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Discusses problems reflecting the crisis in education, presenting general approaches for resolving the problems. Also discusses the California Earthquake Education Project (CALEEP), a program which provides education of current concern (including personal emergency preparedness) to the public. CALEEP activities are successful due to many social, economic, and political reasons. (DH)

We propose a new approach capable of measuring local seismic anisotropy from 6C (three‐component translation and three‐component rotation) amplitude observations of ambient seismic noise data. Our recent theory demonstrates that the amplitude ratio of 6C cross‐correlation functions (CCFs) enables retrieving the local phase velocity. This differs from conventional velocity extraction methods based on the travel time. Its local sensitivity kernel beneath the 6C seismometer allows us to study anisotropy from azimuth‐dependent CCFs, avoiding path effects. Such point measurements have great potential in planetary exploration, ocean bottom observations, or volcanology. We apply this approach to a small seismic array at Pin˜ $\widetilde{n}$on Flat Observatory (PFO) in southern California, array‐deriving retrieves rotational ground motions from microseismic noise data. The stress‐induced anisotropy is well resolved and compatible with other tomography results, providing constraints on the origin of depth‐dependent seismic anisotropy. Plain Language Summary: In contrast to the well‐known translational displacement of seismic waves, rotation measures the angle of rotation of a point in a medium as it deforms. Traditionally, the travel time of cross‐correlation functions of translational displacement of ambient seismic noise and its corresponding propagation distance is used to calculate the velocity of the subsurface medium. However, our recent theory shows that the amplitude ratio of translational displacement and rotation allows us to reveal the local velocity of the medium beneath the receiver from ambient seismic noise data and to study anisotropy from earthquake events. This paper points out for the first time that the amplitude observations of 6C cross‐correlation functions of ambient seismic noise provide a new approach to measure the azimuth‐dependent velocity in anisotropic media. Key Points: We show a new approach enabling the extraction of local phase velocity from 6C cross‐correlation functions of ambient noise dataAzimuth‐dependent 6C cross‐correlation functions allow us to study local seismic anisotropy and its depth dependenceLocal anisotropy at Pin˜ $\widetilde{n}$on Flat Observatory is compatible with compression stress, providing constraints on stress‐induced anisotropy [ABSTRACT FROM AUTHOR]

We evaluate seismicity and tectonics along the San Andreas Fault (SAF) in southern California to elucidate ongoing near‐field crustal deformation processes. The principal slip surfaces (PSSs) or the fault core that accommodate major earthquakes, form the boundary between the tectonic plates. We analyze seismicity catalogs extending back to 1857, 1932, and 1981 with progressively improved magnitude of completeness and spatial resolution. The 1857 to present statewide catalog that is complete at M5.5+ documents minimal aftershock activity for the Mw7.9 1857 and 1906 Mw7.8 San Francisco earthquakes. The higher quality 1932 and 1981 catalogs show that the PSSs (the rupture zone) of the 1857 Mw7.9 Fort Tejon earthquake exhibits remarkable seismic quiescence both in the core and in the adjacent extended‐damage zone. Further south, the fault core is still aseismic but the shape of the SAF is more complex, and the rate of adjacent seismicity is much higher. This fault complexity and the seismicity rate are larger the more the strike of the SAF deviates from the Pacific plate velocity‐vector direction. The focal mechanisms of the SAF adjacent earthquakes are also heterogeneous and rarely have strikes and dips that are consistent with slip on the nearby PSSs. We infer that the southern SAF is locked, and a lack of seismicity at the core of the fault may be a standard feature of faults that almost exclusively accommodate high‐slip rates by producing major earthquakes. Correspondingly future aftershock sequences of major earthquakes on the southern SAF will likely have below average aftershock productivity. Plain Language Summary: The fast‐moving San Andreas Fault (SAF) that runs up the spine of California from the Salton Sea to Cape Mendocino, forms the plate boundary between the Pacific and North America plates. It has accommodated California's two largest earthquakes, the 1857 Mw7.9 Fort Tejon and the 1906 Mw7.8 San Francisco, which ruptured the fault for hundreds of kilometers. We focus on analyzing the seismicity of the southern SAF from 1857 to present. Almost no small earthquakes occur along the core of the southern SAF, from south of Parkfield to the Salton Sea. To the north, the 1857 Mw7.9 earthquake was followed by relatively few immediate aftershocks, and since 1932 this section of the fault has been very seismically quiescent. To the south of the 1857 rupture, the rate of extended‐damage zone seismicity inversely correlates with the SAF strike deviation from the Pacific plate vector relative to the SAF strike. In particular, the east‐west striking 28 km long Mill Creek segment may impede SAF slip, which results in abundant off‐fault seismicity and transfer of tectonic strain into the eastern California shear zone and the Peninsular Ranges. Key Points: The near‐field seismicity of the southern San Andreas Fault (SAF) is limited to the adjacent extended‐damage zone and mostly absent from the fault coreStep changes in the 95% depth and in the texture of seismicity depth distribution likely define the presence of the SAF fault coreSeismicity rate in the damage zone positively correlates with SAF deviation from Pacific plate vector except in the quiescent 1857 rupture [ABSTRACT FROM AUTHOR]

Earthquake nowcasting has been proposed as a means of tracking the change in large earthquake potential in a seismically active area. The method was developed using observable seismic data, in which probabilities of future large earthquakes can be computed using Receiver Operating Characteristic methods. Furthermore, analysis of the Shannon information content of the earthquake catalogs has been used to show that there is information contained in the catalogs, and that it can vary in time. So an important question remains, where does the information originate? In this paper, we examine this question using stochastic simulations of earthquake catalogs. Our catalog simulations are computed using an Earthquake Rescaled Aftershock Seismicity ("ERAS") stochastic model. This model is similar in many ways to other stochastic seismicity simulations, but has the advantage that the model has only 2 free parameters to be set, one for the aftershock (Omori‐Utsu) time decay, and one for the aftershock spatial migration away from the epicenter. Generating a simulation catalog and fitting the two parameters to the observed catalog such as California takes only a few minutes of wall clock time. While clustering can arise from random, Poisson statistics, we show that significant information in the simulation catalogs arises from the "non‐Poisson" power‐law aftershock clustering, implying that the practice of de‐clustering observed catalogs may remove information that would otherwise be useful in forecasting and nowcasting. We also show that the nowcasting method provides similar results with the ERAS model as it does with observed seismicity. Plain Language Summary: Earthquake nowcasting was proposed as a means of tracking the change in the potential for large earthquakes in a seismically active area, using the record of small earthquakes. The method was developed using observed seismic data, in which probabilities of future large earthquakes can be computed using machine learning methods that were originally developed with the advent of radar in the 1940s. These methods are now being used in the development of machine learning and artificial intelligence models in a variety of applications. In recent times, methods to simulate earthquakes using the observed statistical laws of earthquake seismicity have been developed. One of the advantages of these stochastic models is that it can be used to analyze the various assumptions that are inherent in the analysis of seismic catalogs of earthquakes. In this paper, we analyze the importance of the space‐time clustering that is often observed in earthquake seismicity. We find that the clustering is the origin of information that makes the earthquake nowcasting methods possible. We also find that a common practice of "aftershock de‐clustering", often used in the analysis of these catalogs, removes information about future large earthquakes. Key Points: Earthquake nowcasting tracks the change in the potential for large earthquakes, using information contained in seismic catalogsWe analyze the information contained in the space‐time clustering that is observed in earthquake seismicityWe find that "aftershock de‐clustering" of catalogs removes information about future large earthquakes that the nowcasting method uses [ABSTRACT FROM AUTHOR]

We use the Godano et al. (2022, https://doi.org/10.1029/2021ea002205) method for evaluating the b maps of the faults associated with the largest earthquakes M ≥ 7.0 that occurred in California. The method allows an independent evaluation of the b parameter, avoiding the overlap of the cells and the omission of some earthquakes, while keeping all the available information in the catalog. We analyzed four large earthquakes: Landers, Hector Mine, Baja California, and Searles Valley. The maps obtained confirm that the b value can be considered as a strain meter and allow us to elucidate the presence of barriers, such as obstacles to the propagation of the fracture, on the fault of the analyzed earthquakes. A further estimated parameter is the time window during which aftershocks occur in the cell, Δt. This quantity is very useful for a better definition of the aftershock generation mechanism. It reveals where the stress is released in a short time interval and how the complexity of the faulting process controls the occurrence of aftershocks on the fault, and also the duration of the entire sequence. Plain Language Summary: The use of the Godano et al. (2022, https://doi.org/10.1029/2021ea002205) method for evaluating the b maps for the on‐fault seismicity during the occurrence of aftershocks allows us to put some constraints on the faulting process during the aftershock sequences. Key Points: Independent cells b value spatial maps evaluationNew operator‐independent griding method for spatial discretization [ABSTRACT FROM AUTHOR]

Seismic moment accumulation rate is a fundamental parameter for assessing seismic hazard. It can be estimated geodetically from either fault‐based modeling, or strain rate‐based calculations, where fault‐based models largely depend on the rheological layering and the number of faults. The strain‐rate method depends on an unknown (Kostrov) thickness used to convert strain rate into moment rate. In Part 1 of this study, we use three published fault‐based models from southern California to establish the value of the Kostrov thickness such that the total moment from the strain‐rate approach, calculated from the fault model‐predicted strain rate, matches the fault‐based approach. Constrained thickness estimates of 7.3, 9.7, and 11.5 km (6.4–13.0 km, including uncertainties) suggest that the 11 km value used in previous studies may be too large and a lower value may be more accurate. In Part 2 we use calibrated values of Kostrov thickness, along with the latest compilation of GNSS velocity data, to partition moment rate into on‐fault and off‐fault moment rate, where off‐fault varies from 32%–43% of the total moment rate. The largest uncertainty is related to the method used to interpolate sparse GNSS data. Lastly, we compare our estimates of total moment rate (mean: 2.13 ± 0.42 × 1019 Nm/yr) with the historical seismic catalog. Results suggest that including uncertainties in Kostrov thickness brings fault‐based geodetic moment rate closer to the seismic moment release (particularly when aseismic afterslip is accounted for), while the (uncertain) values of off‐fault moment rate push geodetic moment rates to be larger than seismic moment rates. Plain Language Summary: One commonly‐used method of evaluating the future earthquake potential of a known fault is to estimate the moment rate accumulating along that fault. In other words, one can estimate how much earthquake energy is accumulating over time, caused by the forces that propel plate boundary motion along fault systems. Two ways to do this are: (a) using detailed fault models of plate boundary motion, which are constrained by satellite‐based measurements of surface motion or (b) using those satellite‐based measurements directly through the estimation of regional strain rate. Here, we investigate the uncertainties present in the latter method by comparing previously published estimates of fault‐based moment rate, with their equivalent strain rate‐based versions. We estimate uncertainties related to the strain‐rate based method, which can lead to an over‐ or under‐estimation of earthquake potential on a given fault system. In addition, we explore the off‐fault moment rate accumulation in southern California which can help identify areas of enhanced moment accumulation and thus increased seismic hazard. Key Points: Previously published fault‐based models can constrain a Kostrov thickness range of 6.4–13.0 km, including uncertaintiesDifferences between strain rate moment estimates and fault‐based estimates suggest 32%–43% off‐fault moment rate in southern CaliforniaThe largest uncertainty in estimating moment rate from strain rate is the degree of smoothing used to estimate strain from geodetic data [ABSTRACT FROM AUTHOR]

I employ an elasticity‐based method to invert a geodetically derived surface velocity field in the western US using for present‐day surface strain rate fields with uncertainties. The method uses distributed body forces in a thin elastic sheet and allows for discontinuities in velocity across creeping faults using the solution for dislocations in a thin elastic plate. I compare the strain rate fields with previously published stress orientations and moment rates from geological slip rate data and previous geodetic studies. Geologic and geodetic moment rates are calculated using slip rate and off‐fault strain rates from the 2023 US National Seismic Hazard Model (NSHM) deformation models. I find that computed total geodetic moment rates are higher than NSHM summed moment rates on faults for all regions of the western US except the highest deforming rate regions including the Western Transverse Ranges and the northern and southern San Andreas Fault (SAF) system in California. Computed geodetic moment rates are comparable to the moment rates derived from the geodetically based NSHM deformation models in all regions. I find systematic differences in orientations of maximum horizontal shortening rate and maximum horizontal compressive stress in the Pacific Northwest region and along much of the SAF system. In the Pacific Northwest, the maximum horizontal stress orientations are rotated counterclockwise 40–90° relative to the maximum horizontal strain rate directions. Along the SAF system, the maximum horizontal stresses are rotated systematically 25–40° clockwise (closer to fault normal) relative to the strain rates. Plain Language Summary: Geodetic measurements of surface velocities can be converted to strain rate which is a measure of the distortion rate of the crust. Strain rate is an important quantity for earthquake studies because it can be related to the rate of elastic stored energy (called moment) available for future earthquakes. In this paper I employ a new method to invert the surface velocity field in the western US for present‐day surface strain rates. I convert the strain rates to crustal moment rates and compare this with moment release rates computed from previous estimates of fault slip rates from geology as well as with model moment rates from the 2023 US National Seismic Hazard Model (NSHM). I find that computed total geodetic moment rates are higher than NSHM model moment rates on faults for many regions of the western US. I also find systematic differences in present‐day strain rates and total stress in the crust indicating that the total stress in the crust is not simply the accrual of present‐day deformation. Key Points: I implement an elasticity‐based inversion of geodetic data for strain rateSystemic differences in orientation of stress and strain rate in Cascadia, San Andreas Fault, and eastern Basin‐RangeThe total geodetic moment rates are higher than geologic moment rates within most regions of the western US [ABSTRACT FROM AUTHOR]

We analyze nearest‐neighbor proximities of earthquakes in California based on the joint distribution (T, R) of rescaled time T and rescaled distance R between pairs of earthquakes (Zaliapin & Ben‐Zion, 2013a, https://doi.org/10.1002/jgrb.50179), using seismic catalogs from several regions and several catalogs for the San Jacinto Fault Zone (SJFZ). The study aims to identify informative modes in nearest‐neighbor diagrams beyond the general background and clustered modes, and to assess seismic catalogs derived by different methods. The results show that earthquake clusters with large and small‐to‐medium mainshocks have approximately diagonal and horizontal (T, R) distributions of the clustered mode, respectively, reflecting different triggering distances of mainshocks. Earthquakes in the creeping section of San Andreas Fault have a distinct "repeaters mode" characterized by very large rescaled times T and very small rescaled distances R, due to nearly identical locations of repeating events. Induced seismicity in the Geysers and Coso geothermal fields follow mostly the background mode, but with larger rescaled times T and smaller rescaled distances R compared to tectonic background seismicity. We also document differences in (T, R) distributions of catalogs constructed by different techniques (analyst‐picks, template‐matching and deep‐learning) for the SJFZ, and detect a mode with very large R and small T in the template‐matching and deep‐learning based catalogs. This mode may reflect dynamic triggering by passing waves and/or catalog artifacts. Plain Language Summary: Earthquake patterns in space and time provide important information about the properties of active faults, the surrounding crust, and different loading mechanisms. Nearest‐neighbor diagrams of earthquake proximities in a combined space‐time‐magnitude domain can be used to detect different types of seismicity patterns. Using such diagrams to analyze several earthquake catalogs in California, we examine characteristics of different sections of the San Andreas and San Jacinto faults along with geothermal areas and the entire southern California. The results highlight features of seismicity beyond the well‐known background and clustered modes of earthquakes, including a mode of repeating events in the creeping section of the San Andreas fault and differences between events in regular tectonic areas and geothermal fields. Analyses of different catalogs for the region around the San Jacinto fault also reveal differences related to the different techniques used to construct the catalogs. The results show that nearest‐neighbor diagrams can provide a simple and efficient way to identify different types of seismicity and assess the quality of earthquake catalogs. Key Points: Nearest‐neighbor diagrams provide an efficient way to identify different types of earthquake patternsRepeating events, large versus small‐moderate mainshocks, and induced seismicity have characteristic signaturesNearest‐neighbor diagrams help to evaluate features in catalogs constructed by different methods [ABSTRACT FROM AUTHOR]

It is critical to characterize submarine landslide hazards near dense coastal populations, especially in areas with active faults, which can trigger slope failure, subsequent tsunamis, and damage seabed infrastructure during earthquake shaking. Offshore southern California, numerous marine geophysical surveys have been conducted over the past decade, and high‐resolution bathymetric and subsurface data now cover about 60 percent of the total region between Point Conception and the United States‐Mexico border from the California coast out to the base of Patton Escarpment ∼200 km offshore. In a comprehensive compilation and interpretive mapping effort, we find evidence of seafloor failure throughout offshore southern California with nearly 1,500 submarine landslide‐related features, including 63 discrete slide deposits with debris and >1,400 slide‐related scarps. In our analysis, we highlight new mapping of submarine landslides in Catalina Basin, the Del Mar slide, the San Gabriel slide complex, and the 232 km2 San Nicolas slide, the largest area of any known submarine landslide mass offshore southern California. Analysis of the spatial distribution of submarine landslide features suggests that most mapped slide features are located relatively near coastal sediment sources, particularly during sea‐level lowstand conditions, which underscores the importance of sediment supply and sediment accumulation on low‐gradient slopes as failure preconditioning processes. Tectonically driven uplift at shelf edges and along basin flanks is another key preconditioning factor, and our results also suggest that earthquakes along active faults trigger mass wasting, especially for repeated, small‐scale failures on tectonically steepened slopes. Plain Language Summary: Submarine landslides can damage seabed infrastructure such as cables and moorings, cause tsunamis, and be triggered by shaking from earthquakes. It is important to understand the risk of submarine landslides near dense coastal populations, particularly where earthquakes also pose hazards. Offshore southern California, we have new high‐resolution seafloor and subsurface imaging data that help us to identify submarine landslide deposits in the marine environment. In our study, we map and compile evidence for submarine landslides and find nearly 1,500 slide‐related features, 63 of which feature significant debris deposits. We describe some of the larger slides in this study for the first time, including submarine landslides in Catalina Basin, the Del Mar slide, the San Gabriel slide complex, and the 232 square kilometer San Nicolas slide, which is one of the largest known submarine landslide masses offshore southern California. Our work suggests that submarine landslide failure processes offshore southern California require a combination of (a) significant sediment supply, which is enhanced during low sea‐level conditions, (b) uplift and steepening along faults, and (c) earthquake shaking to trigger slide events. Key Points: Comprehensive analysis of submarine landslides in southern California provides new metrics on their size, distribution, timing, and geologySubmarine landslide failure processes are controlled by a combination of sediment deposition, tectonic uplift, and earthquake triggeringSmall‐scale failures dominate steep areas near Quaternary faults; large slides tend to occur on lower slopes farther from faults [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]

Discrimination between tectonic earthquakes (EQs) and quarry blasts is important for accurate EQ cataloguing and seismic hazard analysis. However, reliable classification of these two types of seismic events is challenging with no prior knowledge of source parameters. Here, we applied deep learning to perform this classification task in southern California and eastern Kentucky. Since the two regions differ significantly in available labelled data, class imbalance and waveform characteristics, we adopted different strategies for them. We directly trained a convolutional neural network (CNN) for southern California due to its data abundancy. To alleviate the class imbalance, the blast data were augmented by repeated sampling. The model for California yields F1-scores of >83.5 per cent when estimated by individual stations and >98.1 per cent by network average (i.e. averaging the CNN's outputs on all available stations for each event). As eastern Kentucky has a much smaller data size, we apply transfer learning to the pre-trained California model to fit the Kentucky data. The transfer-learned model yields F1-scores of >86.9 per cent when estimated by individual stations and >96.7 per cent by network average. The transfer-learned model outperforms the model re-trained from scratch for the Kentucky data. Gradient-weighted class activation mapping shows the S onset and the S long-period coda are important to identify EQs and blasts, respectively. By visual inspections of the seismograms, the source locations, the origin time and the P -wave polarities, we verified that most of the events falsely predicted by our models are actually mislabelled by seismic analysts. Our models thus show great potential in helping seismic analysts find those mislabelled events which remain hidden in the historical catalogue. Our results demonstrate that deep learning can achieve high accuracy in seismic event discrimination and that transfer learning is effective and efficient to generalize deep learning models across different regions. [ABSTRACT FROM AUTHOR]

Repeating earthquakes repeatedly rupture the same fault asperities, which are likely loaded to failure by surrounding aseismic slip. However, repeaters occur less often than would be expected if these earthquakes accommodate all of the long‐term slip on the asperities. Here, we assess a possible explanation for this slip discrepancy: partial ruptures. On asperities that are much larger than the nucleation radius, a fraction of the slip could be accommodated by smaller ruptures on the same asperities. We search for partial ruptures of repeating earthquakes in Parkfield using the Northern California earthquakes catalog. We find 3991 individual repeaters which have 4468 partial ruptures. The presence of partial ruptures suggests that the asperities of repeating earthquakes are much larger than the nucleation radius. However, we find that partial ruptures could accommodate only around 25% of the slip on repeating earthquake patches. A 25% increase in the slip budget can explain only a small portion of the long recurrence intervals of repeating earthquakes. Plain Language Summary: Repeating earthquakes happen on the same fault patch over and over again. They are thought to happen on locked patches surrounded by a slowly moving section of the fault. This slow‐moving fault loads the patch to failure. However, the observed slip on the repeating earthquake patches does not match the long‐term slip on the surrounding fault. This slip deficit means the time between earthquakes is longer than expected. We explore the possibility that some of the slip deficit is explained by slip happening in smaller earthquakes ("partial ruptures") in between the time of the larger magnitude repeating earthquakes. We search for partial ruptures in Parkfield, California using the Northern California earthquakes catalog, which contains many well‐located repeating earthquake sequences. We find that partial ruptures could accommodate up to 25% of the slip on repeating earthquake patches, but this is still not enough slip to explain why small repeating earthquakes occur about five times less often than one would expect. Key Points: We search for partial ruptures of repeating earthquakes in Parkfield, CaliforniaWe find partial ruptures, which suggests repeating earthquake asperities are many times larger than the nucleation radiusIncluding partial ruptures in the slip budget does not account for the repeaters' surprisingly long recurrence intervals [ABSTRACT FROM AUTHOR]

We use sparse dictionary learning to develop transformations between seismic velocity models of different resolution and spatial extent. Starting with data in the common region of both models, the method can enhance a regional lower‐resolution model to match the style and resolution of local higher‐resolution results while preserving its regional coverage. The method is demonstrated by applying it to two‐dimensional VS and three‐dimensional VP and VS regional and local velocity models in southern California. The enhanced reconstructed regional results exhibit clear visual improvements, especially in the reconstructed VP/VS ratios, and better correlations with geological features. Moreover, the reconstructed regional VP, VS models outperform the original ones in comparison of simulated earthquake waveforms to observations. The improved fitting to observed waveforms extends beyond the domain of the overlapping region. The developed dictionary learning approach provides physically interpretable results and offers a powerful tool for additional applications of data enhancement in earth sciences. Plain Language Summary: Seismic velocity models are essential for many applications including derivation of earthquake properties, studies of tectonic processes, and analysis of earthquake ground motions. In various places there are velocity models of different spatial coverage and resolution. To benefit from the complementary strengths of such models, we develop a new method for enhancing regional velocity models of relatively low‐resolution with information contained in local higher‐resolution models. The method builds a transformation between different models in their common local region based on a sparse dictionary learning and then uses the transformation outside the common region. We apply the method to seismic velocity models in southern California and show that the enhanced regional P and S velocity models outperform the original models in fitting earthquake waveforms and also have better consistency with geological features. The method has a potential for enhancing other data sets in earth sciences. Key Points: We present a new method for merging multi‐scale velocity models with different resolutions and spatial coveragesWe reconstruct multi‐scale VP, VS regional seismic velocity models for southern CaliforniaValidation with waveform simulations shows that the reconstructed models outperform the original ones [ABSTRACT FROM AUTHOR]

The paper presents implementation of state-of-the-art pseudo-dynamic rupture in a 3D viscoelastic fourth-order staggered-grid time-domain finite-difference code for the physics-based broadband strong ground motion synthetics. The achieved quantitative improvements in the efficacy of the considered reference pseudo-dynamic rupture model (comprising of random distribution of slip, rake, rise-time of source time function, peak-time as 0.13 times rise-time and rupture arrival time) after the explicit addition of damage zone, fault-roughness and perturbation to the peak-time are highly stimulating in proficient broadband seismic energy radiation and reduction of coherency effects on the high frequency radiations. A final pseudo-dynamic rupture model is implemented with random distribution of all the source parameters along with damage-zone and fault-roughness. An excellent match of the computed pseudo-spectral acceleration using the simulated ground motion by means of the final pseudo-dynamic rupture model with that obtained using NGA-West2 GMPEs for a hypothetical Mw6.5 strike-slip earthquake validates the efficiency of final implemented rupture model. Further, the obtained average of spectral ratio of fault normal and fault parallel ground motions of the order 1.28 (around 1.0) for frequencies 0.8–10 Hz reflects the efficacy to reduce the coherency effect on the high frequency radiations. The observed good match of the simulated ground motion due to the 2004 (Mw6) Parkfield, California earthquake with the earthquake records on rock further validates the efficiency of the implemented final-pseudo-dynamic rupture model in the 3D finite-difference code. [ABSTRACT FROM AUTHOR]

We investigate the relation between frictional heating on a fault and the resulting conductive surface heat flow anomaly using the fault's long‐term energy budget. Analysis of the surface heat flow surrounding the fault trace leads to a constraint on the frictional power generated on the fault—the mechanism behind the San Andreas fault (SAF) heat flow paradox. We revisit this paradox from a new perspective using an estimate of the long‐term accumulating elastic power in the region surrounding the fault, and analyze the paradox using two parameters: the seismic efficiency and the elastic power. The results show that the constraint on frictional power from the classic interpretation is incompatible with the accumulating elastic power and the radiated power from earthquake catalogs. We then explore four mechanisms that can resolve this extended paradox. First, stochastic fluctuations of surface heat flow could mask the fault‐generated anomaly (we estimate 21% probability). Second, the elastic power accumulating in the region could be overestimated (≥550 MW required). Third, the seismic efficiency—ratio of radiated energy to elastic work—of the SAF could be higher than that of the remaining faults in the region (≥5.8% required). Fourth, the scaled energy—ratio of radiated energy to seismic moment—on the SAF could be lower than on the remaining faults in the region (a factor 5 difference required). In the last three hypotheses, we analyze the interplay of the energy budget on a single fault with the total energy budget of the region. Plain Language Summary: When earthquakes move rock against rock, friction heats the contact surface. If this frictional resistance were like laboratory measurements of typical crustal rock, the heat would cause a considerable heat flow signature ("anomaly") at Earth's surface. For the San Andreas fault (SAF) in Southern California, such a signature has not been observed. One solution to this paradox is that the fault is weak. We approach the paradox from a new angle by using additionally the rate at which elastic energy accumulates in California. This elastic power is incompatible with the radiated power from earthquake catalogs and the maximum rate of frictional heating from the paradox if only simple assumptions are made. We call this conflict the extended heat flow paradox. Four mechanisms could individually resolve the extended paradox: randomness in regional heat flow measurements could conceal the anomaly, the elastic power on the SAF could be overestimated, the seismic efficiency (ratio of radiated energy per input work) on the SAF could be comparatively high, or the scaled energy (ratio of radiated energy per seismic moment) on the SAF could be comparatively low. A combination of multiple effects is possible. Key Points: Heat flow around the San Andreas fault is incompatible with radiated power and elastic input power under simple assumptionsRegionally, a stochastic view on heat flow and/or an overestimated total elastic power can resolve the paradoxLocally, a high seismic efficiency or a low scaled energy (radiated energy/seismicmoment) on the San Andreas fault can resolve the paradox [ABSTRACT FROM AUTHOR]

Migration-based earthquake location methods may encounter the polarity reversal issue due to the non-explosive components of seismic sources, leading to an unfocused migration image. Such a problem usually makes it difficult to accurately retrieve the optimal location from the migrated source image. In this study, by taking advantage of the general pattern recognition ability of the convolutional neural network, we propose a novel deep-learning image condition (DLIC) to address this issue. The proposed DLIC measures the goodness of waveform alignments for both P and S waves, and it follows the geophysical principle of seismic imaging that the best-aligned waveforms represent fully a best-imaged source location. A synthetic test shows that the DLIC can effectively overcome the polarity reversal issues. Real data applications to southern California show that the DLIC can enhance the focusing of the migrated source image over the classic source scanning algorithm. Further tests show that the DLIC applies to continuous seismic data, to regions with few previously recorded earthquakes, and has the potential to locate small earthquakes. The proposed DLIC shall benefit the migration-based source location methods. [ABSTRACT FROM AUTHOR]

Scientists mapped the seismic time series into networks by considering the geographical location of events as nodes and establishing links between the nodes with different rules. Applying the successively defined models to construct the networks of seismic data, a variety of features of earthquake networks are detected (scale-free and small-world structures). Network construction models had changed in detail to optimize the performance of the verification of the minimum geographical size defined for the node. In all the studies, people try to use large data sets like years of data to ensure their results are good enough. In this work, by proposing the temporal network construction and employing the small-worldness property for data from Iran and California, we could achieve the minimum time scale needed for the best results. We verified the importance of this scale by analyzing two significant centrality measures (degree centrality and PageRank) introduced in the concept of earthquake network. [ABSTRACT FROM AUTHOR]

Abstract—A platform for monitoring and analysis of the seismogenic processes is described. The platform consists of two separate GISs. The first system, Web GIS Prognosis, downloads and processes data from remote servers and is used in the systematic Web earthquake prediction and preparation of a project for further analysis. The second system, spatiotemporal GIS GeoTime 3, provides opportunities for detailed study of the data prepared in the GIS Prognosis. Examples of the data analysis results on this platform are presented. The potential of systematic observation of the seismological situation in the GIS Prognosis is demonstrated by the example of California. Using the GIS GeoTime, the efficiencies of the methods for estimating the earthquake epicenter density fields when predicting earthquakes in Kamchatka are compared and it is shown that adaptive weight smoothing yields the best result. [ABSTRACT FROM AUTHOR]

The Southern San Andreas Fault (SSAF) in California is one of the most thoroughly studied faults in the world, but its configuration at seismogenic depths remains enigmatic in the Coachella Valley. We use a combination of space geodetic and seismic observations to demonstrate that the relatively straight southernmost section of the SSAF, between Thousand Palms and Bombay Beach, is dipping to the northeast at 60–80° throughout the upper crust (<10 km), including the shallow aseismic layer. We constrain the fault attitude in the top 2–3 km using inversions of surface displacements associated with shallow creep, and seismic data from a dense nodal array crossing the fault trace near Thousand Palms. The data inversions show that the shallow dipping structure connects with clusters of seismicity at depth, indicating a continuous throughgoing fault surface. The dipping fault geometry has important implications for the long‐term fault slip rate, the intensity of ground shaking during future large earthquakes, and the effective strength of the southern SAF. Plain Language Summary: The San Andreas Fault (SAF) is capable of large, destructive earthquakes and poses significant seismic hazard in California. In the Coachella Valley, the geometry of the SAF beneath the Earth's surface has been the subject of much debate. Here, we present new constraints on the fault's structure in the uppermost 2–3 km of the crust from several new sets of observations. We measure slow movement of the fault at the surface using satellite‐based radar and perform simulations of what fault geometry is most likely to produce the observed motion. We also installed an array of sensors across the SAF to measure seismic waves that travel through or reflect off the fault. Analysis of both data sets indicates that the shallow portion of the SAF dips to the northeast in the Coachella Valley. Precise locations of small earthquakes at greater depths exhibit a similar trend—we thus suggest the fault dips throughout the Earth's crust. Better understanding the geometry of this section of the SAF has implications for earthquake hazards in Southern California, as well as the fault's geologic history. Key Points: InSAR measurements and models of shallow creep on the southernmost San Andreas Fault indicate a moderate‐to‐steep northeast fault dipSeismic imaging using data from a dense array deployed near Thousand Palms reveal northeast dipping fault damage zonesA joint interpretation of available data suggests that the northeast dip persists throughout the upper 10 km of the crust [ABSTRACT FROM AUTHOR]

Simple Summary: Earthquakes are a common occurrence at The Geysers geothermal field in California which, over the years, have led to general land sinking in the area. In our study, we explore the correlation of geothermal data and seismicity and find that the injection rate exhibits a high correlation with the number of earthquakes. Additionally, we rely on satellite imagery which measures the extent of the land subsidence and develop a machine learning model that predicts future land subsidence, finding that it has a relatively low error rate. Moreover, we incorporate geothermal data into this model and find that it performs even better. All in all, this encourages the use of machine learning models in hazard mitigation in order to minimize the potential impacts that land subsidence might bring. The Geysers geothermal field in California is experiencing land subsidence due to the seismic and geothermal activities taking place. This poses a risk not only to the underlying infrastructure but also to the groundwater level which would reduce the water availability for the local community. Because of this, it is crucial to monitor and assess the surface deformation occurring and adjust geothermal operations accordingly. In this study, we examine the correlation between the geothermal injection and production rates as well as the seismic activity in the area, and we show the high correlation between the injection rate and the number of earthquakes. This motivates the use of this data in a machine learning model that would predict future deformation maps. First, we build a model that uses interferometric synthetic aperture radar (InSAR) images that have been processed and turned into a deformation time series using LiCSBAS, an open-source InSAR time series package, and evaluate the performance against a linear baseline model. The model includes both convolutional neural network (CNN) layers as well as long short-term memory (LSTM) layers and is able to improve upon the baseline model based on a mean squared error metric. Then, after getting preprocessed, we incorporate the geothermal data by adding them as additional inputs to the model. This new model was able to outperform both the baseline and the previous version of the model that uses only InSAR data, motivating the use of machine learning models as well as geothermal data in assessing and predicting future deformation at The Geysers as part of hazard mitigation models which would then be used as fundamental tools for informed decision making when it comes to adjusting geothermal operations. [ABSTRACT FROM AUTHOR]

Detection thresholds in earthquake catalogs frequently change in time due to station coverage improvements and network saturation effects during active periods such as mainshock‐aftershock cascades. This presents a challenge to seismicity‐rate estimation; there is a tradeoff between using as low a minimum magnitude as possible to maximize data while not undercounting the rate due to catalog incompleteness. Here we present a simple method, "a‐positive," which makes use of differential statistics to robustly estimate the seismicity rate in catalogs with time‐varying detection thresholds. We demonstrate the effectiveness of this method for a centuries‐long, hybrid earthquake catalog with both historical and instrumentally‐detected earthquakes in the Central and Eastern U.S., as well as for the 2019 Ridgecrest aftershock sequence in California, which has rapid changes in completeness due to network saturation. We find that the a‐positive method leads to more precise and less biased estimates of seismicity rate than traditional methods. In addition, with our improved estimate of earthquake rate early in the aftershock cascade, we find no evidence of rate‐saturation at short times from the mainshock; that is, the Omori c‐value is not distinguishable from zero. Plain Language Summary: The seismicity rate is a fundamental aspect of statistical seismology, and it is used to create long‐ term models that inform building codes and seismic hazard assessments. However, earthquake catalogs are imperfect records of earthquakes, due to both changing network coverage and short‐ term aftershock incompleteness, where large earthquakes obscure smaller ones in times of high activity. We here present a new method "a‐positive" to estimate the seismicity rate in catalogs with time‐varying detection thresholds. This method uses differential statistics to estimate the seismicity rate from interevent times, where interevent times are measured to the next larger earthquake, with no reference to a completeness magnitude. The "a‐positive" method provides more precise and less biased estimates of seismicity rate than traditional methods. The method has been tested on a centuries‐long, hybrid earthquake catalog with both historical and instrumentally‐detected earthquakes in the Central and Eastern U.S. and on the 2019 Ridgecrest aftershock sequence in California, which has rapid changes in completeness due to short‐term aftershock incompleteness. The results show that the "a‐positive" method is minimally sensitive to catalog incompleteness and provides an unbiased estimate of the earthquake rate even in the first moments of an aftershock sequence. Key Points: Earthquake catalogs are imperfect records of the earthquake rate, suffering from both network sparseness and short‐term rate saturation"a‐positive" estimates seismicity rate based on the time to the next larger earthquake, with no reference to completeness magnitudeUnbiased measurement of the aftershock rate shows no evidence for rate saturation at early times [ABSTRACT FROM AUTHOR]

We introduce a seismic identification method for collapse events using moment tensors (MTs). We start by computing full (six-element) MT solutions for 43 identified collapse events from around the world, and statistically characterizing the population on the MT hypersphere. We then test a large data set of over 1000 full MTs for the western U.S. against the distribution of collapses using a MT-based identification method similarly as used for testing explosions. Known collapses and explosions are readily identified, along with other anomalous events in the Geysers and central California coast. Misidentification rates are determined for various screening angles with optimal misidentification rates between earthquakes and collapses on the order of 3 per cent. The method is demonstrated to be very effective at identifying non-earthquake sources with a 97–98 per cent accuracy. It is likely to be transportable to other regions, and can be used for event identification anywhere full MT solutions are routinely calculated. [ABSTRACT FROM AUTHOR]

The hypocentres of natural earthquake swarms and injection-induced seismicity usually show systematic migration, which is considered to be a manifestation of their triggering mechanism. In many of these cases, the overall growth of the earthquake distribution is accompanied by short episodes of rapid migration, the origin of which is still not sufficiently clarified. We review the possible triggering mechanisms of these migrating episodes and propose a graphical method for distinguishing internal and external triggering forces. We also analyse the theoretical relationship between the evolution of the cumulative seismic moment and the rupture area and propose two models, the crack model and the rupture front model, which can explain the spreading of hypocentres. We developed an automatic algorithm for detecting fast migration episodes in seismicity data and applied it to relocated catalogues of natural earthquake swarms in California, West Bohemia, and Iceland, and to injection-induced seismicity. Fast migration episodes is shown to be relatively frequent during earthquake swarms (8–20 per cent of all events) compared to fluid-induced seismicity (less than 5 per cent of the events). Although the migration episodes were detected independently of time, they grew monotonically with time and square-root dependence of radius on time was found suitable for majority of sequences. The migration velocity of the episodes of the order of 1 m s−1 was found and it anticorrelated with their duration, which results in a similar final size of the clusters scattering around 1–2 km. Comparison of seismic moment growth and activated fault area with the predictions of the proposed models shows that both the rupture front model and the crack model are able to explain the observed migration and that the front model is more consistent with the data. Relatively low estimated stress drops in the range of 100 Pa to 1 MPa suggest that aseismic processes are also responsible for cluster growth. Our results show that the fast migrating episodes can be driven by stress transfer between adjacent events with the support of aseismic slip or fluid flow due to dynamic pore creation. [ABSTRACT FROM AUTHOR]

We infer a ~ 2,700-year history of Cascadia megathrust and other earthquakes from two small mountain lakes located 100 km inland of the coast near the California/Oregon border. We use the characteristics of disturbance deposits in the historic portion of the sediment cores from the lower lake to identify a deposit from the 1700 CE Cascadia earthquake (deposit J). This deposit is composed of light-coloured silt (indicating it is enriched in watershed-sourced sediment), without visible mica grains (which would indicate a lake bedrock source), organic grading of the deposit tail, and a basal contact with evidence of rapid loading. Seven deposits downcore have some of the characteristics of deposit J. An age-depth model suggests that the five deposits most similar to deposit J (including deposit J) are temporal correlatives to the largest margin-wide marine turbidite event deposits from Goldfinger et al., 2012, (T1, T2, T3, T4, T5 and T6), whereas the two deposits with some of the characteristics are potential correlatives of smaller turbidites T5a and T5b. Other thinner deposits are temporal correlatives of T2a and T3a and other smaller deposits of uncertain origin. Lake core physical property data can be correlated to those from other regional lake records and offshore cores. These results suggest that small Cascadia lakes with sufficient sedimentation rates (~ 1–2 cm/decade) with mixed clastic and organic sedimentation may be good recorders of earthquakes, that subduction earthquake deposits are different from those from other types of earthquake deposits and deposits from other types of disturbances, such as floods. [ABSTRACT FROM AUTHOR]

We report solid-earth tidal modulation of early aftershocks of the July 2019 Ridgecrest earthquake sequence, which occurred close to the southeastern edge of the Coso geothermal field. We found that the frequency of early aftershocks in the northern part, close to the Coso geothermal field, was modulated by the solid earth tides as they exhibit a strong correlation with the peak shear stress and Coulomb stress imparted by the solid earth tides. However, aftershocks that occurred farther south of the Coso geothermal field in the same sequence exhibit a weak correlation with the solid earth tidal stress. Our analysis implies that the tidal modulation of the earthquake sequence in the northern part is due to its vicinity to the Coso geothermal fields in southern California, which contain high-pressure fluids and are well known for their susceptibility towards tidal triggering. [ABSTRACT FROM AUTHOR]

This paper proposes a Bayesian model selection framework to determine optimal, parsimonious models for given building structures under ground motion excitation using stochastic filtering. Structural system identification at a regional scale after an earthquake is nominally a monumental task due to its inherently high computational cost. This challenge is currently addressed in the literature by using simplified structural models that bear analytical solutions, such as Timoshenko or shear beams, and shear building models. However, a low computational effort usually leads to an increased prediction error and consequently, a higher model uncertainty. This poses the dilemma of prediction accuracy versus model complexity when selecting a model class to represent a building structure. The proposed framework selects the model class that strikes the best balance between the two. To this end, the notion of cumulative evidence is introduced here as the integral of local evidence over the ground motion duration, which is then formulated as the difference between the cumulative likelihood of the observed measurements and the cumulative penalty. The likelihood measure promotes models whose predictions better match the observations. This is counteracted by the penalty, which is devised as an "Ockham factor," and penalizes models with higher information gains due to their higher complexities, for example, a larger number of parameters. The proposed approach also yields the best tuning parameters of the stochastic filter for each model class and the best initial values of the identification parameters. The proposed framework is verified using a synthetic example, and validated using recorded data from the Millikan Library building in California and the ANX building in Japan. [ABSTRACT FROM AUTHOR]

Earthquake focal mechanisms provide critical in-situ insights about the subsurface faulting geometry and stress state. For frequent small earthquakes (magnitude< 3.5), their focal mechanisms are routinely determined using first-arrival polarities picked on the vertical component of seismometers. Nevertheless, their quality is usually limited by the azimuthal coverage of the local seismic network. The emerging distributed acoustic sensing (DAS) technology, which can convert pre-existing telecommunication cables into arrays of strain/strain-rate meters, can potentially fill the azimuthal gap and enhance constraints on the nodal plane orientation through its long sensing range and dense spatial sampling. However, determining first-arrival polarities on DAS is challenging due to its single-component sensing and low signal-to-noise ratio for direct body waves. Here, we present a data-driven method that measures P-wave polarities on a DAS array based on cross-correlations between earthquake pairs. We validate the inferred polarities using the regional network catalog on two DAS arrays, deployed in California and each comprising ~ 5000 channels. We demonstrate that a joint focal mechanism inversion combining conventional and DAS polarity picks improves the accuracy and reduces the uncertainty in the focal plane orientation. Our results highlight the significant potential of integrating DAS with conventional networks for investigating high-resolution earthquake source mechanisms. Determining earthquake faulting orientation is essential for understanding earthquake-stress interactions. Here, the authors present a technique that leverages telecom fiber optic cables to improve the estimation of this fundamental parameter. [ABSTRACT FROM AUTHOR]

Earthquakes can be dynamically triggered by the passing waves of other distant events. The frequent occurrence of dynamic triggering offers tangible hope in revealing earthquake nucleation processes. However, the physical mechanisms behind earthquake dynamic triggering have remained unclear, and contributions of competing hypotheses are challenging to isolate with individual case studies. To gain a systematic understanding of the spatiotemporal patterns of dynamic triggering, we investigate the phenomenon in southern California from 2008 to 2017. We use the Quake Template Matching catalog and an approach that does not assume an earthquake occurrence distribution. We develop a new set of statistics to examine the significance of seismicity‐rate changes as well as moment‐release changes. Our results show that up to 70% of 1,388 global M ≥ 6 events may have triggered earthquakes in southern California. The triggered seismicity often occurred several hours after the passing seismic waves. The Salton Sea Geothermal Field, San Jacinto fault, and Coso Geothermal Field are particularly prone to triggering. Although adjacent fault segments can be triggered by the same earthquakes, the majority of triggered earthquakes seem to be uncorrelated, suggesting that the process is primarily governed by local conditions. Further, the occurrence of dynamic triggering does not seem to correlate with ground motion (e.g., peak ground velocity) at the triggered sites. These observations indicate that nonlinear processes may have primarily regulated the dynamic triggering cases. Plain Language Summary: Earthquakes interact with each other, such as mainshocks triggering nearby aftershocks. Earthquake dynamic triggering is a type of interaction where seismic waves from an earthquake trigger other earthquakes beyond several fault lengths, and sometimes, up to thousands of kilometers away. Triggered earthquakes may occur upon the arrival of the seismic waves but may also be delayed hours after the wave passage, suggesting the involvement of time‐dependent processes. Identifying delayed cases relies on robust measures of seismicity‐rate changes. Here we present a new method that can identify triggering cases without many assumptions. We find that earthquakes in southern California are frequently triggered by distant earthquakes around the globe, and the triggered earthquakes tend to cluster in space and time. We also find that the triggering incidences do not seem to correlate with the seismic wave characteristics of the distant earthquakes. Our findings suggest that dynamically triggered earthquakes in southern California are likely caused by time‐dependent, local complex processes. Key Points: Earthquake dynamic triggering is ubiquitous in southern CaliforniaTriggered earthquakes are frequently associated with significant moment‐release anomalies and are likely controlled by local processesOur procedure does not assume that seismicity follows a Poissonian distribution when identifying dynamic triggering [ABSTRACT FROM AUTHOR]

It is well known that large earthquakes often exhibit significant rupture complexity such as well separated subevents. With improved recording and data processing techniques, small earthquakes have been found to exhibit rupture complexity as well. Studying these small earthquakes offers the opportunity to better understand the possible causes of rupture complexities. Specifically, if they are random or are related to fault properties. We examine microearthquakes (M  < 3) in the Parkfield, California, area that are recorded by a high-resolution borehole network. We quantify earthquake complexity by the deviation of source time functions and source spectra from simple circular (omega-square) source models. We establish thresholds to declare complexity, and find that it can be detected in earthquakes larger than magnitude 2, with the best resolution above M2.5. Comparison between the two approaches reveals good agreement (>90 per cent), implying both methods are characterizing the same source complexity. For the two methods, 60–80 per cent (M 2.6–3) of the resolved events are complex depending on the method. The complex events we observe tend to cluster in areas of previously identified structural complexity; a larger fraction of the earthquakes exhibit complexity in the days following the M w 6 2004 Parkfield earthquake. Ignoring the complexity of these small events can introduce artefacts or add uncertainty to stress drop measurements. Focusing only on simple events however could lead to systematic bias, scaling artefacts and the lack of measurements of stress in structurally complex regions. [ABSTRACT FROM AUTHOR]

From the early stage of seismological research, a complex network is one of the statistical methods to investigate the complexity of earthquake systems. The benefit of using this method is to inspect the systems with minimum information about their entities and corresponding interactions. Achieving a high interest in studying the seismic events using the complex network resulted in defining models to map the seismic data into networks. Application of these models to the seismic data sets in nonidentical geographical regions has yielded promising results independent of time and location. In this review, we bring in the recent famous models varying from monolayer to multiplex and compare their proficiency in capturing the complexity of the seismicity by using two data sets from Iran and California. [ABSTRACT FROM AUTHOR]

The rate of fault zone restrengthening between earthquakes can be influenced by both frictional and cohesive healing processes. Friction is dependent on effective normal stress while cohesion is independent of normal stress, potentially explaining—in part—the lack of depth dependence of earthquake stress drops. Although amenable to laboratory testing, few studies have systematically addressed the normal stress dependence of restrengthening rate. This is partially due to difficulty in separating relative contributions of friction and cohesion in recovery of fault strength. We present results from a series of slide‐hold‐slide tests on thin layers (≤10 휇m) of ultrafine quartz gouge that develop during shearing of initially bare‐surface quartzite. Tests were conducted at 10 MPa constant pore pressure, 20–200 MPa constant effective normal stress, and temperatures of 22°–200°C. Restrengthening, defined as the difference between peak shear stress measured after resumption of sliding and steady‐state sliding shear stress, increases with the log of hold duration. The 200°C healing rate, 0.014 per e‐fold increase in time, is comparable to that determined from seismological observations along the Calaveras Fault, California. Construction of Mohr‐Coulomb failure envelopes shows that changes in cohesion are small (<1 MPa) and independent of hold durations to 105 s, indicating that the increased strength is due to changes in the friction coefficient. These experimental results are inconsistent with the hypothesis that cohesive healing explains the depth independence of earthquake stress drop, but higher temperatures, longer time‐scales, and more complex mineralogy could facilitate cohesive healing in natural fault systems. Plain Language Summary: Faults recover strength, or heal, following earthquakes. This occurs due to processes that can increase the frictional or cohesive strength of the fault surface, but the relative contributions of friction and cohesion to fault strength are difficult to constrain. By conducting a series of slide‐hold‐slide friction experiments, where a simulated fault in quartzite alternates between periods of shear displacement and quasi‐stationary contact, at a range of temperatures and effective normal stresses, we can independently determine the magnitude of frictional and cohesive healing. The strength of the simulated faults increases with time that the surfaces are held in contact. We show that cohesive healing is negligible and the rate of frictional healing is comparable to earthquake observations. This study has implications for our understanding of earthquake recurrence and fault stability. Key Points: Strength recovery is similar at 22° and 200°C but minimized at 100°C, indicating the mechanism is not a simple Arrhenius‐type processCohesive healing in these experiments is negligible and strength recovery is predominately due to frictional healing processesExperimentally determined frictional healing rate is comparable to seismological observations [ABSTRACT FROM AUTHOR]

Faults often form through reactivation of pre-existing structures, developing geometries and mechanical properties specific to the system's geologic inheritance. Competition between fault geometry and other factors (e.g., lithology) to control slip at Earth's surface is an open question that is central to our knowledge of fault processes and seismic hazards. Here we use remote sensing data and field observations to investigate the origin of the 2019 M7.1 Ridgecrest, California, earthquake rupture geometry and test its impact on the slip distribution observed at Earth's surface. Common geometries suggest the fault system evolved through reactivation of structures within the surrounding Independence dike swarm (IDS). Mechanical models testing a range of fault geometries and stress fields indicate that the inherited rupture geometry strongly controlled the M7.1 earthquake slip distribution. These results motivate revisiting the development of other large-magnitude earthquake ruptures (1992 M7.3 Landers, 1999 M7.1 Hector Mine) and tectonic provinces within the IDS. Faults responsible for the 2019 M7.1 Ridgecrest, California earthquake likely evolved through reactivation of pre-existing Independence dike swarm structures. The inherited rupture geometry strongly controlled the earthquake slip distribution. [ABSTRACT FROM AUTHOR]

To constrain fault processes and hazard, fault slip rates may be extrapolated over different fault lengths or time intervals. Here, we investigate slip rates for the Cucamonga Fault (CF). The CF is located at the junction of the Transverse Range fault system with the San Andreas and San Jacinto Faults, and it is hypothesized to connect with these faults, promoting the propagation of large, multi‐fault earthquakes. Previous work has shown that CF displacements on late Quaternary alluvial fan surfaces are highly variable along strike. We present two new 10Be surface exposure ages from depth profiles on the alluvial fans. Slip rates are consistent with a rate of 1.4 ± 0.3 m/kyr over time intervals of ∼20, ∼30, and ∼40 kyr. If the CF participates in multi‐fault ruptures, then these earthquakes occur either rarely or with sufficient regularity to maintain apparently steady rates over multiple intervals. We also explore along‐strike fault displacement variability using a calibrated morphological model. The model successfully reproduces scarp profiles and indicates that fault displacement variability can be explained in part by scarp age but not uplift rate. We infer that both erosion by ephemeral gullying and distributed deformation contribute to fault displacement variability, although both are difficult to detect confidently without excavations across the scarp. These investigations show that better characterization of cumulative‐slip variability along strike may improve accuracy and precision of slip rates. Slip rates that do not consider epistemic uncertainties may not be suitable for extrapolation over longer fault sections. Plain Language Summary: Geologic slip rates describe total fault displacement over a specific time interval and are important for earthquake hazard assessment. On the Cucamonga Fault, a component of hazard is the potential to participate in multi‐fault earthquakes, which can be particularly large and damaging. We measure the displacements that have accumulated during three overlapping intervals of time. The slip rate appears to be constant, but our resolution is limited by large uncertainties, especially uncertainties arising from variable displacement along the length of the fault. We model fault scarp evolution to better understand the sources for this variability. Model results indicate that alteration of the scarp by erosion is important, but other sources also contribute that are difficult to identify from topographic analysis alone. Our slip‐rate data suggest that large, multi‐fault earthquakes are either very rare or frequent enough to give the appearance of steady slip rates through time. Key Points: Cucamonga Fault slip rates are consistent with a single rate of 1.4 ± 0.3 m/kyr at intervals of ∼20, 30, and 40 kyrIf the fault participates in multi‐fault earthquakes, then they occur either rarely or frequently enough to maintain apparently steady ratesSpatial slip‐rate variability is difficult to mitigate; local measurements should consider epistemic uncertainty on dip‐slip faults [ABSTRACT FROM AUTHOR]

Accurate earthquake focal mechanisms are essential for solving fault zone structure, estimating stress variations, and assessing seismic hazards. Small earthquakes' focal mechanisms are usually solved using P‐wave first‐motion polarities and/or S‐/P‐wave amplitude ratios, which are limited due to the low signal‐to‐noise ratio of small‐earthquake waveforms and a limited number of three‐component seismograms. To increase the number of high‐quality focal mechanisms, we develop a method that utilizes the inter‐event relative radiation patterns to perform a joint focal mechanism inversion of numerous clustered events (called REFOC). The method first uses P‐wave polarities and S‐/P‐wave amplitude ratios to constrain the initial solutions and then combines these solutions and the inter‐event P‐/P‐wave and S‐/S‐wave amplitude ratios to refine solutions. For example, we apply the method to 38,413 earthquakes in the Parkfield region, California. The REFOC outperforms traditional methods with 57% more solutions with <55° uncertainties (>70% of the catalog events) and 126% more solutions with <25° uncertainties (>40% of the catalog events), illuminating unprecedented fine‐scale rupture processes. Instead of rupturing along the main fault, many M < 2 focal mechanisms show >45° angular differences from the 2004 Mw 6.0 Parkfield earthquake. The variation of focal mechanism properties is spatially related to the variation of fault strength and geometry, and temporally correlated with the shear stress variations before, during, and after the 2004 Mw 6.0 earthquake. The observations highlight the potential of applying REFOC to monitor unprecedented details of fault zone structure and stress field, providing new insights into fault rupture physics, seismotectonic processes, and seismic hazards. Plain Language Summary: Earthquakes and other tectonic activities are largely controlled by the stress field and structures within the Earth's crust, which are difficult to measure because of the lack of direct observations in depth. Earthquake focal mechanisms, containing fault plane and slip motion information, are the key to monitoring crustal state in seismogenic crust. However, solving the focal mechanisms of M < 3 earthquakes is very challenging due to the limited number of high‐quality records. In this study, we utilize the relative amplitudes of closely located earthquakes as additional observational parameters to constrain small‐earthquake focal mechanisms. The new method was applied to earthquakes in the Parkfield segment of the San Andreas fault and provided a twofold increase in the number of the focal mechanism solutions. Our results show that many small‐earthquake focal mechanisms are not consistent with the main fault slip. We suggest that the variation of focal mechanism properties is spatially related to fault strength and geometry, and temporally correlated with the shear stress variations before and after 2004 Mw 6.0 earthquake. The observations suggest that the large number of focal mechanisms solved using our new algorithm have the potential to monitor unprecedented small‐scale stress perturbations and fault structures and help better understand crustal dynamics. Key Points: We present a new focal mechanism calculation method using polarities, S/P amplitude ratios and inter‐event P/P and S/S amplitude ratiosThe application to the Parkfield area shows 43% of the events have high‐quality focal mechanisms (∼2 times the traditional catalog)Focal mechanism variations are spatially related to fault strength and geometry, and temporally correlated with the 2004 Mw 6.0 earthquake [ABSTRACT FROM AUTHOR]