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Through hydrocarbon explorations, deep carbonate reservoirs within a craton were determined to be influenced by deep strike-slip faults, which exhibit small displacements and are challenging to identify. Previous research has established a correlation between seismic attributes and deep geological information, wherein large-scale faults can cause abrupt waveform discontinuities. However, due to the inherent limitations of seismic datasets, such as low signal-to-noise ratios and resolutions, accurately characterizing complex strike-slip faults remains difficult, resulting in increased uncertainties in fault characterization and reservoir prediction. In this study, we integrate advanced techniques such as principal component analysis and structure-oriented filtering with a fault-centric imaging approach to refine the resolution of seismic data from the Tarim craton. Our detailed evaluation encompassed 12 distinct seismic attributes, culminating in the creation of a sophisticated model for identifying strike-slip faults. This model incorporates select seismic attributes and leverages fusion algorithms like K-means, ellipsoid growth, and wavelet transformations. Through the technical approach introduced in this study, we have achieved multi-scale characterization of complex strike-slip faults with throws of less than 10 m. This workflow has the potential to be extended to other complex reservoirs governed by strike-slip faults in cratonic basins, thus offering valuable insights for hydrocarbon exploration and reservoir characterization in similar geological settings. [ABSTRACT FROM AUTHOR]

In recent years, self‐supervised procedures have advanced the field of seismic noise attenuation, due to not requiring a massive amount of clean labelled data in the training stage, an unobtainable requirement for seismic data. However, current self‐supervised methods usually suppress simple noise types, such as random and trace‐wise noise, instead of the complicated, aliased ground roll. Here, we propose an adaptation of a self‐supervised procedure, namely, blind‐fan networks, to remove aliased ground roll within seismic shot gathers without any requirement for clean data. The self‐supervised denoising procedure is implemented by designing a noise mask with a predefined direction to avoid the coherency of the ground roll being learned by the network while predicting one pixel's value. Numerical experiments on synthetic and field seismic data demonstrate that our method can effectively attenuate aliased ground roll. [ABSTRACT FROM AUTHOR]

Machine learning-based seismic processing models are typically trained separately to perform seismic processing tasks (SPTs) and, as a result, require plenty of high-quality training data. However, preparing training data sets is not trivial, especially for supervised learning (SL). Despite the variability in seismic data across different types and regions, some general characteristics are shared, such as their sinusoidal nature and geometric texture. To learn the shared features and thus, quickly adapt to various SPTs, we develop a unified paradigm for neural network-based seismic processing, called Meta-Processing, that uses limited training data for meta learning a common network initialization, which offers universal adaptability features. The proposed Meta-Processing framework consists of two stages: meta-training and meta-testing. In the former, each SPT is treated as a separate task and the training dataset is divided into support and query sets. Unlike conventional SL methods, here, the neural network (NN) parameters are updated by a bilevel gradient descent from the support set to the query set, iterating through all tasks. In the meta-testing stage, we also utilize limited data to fine-tune the optimized NN parameters in an SL fashion to conduct various SPTs, such as denoising, interpolation, ground-roll attenuation, image enhancement, and velocity estimation, aiming to converge quickly to ideal performance. Extensive numerical experiments are conducted to assess the effectiveness of Meta-Processing on both synthetic and real-world data. The findings reveal that our approach leads to a substantial improvement in the convergence speed and predictive performance of the NN. [ABSTRACT FROM AUTHOR]

Copyright of Petroleum Geology & Oilfield Development in Daqing is the property of Editorial Department of Petroleum Geology & Oilfield Development in Daqing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The presence of coherent noise in seismic data leads to errors and uncertainties, and as such it is paramount to suppress noise as early and efficiently as possible. Self‐supervised denoising circumvents the common requirement of deep learning procedures of having noisy‐clean training pairs. However, self‐supervised coherent noise suppression methods require extensive knowledge of the noise statistics. We propose the use of explainable artificial intelligence approaches to 'see inside the black box' that is the denoising network and use the gained knowledge to replace the need for any prior knowledge of the noise itself. This is achieved in practice by leveraging bias‐free networks and the direct linear link between input and output provided by the associated Jacobian matrix; we show that a simple averaging of the Jacobian contributions over a number of randomly selected input pixels provides an indication of the most effective mask to suppress noise present in the data. The proposed method, therefore, becomes a fully automated denoising procedure requiring no clean training labels or prior knowledge. Realistic synthetic examples with noise signals of varying complexities, ranging from simple time‐correlated noise to complex pseudo‐rig noise propagating at the velocity of the ocean, are used to validate the proposed approach. Its automated nature is highlighted further by an application to two field data sets. Without any substantial pre‐processing or any knowledge of the acquisition environment, the automatically identified blind masks are shown to perform well in suppressing both trace‐wise noise in common shot gathers from the Volve marine data set and coloured noise in post‐stack seismic images from a land seismic survey. [ABSTRACT FROM AUTHOR]

A 3D vertical seismic profiling (VSP) survey was acquired using a distributed acoustic sensing (DAS) system in the Permian Basin, West Texas. In total, 682 shot points from a pair of vibroseis units were recorded using optical fibers installed in a 9000 ft (2743 m) vertical part and 5000 ft (1524 m) horizontal reach of a well. Transmitted and reflected P, S, and converted waves were evident in the DAS data. From first-break P and S arrivals, we found average P-wave velocities of approximately 14,000 ft/s (4570 m/s) and S-wave velocities of 8800 ft/s (3000 m/s) in the deep section. We modified the conventional geophone VSP processing workflow and produced P–P reflection and P–S volumes derived from the well's vertical section. The Wolfcamp formation can be seen in two 3D volumes (P–P and P–S) from the vertical section of the well. They cover an area of 3000 ft (914 m) in the north–south direction and 1500 ft (460 m) in the west–east direction. Time slices showed coherent reflections, especially at 1.7 s (~11,000 ft), which was interpreted as the bottom of the Wolfcamp formation. Vp/Vs values from 2300 ft (701 m) –8800 ft (2682 m) interval range were between 1.7 and 2.0. These first data provide baseline images to compare to follow-up surveys after hydraulic fracturing as well as potential usefulness in extracting elastic properties and providing further indications of fractured volumes. [ABSTRACT FROM AUTHOR]

Through hydrocarbon explorations, deep carbonate reservoirs within a craton were determined to be influenced by deep strike-slip faults, which exhibit small displacements and are challenging to identify. Previous research has established a correlation between seismic attributes and deep geological information, wherein large-scale faults can cause abrupt waveform discontinuities. However, due to the inherent limitations of seismic datasets, such as low signal-to-noise ratios and resolutions, accurately characterizing complex strike-slip faults remains difficult, resulting in increased uncertainties in fault characterization and reservoir prediction. In this study, we integrate advanced techniques such as principal component analysis and structure-oriented filtering with a fault-centric imaging approach to refine the resolution of seismic data from the Tarim craton. Our detailed evaluation encompassed 12 distinct seismic attributes, culminating in the creation of a sophisticated model for identifying strike-slip faults. This model incorporates select seismic attributes and leverages fusion algorithms like K-means, ellipsoid growth, and wavelet transformations. Through the technical approach introduced in this study, we have achieved multi-scale characterization of complex strike-slip faults with throws of less than 10 m. This workflow has the potential to be extended to other complex reservoirs governed by strike-slip faults in cratonic basins, thus offering valuable insights for hydrocarbon exploration and reservoir characterization in similar geological settings.

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During 1975–1988, an academic research ship, R/V Robert D. Conrad, acquired more than 150,000‐line‐km of multichannel seismic reflection profile data from each of the world's main ocean basins and their margins. This extensive legacy seismic data set, which involved both single ship and two‐ship data acquisition, has been widely used by the marine geoscience community. We report on our experience in reprocessing seismic reflection profile data acquired during Conrad cruise RC2308 to the Hawaiian Islands region in August/September 1982. We show that the application of modern, industry standard processing techniques, including filtering, de‐bubble, deconvolution, and migration, can significantly enhance 40+ year old legacy seismic reflection profile data. The reprocessed data reveals more precisely, and with much less scatter, the flexure of Cretaceous Pacific oceanic crust caused by the Pliocene‐Recent volcanic loads that comprise the Hawaiian Islands. A comparison of observed picks of top oceanic crust which has been corrected for the Hawaiian swell and the Molokai Fracture Zone with the calculations of a simple 3‐dimensional elastic plate (flexure) model reveals a best fit elastic plate thickness of the lithosphere, Te, of 26.7 km, an average infill density of 2,701 kg m−3, and a Root Mean Square difference between observations and calculations of 305 m. Tests show these results depend weakly on the load density assumed and that the average infill density is close to what would be predicted from an arithmetic average of the flanking moat infill density and the infill density that immediately underlies the volcanic edifice. Plain Language Summary: The mid‐1970s to mid‐1980s saw a rapid increase in the acquisition of marine seismic reflection profile data by academic research vessels using large airgun arrays and long multichannel streamers. The data have provided some of the best images we have of the structure of mid‐ocean ridges, transform faults and fracture zones, and deep‐sea trenches; however, the processing of these data was rudimentary in comparison to modern standards. We show here how reprocessing of seismic reflection profile data acquired some 40+ years ago in the vicinity of the Hawaiian Islands that utilize modern methods of filtering, deconvolution, and migration technologies have significantly improved imaging of the top and bottom of oceanic crust, reducing the scatter in reflector depths from a legacy data set by almost a factor of two. This, in turn, has led to improved resolution of the large‐scale deformation caused by the individual volcano loads that comprise the Hawaiian Islands and, since the ages of these loads and the ocean floor on which these loads have been emplaced are known, to a better understanding of the stress state, strength, and rheological properties of Earth's tectonic plates. Key Points: Modern methods of deconvolution and migration technologies have significantly enhanced the value of a 40+ year seismic data setReprocessing of legacy seismic data acquired at Hawaii provide the best examples we have of plate flexure caused by volcano loadingCurrent difficulties in mounting new seismic experiments at sea require the reprocessing of legacy seismic data sets using modern methods [ABSTRACT FROM AUTHOR]

The CROP95-M18 seismic profile was acquired in 1995 as part of the "CROsta Profonda" project, a regional survey carried out to explore the deeper structures of the Earth's crust in Italy. Although the high energy of the source allowed a high penetration depth, the vintage stacked section showed some areas of low signal-to-noise ratio, due to the acquisition in shallow water environments and related reverberations of surface and guided-waves. To improve the quality of the image, we reprocessed the 2D seismic line with the up-to-date processing techniques by adopting a specific workflow in time domain up to pre-stack time migration. In addition, we created an accurate interval velocity model by combining the coherency inversion technique and reflection tomography. This resulted in depth migration of the uppermost part of the profile (to a depth of 9 km), which was confirmed by the stratigraphy of the Amanda 1bis well. The reprocessing significantly improved the image of the profile and enabled new information on the seismo-stratigraphy to be gleaned from the vintage data. This was particularly important for science and research, as new seismic data with high penetration depths, such as the CROP profiles, are currently difficult to acquire. [ABSTRACT FROM AUTHOR]

Copyright of World Nuclear Geoscience is the property of World Nuclear Geoscience Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A 3D vertical seismic profiling (VSP) survey was acquired using a distributed acoustic sensing (DAS) system in the Permian Basin, West Texas. In total, 682 shot points from a pair of vibroseis units were recorded using optical fibers installed in a 9000 ft (2743 m) vertical part and 5000 ft (1524 m) horizontal reach of a well. Transmitted and reflected P, S, and converted waves were evident in the DAS data. From first-break P and S arrivals, we found average P-wave velocities of approximately 14,000 ft/s (4570 m/s) and S-wave velocities of 8800 ft/s (3000 m/s) in the deep section. We modified the conventional geophone VSP processing workflow and produced P–P reflection and P–S volumes derived from the well’s vertical section. The Wolfcamp formation can be seen in two 3D volumes (P–P and P–S) from the vertical section of the well. They cover an area of 3000 ft (914 m) in the north–south direction and 1500 ft (460 m) in the west–east direction. Time slices showed coherent reflections, especially at 1.7 s (~11,000 ft), which was interpreted as the bottom of the Wolfcamp formation. Vp/Vs values from 2300 ft (701 m) –8800 ft (2682 m) interval range were between 1.7 and 2.0. These first data provide baseline images to compare to follow-up surveys after hydraulic fracturing as well as potential usefulness in extracting elastic properties and providing further indications of fractured volumes.

Seismic surveys are often affected by environmental obstacles or restrictions that prevent regular sampling in seismic acquisition. To address missing data, various methods, including deep learning techniques, have been developed to extract features from complex information, albeit with the limitation of requiring external seismic databases. While previous works have primarily focused on trace reconstruction, missing shot-gathers directly impact the seismic processing flow and represent a major challenge in seismic data regularization. In this paper, we propose DIPsgr, a seismic shot-gather reconstruction method that uses only the incomplete seismic acquisition measurements to estimate their missing information employing unsupervised deep learning. Numerical experiments on three databases demonstrate that DIPsgr recovers the complete set of traces in each shot-gather, with preserved information and seismic events.

In this study, we analyze the quality factor- Q derived from the Vertical Seismic Profiling (VSP) and sonic log data that was performed in the DS-2 borehole located in the Carpathian Foredeep. We use the obtained quality factor distributions in the inverse Q-filtering (IQF) procedure during active seismic data processing. Another very important and, unfortunately, subjective in nature parameter is the gain function applied to seismic data while performing the IQF. To calculate Q from VSP data we use an iterative approach to the spectral ratio method where we use several shallowest receivers as reference. We observe high variability of the obtained Q values, depending on the reference receiver, however, depth distribution of Q was consistent regardless To increase reliability of the obtained values, an additional method of Q measurements is incorporated, i.e., acoustic travel time dispersion method. We show that incorporating inverse Q-filtering to active seismic data processing flow allows to enhance the vertical resolution of 2D seismic data. [ABSTRACT FROM AUTHOR]

Seismic surveys are often affected by environmental obstacles or restrictions that prevent regular sampling in seismic acquisition. To address missing data, various methods, including deep learning techniques, have been developed to extract features from complex information, albeit with the limitation of requiring external seismic databases. While previous works have primarily focused on trace reconstruction, missing shot-gathers directly impact the seismic processing flow and represent a major challenge in seismic data regularization. In this paper, we propose DIPsgr, a seismic shot-gather reconstruction method that uses only the incomplete seismic acquisition measurements to estimate their missing information employing unsupervised deep learning. Numerical experiments on three databases demonstrate that DIPsgr recovers the complete set of traces in each shot-gather, with preserved information and seismic events. [ABSTRACT FROM AUTHOR]

In an ever more challenging context for the acquisition of seismic data in the Mediterranean Sea, reprocessing to improve the quality of legacy data has become increasingly important. This work presents the newly reprocessed, open access dataset SALTFLU acquired in the Algerian basin by the National Institute of Oceanography and Applied Geophysics (OGS) in 2012. We apply a ‘broadband’ reprocessing strategy adapted for offset-limited (3 km streamer for a target 4 km below the sea level) airgun reflection seismic data acquired in deep water settings. We then assess if the reprocessed images provide new geological insights on the Mediterranean sub-surface. The workflow relies on an integrated approach combining geophysics and geological interpretation to iteratively build the velocity model. In this way we aim to tackle some of the challenges linked to imaging deep complex geological structures containing high velocity contrasts with 2-D, offset-limited seismic data. We first broaden the bandwidth of the data through multi-domain de-noising, deghosting and a source designature using an operator derived from the seabed reflection. We then perform iterative migration velocity analysis, pre-stack time migration and multiple attenuation in the Radon domain to obtain time-migrated images. The initial velocity model is derived from the resulting time migration velocities, and geologically driven model updates are generated using a combination of travel-time tomography, seismic interpretation of the major salt horizons and velocity gradient flooding. The gradient flooding aims to reproduce the large scale first-order velocity variations, while the travel-time tomography aims to resolve the smaller second-order velocity variations. The results improve our deep geological knowledge of the under-explored Algerian basin down to the base salt and the pre-salt. Fluid indicators are imaged within the Plio-Quaternary of the Algerian basin, which we interpret as thermogenic or biogenic gas sourced from either the Messinian Upper Unit or from the pre-salt, migrating through a hydro-fractured salt. The reprocessed data image lateral and vertical seismic facies variation within the Messinian units that could shed new light on the tectono-stratigraphic processes acting during the Messinian Salinity Crisis. It also reveals numerous previously unresolved volcanic structures within the Formentera basin. [ABSTRACT FROM AUTHOR]

When processing seismic survey data, it is desirable to reduce random noise without lowering the seismic resolution or lessening the number of seismic stacking folds. To this end, here we introduce a wavelet denoising of multi-angle (WDMA) method for prestack seismic data. Unlike the traditional method of directly stacking multi-angle gathers, our proposed WDMA method does not rely on the simple averaging of multi-angle prestack data, and when denoising the final result does not require averaging of direct stacking. Instead, WDMA first decomposes single-angle gathers through wavelet decomposition to determine local noise and obtain a detailed estimation, following which the average weighting coefficient is calculated and the data are reconstructed. Based on the above steps, we mainly studied the wavelet decomposition, optimization of the coefficient weight and weight mode, and the number of angle gathers. We used synthetic prestack angle gathers and field seismic data to validate the effectiveness of the proposed method. Even when the ratio of maximum noise amplitude to maximum effective signal amplitude was 60%, our proposed method could still achieve a good denoising effect by conducting WDMA two or more times. Compared with soft threshold wavelet denoising or the traditional stacking method, our results demonstrate that the proposed WDMA method can obtain high-quality seismic data using fewer frames of angle gathers and can simultaneously perform denoising and stacking. [ABSTRACT FROM AUTHOR]

Reducing multiple contaminations in reflection seismic data remains one of the primary challenges in marine seismic data processing. Besides geological settings, its effectiveness is also dependent on the multiple removal methods. In this study, we undertook two legacy 2D multi-channel seismic data crossing the accretionary wedge off SW Taiwan to test the efficiency of various multiple-attenuation scenarios. The tectonic domain has resulted from the incipient arc-continent collision between the northern rifted margin of the South China Sea and the Luzon volcanic arc. The wedge extends from shallow water to deep water bathymetries, hence promoting both short-period and long-period multiples within the seismic records. A cascade of de-multiple methods was tested to attenuate multiple energy under various seafloor bathymetry and tectonic areas. The first step relies on the periodicity nature of multiples. Spatial dependent predictive deconvolution in the x-t domain was performed to attenuate reverberations and improve temporal resolution in the time domain. Wave-equation multiple attenuation (WEMA) was applied to suppress the water layer multiples based on a combination of numerical wave extrapolation in the shot domain through water layer and water bottom reflectivity. Surface-related multiple elimination (SRME) aimed to attenuate the residual water bottom multiple and peg-leg multiple by assuming surface-related multiples can be kinematically predicted via convolution of pre-stack seismic traces at possible surface multiple reflection locations. The second step exploits the spatial move-out difference behavior between primaries and multiples. Parabolic Radon transforms far-offset multiples by subtracting noise energy in the τ-p domain, whereas the frequency-wave number (F-K) filter aimed to eliminate any residual multiples energy in the F-K domain. Predictive deconvolution improved seismic resolution and suppressed sea-bottom reverberation energy in the continental and lower wedge slopes, but not in the upper wedge slope. WEMA, Radon filter, and F-K filter reduced multiples energy both at the continental slope and wedge slope; whereas SRME made minimal impact on both areas. Since the reflection seismic datasets stretch diverse tectonic environments and water depth, there was no single multiple attenuation method capable to suppress multiples in all tectonic environments and bathymetry. [ABSTRACT FROM AUTHOR]

High-resolution seismoacoustic data represent a useful tool for the investigations of gas-charged sediments occurring beneath the seabed through the identification of the diagnostic intrasedimentary features associated with them. Acoustic blanking revealed shallow gas pockets in the seismostratigraphic units of the inner shelf off the Northern Cilento promontory. Six main seismostratigraphic units were recognized based on the geological interpretation of the seismic profiles. Large shallow gas pockets, reaching a lateral extension of 1 km, are concentrated at the depocenter of Late Pleistocene–Holocene marine sediments that are limited northwards by the Solofrone River mouth and southwards by the Licosa Cape promontory. A morphobathymetric interpretation, reported in a GIS environment, was constructed in order to show the main morphological lineaments and to link them with the acoustic anomalies interpreted through the Sub-bottom chirp profiles. A newly constructed workflow was assessed to perform data elaboration with Seismic Unix software by comparing and improving the seismic data of the previously processed profiles that used Seisprho software. The identification of these anomalies and the corresponding units from the offshore Cilento promontory represent a useful basis for an assessment of marine geohazards and could help to plan for the mitigation of geohazards in the Cilento region. [ABSTRACT FROM AUTHOR]

The Nyquist sampling theorem must be followed in conventional seismic data acquisition; however, due to missing traces or exploration cost constraints, field data acquisition cannot meet the sampling theorem, so prestack data must be reconstructed to meet the requirements. We proposed a new seismic data reconstruction approach based on a fast projection onto convex sets (POCS) algorithm with sparsity constraint in the seislet transform domain, based on compressive sensing (CS) theory in the signal-processing field. In comparison to traditional projection onto convex sets (POCS), the faster projection onto convex sets (FPOCS) can achieve much faster convergence (about two-thirds of conventional iterations can be saved). To address the slow convergence of typical threshold parameters during reconstruction, an exponential square root lowered threshold and process 2-D seismic data reconstruction with hard thresholding has been purposed. Because of the much sparser structure in the seislet transform domain, the seislet transform-based reconstruction approach can clearly achieve better data recoveryresults than f-k transform-based scenarios, in terms of both signal-to-noise ratio (SNR) and visual observation. The proposed approach’s performance is demonstrated using both synthetic and field data examples. [ABSTRACT FROM AUTHOR]

Currently, East Siberia as a whole and the Republic of Sakha (Yakutia) in particular are the territories of extensive geological exploration. Field geophysical surveys are mainly performed by seismic technologies used to discover new deposits and provide data for detailed appraisal of the known fields. Three-dimensional seismic surveys are effectively used for clarifying the details of geologic structures and obtaining the data for drilling programs. Upon further development of technologies, improvements are introduced in field operations, seismic data processing and interpretation, and drilling programs. As the information content of the survey data increases, it becomes possible to revise and update geological models and/or develop them in a more detail. With the use of modern data processing technologies that preserve the signals and depth migration data, the tectonic model of the Srednebotuobinskoe Oil and Gas Condensate Field was fully modified. Furthermore, additional isolated tectonic blocks were discovered in the eastern part of the Central deposit, and their map was constructed. The geological exploration based on the updated tectonic model is successful – new mineral reserves have been discovered, and the vector of geological exploration has been completely changed.

Seismic data processing heavily relies on the solution of physics-driven inverse problems. In the presence of unfavourable data acquisition conditions (e.g., regular or irregular coarse sampling of sources and/or receivers), the underlying inverse problem becomes very ill-posed and prior information is required to obtain a satisfactory solution. Sparsity-promoting inversion, coupled with fixed-basis sparsifying transforms, represent the go-to approach for many processing tasks due to its simplicity of implementation and proven successful application in a variety of acquisition scenarios. Nevertheless, such transforms rely on the assumption that seismic data can be represented as a linear combination of a finite number of basis functions. Such an assumption may not always be fulfilled, thus producing sub-optimal solutions. Leveraging the ability of deep neural networks to find compact representations of complex, multi-dimensional vector spaces, we propose to train an AutoEncoder network to learn a nonlinear mapping between the input seismic data and a representative latent manifold. The trained decoder is subsequently used as a nonlinear preconditioner for the solution of the physics-driven inverse problem at hand. Through synthetic and field data examples, the proposed nonlinear, learned transformations are shown to outperform fixed-basis transforms and converge faster to the sought solution for a variety of seismic processing tasks, ranging from deghosting to wavefield separation with both regularly and irregularly subsampled data.

In this study, we analyze the quality factor- Q derived from the Vertical Seismic Profiling (VSP) and sonic log data that was performed in the DS-2 borehole located in the Carpathian Foredeep. We use the obtained quality factor distributions in the inverse Q-filtering (IQF) procedure during active seismic data processing. Another very important and, unfortunately, subjective in nature parameter is the gain function applied to seismic data while performing the IQF. To calculate Q from VSP data we use an iterative approach to the spectral ratio method where we use several shallowest receivers as reference. We observe high variability of the obtained Q values, depending on the reference receiver, however, depth distribution of Q was consistent regardless To increase reliability of the obtained values, an additional method of Q measurements is incorporated, i.e., acoustic travel time dispersion method. We show that incorporating inverse Q-filtering to active seismic data processing flow allows to enhance the vertical resolution of 2D seismic data.

Smartphones equipped with motion sensors can be manipulated as a community seismic network for earthquake detection. But, there still have many challenges such as the limited sensing capability of the off-the-shelf sensors and unpredictable diversity of daily operations by phone users in current smartphones, which yield poor monitoring quality. So we present a suite of algorithms towards detecting anomalous seismic events from sampling data contaminated by users operations, including a lightweight signal preprocessing method, a two-phase events picking and timing scheme on local smartphones, and a decision fusion scheme to maximize anomaly detection performance at the fusion center while meeting the requirements on system false alarm rate. We experimentally evaluate the proposed approach on networked smartphones and shake tables. The results verify the effectiveness of our approach in distinguishing anomalous seismic events from noises due to the normal daily operation. [ABSTRACT FROM AUTHOR]

The seismic reflection method is one of the most important methods in geophysical exploration. There are three stages in a seismic exploration survey: acquisition, processing, and interpretation. This paper focuses on a pre-processing tool, the Non-Local Means (NLM) filter algorithm, which is a powerful technique that can significantly suppress noise in seismic data. However, the domain of the NLM algorithm is the whole dataset and 3D seismic data being very large, often exceeding one terabyte (TB), it is impossible to store all the data in Random Access Memory (RAM). Furthermore, the NLM filter would require a considerably long runtime. These factors make a straightforward implementation of the NLM algorithm on real geophysical exploration data infeasible. This paper redesigned and implemented the NLM filter algorithm to fit the challenges of seismic exploration. The optimized implementation of the NLM filter is capable of processing production-size seismic data on modern clusters and is 87 times faster than the straightforward implementation of NLM. [ABSTRACT FROM AUTHOR]

The exploration of biogenic gas in the Adriatic Sea is highly developed and the investigation of the residual potential and located complex areas, requires an improvement in the quality of available data. This paper illustrates a 2-year seismic reprocessing program intended to assess the residual gas potential in this still productive hydrocarbon area by leveraging on integrated geological and geophysical workflows, modern processing techniques and High Parallel Computing capabilities. Legacy or vintage data sets can usually be re-examined using new processing technologies and the re-processing of available data is the only solution to re-evaluate the residual potential of developed-explored areas. [ABSTRACT FROM AUTHOR]

High-resolution seismoacoustic data represent a useful tool for the investigations of gas-charged sediments occurring beneath the seabed through the identification of the diagnostic intrasedimentary features associated with them. Acoustic blanking revealed shallow gas pockets in the seismostratigraphic units of the inner shelf off the Northern Cilento promontory. Six main seismostratigraphic units were recognized based on the geological interpretation of the seismic profiles. Large shallow gas pockets, reaching a lateral extension of 1 km, are concentrated at the depocenter of Late Pleistocene–Holocene marine sediments that are limited northwards by the Solofrone River mouth and southwards by the Licosa Cape promontory. A morphobathymetric interpretation, reported in a GIS environment, was constructed in order to show the main morphological lineaments and to link them with the acoustic anomalies interpreted through the Sub-bottom chirp profiles. A newly constructed workflow was assessed to perform data elaboration with Seismic Unix software by comparing and improving the seismic data of the previously processed profiles that used Seisprho software. The identification of these anomalies and the corresponding units from the offshore Cilento promontory represent a useful basis for an assessment of marine geohazards and could help to plan for the mitigation of geohazards in the Cilento region.

Summary: Most of the applications in the seismology field rely on the processing of up to hundreds of terabytes of data and their performance is strongly affected by IO operations. In this article, we analyze the main file structures currently used to store seismic data and propose a new intermediate data structure to improve IO performance while still complying with established standards. We show that, throughout a common workflow in seismic data analysis, our IO performance gain greatly surpasses the overhead of translating data to the intermediate structure. This approach enables a speedup of up to 208 times in reading time when using classical standards (e.g., SEG‐Y) and our intermediate structure is up to 1.8 times more efficient than modern formats (e.g., ASDF). Considering cache‐friendly applications, our speedups over the direct use of SEG‐Y reach 8000 times. We also performed a cost analysis on the AWS cloud showing that, in our approach, HDDs can be 1.25 times more cost‐effective than SSDs. [ABSTRACT FROM AUTHOR]

Interval velocity analysis increases the precision of seismic velocity data with a complex structure and a high variation of velocity both laterally and vertically. In this study, interval velocity analysis was performed by applying the exponential asymptotically bounded function approach. An exponentially asymptotically bounded function was applied to calculate the interval velocity obtained from the root mean square velocity of seismic data using the Dix equation for conversion. To control this operation, a velocity constraint was applied in the interval velocity conversion. The velocity constraint used was the velocity trend gained from the root mean square velocity. This method is called constrained velocity inversion. In this study, interval velocity analysis using constrained velocity inversion was applied to seismic data from the North Sumatra Basin area. The seismic data interpretation resulted from the interval velocity analysis using constrained velocity inversion described the subsurface structure clearly. A corresponding anomaly at a time depth from 2000 ms to 2400 ms in the seismic time-domain data indicated a fault beneath an anticline. This result indicates that the interval velocity analysis of seismic data is more rigorous than the root mean square velocity analysis. [ABSTRACT FROM AUTHOR]

Synchrosqueezing transform (SST) is an effective time–frequency analysis (TFA) approach for the processing of nonstationary signals. The SST shows a satisfactory ability of the TF localization of the nonlinear signal with a slowly time-varying instantaneous frequency (IF). However, for the signal of which ridge curves in the TF domain are fast varying, or even almost parallel to the frequency axis, the SST will provide a blurred TF representation (TFR). To solve this issue, the transient-extracting transform (TET) was recently put forward. The TET can effectively characterize and extract transient features in the much concentrated TFR for the strongly frequency-modulated (FM) signal, especially the impulse-like signal. However, contrary to the SST, it is not suitable for weak FM modes. In this study, we propose a TFA method called the time-synchroextracting general chirplet transform (TEGCT). The TEGCT can achieve a highly concentrated TFR for strong FM signals as well as weak FM ones. Quantized indicators, the concentration measurement and the peak signal-to-noise ratio, are used to analyze the performances of the proposed method compared with those of other methods. The comparisons show that the TEGCT can provide a result with better TF localization. Then, the proposed method was applied to the spectrum analysis of the seismic data for oil reservoir characteristics. The horizontal slices of the offshore 3-D seismic data show that the TEGCT delineates more distinct and continuous subsurface channels in a fluvial-delta deposition system. All the results illustrate that our proposed method is a good potential tool for seismic processing and interpretation in the geoscience. [ABSTRACT FROM AUTHOR]

Fast Marching Method (FMM) boasts high calculation efficiency and strong adaptability and stability while being applied to seismic traveltime. However, when it is applied to the largescale model, the calculation precision of FMM is insufficient. FMM has poor calculation precision near the source, which is an essential reason for the low accuracy of the whole algorithm. This paper puts forward a joint traveltime calculation method to address the problem. Wavefront Construction (WFC) with a relatively high calculation accuracy rather than FMM is adopted for calculation of the grid nodes’ traveltime near the source. After that, FMM is used to calculate the seismic traveltime in the remaining area. Joint traveltime calculation method greatly improves the calculation accuracy of the source’s surrounding area and the calculation accuracy of FMM. According to the new method, FMM is still adopted for the calculation of most grid nodes in the model, so the high calculation efficiency of FMM is maintained. Multiple numerical models are utilized to verify the above conclusions in the paper.

Common Midpoint (CMP) and Common Reflection Surface (CRS) are widely used methods for improving the signal-to-noise ratio in the field of seismic processing. These methods are computationally intensive and require high-performance computing. This article optimizes these methods on the Sunway many-core architecture and implements large-scale seismic processing on the Sunway Taihulight supercomputer. We propose the following three optimization techniques: 1) we propose a software cache method to reduce the overhead of memory accesses, and share data among CPEs via the register communication; 2) we re-design the semblance calculation procedure to further reduce the overhead of memory accesses; 3) we propose a vectorization method to improve the performance when processing the small volume of data within short loops. The experimental results show that our implementations of CMP and CRS methods on Sunway achieve 3.50× and 3.01× speedup on average compared to the-state-of-the-art implementations on CPU. In addition, our implementation is capable to run on more than one million cores of Sunway TaihuLight with good scalability. [ABSTRACT FROM AUTHOR]

Gas hydrates are a potential energy source. Seismic methods have been extensively applied to detecting gas hydrates based on the interpretation of bottom-simulating reflectors (BSRs), for which the seismic response is characterized by many features such as a strong reflection and low-frequency shadow (LFS). Moreover, the frequency content depends of the offset, with the BSR response having a lower dominant frequency at large offsets. In this work, we intend to enhance the low amplitude of the BSR and to observe the LFS phenomena at the southern Hikurangi margin of New Zealand. To improve the resolution of the seismic image, we processed a multichannel seismic profile acquired over a distance of 125 km. The standard processing sequence for seismic reflection data has been performed with an emphasis on using high fold. The results reveal that the BSR is characterized by strong reflections and high amplitude owing to the seismic data being stacked at higher fold. In addition, a continuation of the BSR event was obtained. Moreover, the use of a higher fold allowed us to determine an LFS below the BSR event, which indicates the presence of gas. The findings support other evidence obtained through contemporary studies on identifying gas hydrates using seismic methods. [ABSTRACT FROM AUTHOR]

High‐frequency multichannel seismic systems provide detailed images of the shallow marine subsurface. In order to exploit the redundancy inherent in such data optimally, traveltime corrections need to account for normal moveout and static effects due to vertical source and receiver variations. Misalignment of reflections in common‐midpoint gathers will significantly lower the frequency content in the final stack, making this correction particularly important for very high‐frequency seismic data. Traditionally, normal moveout correction involves labour‐intensive picking of stacking velocities, while static corrections can be, by some techniques, performed automatically. In this paper, we present a high‐frequency seismic case study from the Baltic Sea, using seismic image matching as a novel, fully automated technique to perform joint moveout and static corrections. Our multichannel test profiles were acquired offshore Rügen island for wind farm development. Owing to the regular passage of up to 1.5 m high ocean waves during data acquisition, these boomer profiles suffer from strong static effects. We perform joint normal moveout and static corrections by defining the nearest common offset section as a fixed reference frame and minimizing its difference in traveltime with respect to all available common offset sections. Time shifts are computed independent of a pre‐defined traveltime curve, using the normalized cross‐correlation as a measure of data similarity while penalizing irregular displacements by a regularization term. Time shifts are converted to stacking velocities based on the traditional hyperbolic traveltime equation. Our results are compared with those derived by conventional manual velocity analysis and subsequent trim static corrections. We find that image matching produces stacks of similar quality and stacking velocity models of similar to slightly better quality compared with the conventionally derived ones, revealing the potential of this technique to automatize and significantly speed up this first part of the seismic processing chain. [ABSTRACT FROM AUTHOR]

Chałupki Dębniańskie seismic profile 2D–3C is located in Carpathian Foredeep basin, Poland, and is an object of interest for geologists and geophysicists due to the presence of gas-bearing layers. Multicomponent seismic plays a significant role in supporting reservoir analysis related to accumulations of crude oil and natural gas. The purpose of the research was the optimal processing workflow design, which integrated seismic images of three-component 2D seismic line (2D–3C seismic). A complete processing flow for vertical and both horizontal components was conducted to obtain stacks and prestack gathers with preserved amplitude relations (RAP processing). The main issue of the research was the interpretation of S-wave velocity, which was not provided by well log data. The obtained results increased the reliability of seismic interpretation within Chałupki Dębniańskie area. The research provided valuable information regarding amplitude anomalies and helped in the verification of the potential gas accumulations. Several reservoir analysis tools were tested, including seismic attributes and AVO analysis. Conducted research confirmed the existence of reservoir which is characterized by good reservoir parameters. [ABSTRACT FROM AUTHOR]

To acquire refined seismic targets, the use of more accurate and more precise methods of seismic signal processing is crucial. Novel seismic processing approach is even more important in case of the complex geology of mountainous regions (e.g., Carpathians, Carpathian Foredeep) where sub-vertical layering of varying rock complex can be found, but also in case of old seismic data reprocessing (especially when the signal-to-noise ratio and general quality are low). Regardless of many research papers and significant progress in seismic processing techniques, a properly processed and interpreted seismic image is still hardly affordable for this region. Obtaining a new value in seismic data processing is a core function of the presented research. The use of dip-guided filtering combined with beam partitioned analysis and Z-domain dip analysis for reprocessing of 3D and 2D seismic data from the Carpathians region has been studied. [ABSTRACT FROM AUTHOR]

Kirchhoff Time migration was applied in Pre and Post-Stack for 2D seismic survey for line AJ-99N, that is located in Ajeel oilfield in Salah Al-Din Governorate, Central Iraq. The process follows several accurate steps to reach the final time migration stage. The results of applied time migration give an accurate image for the Ajeel anticline reservoir and to improve the signal to noise ratio. Pre-Stack shows a clearer image for the structure in the study area, and the time-frequency analysis insure the result. [ABSTRACT FROM AUTHOR]

Sparse seismic acquisition is a new trend in seismic exploration, as it costs much less than conventional methods. To maintain the initial resolution of the seismic image, we propose several ways to sample data irregularly but periodically. These were tested by decimating the synthetic data, then interpolating, imaging and inversion. At every processing step, we quantified the effect of interpolation by comparing the results with those from the fully sampled data. Once the numerical test suggested the best decimation scheme, we were able to proceed to test the real dataset. This test confirmed that sparse acquisition using 60% of the available data is feasible. To maintain the initial resolution of the seismic image from seismic sparse acquisition, we propose several ways to sample data irregularly but periodically. At every processing step, we quantified the effect of interpolation by comparing the results with those from the fully sampled data. The result shows that using 60% of the available data is feasible. [ABSTRACT FROM AUTHOR]

We present an open-source multi-platform package for the damped rank-reduction (DRR) method, that has been widely used in various problems in seismology. The DRR method is an optimally improved rank-reduction (RR) method by damping the singular values using an analytically derived threshold. The DRR method can separate the useful signal and unwanted noise in multi-channel seismic data better than the standard RR method, resulting in spatially more coherent seismic events without damaging the critical subtleties. There have been some advanced methodological improvements or ingenious implementations of the DRR method in the past decade, as well as dozens of new applications in different areas of seismology, most of which will be included in the presented package. The DRR package is composed of two parts: MATdrr and Pydrr, corresponding to the Matlab and Python versions, respectively. The current DRR package includes localized DRR, optimally damped rank-reduction (ODRR), off-the-grid reconstruction, de-aliased reconstruction, and many reproducible examples in different areas of seismology. The DRR package will be continuously developed to include more functionalities in the future. • We present an open-source multi-platform package, DRR, for various applications in seismology. • The DRR method can separate the useful signal and unwanted noise in multi-channel seismic data. • The DRR package is composed of two parts: MATdrr and Pydrr. • The DRR package will be continuously developed to include more functionalities. [ABSTRACT FROM AUTHOR]