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Detailed knowledge of the subsurface elastic properties might provide much‐needed insight into the subduction‐zone structure and the estimation of reservoir parameters. Compressional and shear wave velocities can be inverted by elastic full‐waveform inversion using multicomponent ocean‐bottom seismometer data. However, this process is computationally intensive, requiring massive, repeated simulations scaling with the number of sources. Although source–receiver reciprocity can streamline elastic full‐waveform inversion, it cannot be applied by directly interchanging the positions of sources and ocean‐bottom seismometers. To reduce the computational cost, we develop a source–receiver reciprocal elastic full‐waveform inversion, in which the reciprocity in multicomponent data can be accomplished by transforming the data matching from the original geometry to a specific form under the reciprocal geometry. This approach significantly reduces computational costs from twice the number of sources, as seen in conventional elastic full‐waveform inversion, to a scale with the count of ocean‐bottom seismometers. The tests on synthetic data verify that the proposed reciprocal elastic full‐waveform inversion maintains accuracy while improving computational efficiency, and further application on the field data from East China Sea also showcases the efficiency of the proposed method, resulting in a speed‐up of 10 times. [ABSTRACT FROM AUTHOR]

Meteorite impacts have proved to be a significant source of seismic signal on the Moon, and have now been recorded on Mars by InSight seismometers. Understanding how impacts produce seismic signal is key to the interpretation of this unique data, and to improve their identification in continuous seismic records. Here, we use the seismic Representation Theorem, and particularly the stress glut theory, to model the seismic motion resulting from impact cratering. The source is described by equivalent forces, some resulting from the impactor momentum transfer, and others from the stress glut, which represents the mechanical effect of plasticity and non linear processes in the source region. We condense these equivalent forces into a point-source with a time-varying single force and nine-component moment tensor. This analytical representation bridges the gap between the complex dynamics of crater formation, and the linear point-source representation classically used in seismology. Using the multiphysics modelling software HOSS, we develop a method to compute the stress glut of an impact, and the associated point-source from hypervelocity impact simulations. For a vertical and an oblique impact at 1000 m s−1, we show that the moment tensor presents a significant deviatoric component. Hence, the source is not an ideal isotropic explosion contrary to previous assumptions, and draws closer to a double couple for the oblique impact. The contribution of the point force to the seismic signal appears negligible. We verify this model by comparing two signals: (1) HOSS is coupled to SPECFEM3D to propagate the near-source signal elastically to remote seismic stations; (2) the point-source model derived from the stress-glut theory is used to generate displacements at the same distance. The comparison shows that the point-source model is accurately simulating the low-frequency impact seismic waveform, and its seismic moment is in trend with Lunar and Martian impact data. High-frequencies discrepancies exist, which are partly related to finite-source effects, but might be further explained by the difference in mathematical framework between classical seismology and HOSS' numerical modelling. [ABSTRACT FROM AUTHOR]

The application of deep‐learning‐based seismic phase pickers has surged in recent years. However, the efficacy of these models when applied to monitoring volcano seismicity has yet to be fully evaluated. Here, we first compile a data set of seismic waveforms from various volcanoes globally. We then show that the performances of two widely used deep‐learning pickers deteriorate systematically as the earthquakes' frequency content decreases. Therefore, the performances are especially poor for long‐period earthquakes often associated with fluid/magma movement. Subsequently, we train new models which perform significantly better, including when tested on two data sets where no training data were used: volcanic earthquakes along the Cascadia subduction zone and tectonic low‐frequency earthquakes along the Nankai Trough. Our model/workflow can be applied to improve monitoring of volcano seismicity globally while our compiled data set can be used to benchmark future methods for characterizing volcano seismicity, especially long‐period earthquakes which are difficult to monitor. Plain Language Summary: Earthquake activity at volcanic regions is often monitored to indicate volcanic activity. Identifying the time when the energy radiated from an earthquake source arrives at a seismometer is essential for locating the earthquake, which can be difficult for volcanic earthquakes because of high noise levels, high event rates, and obscured onsets. Previous studies have demonstrated that deep learning can excel in picking the arrival times of regular earthquakes. However, it is unclear how sensitive these detectors are to earthquakes in volcanic regions. Here, we first compile a data set of earthquakes from various volcanoes globally. We then show that existing deep‐learning‐based detectors can miss a large fraction of these earthquakes, especially those without an abrupt change in signal amplitude. We then provide two new models which can better detect volcanic earthquakes than existing models. Our model/workflow can be applied to improve monitoring of volcanic earthquakes globally. Key Points: We compile a data set of seismic waveforms from various volcanic regions globallyWe show that existing deep‐learning phase pickers' performances deteriorate with decreasing earthquake frequency contentOur retrained models perform better and are more generalizable for monitoring volcano seismicity, especially long‐period earthquakes [ABSTRACT FROM AUTHOR]

Dense and broad‐coverage ocean‐bottom observation networks enable us to obtain near‐fault displacement records associated with an offshore earthquake. However, simple integration of ocean‐bottom strong‐motion acceleration records leads to physically unrealistic displacement records. Here we propose a new method using a Kalman filter to estimate coseismic displacement waveforms using the collocated ocean‐bottom seismometers and pressure gauges. First, we evaluate our method using synthetic records and then apply it to an offshore Mw 6.0 event that generated a small tsunami. In both the synthetic and real cases, our method successfully estimates reasonable displacement waveforms. Additionally, we show that the computed waveforms improve the results of the finite fault modeling process. In other words, the proposed method will be useful for estimating the details of the rupture mechanism of offshore earthquakes as a complement to onshore observations. Plain Language Summary: Ocean‐bottom observations enable us to obtain near‐fault displacement records associated with an offshore earthquake. However, simple integration of ocean‐bottom acceleration records leads to physically unrealistic displacement records. Here we propose a new method to estimate offshore coseismic displacement waveforms. First, we evaluate our method using synthetic records and then apply it to an offshore earthquake that generated a small tsunami. In both cases, our method successfully estimates reasonable displacements. Additionally, we show that the computed waveforms improve the results of the earthquake source modeling process. In other words, the proposed method will be useful for estimating the details of the rupture mechanism of offshore earthquakes as a complement to onshore observations. Key Points: Propose a new method to estimate the near‐fault displacement waveform associated with an offshore earthquakeOur method adopts the Kalman filter approach to combine the collocated strong‐motion seismometer and ocean‐bottom pressure gaugeSince the obtained displacement waveforms have fault rupture information, it can improve the finite fault model [ABSTRACT FROM AUTHOR]

We investigated the effects of the propagation path and site amplification of shallow tremors along the Nankai Trough. Using far‐field S‐wave propagation from intraslab earthquake data, the amplification factors at the DONET stations were 5–40 times against an inland outcrop rock site. Thick (∼5 km) sedimentary layers with VS of 0.6–2 km/s beneath DONET stations have been confirmed by seismological studies. To investigate the effects of thick sedimentary layers, we synthesized seismograms of shallow tremors and intraslab earthquakes at seafloor stations. The ratios of the maximum amplitudes from the synthetic intraslab seismograms between models with and without thick sedimentary layers were 1–2. This means that thin lower‐velocity (<0.6 km/s) sediments just below the stations primarily control the estimated large amplifications. Conversely, at near‐source (≤20 km) distances, 1‐order amplifications of seismic energies for a shallow tremor source can occur due to thick sedimentary layers. Multiple S‐wave reflections between the seafloor and plate interface are contaminated in tremor envelopes; consequently, seismic energy and duration are overestimated. If a shallow tremor occurs within underthrust sediments, the overestimation becomes stronger because of the invalid rigidity assumptions around the source region. After 1‐order corrections of seismic energies of shallow tremors along the Nankai Trough, the scaled energies of seismic slow earthquakes were 10−10–10−9 irrespective of the region and source depth. Hence, the physical mechanisms governing seismic slow earthquakes can be the same, irrespective of the region and source depth. Plain Language Summary: The deployment of campaigns and permanent ocean bottom seismometers (OBSs) has enabled us to investigate the activity and physical properties of offshore seismic phenomena. Our knowledge of offshore subsurface structures is still limited; consequently, many studies have used conventional analysis methods with the simplest assumptions. Using observed and synthetic seismograms near the Nankai Trough, we found a limitation in the conventional analysis method applied to OBS data. Thick sedimentary layers, which have been confirmed by seismological studies along the Nankai Trough just below the OBSs, cause an approximately 1‐order overestimation of source parameters for seismic phenomena occurring around the shallow plate boundary. This overestimation may have occurred during the seismic energy estimation of shallow slow earthquakes in Hikurangi, Costa Rica, and Mexico. After correcting for the effects of thick sedimentary layers, we found that the scaled energies of seismic slow earthquakes were 10−10–10−9 irrespective of the region and source depth. This suggests that the physical mechanisms governing seismic slow earthquakes can be the same, regardless of region and source depth. Key Points: Effects of path and site on the seismic energy estimation of slow earthquakes at shallow plate boundaries were investigatedThe assumption of far‐field body waves without thick sediments causes an overestimation of seismic energies for shallow tremorsScaled energies of seismic slow earthquakes at both shallow and large depths range from 10−10 to 10−9 [ABSTRACT FROM AUTHOR]

A strong motion seismometer is a kind of inertial sensor, and it can record middle- to high-frequency ground accelerations. The double-integration from acceleration to displacement amplifies errors caused by tilt, rotation, hysteresis, non-linear instrument response, and noise. This leads to long-period, non-physical baseline drifts in the integrated displacements. GNSS enables the direct observation of the ground displacements, with an accuracy of several millimeters to centimeters and a sample rate of 1 Hz to 50 Hz. Combining GNSS and a strong motion seismometer, one can obtain an accurate displacement series. Typically, a Kalman filter is adopted to integrate GNSS displacements and strong motion accelerations, using the empirical values of noise uncertainty. Considering that there are significantly different errors introduced by the above-mentioned tilt, rotation, hysteresis, and non-linear instrument response at different stations or at different times at the same station, it is inappropriate to employ a fixed noise uncertainty for strong motion accelerations. In this paper, we present a Sage–Husa Kalman filter, where the noise uncertainty of strong motion acceleration is adaptively estimated, to integrate GNSS and strong motion acceleration for obtaining the displacement series. The performance of the proposed method was validated by a shake table simulation experiment and the GNSS/strong motion co-located stations collected during the 2023 Mw 7.8 and Mw 7.6 earthquake doublet in southeast Turkey. The experimental results show that the proposed method enhances the adaptability to the variation of strong motion accelerometer noise level and improves the precision of integrated displacement series. The displacement derived from the proposed method was up to 28% more accurate than those from the Kalman filter in the shake table test, and the correlation coefficient with respect to the references arrived at 0.99. The application to the earthquake event shows that the proposed method can capture seismic waveforms at a promotion of 46% and 23% in the horizontal and vertical directions, respectively, compared with the results of the Kalman filter. [ABSTRACT FROM AUTHOR]

Full-waveform inversion (FWI) of seismic data provides quantitative constraints on subsurface structures. Despite its widespread success, FWI of data around the critical angle is challenging because of the abrupt change in amplitude and phase at the critical angle and the complex waveforms, especially in the presence of a sharp velocity contrast, such as at the Moho transition zone (MTZ). Furthermore, the interference of refracted lower crustal (Pg) and upper mantle (Pn) arrivals with the critically reflected Moho (PmP) arrivals in crustal and mantle studies makes the application of conventional FWI based on linearized model updates difficult. To address such a complex relationship between the model and data, one should use an inversion method based on a Bayesian formulation. Here, we propose to use a Hamiltonian Monte Carlo (HMC) method for FWI of wide-angle seismic data. HMC is a non-linear inversion technique where model updates follow the Hamiltonian mechanics while using the gradient information present in the probability distribution, making it similar to iterative gradient techniques like FWI. It also involves procedures for generating distant models for sampling the posterior distribution, making it a Bayesian method. We test the performance and applicability of HMC based elastic FWI by inverting the non-linear part of the synthetic seismic data from a three-layer and a complex velocity model, followed by the inversion of wide-angle seismic data recorded by two ocean bottom seismometers over a 70 Ma old oceanic crustal segment in the equatorial Atlantic Ocean. The inversion results from both synthetic and real data suggest that HMC based FWI is an appropriate method for inverting the non-linear part of seismic data for crustal studies. [ABSTRACT FROM AUTHOR]

For a narrow-brand seismograph with a flat response range limited, it cannot precisely record the signal of a ground motion and output the records with the low-frequency components cut down. A transfer function is usually used to spread the spectrum of the narrow-brand seismic records. However, the accuracy of the commonly used transfer function is not high. The authors derive a new transfer function based on the Laplace transform, bilinear transform, and Nyquist sampling theory to solve this problem. And then, the derived transfer function is used to correct the narrow-band velocity records from the Hi-net. The corrected velocity records are compared with the velocities integrated from the synchronously recorded broad-band acceleration at the same station with Hi-net. Meanwhile, the corrected records are compared with those corrected by the Nakata transfer function. The results show that the calculation accuracy of the derived transfer function is higher than the Nakata transfer function. However, when the signal-to-noise ratio is below 24, its accuracy diminishes, and it is unable to recover signals within the 0.05–0.78Hz frequency band. [ABSTRACT FROM AUTHOR]

Accurate source parameters of global submarine earthquakes are essential for understanding earthquake mechanics and tectonic dynamics. Previous studies have demonstrated that teleseismic P coda waveform complexities due to near‐source 3‐D structures are highly sensitive to source parameters of marine earthquakes. Leveraging these sensitivities, we can improve the accuracy of source parameter inversion compared to traditional 1‐D methods. However, modeling these intricate 3‐D effects poses significant computational challenges. To address this issue, we propose a novel reciprocity‐based hybrid method for computing 3‐D teleseismic Green's functions. Based on this method, we develop a grid‐search inversion workflow for determining reliable source parameters of moderate‐sized submarine earthquakes. The method is tested and proven on five Mw5+ earthquakes at the Blanco oceanic transform fault (OTF) with ground truth locations resolved by a local ocean bottom seismometer array, using ambient noise correlation and surface‐wave relocation techniques. Our results show that fitting P coda waveforms through 3‐D Green's functions can effectively improve the source location accuracy, especially for the centroid depth. Our improved centroid depths indicate that all the five Mw5+ earthquakes on the Blanco transform fault ruptured mainly above the depth of 600°C isotherm predicted by the half‐space cooling model. This finding aligns with the hypothesis that the rupture zone of large earthquakes at OTFs is confined by the 600°C isotherm. However, it is noted that the Blanco transform fault serves as a case study. Our 3‐D source inversion method offers a promising tool for systematically investigating global oceanic earthquakes using teleseismic waves. Plain Language Summary: Understanding where earthquakes happen and the geometric characteristics of the faults are crucial for studying earthquakes and the Earth's tectonics. Sufficient data collected by seismometers near the fault can help us figure out these details. However, usually only distant seismometers located on land are available for most underwater earthquakes in the vast oceans. Simulating seismic waves propagating from these remote earthquakes all the way to seismometers on land is computationally expensive. To save costs, traditional simulations typically use a depth‐dependent Earth model. In other words, the ocean is excluded or is included with a flat seafloor. These simplifications cause significant error because the real non‐flat seafloor results in much more complicated seismic waves. Here, we develop a computationally inexpensive method to accurately model how seismic waves propagate in an Earth model with non‐flat seafloor. We demonstrate that this more accurate modeling can dramatically improve the earthquake source parameter estimation, especially the depth. We test this method using five moderate‐sized earthquakes on the Blanco transform fault and confirm its high accuracy. The reliable depths indicate that all these earthquakes happened at depths with temperatures lower than 600°C. This method can be applied globally to study submarine earthquakes. Key Points: A hybrid method is proposed to efficiently model complex teleseismic body waves due to 3‐D source‐side effects of submarine earthquakesThe source parameters estimation of moderate‐sized submarine earthquakes is improved by using the 3‐D teleseismic Green's functionsThe improved centroid depths of five Mw5+ earthquakes support a seismogenic zone above the 600°C isotherm at the Blanco transform fault [ABSTRACT FROM AUTHOR]

Clock drift is common for the autonomous ocean bottom seismometer, and a technique of linear drift correction is usually used to fix this kind of timing error assuming that the internal clock has constant drift rate during deployment. However, non‐linear component of clock drift has also been found recently. In order to test the behaviours of the clock drift for 19 Chinese ocean bottom seismometers from a three‐dimensional controlled‐source seismic survey, we have analysed two types of differences in direct wave travel‐times, including one from all shots to the ocean bottom seismometer onboard and another one from the nearest two shots on different shooting lines to the ocean bottom seismometer on the seafloor. Comparison of the differences in direct wave travel‐times from seismic data with and without clock drift correction shows that linear drift correction can commendably eliminate them for most of the ocean bottom seismometers, but some large residuals remain for part of the ocean bottom seismometers. These residuals are inferred to be possibly related to the non‐linear component of clock drift caused by the temperature shock during deployment. The non‐linear clock drift usually varies from different ocean bottom seismometers and thus difficult to correct completely and resultantly will influence the timing accuracy somewhat. Hence, higher uncertainties are suggested to assign to the picked travel‐times for the ocean bottom seismometer with possibly larger non‐linear clock drift in the further tomographic modelling and the internal clock for this kind of ocean bottom seismometer should be recalibrated prior to the next experiment if which requires higher timing accuracy. [ABSTRACT FROM AUTHOR]

The data collected by ocean‐bottom seismometers are unique compared to that of land seismic stations and require preprocessing before use. In this study, we focus on preprocessing seismic data obtained from a passive‐source experiment in the Mariana subduction zone conducted by the Institute of Deep‐Sea Science and Engineering, Chinese Academy of Sciences from 2018 to 2019. We outline the steps involved in preprocessing the data, including time correction using the ocean background noise cross‐correlation function, tilt correction using a transfer function and orientation correction based on seismic wave polarization. Our results show that the maximum clock drifting was ∼4 s on two stations (K06 and K20), whereas the rest of the station waveforms have clock drifting of less than 2 s. Among stations, only the K06 station is tilted, and the tilt angle is 19.82°. We use the P‐wave and Rayleigh wave polarization methods to determine the orientation. Our results indicate that the error in the result obtained using the P‐wave polarization method is less than that of the Rayleigh wave polarization method. Additionally, we address the advantages and disadvantages of these preprocessing techniques. Our goal is to offer a comprehensive overview of the preprocessing process, to assist beginners in the field and to serve as a foundation for future research in passive‐source ocean‐bottom seismometers. [ABSTRACT FROM AUTHOR]

The Sulu orogenic belt, formed by the collision between Sino‐Korean and South China blocks, has important geological implications due to its unique tectonic setting. To understand the deep crustal structure of the extension of the Sulu orogenic belt in the Yellow Sea, we presented a 220‐km long active‐source ocean‐bottom seismometer wide‐angle reflection/refraction profile across the Yellow Sea. The obtained P‐wave velocity structure exhibits significant horizontal and vertical variations. The middle and upper crustal regions of the north beneath the survey line exhibit a higher velocity and larger thickness than those of the south, whereas the opposite is the case with the lower crust. The lower crustal high‐velocity zone in the Northern Depression of the South Yellow Sea Basin is a manifestation of the mantle material upwelling and accretion to the lower crust. Magmatic intrusions occurred along the faults and formed a high‐velocity zone in the middle and upper crusts. Due to undulating Moho interface and varying crustal velocities, the Qingdao–Rongcheng fault is identified as a fault in the Sulu orogenic belt without the significance of tectonic zoning. This study shows that the Sino‐Korean Block, Sulu orogenic belt and South China Block are bounded by the Jimo–Muping faults (an extension to the sea), Qianliyan fault and deep fault of the South Yellow Sea. Furthermore, the Sulu orogenic belt may have been formed by the northward subduction of the South China Block beneath the Sino‐Korean Block during the Indosinian orogeny to the east of the Shandong Peninsula. [ABSTRACT FROM AUTHOR]

First‐arrival travel‐time tomography is frequently used in ocean‐bottom seismometer surveys for estimating subsurface velocity. However, due to a lack of seismometer stations or ray sampling, the tomographic images' spatial resolution and quality are typically low. Inspired by the multiple imaging of ocean‐bottom seismometer data, in this study, I developed a mirror tomography method to incorporate the travel times of refraction multiples in first‐arrival travel‐time tomography. Specifically, the travel times of refraction multiples were treated as virtual first‐arrival travel times from the mirror positions of the stations. This technique enhances ray coverage and stabilizes the inversion. I confirmed that the travel times of refraction multiples were consistent with the first‐arrival travel times calculated using numerical modelling at the mirror position of the station. Synthetic examples showed that the mirror tomography scheme may enhance ray coverage and model resolution. Mirror tomography may compensate for the uneven distribution of travel‐time picks caused by the loss of the ocean‐bottom seismometer. [ABSTRACT FROM AUTHOR]

Ocean bottom seismometer data usually contain a large amount of random noise, which seriously reduces the signal‐to‐noise ratio of the data and affects subsequent imaging. Hence, random noise attenuation is one of the most essential steps in ocean bottom seismometer data processing. In this paper, a novel approach is proposed to attenuate the marine seismic random noise of ocean bottom seismometers based on a six‐dense‐layer denoising autoencoder. We input the t−x$t-x$ domain data into the denoising autoencoder, the encoder compresses the signal and noise and extracts the main features and the decoder finally reconstructs the denoised data with the same dimension as the input. In this approach, because few raw labelled examples are available, we first constructed the pretraining, training and test data sets by patch processing. Then, we pretrained the encoder based on clean synthetic seismic data through unsupervised learning and pretrained the decoder based on noisy synthetic seismic data through supervised learning. Next, the pretrained model was fine‐tuned with the encoder–decoder on a raw seismic data set in an unsupervised manner. Finally, we used the model to attenuate the random noise in raw ocean bottom seismometer data for testing. Synthetic and raw examples are used to compare the f−x$f-x$ deconvolution, multichannel singular spectrum analysis, deep denoising autoencoder and substep deep denoising autoencoder approaches. Experimental tests demonstrate that the proposed method has higher processing efficiency and precision. [ABSTRACT FROM AUTHOR]

Seafloor ambient noise in the Arctic Ocean is related to sea ice. The characteristics of low‐frequency seafloor ambient noise (<10 Hz) in the northern Chukchi Sea have rarely been reported. Here, we investigated the seafloor ambient noise recorded using ocean‐bottom seismometers in the northern Chukchi Sea under four different sea ice‐concentration periods from 3 to 23 August 2020, in the 11th Chinese National Arctic Research Expedition. The causes and mechanisms of the changes in seafloor ambient noise that correspond to the variation in sea ice concentration were discussed. The energy of infragravity waves and primary microseisms is weak compared with other oceans. Combined with the analysis of land station, we argue that the variations in sea ice extent have little effect on the energy of the primary microseisms and infragravity waves. The power of secondary microseisms is growth with the loss of sea ice and is influenced by storms. We find that a high concentration of sea ice can impede the process of storm‐sea surface interaction, which in turn affects the power of microseisms inspired by storm. [ABSTRACT FROM AUTHOR]

With the increasing maturity of ocean bottom seismometer technology in gas hydrate exploration, more and more researchers apply ocean bottom seismometer exploration technology for offshore oil exploration. However, due to the special observation mode of ocean bottom seismometer, it is impracticable to process ocean bottom seismometer data using traditional data processing methods, such as velocity analysis and pre‐stack time migration. This manuscript proposed a new velocity analysis method for ocean bottom seismometer data, which obtains more accurate root‐mean‐square velocity than the existing method. Then we deduced the Kirchhoff pre‐stack time migration formula for ocean bottom seismometer data. Two models demonstrate the correctness of the velocity analysis and migration methods. Finally, the two methods were applied to the actual ocean bottom seismometer data, and the obtained migration profile is consistent well with the profile of towed streamer data in the nearby area. [ABSTRACT FROM AUTHOR]

Refraction seismic data acquired by wide‐angle ocean‐bottom seismometers are typically used to invert geological structures. Accurate and reliable first‐arrival traveltime picks are therefore critical to successful refraction data processing. Unfortunately, signal‐to‐noise ratios of refraction data acquired by wide‐angle ocean‐bottom seismometers at far offsets are usually low; thus, conventional methods such as the energy ratio method and correlation analysis method usually fail to accurately and efficiently determine the refraction phase. We therefore develop a novel first‐arrival wave recognition method based on the optimal dominant energy spectrum to improve the accuracy of refraction wave picking on wide‐angle ocean‐bottom seismometer data. The method consists of three steps. The first step is to obtain the optimal dominant energy spectrum according to the power spectrum density of sample scanning. Second, the initial position of the first‐arrival wave is recognized by combining the range of the optimal dominant energy spectrum and the maximum point of its derivative. Third, the modified cross‐correlation method adjusts the initial position to obtain a more accurate arrival time. In this paper, we illustrate the workflow and feasibility of the proposed method via testing on model data. Then, the method is used to determine the first‐arrival time of field wide‐angle ocean‐bottom seismometer data acquired in the Southwest Pacific Ocean and Xisha Islands. The results show that our method can accurately pick the refraction phase at offsets as far as 80 km, thus significantly reducing the uncertainties in first‐arrival phase picking. [ABSTRACT FROM AUTHOR]

In distributed acoustic sensing (DAS), optical fibre is used as sensors, which enables us to observe strain over tens of kilometres at intervals of several metres. S -wave velocity (V s) structures of shallow sediments of high resolution have been obtained from surface wave dispersion curves by applying seismic interferometry to DAS data both onshore and offshore. However, it is known that there is a disadvantage to DAS seismic interferometry. In addition to Rayleigh waves, Love waves are also included. Consequently, the accuracy of the estimated phase velocities for Rayleigh waves is reduced due to the contamination of Love waves. To address this shortcoming, we suggest a spatial autocorrelation (SPAC) method between DAS and the vertical component of seismometer data. The SPAC method is equivalent to seismic interferometry and is useful for obtaining phase-velocity dispersion curves of surface waves from the cross-correlation functions (CCFs) between the records of two receivers. The CCFs obtained from a combination of DAS and vertical seismometer data should contain only Rayleigh waves because Love waves have no vertical component. CCFs between DAS and vertical seismometer data are therefore expected to give more accurate phase velocities of Rayleigh waves than CCFs with DAS data only. In this study, we first formulated analytical expressions of cross-spectra for DAS and three-component seismometer data because seismic observation is generally carried out using a three-component seismometer. A new SPAC method is presented in the form of analytical expressions. We showed that our formulation only includes Rayleigh and not Love waves in the cross-spectra with DAS and the vertical-component seismometer data. We applied our SPAC method to actual DAS and vertical seismometer data recorded on the seafloor. Then, we compared our new SPAC method for DAS and vertical seismometer data with a conventional SPAC method for only DAS data. The results reveal that our new SPAC method can estimate the phase velocities of Rayleigh waves more accurately than the conventional method. In addition, the analytical formulations of the cross-spectrum between DAS and three-component seismometer data, which we obtained in this study, are expected to be useful for the estimation of accurate 3-D structures in the future, although this is not available at the moment due to the lack of an applicable data set. [ABSTRACT FROM AUTHOR]

Rapid and accurate earthquake magnitude estimations are essential for earthquake early warning (EEW) systems. The distance information between the seismometers and the earthquake hypocenter can be important to the magnitude estimation. We designed a deep-learning, multiple-seismometer-based magnitude estimation method using three heterogeneous multimodalities: three-component acceleration seismograms, differential P-arrivals, and differential seismometer locations, with a specific transformer architecture to introduce the implicit distance information. Using a data-augmentation strategy, we trained and selected the model using 5365 and 728 earthquakes. To evaluate the magnitude estimation performance, we use the root mean square error (RMSE), mean absolute error (MAE), and standard deviation error (ϭ) between the catalog and the predicted magnitude using the 2051 earthquakes. The model could achieve RMSE, MAE, and ϭ less than 0.38, 0.29, and 0.38 when the passing time of the earliest P-arrival is 3 s and stabilize to the final values of 0.20, 0.15, and 0.20 after 14 s. The comparison between the proposed model and model ii, which is retrained without the specific architecture, indicates that the architecture contributes to the magnitude estimation. The P-arrivals picking error testing indicates the model could provide robust magnitude estimation on EEW with an absolute error of less than 0.2 s. [ABSTRACT FROM AUTHOR]

Accurate seismic phase detection and onset picking are fundamental to seismological studies. Supervised deep-learning phase pickers have shown promise with excellent performance on land seismic data. Although it may be acceptable to apply them to Ocean Bottom Seismometer (OBS) data that are indispensable for studying ocean regions, they suffer from a significant performance drop. In this study, we develop a generalized transfer-learned OBS phase picker—OBSTransformer, based on automated labelling and transfer learning. First, we compile a comprehensive data set of catalogued earthquakes recorded by 423 OBSs from 11 temporary deployments worldwide. Through automated processes, we label the P and S phases of these earthquakes by analysing the consistency of at least three arrivals from four widely used machine learning pickers (EQTransformer, PhaseNet, Generalized Phase Detection and PickNet), as well as the Akaike Information Criterion (AIC) picker. This results in an inclusive OBS data set containing ∼36 000 earthquake samples. Subsequently, we use this data set for transfer learning and utilize a well-trained land machine learning model—EQTransformer as our base model. Moreover, we extract 25 000 OBS noise samples from the same OBS networks using the Kurtosis method, which are then used for model training alongside the labelled earthquake samples. Using three groups of test data sets at subglobal, regional and local scales, we demonstrate that OBSTransformer outperforms EQTransformer. Particularly, the P and S recall rates at large distances (>200 km) are increased by 68 and 76 per cent, respectively. Our extensive tests and comparisons demonstrate that OBSTransformer is less dependent on the detection/picking thresholds and is more robust to noise levels. [ABSTRACT FROM AUTHOR]

Hyuga-nada, off the Pacific coast of Kyushu along the Nankai Trough in southwest Japan, is one of the most active slow earthquake regions around Japan. We estimated the energies of shallow tremors and moments of shallow very low frequency earthquakes (VLFEs) in Hyuga-nada using data from a permanent onshore broadband network and temporary ocean–bottom seismometer observations. The energies and moments of these slow earthquakes have a similar along-strike variation and are generally higher south of the subducted Kyushu–Palau Ridge than near the top of the ridge. This spatial variation is also related to the characteristics of slow earthquake migration. The along-strike migration speed was faster at initiation in the south, where the moments of slow earthquakes are higher. After migration entered the subducted Kyushu–Palau Ridge, its speed was decelerated with a parabolic pattern and their moments became smaller. Assuming a constant patch size of slow earthquakes, we estimated that the stress drop of VLFEs in the south of the subducted ridge was approximately three times higher than that near the top of the subducted ridge. According to our observations and a physical model, this stress drop difference between adjacent regions may cause parabolic migration. We also estimated the scaled energy of slow earthquakes from the ratio of the seismic energy rates of tremors to the seismic moment rates of accompanying VLFEs. The spatial variation in scaled energy is not identified inside the Hyuga-nada. Since the range of scaled energy is similar between the south and near the top of the subducted ridge, the apparent stress may be similar if the rigidity is the same. The dominant range of scaled energy of slow earthquakes in Hyuga-nada is 10−11.5–10−8.5. In addition to having similar or one order smaller values compared to other slow earthquake regions, the range of scaled energy in Hyuga-nada is broader. This broader range suggests wide range of characteristic time and various spectral features of slow earthquakes in Hyuga-nada. Based on a Brownian slow earthquake model, the wide range of characteristic time in this area suggests width variations of slow earthquake source area. [ABSTRACT FROM AUTHOR]

For a more quantitative discussion of slow earthquake activity, we evaluated the detectable limits of very low frequency earthquakes (VLFEs), which are seismic slow earthquakes observed in very low-frequency (< 0.05 Hz) bands in the Nankai subduction zone. We performed numerical simulations using a local 3-D model and used the observed noise level of permanent broad-band seismometers. First, we investigated the effects of the source-time functions on the maximum amplitudes of the VLFE signals at a certain station. The maximum amplitudes of the VLFE signals were controlled by the VLFE moment rate. The detectable limit of VLFEs at each source location can be defined as the lowest moment rate of detectable VLFEs, which radiate signals larger than the noise levels of any component at ≥ 3 stations. For inland seismometers only, the detectable limits of VLFEs at deep (30–40 km) and shallow (≤ 10 km) depths were 1012–1012.3 and 1012.7 N·m s−1, respectively. Due to the geometrical spreading of VLFE signals and large noise levels in horizontal components, offshore seismometers improved the detectability of shallow VLFEs in regions where seismometers were densely deployed. Based on our detectability and published catalogues, shallow slow earthquakes are less active south-southwest off the Kii Peninsula, where geodetic studies expect mechanical coupling. [ABSTRACT FROM AUTHOR]

Mimoun et al. (Space Sci Rev 211(1–4):383–428, 2017) developed a pre-landing noise model of the Martian seismometer package SEIS onboard InSight that analysed all the external and internal noise sources. We updated the environmental and instrumental parameters of the model as well as the ground properties with InSight mission data. We compared the output of the in situ noise model to the actual noise measured during the full mission for each individual noise source as well as for the full noise model. We evaluate in detail the efficiency of the model to fit the measured data and discuss the transient noise and other sources that were not included in the model. The main noise sources in the seismic bandwidth are the pressure noise and the lander noise, which is increased from the pre-landing model and overestimated when compared to the data; the magnetic field noise was overestimated in the pre-landing model and is now found to be negligible. The conclusions and models from this study could benefit future space missions. [ABSTRACT FROM AUTHOR]

This study probes the lithosphere‐asthenosphere system beneath 155 Ma Pacific seafloor using teleseismic S‐to‐p receiver functions at the Pacific Lithosphere Anisotropy and Thickness Experiment project ocean‐bottom‐seismometers. Within the lithosphere, a significant velocity decrease at 33–50 km depth is observed. This mid‐lithospheric discontinuity is consistent with the velocity contrast between the background mantle and thin, trapped layers of crystallized partial melt, in the form of either dolomite or garnet granulite. These melts possibly originated from deeper asthenospheric melting beneath the flanks of spreading centers, and were transported within the cooling lithosphere. A positive velocity increase of 3%–6% is observed at 130–155 km depth and is consistent with the base of a layer with partial melt in the asthenosphere. A shear velocity decrease associated with the lithosphere‐asthenosphere boundary at 95–115 km depth is permitted by the data, but is not required. Plain Language Summary: Using seismic waves from distant earthquakes recorded by seismometers deployed on the seafloor, we investigated the seismic velocity structure beneath old Pacific seafloor. Within the lithosphere, we see a decrease in velocity at depths of roughly 40 km, which can be modeled as crystallized layers of partial melt that were emplaced in the lithosphere when it was young. The data indicate the presence of a velocity increase at depths of 130–155 km, and are consistent with (but do not require) a velocity decrease at depths of 95–115 km. These boundaries could mark the lower and upper boundaries of a layer in the asthenosphere that contains higher fractions of partially molten rock. Key Points: Lithosphere and asthenosphere beneath 155 Ma western Pacific crust are measured by S‐to‐p receiver functions using the Pacific Lithosphere Anisotropy and Thickness Experiment ocean‐bottom seismometers dataA mid‐lithospheric discontinuity at ∼40 km depth is observed and can be explained by layers of crystallized partial meltA partial melt‐bearing layer from 95 to 155 km depth is consistent with the receiver function observations [ABSTRACT FROM AUTHOR]

Post-rift magmatism along continental margins is usually focused on highly stretched basins or aborted rifts. Adjacent microplates with relatively thick lithosphere are not expected to exhibit intense postrift magmatism. This study identifies 20 mounded structures and associated pathways using two-dimensional, multichannel seismic data and ocean bottom seismometer (OBS) data across the southeastern Xisha Massif of the northwestern South China Sea. This massif is a relatively thick (>20 km) region of crust that forms a microplate between two rift branches. The mounded structures are interpreted as volcanoes, based on their seismic reflections and morphological characteristics. Detachment faults that extend into the middle crust captured the magma and provided pathways for vertical migration. During the rise of magma into the sedimentary stratum, detachment faults still served as the main channels of magmatic migration. The rigidity differences between the basement and the overlying sediments, as well as the stress field, facilitated subordinate pathways for magmatic migration, particularly at the depocenters and flanks of half-grabens. Consequently, larger volcanoes are present above the basement highs, while smaller volcanoes are located in the centers of half-grabens. This study provides criteria for identifying submarine mounded structures of different origins that are applicable beyond the study area. Moreover, this study highlights that detachment faults play a key role in the volcanic systems of the relatively rigid microplates of heterogeneous crustal structure. It also promotes our understanding of post-rift magmatism and the dynamic evolution of continental margins, and the results could be applicable to other areas with similar geological settings. [ABSTRACT FROM AUTHOR]

Ocean bottom seismometers (OBSs) have been used to detect submarine structural and tectonic information for decades. According to signal source controllability, OBS data have generally been classified into active and passive source data categories. The former mainly focuses on the compressional wave (P‐wave) velocity inversion and always lacks valid information about the shear wave (S‐wave) velocity structure. While the latter provides structural information with limited resolution due to the aperture of the stations. Overcoming the barriers between processing these two data types will allow the reuse of a vast amount of data from active source experiments to explore the submarine S‐wave velocity structural properties. Here, we creatively applied ambient noise interferometry to invert the S‐wave velocity structure using data from active source OBS deployment conducted in the southernmost Mariana subduction zone, which had already been utilized to detect submarine P‐wave velocity structure. Considering the short time duration and relatively low quality of this type of data, a combined method of short‐segment cross‐correlation and selected time‐frequency domain phase‐weighted stacking was adopted to obtain stable cross‐correlation functions, which were subsequently used to invert S‐wave velocity structures. Compared to previous studies using different methods, our result sheds new light on the crust and upper mantle structure of the southernmost Mariana subduction zone. This method could be used to detect more information based on the reutilization of existing active source OBS data. Plain Language Summary: Active source ocean bottom seismometer (OBS) data are usually used to detect P‐wave velocity information of submarine areas using signals from air‐gun shootings. However, most of the waveform recording ambient noise has yet to be utilized. A large amount of valuable data needs to be fully exploited for the high cost of OBS experiments. In addition, we still lack shear wave velocity information in many critical marine areas due to the rarely picked shear wave phases. However, this information is of great importance for marine tectonic analysis, such as magmatism, dehydration or serpentinization. Using the recorded ambient noise in OBS data from active source experiments can afford the opportunity to obtain shear‐wave velocity structures. We adopted different processing methods to calculate high‐quality surface wave dispersion data, which were subsequently used to invert shear wave structures in the southernmost Mariana Trench. This method can also provide structural information with a different resolution that is lacking using passive source OBS data. Key Points: Ambient noise interferometry was applied to ocean bottom seismometer data from an active source deployment to invert the shear‐wave velocity structureA selected time‐frequency domain phase‐weighted stack was adopted to improve the quality of cross‐correlationsWater content and hydrated layer thickness of the subducting plate were estimated in the southernmost Mariana Trench [ABSTRACT FROM AUTHOR]

A T‐wave is a seismo‐acoustic wave that can travel a long distance in the ocean with little attenuation, making it valuable for monitoring remote tectonic activity and changes in ocean temperature using seismic ocean thermometry (SOT). However, current high‐quality T‐wave stations are sparsely distributed, limiting the detectability of oceanic seismicity and the spatial resolution of global SOT. The use of ocean bottom distributed acoustic sensing (OBDAS), through the conversion of telecommunication cables into dense seismic arrays, is a cost‐effective and scalable means to complement existing seismic stations. Here, we systematically investigate the performance of OBDAS for oceanic seismicity detection and SOT using a 4‐day Ocean Observatories Initiative community experiment offshore Oregon. We first present T‐wave observations from distant and regional earthquakes and develop a curvelet denoising scheme to enhance T‐wave signals on OBDAS. After denoising, we show that OBDAS can detect and locate more and smaller T‐wave events than regional OBS network. During the 4‐day experiment, we detect 92 oceanic earthquakes, most of which are missing from existing catalogs. Leveraging the sensor density and cable directionality, we demonstrate the feasibility of source azimuth estimation for regional Blanco earthquakes. We also evaluate the SOT performance of OBDAS using pseudo‐repeating earthquake T‐waves. Our results show that OBDAS can utilize repeating earthquakes as small as M3.5 for SOT, outperforming ocean bottom seismometers. However, ocean ambient natural and instrumental noise strongly affects the performance of OBDAS for oceanic seismicity detection and SOT, requiring further investigation. Plain Language Summary: Oceanic earthquakes can produce loud sounds in the ocean. These sounds usually arrive at a seismic station as the tertiary wave, a so‐called T‐wave, following the arrival of the primary P‐wave and secondary S‐wave. T‐waves can propagate thousands of kilometers in the ocean's SOund Fixing And Ranging (SOFAR) channel with little energy loss. Thus, they are useful for monitoring earthquakes and ocean temperature changes. However, currently available instruments for measuring these waves are limited. Recently, a new type of technique, Distributed Acoustic Sensing, provides an opportunity to expand the seismic‐recording capability in the ocean. Ocean bottom distributed acoustic sensing (OBDAS) can effectively turn submarine telecommunication cables into dense seismic sensors that complement conventional seismometers. In this study, we explore the OBDAS potential for T‐wave detection. With a 4‐day OBDAS community experiment offshore Oregon, we demonstrate that OBDAS does a better job than a conventional seismic network for detecting T‐waves when a specifically designed denoising scheme is applied. In addition, OBDAS has the potential to measure ocean temperature changes using more repeating earthquakes of smaller magnitudes, outperforming conventional sensors. However, the accuracy of the OBDAS system can be strongly affected by various types of noise, which requires further research. Key Points: We develop a curvelet denoising scheme for ocean bottom distributed acoustic sensing to enhance T‐wave signalsThe denoised distributed acoustic sensing data detects three times more T‐wave events than cabled ocean bottom seismometersThe improved detection capability makes more small repeating earthquakes useable for seismic ocean thermometry [ABSTRACT FROM AUTHOR]

Large earthquakes can trigger smaller seismic events, even at significant distances. The process of earthquake triggering offers valuable insights into the evolution of local stress states, deepening our understanding of the mechanisms of earthquake nucleation. However, our ability to detect these triggered events is limited by the quality and spatial density of local seismometers, posing significant challenges if the triggered event is hidden in the signal of a nearby larger earthquake. Distributed acoustic sensing (DAS) has the potential to enhance the monitoring capability of triggered earthquakes through its high spatial sampling and large spatial coverage. Here, we report on an uncatalogued magnitude (M) 5.1 event in northeast Turkey, which was likely dynamically and instantaneously triggered by the 2023 M7.8 earthquake in southeast Turkey, located 400 km away. This event was initially discovered on ∼1,100 km of active DAS recordings that are part of an 1,850‐km linear array. Subsequent validation using local seismometers confirmed the event's precise time, location, and magnitude. Interestingly, this dynamically triggered event exhibited precursory signals preceding its P arrivals on the nearby seismometers. It can be interpreted as the signal from other nearby, uncatalogued, smaller triggered events. Our results highlight the potential of high‐spatial‐density DAS in enhancing the local‐scale detection and the detailed analysis of earthquake triggering. Plain Language Summary: Large earthquakes can trigger smaller ones even far away. This helps us understand more about how earthquakes start and develop. However, finding these smaller earthquakes can be difficult as sometimes they are hidden in the chaos of a bigger, nearby earthquake. There is a novel technology called Distributed Acoustic Sensing (DAS) that might help. DAS can listen for earthquakes over large areas and gives a more detailed picture of what's happening underground. In this study, we discovered a moderate‐size earthquake with a magnitude of 5.1 in Northeast Turkey that was triggered by a large earthquake with a magnitude of 7.8 in Southeast Turkey, using a DAS system that stretched over a thousand kilometers. We then checked this finding with conventional seismometers to be sure. Interestingly, this triggered earthquake showed precursory signals before its main shaking started, which could be signs of other smaller earthquakes happening nearby. Our findings deepen our understanding of how earthquakes interact with each other and offer insights into how earthquakes start. Our results also suggest that DAS could help us find and understand these triggered earthquakes, providing us with invaluable information for future seismology studies. Key Points: We find an uncatalogued M5.1 earthquake in NE Turkey dynamically and instantaneously triggered by the 2023 Mw 7.8 SE Turkey earthquakeWe uncover this event on a thousand‐kilometer linear distributed acoustic sensing array, then confirm its precise time, location, and magnitude with seismometersPrecursory signals of the M5.1 event are observed and are most likely resulting from other nearby events rather than its nucleation phase [ABSTRACT FROM AUTHOR]

This study focuses on developing and evaluating the broadband ocean bottom seismometer (Yardbird-BB OBS) in Taiwan. The Yardbird-BB OBS is a crucial instrument for recording seismic signals in deep-sea environments. Rigorous testing ensures optimal performance and data recording capabilities. Following assembly, the Yardbird-BB OBS undergoes a 3–6 month deployment test in the deep sea, capturing seismic signals worldwide. Data from 2016 and 2017 deployments in the Okinawa Trough analyze significant seismic events, including a magnitude 7.8 earthquake in New Zealand and a magnitude 6.3 earthquake from a North Korean nuclear test. Waveform analysis, focusing on tele-seismic events and waveform quality, assesses the OBS's performance, highlighting successful automatic leveling adjustment. These high-quality recordings benefit research, aiding the study of plate tectonics, crustal age estimation, seafloor ambient noise determination, and earthquake location accuracy improvement. The study also details methods for verifying instrumental self-noise, dynamic range, digitization sensitivity, linearity error, clock drift, and data logger power consumption. Calibration procedures and evaluation methods provide insights into Yardbird-BB OBS performance characteristics, contributing to its understanding and enhancement for effective long-term underwater data recording and valuable scientific research. [ABSTRACT FROM AUTHOR]

On September 5, 2019, a moderate earthquake of Mw5.4 unexpectedly occurred in the apparently quiescent central basin of the South China Sea. We immediately carried out a seismicity monitoring experiment around the epicenter by using broadband ocean bottom seismometers (OBS) for the following three scientific targets. The first is knowing the earthquake seismogenic mechanism, fault structure and further development. The second is finding the role of the residual spreading ridge playing in earthquake processes and further revealing the deep structures of the ridge directional turning area. The third is confirming the existence and significance of the so called "Zhongnan fault". This paper reports the preliminary results of the first phase experiment. Five OBSs were deployed for seismicity monitoring with a duration of 288 days, but only three were recovered. Micro-earthquakes were firstly detected by an automatic seismic phase picking algorithm and then were verified by analyzing their seismic phases and time-frequency characteristics in detail. A total of 21, 68 and 89 micro-earthquakes were picked out from the three OBSs respectively within the distance of 30 km. The dominant frequency of these micro-earthquakes is 12–15 Hz, indicating tectonic fracturing. During the first two months after the mainshock the seismicity was relatively stronger, and micro-earthquakes were still occurring occasionally till the end of observation, indicating the epicenter area is active. We used Match&Locate method to locate 57 micro-earthquakes preliminarily. Their spatial distribution shows that the seismicity is developed mainly along the NE direction roughly parallel to the residual ridge with depth variations between 10–20 km. [ABSTRACT FROM AUTHOR]

We present an integrated algorithm for high‐resolution crustal‐scale imaging utilizing long‐offset wide‐angle ocean‐bottom seismometer (OBS) data and short‐offset multichannel streamer (MCS) data. The algorithm adopts a two‐step imaging strategy, initially using the OBS data to enhance deep structure imaging and capture long‐wavelength features of the migration velocity. Subsequently, the MCS data are migrated to recover detailed short‐wavelength components and shallow structures using the migration velocity model modified by the OBS result. Both steps employ the least‐squares reverse time migration (LSRTM) method with appropriate regularization techniques. The algorithm is implemented using the Bregmanized operator splitting (BOS) approach, known for its efficiency and adaptability in handling non‐smooth regularization, such as total‐variation (TV) constraints. To improve computational efficiency, compressed sensing is employed to store incident wavefields at half their actual size and reconstruct them accurately when required. Convergence is expedited through the use of preconditioners and the Anderson acceleration method. The proposed algorithm is validated through large‐scale numerical examples, demonstrating its robustness even with an initially imprecise velocity model. Results showcase improved resolution and accuracy achieved through the integration of OBS and MCS data. This study offers a comprehensive framework for crustal‐scale imaging, addresses computational complexities, and provides enhanced imaging capabilities. Plain Language Summary: We present a method for high‐resolution crustal‐scale imaging using a combination of long‐offset wide‐angle data from stationary receivers deployed on the seafloor with short‐offset data from floating receivers. Our method is structured as a two‐step imaging algorithm that exploits the unique strengths of each data set. In the first step, the stationary recorded data is used to recover the long‐wavelength features. Subsequently, we integrate the floating receiver data with the preceding image to recover the short‐wavelength components, adding details. The efficacy of our method relies on the utilization of least‐squares reverse time migration, which is enhanced by incorporating appropriate regularization techniques. This enables us to address critical challenges such as memory overhead, convergence speed, and image stability. These key challenges are addressed by incorporating three advanced strategies: compressed sensing theory, Anderson acceleration, and total variation regularization. Accordingly, we enhance computational efficiency, expedite convergence, and elevate imaging quality, which helps provide accurate, high‐resolution images of the subsurface that facilitate better geological interpretation of deep structures and therefore improve our understanding of the target's regional geodynamic context. Moreover, this algorithm allows imaging with less initial knowledge about the image and more robustness to the frequency range. Key Points: Enhanced crustal‐scale seismic imaging from academic dataOcean bottom seismometer and multi‐channel streamer data integrationHigh‐resolution least‐squares reverse time migration [ABSTRACT FROM AUTHOR]

Ocean bottom seismometer networks can record opportunistic data sets of 20-Hz fin whale calls. Because networks are often too sparse for multi-station tracking, single-station methods are needed to estimate call density. We investigated a method to range to singing fin whales at full ocean depths using the spacing of water column multiples. Calls were detected by cross-correlating a spectrogram with a template call. To enhance multipath signals, we considered 20-min windows and either summed the spectrograms of all calls aligned on the strongest detection before measuring the multipath spacing or measured the spacing directly from the autocorrelation of the cross correlation time series. We evaluated the methods at five sites with contrasting seafloor and subsurface properties, bathymetric relief, and water depths of 4000–6000 m, using fin whale songs at four sites and a sei whale song at the fifth. The autocorrelation method works best, and ranges can be obtained to >15 km. Ranging at sedimented sites requires careful accounting for subsurface reflections. Ranges have considerable uncertainty in regions of bathymetric relief. The method requires that the time between calls is different from that of the multipaths and does not work reliably when more than one whale is singing nearby. [ABSTRACT FROM AUTHOR]

Template matching has proven to be an effective method for seismic event detection, but is biased toward identifying events similar to previously known events, and thus is ineffective at discovering events with non‐matching waveforms (e.g., those dissimilar to existing catalog events). In principle, this limitation can be overcome by cross‐correlating every segment (possible template) of a seismogram with every other segment to identify all similar event pairs, but doing so has been previously considered computationally infeasible for long time series. Here we describe a method, called the 'Matrix Profile' (MP), a "correlate everything with everything" calculation that can be efficiently and scalably computed. The MP returns the maximum value of the correlation coefficient of every sub‐window of continuous data with every other sub‐window, as well as the best‐correlated sub‐window location. Here we show how MP methods can obtain valuable results when applied to months and years of continuous seismic data in both local and global case studies. We find that the MP can identify many new events in Parkfield, California seismicity that are not contained in existing event catalogs and that it can efficiently find clusters of similar earthquakes in global seismic data. Either used by itself, or as a starting point for subsequent template matching calculations, the MP is likely to provide a useful new tool for seismology research. Plain Language Summary: Detecting and cataloging earthquakes through analysis of seismic data—the shapes of seismic waves, recorded by seismometers—is foundational to our understanding of Earth's interior structure and processes, as well as geological hazards such as earthquakes. Methods to improve the efficiency and sensitivity of earthquake detection while maintaining accuracy are critical, as seismic data volumes have grown exponentially in recent decades. Recently, methods that use recorded earthquakes as template patterns to identify in seismic data, have proven capable of detecting several times more earthquakes than traditional methods. However, such methods require knowledge of the template earthquakes ahead of time, and are best at identifying earthquakes whose waveforms are similar to the templates. We present here a new method, called the 'Matrix Profile' (MP), that takes short windows of data from a seismic data stream, and compares them to all other parts of that data stream, identifying parts of the data that are highly similar. The MP effectively identifies earthquakes, even those which are hidden within noise, does not require any templates to be provided upfront, and can be efficiently calculated. We demonstrate its success at detecting earthquakes in different types of seismic data, and provide practical guidelines for applying the method and interpreting the results. Key Points: The matrix profile finds the maximum autocorrelation of every subsequence in a time seriesPeaks of high similarity in the matrix profile can be used to identify seismic events and similar event pairsThe matrix profile method has similar sensitivity to template matching but does not require a priori templates [ABSTRACT FROM AUTHOR]

Teleseismic back‐projection imaging has emerged as a powerful tool for understanding the rupture propagation of large earthquakes. However, its application often suffers from artifacts related to the receiver array geometry. We developed a teleseismic back‐projection technique that can accommodate data from multiple arrays. Combined processing of P and pP waveforms may further improve the resolution. The method is suitable for defining arrays ad‐hoc to achieve a good azimuthal distribution for most earthquakes. We present a catalog of short‐period rupture histories (0.5–2.0 Hz) for all earthquakes from 2010 to 2022 with MW ≥ 7.5 and depth less than 200 km (56 events). The method provides automatic estimates of rupture length, directivity, speed, and aspect ratio, a proxy for rupture complexity. We obtained short‐period rupture length scaling relations that are in good agreement with previously published relations based on estimates of total slip. Rupture speeds were consistently in the sub‐Rayleigh regime for thrust and normal earthquakes, whereas a tenth of strike‐slip events propagated at supershear speeds. Many rupture histories exhibited complex behaviors, for example, rupture on conjugate faults, bilateral propagation, and dynamic triggering by a P wave. For megathrust earthquakes, ruptures encircling asperities were frequently observed, with downdip, updip, and balanced patterns. Although there is a preference for short‐period emissions to emanate from central and downdip parts of the megathrust, emissions updip of the main asperity are more frequent than suggested by earlier results. Plain Language Summary: Back‐projection is an earthquake imaging method based on seismic waveforms recorded remotely at a group of seismometers (seismic array). Here, we develop a new approach to combine backprojections from multiple arrays and seismic waveforms and use it to derive a catalog of large earthquake rupture histories from 2010 to 2022, providing a map view of the high‐frequency radiation emitted along the fault. The method automatically estimates the earthquake rupture length, speed, directivity, and aspect ratio. Based on these estimates, we obtained scaling relations between the earthquake magnitude and rupture length that agree with classical relationships. We identified strike‐slip earthquakes propagating at supershear, that is, faster than the shear wave speed, the usual limit for self‐sustaining rupture propagation. We observed complex rupture behaviors, for example, multiple faults activated, bilateral ruptures, and triggering of the main phase of a rupture by a primary (P) wave from the earliest part of the rupture. For subduction earthquakes, high‐frequency emissions were often observed, forming a ring around the fault interface patches (asperities) where the main slip occurs. There was a preference for high‐frequency radiation to emanate from central and deeper parts of the subducting plate interface, but shallower emissions were more frequent than expected from previous literature. Key Points: We provide a complete catalog of high‐frequency rupture histories for M ≥ 7.5 events 2010–2022We develop a semi‐automatic method for estimating rupture length, speed, directivity, and aspect ratioBoth encircling ruptures and emissions updip of slip asperities common in megathrust earthquakes [ABSTRACT FROM AUTHOR]

Microseismic monitoring has been used in geo-energy related activities, such as shale-gas exploitation, mining, deep geothermal exploitation, geotechnical and structural engineering, for detecting and locating fractures, rock failures and micro-earthquakes. The success of microseismic monitoring depends on reliable detection and location of the recorded microseismicity. Multichannel coherence migration (MCM) is a detection and location waveform migration-based approach which does not require phase picking, identification and association and performs well on noisy data. Its caveat is a high computational cost, which impedes its application of MCM on large data sets or for real-time monitoring. To address this issue, we propose an improved approach, the multichannel coherence migration grid search (MCMgs), by introducing an adaptive grid optimization technique. Based on results from synthetic and real data, we show that MCMgs reduces the computation time up to 64 times. In addition, MCMgs generates multiple maximum coherence values with various grid sizes instead of a single (maximum) coherence value that links to a single gridpoint and size, thus resulting in more accurate locations. Our simulation results on different deployment geometries demonstrate that MCMgs is effective even with a small number of recordings available—a minimum of seven. We conduct a sensitivity analysis to assess how the detectability of events is affected by the spatial arrangement of the deployed monitoring array. If a limited number of seismometers are available for deployment, our analysis favours a patch array deployment geometry. We show that 12 seismometers deployed at a patch array geometry can have similar detection and localization capability as a large rectangular array of more than 100 seismometers but at a much lower computational and deployment cost. [ABSTRACT FROM AUTHOR]

Scientific objectives, instruments, and measurement program of the scientific instrumentation of the Kazachok stationary landing platform of the State Corporation Roscosmos and the European Space Agency (ESA) ExoMars-2022 project are presented. The scientific objectives of research on the landing platform included the long-term climate monitoring, the studies of the atmospheric composition, the mechanisms for dust lifting and related electrical phenomena, atmosphere–surface interactions, the subsurface water abundance, monitoring the radiation situation, and the study of Mars internal structure. To address these problems, 11 Russian and two European instruments with a total mass of 45 kg were built, tested and integrated into the spacecraft. These include a television camera system, meteorological complexes, a suite for studying dust and related electrical phenomena, optical spectrometers and an analytical complex for studying the atmospheric composition, a microwave radiometer, the neutron and gamma spectrometers for surface research, a seismometer, magnetometers and a Mars proper motion experiment to study its internal structure. Although the ExoMars-2022 project has been discontinued, the scientific objectives of the landing platform have not lost their relevance, and the technical solutions and developments implemented in scientific equipment are of interest and promising for further Mars exploration. [ABSTRACT FROM AUTHOR]

We propose a method for detecting earthquakes for high-speed trains based on unsupervised anomaly-detection techniques. In particular, we utilized autoencoder-based deep learning models for unsupervised learning using only normal training vibration data. Datasets were generated from South Korean high-speed train data, and seismic data were measured using seismometers nationwide. The proposed method is compared with the conventional Short Time Average over Long Time Average (STA/LTA) model, considering earthquake detection capabilities, focusing on a Peak Ground Acceleration (PGA) threshold of 0.07, a criterion for track derailment. The results show that the proposed model exhibit improved earthquake detection capabilities than STA/LTA for PGA of 0.07 or higher. Furthermore, the proposed model reduced false earthquake detections under normal operating conditions and accurately identified normal states. In contrast, the STA/LTA method demonstrated a high rate of false earthquake detection under normal operating conditions, underscoring its propensity for inaccurate detection in many instances. The proposed approach shows promising performance even in situations with limited seismic data and offers a viable solution for earthquake detection in regions with relatively few seismic events. [ABSTRACT FROM AUTHOR]

Detecting phase arrivals and pinpointing the arrival times of seismic phases in seismograms is crucial for many seismological analysis workflows. For land station data, machine learning methods have already found widespread adoption. However, deep learning approaches are not yet commonly applied to ocean bottom data due to a lack of appropriate training data and models. Here, we compiled an extensive and labeled ocean bottom seismometer (OBS) data set from 15 deployments in different tectonic settings, comprising ∼90,000 P and ∼63,000 S manual picks from 13,190 events and 355 stations. We propose PickBlue, an adaptation of the two popular deep learning networks EQTransformer and PhaseNet. PickBlue joint processes three seismometer recordings in conjunction with a hydrophone component and is trained with the waveforms in the new database. The performance is enhanced by employing transfer learning, where initial weights are derived from models trained with land earthquake data. PickBlue significantly outperforms neural networks trained with land stations and models trained without hydrophone data. The model achieves a mean absolute deviation of 0.05 s for P‐waves and 0.12 s for S‐waves, and we apply the picker on the Hikurangi Ocean Bottom Tremor and Slow Slip OBS deployment offshore New Zealand. We integrate our data set and trained models into SeisBench to enable an easy and direct application in future deployments. Plain Language Summary: Ocean bottom seismometers (OBS) are seismic stations on the seafloor. Just like their counterparts on land, they record many earthquakes on three component sensors but are additionally equipped with underwater hydrophones. To determine the location of an earthquake, seismologists must precisely measure the arrival times of seismic waves. For onshore data, machine learning (ML) has been highly successful in determining earthquake arrival times. However, the noise and the signal are different in the ocean environment. For example, the recordings can contain whale songs and water layer reverberations and are disturbed by ocean bottom currents. We have assembled an extensive database of ocean bottom recordings and trained artificial neural networks to use the underwater hydrophone information and cope with the ocean noise environment. We demonstrate that the resulting ML picker picks are similar to those of human experts and outperform phase pickers based on land data only. We compare earthquake catalogs based on different pickers created from an OBS deployment offshore New Zealand and demonstrate that PICKBLUE outperforms previous pickers. We make the database and ML picker available with a standard interface so that it is easy for other scientists to apply them in their studies. Key Points: We assembled a database of ocean Bottom Seismometer (OBS) waveforms and manual P and S picks, on which we train PickBlue, a deep learning pickerOur picker significantly outperforms pickers trained with land‐based data with confidence values reflecting the likelihood of outlier picksThe picker and database are available in the SeisBench platform, allowing easy and direct application to OBS traces and hydrophone records [ABSTRACT FROM AUTHOR]

Distributed acoustic sensing (DAS) using existing optical fiber cables facilitates high‐density seismic observation. However, few studies have examined the reliability of the seismic waveform amplitude recorded by DAS. In this study, a DAS network was connected to optical fiber cables installed over a distance of 75 km along a high‐speed train (Shinkansen) railway in the Kumamoto prefecture, Japan. We successfully observed strong motions of the Mj6.6 earthquake (approximately 150 km from the fiber) on 22 January 2022, in Hyuga‐nada, in addition to several small local earthquakes. The observed strong motions from the Mj6.6 earthquake, using DAS, exhibited cycle skipping (clipping) issues due to dynamic range limitations at numerous channels. To address this, we estimated the shaking map, representing maximum strain distributions for Mj6.6, by replacing the clipped data with information from nearby unclipped channels and scaling their RMS amplitudes based on S‐coda (unclipped). Furthermore, we verified the reliability of the amplitude information obtained from DAS by estimating the distance attenuation of seismic waves while correcting for the differences in the structure type and coupling as much as possible. The distance attenuation property of local earthquakes was consistent with that of the peak ground velocities obtained from seismometers, indicating that DAS data acquired using fibers installed on infrastructure (various structures) can also be utilized to assess the spatial distribution of the relative amplitude values along the fiber. Obtaining high‐density seismic motion distributions is important for earthquake early warning and accurate damage estimation of strong motions. Plain Language Summary: In the past decade, the use of distributed acoustic sensing (DAS) for seismological purposes has grown rapidly. By utilizing existing dark fibers, such as telecommunication cables, high‐density seismic observation networks can be established in residential areas. In this study, DAS was applied to an existing optical fiber cable installed along a railway line, which recorded strong ground motions of Mj6.6 originating from a subduction zone, as well as numerous regional earthquakes. Although the technique uses optical phase differences for strain measurements and therefore has a poor response to strong ground motions, we resolve this issue using the correct data from neighboring stations as a reference and estimated the shaking distribution every 5 m along 75 km of the railway line. Additionally, when we analyzed the distance attenuation of small regional earthquakes, we found that the attenuation characteristics were similar to those obtained from permanent seismometers. These findings will significantly impact society in the future, particularly as the fiber cables replace conventional seismic observation networks. As a result, ultra‐high‐density observation networks will be capable of issuing more accurate earthquake early warnings and estimating damage following earthquakes. Key Points: We successfully observed strong motion from the Mj6.6 earthquake in Hyuga‐nada using Distributed acoustic sensing (DAS) over 75 km along railwayThe shaking map of the Mj6.6 was estimated by replacing the clipped channels with nearby unclipped channels and scaling their RMS amplitudes of S‐codaDistance attenuation property of local earthquakes of DAS consistent with that of peak ground velocities from continuous seismometers [ABSTRACT FROM AUTHOR]

A detailed investigation has been conducted of the interaction between a Martian regolith simulant and the foot of a seismometer (SEIS) recently deployed on the surface of Mars within the NASA InSight mission. A specific device used to investigate the SEIS/ground interaction was improved to provide accurate measurements of low forces and displacements, with a higher system stiffness and appropriate thermal insulation. A series of tests was carried out with a 60 mm dia. disc and the SEIS foot (disc with a spike at its centre). The maximum disc penetration in the loose sand used as simulant under the SEIS weight (10 N) was between 400 and 600 μm, with a tiny effect of the spike. Load cycles under various forces were performed to investigate the elastic interaction, with good reversibility and a linear change of the Young's modulus with respect to the average vertical stress. The tests provided comparable values, showing that the Young's modulus was around 20 MPa – which is compatible with that of loose terrestrial sands – and agreed well with the seismic wave velocities at the surface (from laboratory experiments and from measuring on the surface of Mars the travel times of waves received by the SEIS seismometer). [ABSTRACT FROM AUTHOR]

Continuous seismic monitoring of volcanoes is challenging due to harsh environments and associated hazards. However, the investigation of volcanic phenomena is essential for eruption forecasting. In seismo-volcanic applications, distributed acoustic sensing (DAS) offers new possibilities for long-duration surveys. We analyse DAS strain rate signals generated by volcanic explosions and tremor at Stromboli volcano (Italy) recorded along 1 km of dedicated fibre-optic cable. We validate DAS recordings with colocated nodal seismometers. Converting node measurements to strain rate, we observe a perfect match in phase between DAS and node waveforms. However, DAS amplitudes appear to be around 2.7 times smaller than those of node records, which we explain as due to the inefficient ground-to-fibre strain transfer in the loose cable. We invert time delays between strain rate waveforms and confirm that the DAS enables us to retrieve a dominant and persistent seismic source in the proximity of active craters. This stable source location is confirmed by node array analyses. Despite an observed high noise level of strain rate signals outside a range of 2–15 Hz, our results demonstrate the potential of this new technology in monitoring volcanic areas. [ABSTRACT FROM AUTHOR]

In this work, we present both 1-D and 3-D shear wave velocity (Vs) models of the oceanic crust and uppermost mantle below the Deep OCean Test ARray area, located ∼ 70 km north of the central section of the Gloria Fault, in the eastern North-Atlantic Ocean. The velocity models are inferred from the dispersion of surface waves recorded on ocean bottom seismometers. Dispersion measurements are obtained from the analysis of ambient seismic noise at short periods (< 14 s) and teleseismic surface waves at long periods (> 14 s) using the two-station method. The 1-D Vs model is inferred from the joint inversion of Rayleigh wave phase velocities and Love wave group and phase velocities. The 3-D tomographic model is obtained by inversion of 2-D Love wave group velocity maps as a function of depth, further constrained by the average of Love wave phase velocities obtained from ambient noise (4–9 s) and the average Rayleigh and Love wave phase velocities calculated from teleseismic data (14–44 s). The 1-D Vs model shows a sediment layer with a low velocity of 1.05 km s−1, similar to previous studies in the region. Below the sediments, we find an oceanic crust with velocities ranging from 3.3 to 4.5 km s−1. The model reaches an unusually high velocity of 4.9 km s−1 in a 20 km thick layer at depths between 16 and 36 km. We interpret this fast velocity layer as indicative of the presence of harzburgite, a residue of enhanced melting that might have been formed by the proximity between the Mid-Atlantic Ridge and the Azores mantle plume. At greater depths the velocity decreases, forming a low-velocity zone that reaches a minimum at ∼ 70 km depth, which we interpret as the maximum depth for the lithosphere–asthenosphere boundary. The 3-D model shows a structure that is mostly horizontally layered, with Vs isocontours at 3.5–4.5 km s−1 highlighting oscillations of the crustal structure with wavelengths of ∼25–30 km. These oscillations may be due to changes in the rate of mantle upwelling and magma supply rate. [ABSTRACT FROM AUTHOR]

The data from a permanent monitoring station, based on a sound card as an AD/DA converter and a magnetic loop antenna for continuous recording of electromagnetic field intensities in the Very Low Frequency (VLF) range, were recorded and compared with data from a seismic station based on Raspberry Pi located in a landslide-affected area. The stations operated 24/7 and were placed in a room below ground level. Correlations were observed between seismic signals in the frequency spectrum from 0.1 Hz to 10 Hz (some extending beyond 20 Hz) and anomalies in the electromagnetic field in the VLF range. These anomalies are likely associated with micro-fracturing, piezo-electromagnetic, and triboelectric phenomena within the landslide body, producing relatively weak VLF emissions and a faint seismic signal. A single-component 4.5 Hz 395 Ohm vertical Racotech RGI-20DX geophone with electronic extension to lower frequencies (<1 Hz) was used with a sampling rate of 100 sps (samples per second). VLF emissions cover almost the entire spectrum from 6 kHz with a peak at 14 kHz to 18 kHz. The received spectrum was divided into following sections: VLF band 4 kHz-6 kHz; 6 kHz - 8 kHz; 8 kHz -10 kHz; 10 kHz - 12 kHz; 12 kHz -14 kHz; 14 kHz - 16 kHz; 16 kHz -18 kHz. Simultaneously, there were changes in these sections analysed and compared with the seismic record within the same time interval. [ABSTRACT FROM AUTHOR]

On February 6, 2023, at 01 : 17 UTC, a M = 7.8 earthquake struck the southern area of Turkey near Gaziantep town and was followed by a second earthquake of M = 7.5 at 10 : 24 UTC with the epicenter in Elbistan city. Both events were associated with the Anatolian Fault System and have claimed over 50,000 victims, as reported by the Disaster and Emergency Management Authority, and caused serious damage in the regions of southern Turkey and northern Syria. Seismic waves related to strong Turkey earthquakes have been recorded both by seismic stations throughout the globe and on other devices such as the ground deformation (GNSS, strainmeters, or tiltmeters) networks. In this paper, we show and analyze the earthquake signals recorded by bore-hole tilt stations that monitor seismic and volcanic activities at Mt. Etna. Tilt stations showed very large variations, despite their distance from the epicenter (approximately 1950 km) with a period between 10 and 25 seconds. We compared tilt and seismic data for a co–located station evidencing a very similar waveform that highlight how tiltmeters respond to translational acceleration rather than ground tilt during a teleseism, suggesting that, for waves with this period, they may behave as horizontal seismometers. By using these signals, we evidence the different behaviors of two of the most used models of tiltmeters on volcanoes (Lily and Pinnacle) and how they are useful for instrument calibration. [ABSTRACT FROM AUTHOR]

In this study, geodetic and seismic phenomena occurring in the subduction zone were observed with two tiltmeters that were installed in seafloor boreholes drilled in the Kumano Basin in the Nankai Trough, southwest Japan. We used one electrolytic-type tiltmeter and one pendulum-type tiltmeter installed 6 and 19 m below the seafloor, respectively. The two tiltmeters have been continuously operated since 2019 and 2021, respectively. The records of the two tiltmeters showed microseisms, seismic signals of normal earthquakes, and variations that correlated well with the M2 tide (tidal response). The noise environment relative to existing seafloor observatories was assessed by comparing the power spectral densities of the tiltmeters and broadband seismometers in the seismic frequency band. The two tiltmeters and the seismometers showed similar spectral peaks in the microseism band, and at lower frequencies below 10–2 Hz, the pendulum tiltmeter had a noise level that was up to 20 dB lower than that of the broadband seismometers. The tidal responses of the tiltmeters were analyzed to reveal corresponding mechanisms because a large amplitude of the tidal response makes it difficult to detect geodetic phenomena. Considerable azimuthal dependence was observed in the NW–SE direction for both tiltmeters. The principal direction was approximately the same as the down-slope direction of the local bathymetry. This tendency was thus interpreted to be caused by the differences in the thickness of the sedimentary layer along the direction of the slope. Furthermore, the validity of the tilt measurements for the two tiltmeters was demonstrated by in-situ loading experiments, where the theoretical response of an elastic half-space medium was computed and compared with the experimental results. The results of the loading experiments can be explained using theoretical values with a set of realistic elastic moduli. [ABSTRACT FROM AUTHOR]

A new, improved approach in the design of broadband seismometers is presented. The design results in the implementation of a high performance, low cost, and simple-to-operate instrument. The proposed seismometer is based on a modified accelerometer followed by a continuous time integrator for providing velocity voltage output. It has a broadband response, flat in velocity from 120 s to 75 Hz, high sensitivity 1200 V/(m/s), and 40 Vpp differential output range. The acceleration integration method provides high performance at low frequencies, with self-noise well below the New Low Noise Model at the range 80 s–16 Hz. The mechanical system provides a perfectly linear response of its displacement sensing system. Evaluation, classification, and noise determination of the presented instrument are performed in terms of direct experimental measurements, simulations, and calculations based on raw data from the proposed sensor and from a commercial product with approximately equivalent performance. Its technical features and performance specifications guarantee accurate sensing of local events, with maximum power at the frequency range of 5 to 10 Hz, but also make it ideal for the recording of distant tectonic activity, where extremely weak motions at long periods are expected. The whole design is robust, lightweight, and weatherproof, comprising in this way a useful tool to geoscientists. [ABSTRACT FROM AUTHOR]

With the development of rotational seismometers, especially the high‐sensitivity optical seismometers, rotational motions have shown increasing benefits for calculation of dispersion curves, estimation of back azimuth and many other applications. Here we present the six‐component observations of 2019 Hualien mww6.1 earthquake simultaneously on both sides of Taiwan Strait. Seismograms and time‐frequency spectrums show that the attenuations for rotations are more significant than translations. Calculations of the surface wave phase velocity are performed for both stations through single‐station records, indicating a distinct difference owning to the instrument installation conditions and site effect. Comparison of theoretical dispersion curves is consistently in trends with estimations, and reveals the influence of higher‐mode surface waves. For the first time, we locate the source of a earthquake through intersection of two stations' back azimuths estimated through rotational methods. Results of comparing to translational polarization verify that rotational observation can distinctly reduce the deviation of source location. Plain Language Summary: We recorded the 2019 Hualien mww6.1 earthquake by two stations on both sides of Taiwan Strait. Both stations consist of a broad‐band seismometer and a fiber‐optic rotational seismometer. We compare the amplitudes and Time‐frequency spectrums of two stations' records and validate the attenuations for translational and rotational motions. Due to the shorter epicentral distance, NA01 station in Taiwan observed stronger signals than QS01 station in Fujian. We then use the rotational methods to compute surface wave phase velocity and dispersion curves, and validate the results by theoretical calculation through crust model. Estimation of back azimuths of two stations are used to locate the earthquake, indicating that rotational motions can reduce the deviation of source location. Key Points: Co‐located observations of three translational motions and three rotational motionsEstimate the phase velocity dispersion curves and back azimuths by rotational methodsLocate the earthquake source by three methods and evaluate the deviations [ABSTRACT FROM AUTHOR]

Elevated seismic noise for moderate‐size earthquakes recorded at teleseismic distances has limited our ability to see their complexity. We develop a machine‐learning‐based algorithm to separate noise and earthquake signals that overlap in frequency. The multi‐task encoder‐decoder model is built around a kernel pre‐trained on local (e.g., short distances) earthquake data (Yin et al., 2022, https://doi.org/10.1093/gji/ggac290) and is modified by continued learning with high‐quality teleseismic data. We denoise teleseismic P waves of deep Mw5.0+ earthquakes and use the clean P waves to estimate source characteristics with reduced uncertainties of these understudied earthquakes. We find a scaling of moment and duration to be M0 ≃ τ4, and a resulting strong scaling of stress drop and radiated energy with magnitude (Δσ≃M00.21 ${\Delta }\sigma \simeq {M}_{0}^{0.21}$ and ER≃M01.24 ${E}_{R}\simeq {M}_{0}^{1.24}$). The median radiation efficiency is 5%, a low value compared to crustal earthquakes. Overall, we show that deep earthquakes have weak rupture directivity and few subevents, suggesting a simple model of a circular crack with radial rupture propagation is appropriate. When accounting for their respective scaling with earthquake size, we find no systematic depth variations of duration, stress drop, or radiated energy within the 100–700 km depth range. Our study supports the findings of Poli and Prieto (2016, https://doi.org/10.1002/2016jb013521) with a doubled amount of earthquakes investigated and with earthquakes of lower magnitudes. Plain Language Summary: The vibration of the Earth's ground recorded at seismometers carries the seismic signatures of distant earthquakes superimposed to the Earth's natural or anthropogenic noise surrounding the seismic station. We use artificial intelligence technology to separate the weak signals of distant earthquakes from other sources of ground vibrations unrelated to the earthquakes. The separated signal provides new insights into earthquakes, especially those within the Earth's deep interior, most of which have not been investigated due to noise levels. In contrast with shallow earthquakes, deep earthquakes are less efficient at radiating energy, though they exhibit a higher rate of increase in both stress drop and radiated energy as they grow. This may suggest that deep earthquakes tend to be more confined fault surfaces. A dual mechanism between nucleation in the subduction‐zone core and propagation of larger events in the dry mantle explains our observations. Key Points: A neural network is used to double the number of earthquakes for source analyses compared to previous studies by improving the data qualityDenoising teleseismic waves improves the source signature in Mw5.0–6.0 events and reduces uncertainties over all magnitudesLarge deep earthquake ruptures are dissipative and compact relative to crustal earthquakes [ABSTRACT FROM AUTHOR]

Seismic activity in the Northern Canadian Cordillera is characterized by diffuse earthquakes that extend hundreds of km northwest from the Yakutat collision zone. We use 25 months of broadband seismic data from Mackenzie Mountain Earthscope Project (MMEP), USArray Transportable Array (TA), and permanent Canadian National Seismic Network stations to present a local earthquake catalog with high sensitivity to small regional events. Deep learning techniques are adopted for both seismic phase detection and association. Event relocations are performed to provide well constrained estimates of earthquake depth distributions. Clusters of seismicity spanning the upper crust are located in the central Richardson Mountains, along the Tintina fault, and in the northeast Selwyn Basin. These clusters suggest that the core of the Richardson Anticlinorium is tectonically active and that the Tintina fault is a locus for low levels of active deformation. We interpret seismicity in the northeast Selwyn Basin as primarily occurring in the hanging wall of the Plateau thrust fault and suggest that some combination of localized duplex structures and lithological strength contrasts both within the Selwyn Basin and between abutting Paleozoic shelf sequences may be responsible for seismicity in the Mackenzie Mountain foreland. Plain Language Summary: The Northern Canadian Cordillera is situated inboard of a transition from a right‐lateral tectonic regime in coastal southeast Alaska to a subduction setting beneath southern Alaska. It presents a unique case study to examine the length scales over which tectonic deformation is able to permeate into a continental interior. In this study we use data from a relatively dense regional network of seismometers in order to present the most spatially complete catalog of earthquakes in the Northern Canadian Cordillera to date. We use machine learning techniques for the detection and classification of small magnitude earthquakes and use our results to analyze regional tectonics. Active faults that host earthquakes are identified and we use the earthquake depth distributions to further distinguish between mechanisms for regional stress transfer. We find evidence that the Tintina fault is presently active along a confined area at a low rate. We also find that low magnitude earthquakes do not generally occur within the Mackenzie Mountain Fold‐Thrust belt but are prevalent within the foreland Selwyn Basin. We also detect a pattern of relatively deep (∼30 km) earthquakes in the northern Richardson Mountains. Key Points: Small magnitude earthquakes extending to 25 km depth are detected throughout the Selwyn Basin in the Mackenzie Mountain forelandThe Tintina fault hosts a range of low magnitude earthquakes in the uppermost 15 km of crust within a 400 km zone along strikeDeformation in the Richardson Mountains is localized onto a narrow band of faults centered on the Richardson Anticlinorium [ABSTRACT FROM AUTHOR]