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SUMMARY: The resolution of velocity models obtained by tomography varies due to multiple factors and variables, such as the inversion approach, ray coverage, data quality, etc. Combining velocity models with different resolutions can enable more accurate ground motion simulations. Toward this goal, we present a novel methodology to fuse multiresolution seismic velocity maps with probabilistic graphical models (PGMs). The PGMs provide segmentation results, corresponding to various velocity intervals, in seismic velocity models with different resolutions. Further, by considering physical information (such as ray path density), we introduce physics-informed probabilistic graphical models (PIPGMs). These models provide data-driven relations between subdomains with low (LR) and high (HR) resolutions. Transferring (segmented) distribution information from the HR regions enhances the details in the LR regions by solving a maximum likelihood problem with prior knowledge from HR models. When updating areas bordering HR and LR regions, a patch-scanning policy is adopted to consider local patterns and avoid sharp boundaries. To evaluate the efficacy of the proposed PGM fusion method, we tested the fusion approach on both a synthetic checkerboard model and a fault zone structure imaged from the 2019 Ridgecrest, CA, earthquake sequence. The Ridgecrest fault zone image consists of a shallow (top 1 km) high-resolution shear-wave velocity model obtained from ambient noise tomography, which is embedded into the coarser Statewide California Earthquake Center Community Velocity Model version S4.26-M01. The model efficacy is underscored by the deviation between observed and calculated traveltimes along the boundaries between HR and LR regions, 38 per cent less than obtained by conventional Gaussian interpolation. The proposed PGM fusion method can merge any gridded multiresolution velocity model, a valuable tool for computational seismology and ground motion estimation. [ABSTRACT FROM AUTHOR]

SUMMARY: To address complex subsurface structures, a high-resolution velocity model must be constructed. Conventionally, algorithms such as full waveform inversion (FWI) have been used to derive accurate high-resolution velocity structures, but obstacles such as high computational costs remain. Therefore, we propose a high-resolution U-NET (HR U-NET) machine learning model to derive a high-resolution velocity model from a low-resolution velocity model. The low-resolution velocity model and migration data obtained through the corresponding velocity information were used as input data for training. In addition, we tried to improve the accuracy of the high-resolution velocity model by using prior information containing accurate velocity values. A prior model generated through geophysical logging data and a weight model including the reliability information of the prior model were also utilized. Therefore, the HR U-NET model was trained using the low-resolution velocity model, the migration data, the prior model and the weight model. Numerical experiments conducted using synthetic and field data demonstrated that the proposed model could accurately construct a high-resolution velocity model and verified that the prior model and weight model play important roles in the training process. Additionally, we confirmed that the proposed method derived almost similar results using only 8.2 percent of the computational cost of the conventional inversion method. In other words, there is an advantage that it is possible to predict high-resolution velocity information more efficiently in terms of computational cost. [ABSTRACT FROM AUTHOR]

The physical mechanisms controlling deep-focus earthquakes, or those observed at depths greater than 300 km, remain enigmatic. The leading processes by which deep-focus earthquakes are thought to occur include transformational faulting, thermal runaway and dehydration embrittlement, but distinguishing observations in support of one or more prevailing mechanisms are needed. In this study, we use a modified back-projection method, data recorded by the Hi-net array in Japan and a 3-D velocity model to produce source images of 19 deep-focus earthquakes within the Izu-Bonin subduction zone. We find that the rupture properties and fault plane orientations of imaged events separate according to reported moment magnitude, indicating the distinct operation of two moment-dependent causal mechanisms of deep-focus earthquakes in this region. We discuss these results in the context of previous observational, laboratory and numerical studies and emphasize the importance of continued research to validate the dual-mechanism hypothesis both in and outside Izu-Bonin. Such work may not only improve our understanding of the nucleation and propagation of deep-focus earthquakes, but also help clarify slab structure and subduction zone dynamics. [ABSTRACT FROM AUTHOR]

Seismic attributes are derived measures from seismic data that help characterize subsurface geological features and enhance the interpretation of subsurface structures: we propose to exploit the hidden layers of Long–Short Time Memory neural network predictions as possible new reflection seismic attributes. The idea is based on the inference process of a neural network, which in its hidden layers stores information related to different features embedded in the input data and which usually are not considered. Neural network applications typically ignore such intermediate steps because the main interest lies in the final output, which is considered as the exclusive exploitable feature of the process. On the contrary, here we analyse the possibility to exploit the intermediate prediction steps, hereafter referred as 'deep attributes' because they are produced by a deep learning algorithm, to highlight features and emphasize characteristics embedded in the data but neither recognizable by traditional interpretation, nor evident with classical attributes or multi-attribute approaches. Nowadays, classical signal attributes are numerous and used for different purposes; we here propose an original strategy to calculate attributes previously never exploited, which are potentially complementary or a good alternative to the classical ones. We tested the proposed procedure on synthetic and field 2-D and 3-D reflection seismic data sets to test and demonstrate the stability, affordability and versatility of the entire approach. Furthermore, we evaluated the performance of deep attributes on a 4-D seismic data set to assess the applicability and effectiveness for time-monitoring purposes and comparing them with the sweetness attribute. [ABSTRACT FROM AUTHOR]

Interferometric synthetic aperture radar (InSAR) decorrelation that creates great challenges to phase unwrapping has been a critical issue for mapping large earthquake deformation. Some studies have proposed a 'remove-and-return model' solution to tackle this problem, but it has not been fully validated yet, and therefore has rarely been applied to real earthquake scenarios. In this study, we use the 2023 M w 7.8 and 7.6 earthquake doublet in Turkey and Syria as a case example to develop an iterative modelling method for InSAR-based coseismic mapping. We first derive surface deformation fields using Sentinel-1 offset tracking and Sentinel-2 optical image correlation, and invert them for an initial coseismic slip model, based on which we simulate InSAR coseismic phase measurements. We then remove the simulated phase from the actual Sentinel-1 phase and conduct unwrapping. The simulated phase is added back to the unwrapped phase to produce the final phase measurements. Comparing to the commonly used unwrapping method, our proposed approach can significantly improve coherence and reduce phase gradients, enabling accurate InSAR measurements. Combining InSAR, offset tracking and optical image correlation, we implement a joint inversion to obtain an optimal coseismic slip model. Our model shows that slip on the Çardak Fault is concentrated on a ∼100 km segment; to both ends, slip suddenly diminished. On the contrary, rupture on the East Anatolian Fault Zone propagated much longer as its geometry is fairly smooth. The iterative coseismic modelling method is proven efficient and can be easily applied to other continental earthquakes. [ABSTRACT FROM AUTHOR]

Muography is an imaging technique that relies on the attenuation of the muon flux traversing geological or anthropogenic structures. Several simulation frameworks help to perform muography studies by combining specialized codes: for muon generation through muon transport to muon detector performance. This methodology is precise but requires significant computational resources and time. We present an end-to-end python-based MUographY Simulation Code, which implements a muography simulation framework capable of rapidly estimating muograms of any geological structure worldwide. This framework considers the generated muon flux as the observation point; the energy loss of muons passing through the geological target; the integrated muon flux detected by the telescope and estimates the 3-D density distribution of the target using algebraic reconstruction techniques. The simulations ignore the relatively small muon flux variance caused by geomagnetic effects, solar modulation and atmospheric conditions. We validate the code performance by comparing our simulation results with data from other frameworks. [ABSTRACT FROM AUTHOR]

Muography is a promising and rapidly developing physical prospecting technique based on the attenuation of muon flux. The feasibility and potential of applying muography to mining were presented in a small number of previous case studies. In this work, we aimed to address three challenges that limit the applicability and efficiency of muography in mineral exploration: (1) application to low-density-contrast ore body exploration, (2) analysis of altitudinal impacts on measurements and (3) precise and efficient reconstruction. We conducted the first case of applying muography to the exploration for low-density-contrast ore bodies. Six muon imaging systems were placed underground to collect surviving muons for roughly half a year. We analysed the altitudinal impact on the data measurements and proposed a simplified method to eliminate it. We also developed the seed algorithm, a novel 3-D reconstruction algorithm, that can reconstruct anomalies located far away from the detectors and avoid their elongation along the observed directions. Benefitting from the seed algorithm, a low-density-contrast ore body and a limonitic siliceous slate structure were reconstructed, demonstrating the sensitivity of this technique in density distinction; discoveries of several mined-out areas are important for accident avoidance; and reconstruction of the stope and scarps served as strong circumstantial evidence of the reliability of the result. The success of this experiment shows the great value of muography in the economic, research and safety aspects of mineral exploration and inspection. Moreover, the overcoming of challenges is helpful for the development of muography, making it an effective and competitive technique in this field. [ABSTRACT FROM AUTHOR]

This paper presents a new methodology to assist geophysicists in determining the first-break event in a 3-D seismic data set using the well-known technique called dynamic time warping algorithm (DTW), which is usually used to find the optimal alignment between two time-series. We used the optimal path from the cost matrix to identify the first break in the seismogram using a few picks (seeds) made by an interpreter as a reference to perform this task. Furthermore, the data were pre-conditioned by the topographic and linear moveout to improve the method's accuracy. To demonstrate the technique's robustness, first, we applied the methodology in a synthetic seismic data. After demonstrating the efficiency of the algorithm, we applied the aforementioned methodology in the Polo-Miranga 3-D seismic cube located in the Recôncavo sedimentary basin, Bahia-Brazil, and in the seismic data acquired from the Blackfoot field in Alberta, Canada. The high-quality results showed consistency in determining the first break in all ranges of offsets, demonstrating an alternative way to accelerate this seismic processing step. Furthermore, we compared the results obtained by the proposed methodology with an algorithm based on comparing the short-time averages with long-time averages. Finally, we performed the static correction calculation to ensure that the time distortion resulting from the terrain and the low-velocity layer was mitigated in shoot gathers and in the stacked section. [ABSTRACT FROM AUTHOR]

Dense array observation and seismic interferometry have revolutionized the imaging schemes of the earth structure. It is becoming possible to directly obtain the lateral variation of the earth's structure by applying array-based methods such as the cross-correlation beamforming (CBF) of the ambient noise to the subsets of the dense array, without tomography. CBF has been proven to extract the azimuth-averaged multimode surface wave dispersion curves. However, the resolution of the dispersion image generated by conventional CBF is low at high frequencies in the frequency–velocity (f - v) domain. Moreover, the irregular array geometry and uneven source distribution would bias the result of CBF, especially for the estimation of azimuth-dependent velocity. In this paper, two beamforming (BF) es are suggested to improve the resolution of multimode dispersion images in the f - v domain. First, the geometrical spreading of the wavefield is corrected to enhance the amplitude at high frequency (or large distance) and thereby improve the resolution of the dispersion image at high frequency. We call this scheme weighted correlation beamforming (WCBF). The azimuth-averaged velocity can be estimated with sufficient resolution using WCBF by stacking the BF output at each azimuth. We show that WCBF is the 2-D Fourier transform of the spatial wavefield from the viewpoint of the wavefield transform. Secondly, a modified beamforming scheme (MCBF) is suggested to reduce the effect of uneven source and/or irregular array geometry. The delay and summation in MCBF are performed only for plane waves incident from the stationary phase region. The azimuth-dependent velocity can therefore be estimated by MCBF with less dependence on the array geometry, as well as on the uneven source distribution. In terms of the estimation of azimuth-averaged phase velocity, we show the F-J method, another array-based method for extracting multimode surface waves from ambient noise using the Fourier–Bessel transform, is the azimuth-averaged version of WCBF. The reliability of WCBF and MCBF is verified based on the synthetic and field data using the array with different geometry. The dispersion image of multimode Rayleigh wave phase velocity at local and regional scales can be generated by WCBF or MCBF with high resolution. In particular, multimode dispersion curves at the local scale can be measured by MCBF with sufficient accuracy using quite short recordings from hours to days. This offers the possibility of a rapid assessment of the media properties. [ABSTRACT FROM AUTHOR]

Earthquake source parameters can be estimated using seismological observations, but the identification of the fault responsible is often complicated by location uncertainties and the inherent ambiguity between nodal planes. Satellite Interferometric Synthetic Aperture Radar (InSAR) can be used to observe ground deformation and model fault geometry but is limited by climate conditions (water vapour) and ground coverage (dense vegetation). In the tropics, the atmosphere is dynamic and most regions are densely vegetated, making detecting coseismic deformation challenging. Here, we perform a systematic inspection of coseismic interferograms from Sentinel-1 SAR images, to assess their suitability for detecting coseismic deformation in Costa Rica. Using data from the seismological network, we target seven earthquakes between 2016 and 2020 with depths |$\le \, 20$| km and magnitudes M w 5.3–6.2. For each event, we use the seismic parameters to compute line-of-sight displacements for ascending and descending geometries and for both nodal planes and generate 12- and 24-d coseismic interferograms where available. We obtain interferograms with coseismic displacement signals for three of the seven earthquakes. We invert the geodetic data to retrieve the earthquake source parameters but the lack of offshore geodetic coverage causes trade-offs between parameters and large uncertainties. The Jacó and Golfito earthquakes likely occurred on the subduction interface and the geodetic locations were 6–9 km closer to the coast than previous seismic estimates. The Burica earthquake occurred on a shallow steeply dipping thrust fault in the outer forearc. For the other earthquakes, no coseismic deformation was detected due to atmospheric noise or poor coherence. These results demonstrate the suitability of 12-d Sentinel-1 interferograms for monitoring shallow earthquakes of magnitude > M w 5.7 in Central America. This approach can be used to begin a surface deformation catalogue for the region, which will ultimately help improve the understanding of active deformation processes and improve hazard maps. [ABSTRACT FROM AUTHOR]

Spectral induced polarization (SIP) and electrical impedance tomography (EIT) are advanced approaches for structural characterisation of soils and rocks based on measurements of the frequency-dependent complex electrical resistivity. Accurate SIP and EIT measurements require the reduction of electrode effects, which have often been attributed to the polarization of metal electrodes in the current pathway. In this study, we demonstrate an alternative electrode effect that influences the accuracy of complex impedance measurements. This alternative electrode effect is caused by a heterogeneous distribution of the contact impedance, which can occur due to ageing or corrosion of the electrode. First, theoretical considerations are given to explain how this electrode effect influences the accuracy of complex impedance measurements. Next, we demonstrate using SIP and EIT measurements that this alternative mechanism better explains the observed electrode effects than previous explanations based only on the presence of metal. The SIP measurements were made on a water-filled container with a metal sphere between two non-polarizing electrodes, which showed that the mere presence of metal in the current pathway only leads to small electrode effects that are typically well below 1 mrad even for small electrode spacings. When the same metal sphere was used as a potential electrode, the observed electrode effect was about 2.5 times larger and varied with rotation. These changes can be explained by a heterogeneous contact impedance resulting in a varying electrode voltage. The EIT measurements were performed with a cylindrical sample holder filled with water. They are used to illustrate how the presented theoretical considerations can be used to investigate the magnitude of the electrode effects for different electrode configurations. We conclude that electrode effects are important to consider for laboratory SIP and EIT measurements and field measurement with closely spaced electrodes. [ABSTRACT FROM AUTHOR]

Seismic interrogation of the upper mantle from the base of the crust to the top of the mantle transition zone has revealed discontinuities that are variable in space, depth, lateral extent, amplitude and lack a unified explanation for their origin. Improved constraints on the detectability and properties of mantle discontinuities can be obtained with P-to-S receiver function (Ps-RF) where energy scatters from P to S as seismic waves propagate across discontinuities of interest. However, due to the interference of crustal multiples, uppermost mantle discontinuities are more commonly imaged with lower resolution S-to-P receiver function (Sp-RF). In this study, a new method called CRISP-RF (Clean Receiver-function Imaging using SParse Radon Filters) is proposed, which incorporates ideas from compressive sensing and model-based image reconstruction. The central idea involves applying a sparse Radon transform to effectively decompose the Ps-RF into its underlying wavefield contributions, that is direct conversions, multiples, and noise, based on the phase moveout and coherence. A masking filter is then designed and applied to create a multiple-free and denoised Ps-RF. We demonstrate, using synthetic experiment, that our implementation of the Radon transform using a sparsity-promoting regularization outperforms the conventional least-squares methods and can effectively isolate direct Ps conversions. We further apply the CRISP-RF workflow on real data, including single station data on cratons, common-conversion-point stack at continental margins and seismic data from ocean islands. The application of CRISP-RF to global data sets will advance our understanding of the enigmatic origins of the upper mantle discontinuities like the ubiquitous mid-lithospheric discontinuity and the elusive X-discontinuity. [ABSTRACT FROM AUTHOR]

Gravity surveys constitute an important method for investigating the Earth's interior based on density contrasts related to Earth material differentials. Because lithology depends on the environment and the period of formation, there are generally clear boundaries between rocks with different lithologies. Inversions with convex functions for approximating the L 0 norm are used to detect boundaries in reconstructed models. Optimizations can easily be found because of the convex transformations; however, the volume of the reconstructed model depends on the weighting parameter and the density constraint rather than the model sparsity. To determine and adapt the modelling size, a novel non-convex framework for gravity inversion is proposed. The proposed optimization aims to directly reduce the L 0 norm of the density matrix. An improved iterative hard thresholding algorithm is developed to linearly reduce the L 0 penalty during the inner iteration. Accordingly, it is possible to determine the modelling scale during the iteration and achieve an expected scale for the reconstructed model. Both simple and complex model experiments demonstrate that the proposed method efficiently reconstructs models. In addition, granites formed during the Yanshanian and Indosinian periods in the Nanling region, China, are reconstructed according to the modelling size evaluated in agreement with the magnetotelluric profile and density statistics of rock samples. The known ores occur at the contact zones between the sedimentary rocks and the reconstructed Yanshanian granites. The ore-forming bodies, periods, and processes are identified, providing guidance for further deep resource exploration in the study area. [ABSTRACT FROM AUTHOR]

SUMMARY We present observations of sidewalk curbs that are offset by creep on the Hayward Fault in Fremont, CA. Using structure-from-motion photogrammetry techniques, we construct 3-D models from suites of 2-D photos taken from 2015 to 2018 at 20 sites along the fault. By aligning and comparing these models, we measure the 3-D displacements of each offset sidewalk through time at precisions of ∼0.5 mm yr−1. We find that on average individual offset curbs record

We studied the benefits of a nine-component (9C) seismic survey over a landslide-prone area in southwest of Sweden to retrieve ultrahigh-resolution shear wave reflection images of the subsurface as well as crucial information on physical properties of the sediments. A complete, 1 m shot and receiver spacing, multicomponent 2-D seismic profile was acquired using three-component microelectromechanical-system-based landstreamer receivers, and a 5-kg sledgehammer strike in vertical and horizontal orientations as three-component seismic source. Given the rich number of shear wave reflections observed on all the 9C data, the processing work focused on their retrievals. It revealed three distinct reflections, two of which associated with coarse-grained materials and one with an extremely undulating bedrock surface. Given the extremely slow shear wave velocities on the order of 60–100 m s−1, we obtained ultrahigh-resolution shear wave sections avoiding temporal and spatial aliasing. Imaging results suggest vertical-source and horizontal-radial receiver (V–HR), and horizontal-transverse source–receiver orientations (HT–HT) provided the most optimum images of the subsurface. A non-hyperbolic algorithm was applied to the normal-moveout corrections justified by the traveltime differences of the bedrock reflection in different shear wave sections. The improved images by incorporating the anisotropy term suggest that the data set reveals moderate shear wave anisotropy along some portions of the profile. The Vp / Vs ratio obtained by using bedrock reflection in P - and S -wave sections suggests values ranging 10–16, which implies high water content. Areas with lower Vp / Vs coincides with greater anisotropic parameters and this can indicate disturbed clays or presence of sensitive clays. [ABSTRACT FROM AUTHOR]

Seismic interferometry (SI) is a technique that allows one to estimate the wavefields accounting for the wave propagation between seismometers, any of which can act as a virtual source (VS). Interferometry, particularly noise interferometry, has been applied to several geophysical disciplines such as passive monitoring and distributed acoustic sensing. In practice, one requires long recordings of seismic noise for noise interferometry. Additionally, one can have missing seismic interferometric traces because some receivers in seismic arrays may be absent or inoperative due to issues of receiver installation and malfunction. Thus, filling the gap of seismic interferometric profile requires wavefield reconstruction and regularization techniques. Compressive sensing (CS) is one such method that can reconstruct seismic interferometric wavefields and help mitigate the limitations by exploiting the sparsity of seismic waves. In our work, we use CS to reconstruct missing seismic interferometric wavefields. One can interpolate interferometric wavefields using correlograms provided by one VS. We call this method of reconstructing an individual VS gather single-source wavefield reconstruction. We propose an alternative technique called multi-source wavefield reconstruction , which applies CS to reconstruct multiple interferometric wavefields using a volume of VS gathers provided from all available VSs. Using numerical examples, we show that one can apply CS to recover interferometric wavefields resulting from interferometry of a linear seismic array. To exploit the sparsity of interferometric wavefields, we apply the Fourier and Curvelet transforms to the two reconstruction schemes. Using the signal-to-noise ratio (SNR) to compare reconstruction of interferometric wavefields, the Fourier multi-source method improves the recovery of interferometric wavefields by approximately 50 dB compared to the Fourier and Curvelet single-source wavefield reconstructions. [ABSTRACT FROM AUTHOR]

Digital rock physics (DRP) is important for characterizing the pore characteristics and petrophysical parameters of rocks from a microscopic perspective. Among the digital rock reconstruction methods, the most widely used is the computer tomography (CT) scan method. However, the pore structure of rocks has multiscale features, and CT scan images with a single resolution cannot completely describe the pore structure characteristics of rocks. In this paper, we analysed reconstructed multisource and multiscale 3-D digital rocks based on the cycle-consistent generative adversarial network (CycleGAN) method. This method fully integrates the advantages of the large field of view of low-resolution images and the high-precision features of high-resolution images. To investigate the performance of the method, two sequences of CT scan images of a sandstone (sample A) and a carbonate rock (sample B) collected from oilfields were studied. Moreover, due to the high resolution of scanning electron microscope (SEM) images, we also constructed 3-D digital rocks with different resolutions of the Berea sandstone based on the combination of CT scan images and SEM images. Finally, the statistical properties and absolute permeabilities were calculated to evaluate the accuracies of reconstructed multisource and multiscale 3-D digital rocks. The results show that the reconstructed multiscale digital rocks based on the CycleGAN method have good accuracy in terms of statistical properties and petrophysical properties. Considering the computational cost and computational accuracy, for high- and low-resolution CT scan images and CT scan images with SEM images as training image data sources, we suggest that the resolution of the reconstructed multiscale digital cores is 4–8 times and 4–16 times higher than that of the low-resolution CT scan images, respectively. The findings of our research will be helpful in gaining insight into the petrophysical properties of heterogeneous rocks. [ABSTRACT FROM AUTHOR]

Full-waveform inversion (FWI) is the current standard method to determine final and detailed model parameters to be used in the seismic imaging process. However, FWI is an ill-posed problem that easily achieves a local minimum, leading the model solution in the wrong direction. Recently, some works proposed integrating FWI with Convolutional Neural Networks (CNN). In this case, the CNN weights are updated following the FWI gradient, defining the process as a Physics-Informed Neural Network (PINN). FWI integrated with CNN has an important advantage. The CNN stabilizes the inversion, acting like a regularizer, avoiding local minima-related problems and sparing an initial velocity model in some cases. However, such a process, especially when not requiring an initial model, is computationally expensive due to the high number of iterations required until the convergence. In this work, we propose an approach which relies on combining supervised learning and physics-informed by using a previously trained CNN to start the DL-FWI inversion. Loading the pre-trained weights configures transfer learning. The pre-trained CNN is obtained using a supervised approach based on training with a reduced and simple data set to capture the main velocity trend at the initial FWI iterations. The proposed training process is different from the initial works on the area which obtained the velocity model from the shots in supervised learning tasks and that required a large amount of labelled data to ensure reasonable model predictions. We investigated in our approach two CNN architectures, obtaining more robust results and a reduced number of parameters when using a modified U-Net. The method was probed over three benchmark models, showing consistently that the pre-training phase reduces the process's uncertainties and accelerates the model convergence using minimal prior information. Besides, the final scores of the iterative process are better than the examples without transfer learning. Thus, transfer learning solved one main limitation of the previous PINN approaches: the unfeasible number of iterations when not using an initial model. Moreover, we tested the method using data with low-frequency band limitations, since the lack of low frequencies is a common issue within real seismic data. The inversion converges to reasonable results probing the method's robustness with restricted frequency content. [ABSTRACT FROM AUTHOR]

Accurate near-surface velocity models are necessary for land seismic imaging. First-arrival traveltime tomography (FTT) routinely used for estimating near-surface velocity models may fail in geological complex areas. Supervised deep learning (SDL) is capable of building accurate velocity models, based on tens of thousands of velocity model-shot gathers training pairs. It takes lots of time and memory space, which may be unaffordable for practical applications. We propose integrating the FTT and SDL to build near-surface velocity models. During the neural network training, the FTT-inverted models rather than the original seismic data are used as the network inputs and corresponding true models are the outputs. The FTT-inverted and true models are the same physical quantities and with the same dimensions. Their relationship is less non-linear than that between shot gathers and true models. Thus, the neural network of the proposed method can be trained well using only a small number of training samples, dramatically reducing the time and memory costs. Numerical tests demonstrate the feasibility and effectiveness of the proposed method. We applied the proposed method to a land data set obtained in mountainous areas in the west of China and obtained satisfactory near-surface velocity models and stacking images. [ABSTRACT FROM AUTHOR]

Seismic velocity analysis is the basis for seismic imaging and understanding complex subsurface geological structures. Although the performance of automatic velocity analysis methods based on Common Middle Point (CMP) data or Velocity Spectra (VS) is encouraging, particularly deep learning methods. However, most methods ignore the complementarity between CMP data and VS data, and only one of them is selected for velocity modelling. We propose a multimodal neural network (MMN) that combines the advantages of CMP data details representation and simplification of VS. MMN includes multilayer convolution structures and auto-encoder structures, which are used to extract time–space amplitude information from CMP gathers and energy groups features from VS data, respectively. This paper compared MMN with the CMP single-modal network (CSN) and the velocity spectra single-modal network (VSSN). Based on synthetic data, we investigated their differences in terms of continuity, accuracy, noise resistance and generalization. The MMN prediction results makes a trade-off between the overall continuity and local details. Visualization analysis of the intermediate feature maps explains the MMN velocity prediction mechanism, that is, the multi-angle representation and complementary fusion of velocity information. Finally, the performance of the proposed method is demonstrated using the braided river deposited field data example. [ABSTRACT FROM AUTHOR]

SUMMARY: With the increasing advances in the oil and gas industry, seismic imaging near or under salt structures has become an important point in deep-water exploration. Detailed velocity models of these areas are particularly interesting not only to characterize hydrocarbon reservoirs but also to identify potential sites for hydrogen and carbon dioxide storage in offshore salt caverns. Thus, we study the full-waveform inversion for the salt reconstruction in acoustic media with constant density considering the time-harmonic wave propagation in a finite element formulation using the topology optimization (TO) method. This problem is challenging due to the strong velocity contrast between salt bodies and the sedimentary background, in addition to the lack of low-frequency data and the inherent ill-posedness of the inverse problem. In this context, we incorporate techniques from the TO field, usually used in design applications, to overcome or reduce these known problems. We initially defined the squared slowness as a combination of two fields, one related to the salt shape and the other to the background. An interpolation rule based on the solid isotropic material with penalization method, combined with filtering and projection schemes, is used to find the shape of the salt bodies with increased sharpness interfaces. A Helmholtz-type filter is applied to modify the gradient aimed to regularize the problem and provide a more stable way for the salt shape to evolve during the inversion process. In particular, we demonstrate that the proposed approach may be relevant for reconstructing media with salt bodies when a suitable starting model is unavailable, and sharp interfaces are required. In addition, we present inversion results from synthetic data generated by a variable density model to demonstrate the approach capability when subjected to a reconstruction application. [ABSTRACT FROM AUTHOR]

SUMMARY: Large continental earthquakes are infrequent, but they are important for understanding active tectonics. Many pre-1990, magnitude greater than 7 earthquakes have not been well documented due to a lack of data. The declassification of KeyHole (KH) imagery provides opportunities for investigating those historical events. In this study, we present new geodetic observations of surface deformation from the 1976 November 24 Ms 7.3 Chaldiran earthquake in eastern Turkey. We use various historical KH imagery and modern Pleiades stereo imagery to determine the coseismic deformation, providing new constraints on the 1976 rupture. Based on the image measurements, we resolve the coseismic slip at depth. The inverted slip from a simple elastic dislocation model, ∼4.0 m at shallow depth of the Hidirmentes segment, is larger than the surface displacement, around 3 m from previous field surveys, suggesting that the coseismic deformation is a combination of slip on the primary fault with 30 ± 16 per cent off-fault deformation. We also measure cumulative offsets at two sites with well-preserved features, and find a constant ratio of horizontal to vertical offsets throughout the past few earthquake cycles. Both the observed constant slip ratio in this study and offset clusters from previous field measurements support that the Chaldiran fault exhibits a characteristic slip behaviour in the late Quaternary. By combining all the geodetic and geological observations in the broader region, we also present a brief analysis of the slip rate of the Chaldiran fault. [ABSTRACT FROM AUTHOR]

Algorithmic and computational advances have made it possible that geophysical survey and earth model design can be aided by many systematic trial inverse-modelling runs with synthetic data. Such may, for example, come up in machine-learning approaches. Automated image appraisal pertaining to such applications will involve common statistical tests for goodness-of-data fit as a primary evaluation method. However, solution non-uniqueness may render multiple images equivalent in terms of their data fit, requiring secondary categorizers. A logical choice for classifying synthetic-imaging results quantifies the goodness of model fit where a known reference model replaces the observational input. The task of model intercomparison in terms of measuring the resemblance to the reference model poses challenges to common distance-based metrics like root mean square error and mean absolute error. First, distance-based metrics can introduce spurious contributions when smooth models with fuzzy target contours are to be compared against a sharp reference. Second, large differences due to parameter-estimation overshoots can dominate distance metrics. The remedy proposed here is referred to as semblance and is based on the idea of logistic functions, where a binary-dependent variable adds non-zero or zero accumulation terms for the, respectively, passing or failing of preset target thresholds. This classifying approach is amenable to an objective where model feature recognition is primary. Numerical comparisons to distance-based metrics provide evidence for the advantages of the semblance in view of this objective. Geophysical imaging in conjunction with machine-learning is seen as a benefitting upcoming application area.

Algorithmic and computational advances have made it possible that geophysical survey and earth model design can be aided by many systematic trial inverse-modelling runs with synthetic data. Such may, for example, come up in machine-learning approaches. Automated image appraisal pertaining to such applications will involve common statistical tests for goodness-of-data fit as a primary evaluation method. However, solution non-uniqueness may render multiple images equivalent in terms of their data fit, requiring secondary categorizers. A logical choice for classifying synthetic-imaging results quantifies the goodness of model fit where a known reference model replaces the observational input. The task of model intercomparison in terms of measuring the resemblance to the reference model poses challenges to common distance-based metrics like root mean square error and mean absolute error. First, distance-based metrics can introduce spurious contributions when smooth models with fuzzy target contours are to be compared against a sharp reference. Second, large differences due to parameter-estimation overshoots can dominate distance metrics. The remedy proposed here is referred to as semblance and is based on the idea of logistic functions, where a binary-dependent variable adds non-zero or zero accumulation terms for the, respectively, passing or failing of preset target thresholds. This classifying approach is amenable to an objective where model feature recognition is primary. Numerical comparisons to distance-based metrics provide evidence for the advantages of the semblance in view of this objective. Geophysical imaging in conjunction with machine-learning is seen as a benefitting upcoming application area.

Reflector-normal angles and reflector-curvature parameters are the principal geometric attributes used in seismic interpretation for characterizing the orientations and shapes, respectively, of geological reflecting surfaces. Commonly, the input data set for their computation consists of fine 3-D grids of scalar fields representing either the seismic-driven reflectivities (e.g. amplitudes of 3-D seismic migrated volumes) or model-driven reflectivities, computed, for example, from the derived elastic impedance parameters. Conventionally, the computation of curvature parameters at each gridpoint is based on analysing the local change in the inline/crossline dips, considering the potential existence of a local quadratic reflecting surface in the vicinity of that point. This assumption breaks down for subsurface points in the vicinity of either complex reflecting surfaces (e.g. brittle/rough/tilted synclines/anticlines, ridges/troughs and saddles) and/or sharp, discontinuous geological features (e.g. fault edges/tips, pinch-outs, fracture systems, channels and small geobodies), where the values of the computed curvature become extremely high. However, while these high values can indicate the existence of non-reflecting objects, they do not deliver their specific geometric characteristics. In this study, we present a novel method that better characterizes the shapes of these complex geological features by extending the assumption of local surfaces (2-D surfaces in 3-D space) into local hypersurfaces (3-D hypersurfaces in 4-D space), with their corresponding (three rather than two) principal (and effective) curvature parameters. We demonstrate the advantages of our method by comparing the conventional dip-based surface curvature parameters with the hypersurface curvature parameters, using a synthetic model/image with different types and shapes of geological features and a seismic image of real data containing a complex fault and hidden buried channels. [ABSTRACT FROM AUTHOR]

Deep learning (DL) has achieved remarkable progress in geophysics. The most commonly used supervised learning (SL) framework requires massive labelled representative data to train artificial neural networks (ANNs) for good generalization. However, the labels are limited or unavailable for field seismic data applications. In addition, SL generally cannot take advantage of well-known physical laws and thus fails to generate physically consistent results. The weaknesses of standard SL are non-negligible. Therefore, we provide an open-source package for geophysics-steered self-supervised learning (SSL; taking application to seismic deconvolution as an example). With the wavelet given, we incorporate the convolution model into the loss function to measure the error between the synthetic trace generated by the ANN deconvolution result and the observed data, steering the ANN's learning process toward yielding accurate and physically consistent results. We utilize an enhanced U-Net as the ANN. We determine a hard threshold operator to impose a sparse constraint on the ANN deconvolution result, which is challenging for current DL platforms because no layer is available. 2-D/3-D ANNs can naturally introduce spatial regularization to the ANN deconvolution results. Tests on synthetic data and 3-D field data with available well logs verify the effectiveness of the proposed approach. The approach outperforms the traditional trace-by-trace method in terms of accuracy and spatial continuity. Experiments on synthetic data validate that sparsity promotion matters for sparse recovery problems. Field data results of the proposed approach precisely identify the layer interfaces and mostly match well with the log. All codes and data are publicly available at https://doi.org/10.5281/zenodo.7233751 (Xintao Chai). [ABSTRACT FROM AUTHOR]

Because the magnetotelluric (MT) method uses natural sources, the electric and magnetic fields recorded in the field acquisition are not directly used but usually converted into other MT response functions for interpretation such as inversion. Considering that inversion results are dependent on types of input data, it can be helpful to analyse different characteristics of MT response functions for inversion. In this study, we examine sensitivity patterns of MT response functions used commonly in MT inversion, which are the impedance tensor, apparent resistivity, phase, tipper, effective impedance and phase tensor; and investigate how their sensitivity patterns affect inversion results. We first describe overall tendencies of 3-D sensitivity patterns of the MT response functions, and then classify the MT response functions into six groups based on 2-D sensitivity patterns computed at the surface, which are briefly called 'surface-sensitivity patterns' in this study. The 'diagonal components of the impedance' and 'off-diagonal components of the phase tensor', which have four petals-shaped surface-sensitivity patterns along the diagonal directions, belong to Group 1, and contribute to imaging 3-D subsurface structures from receivers installed evenly at the surface. Group 2 contains the ' xy -components of the impedance, apparent resistivity and phase' and ' yy -component of the phase tensor' whose surface-sensitivity patterns are linear in the y -axis. The ' yx -components of the impedance, apparent resistivity and phase' and ' xx -component of the phase tensor' that have strong linear surface-sensitivity patterns along the x -axis are classified into Group 3. The MT response functions of Groups 2 and 3 are useful for inversion of structures close to 2-D, whose strike extends along the y - and x -axes, respectively. Groups 4 and 5 include the ' x - and y -components of tipper' that possess linearly aligned two petals-shaped surface-sensitivity patterns in the x - and y -axes, respectively. The tipper can be helpful in imaging both 2-D and 3-D structures. The 'effective impedance' belongs to Group 6, whose surface-sensitivity patterns appear as a small circle. The surface-sensitivity patterns allow the effective impedance to have an advantage in interpretation of 1-D structures. By using several MT response functions for specific cases of 1-D, 2-D and 3-D interpretation of MT data, we investigate whether characteristics of the sensitivity patterns are reflected in modelling (simulating field data) and inversion results, and then suggest optimal MT response functions for those cases. In doing so, we show how to utilize the characteristics of the sensitivity patterns in inversion, and recommend the input MT response functions for inversion according to MT exploration situations. Our study provides basic information on similarities and differences of major MT response functions for inversion and insights on which MT response functions are suitable to increase the feasibility of MT inversion for different field situations based on the sensitivity patterns. [ABSTRACT FROM AUTHOR]

On 2013 September 24, an earthquake of magnitude M w 7.7 occurred on the Hoshab Fault in southern Pakistan, south of the 650-km-long Chaman Fault, within the eastern Makran accretionary wedge. This earthquake was caused predominantly by strike-slip faulting. To quantify the post-seismic deformation following this large earthquake, we used ascending and descending Sentinel-1 data over the period 2014 November–2017 December. The deformation time-series over these 3 years shows that the post-seismic deformation was not linear over the time. To determine the mechanisms that may have driven post-seismic deformation, such as after-slip or a combination of after-slip and viscous relaxation, we explored some models, including after-slip only, and a combination of after-slip and viscous relaxation, to identify the best fit between the observed and simulated deformation time-series. Our results indicate that after-slip was the main mechanism controlling the post-seismic deformation. The introduction of a basal flat fault in the modelling improves the fit between the model results and the time-series obtained from the satellite images This basal fault is located at a depth of 18 km, has a northward dip of 7° and corresponds to the basal décollement level of the Makran accretionary prism. [ABSTRACT FROM AUTHOR]

Subsurface reflectivity imaging is one of the most important geophysical characterization methods for revealing subsurface structures. In many cases, accurate subsurface reflectivity imaging is challenging because of, for example, random or coherent noise in the data and sparse source–receiver observation geometry. We develop a deep-learning-guided iterative imaging method to improve subsurface structure imaging. Specifically, we train a supervised neural network to infer a noise-free, high-resolution image from a noisy, low-resolution image and use this estimated image as guidance to regularize least-squares imaging. We develop a systematic method to generate high-quality synthetic training data (data-label pairs) to train the guidance neural network. The trained neural network can provide high-fidelity predictions even for field-data images that are not in the training data. We validate our new imaging method using one synthetic and two field ground-penetrating radar data examples, and find that our method can produce clean, high-resolution subsurface reflectivity images where existing single-pass and least-squares imaging methods fail due to noise and insufficient data coverage. [ABSTRACT FROM AUTHOR]

The performance of supervised deep learning-based denoising methods relies on massive amounts of high-quality training data set with labels. However, data labelling is a time-consuming and tedious process, and the lack of labelled data set has become a major bottleneck affecting the development of supervised deep learning-based denoising methods. In recent years, denoising methods that only use unlabelled noisy data set for training have received more and more attention. Although these methods get rid of the dependence on labels, they usually have some specific requirements on the training data set. For example, the paired training data are required to be multiple noisy observations for each scene or obey a specific noise distribution, etc., which are often very challenging to meet in practical applications. In this study, we propose an improved self-supervised denoising framework based on Noise2Noise that only uses noisy seismic data set for training, and we name it sample2sample. The proposed denoising framework does not require multiple repeated acquisitions of seismic data to obtain multiple independent noisy observations for each scene used for training, and has no specific requirement for the noise distribution prior. Specifically, we introduce a random sampler to generate paired subsamples from some individual noisy seismic data for training. The corresponding elements in the two paired subsamples are adjacent in the original seismic data and approximately meet the training premise of Noise2Noise, that is the paired training data have the same signal. In addition, considering that there are some subtle differences in the signals of the paired subsamples generated by sampling, we also introduce a regularization loss to compensate for this. We conducted a qualitative and quantitative analysis of the denoising performance of the proposed method through further experiments, including synthetic data experiments and field data experiments. [ABSTRACT FROM AUTHOR]

The accurate and fast estimation of velocity models is crucial in seismic imaging. Conventional methods, such as tomography, stereotomography, migration velocity analysis and full-waveform inversion (FWI), obtain appropriate velocity models; however, they require intense and specialized human supervision and consume much time and computational resources. In recent years, some works investigated deep learning (DL) algorithms to obtain the velocity model directly from shots or migrated angle panels, obtaining encouraging predictions of synthetic models. This paper proposes a new flow to recover structurally complex velocity models with DL. Inspired by the conventional geophysical velocity model building methods, instead of predicting the entire model in one step, we predict the velocity model iteratively. We implement the iterative nature of the process when, at each iteration, we train the DL algorithm to determine the velocity model with a certain level of precision/resolution for the next iteration; we name this process as 'Deep-Tomography'. Starting from an initial model, that is an ultrasmooth version of the true model, Deep-Tomography is able to predict an appropriate final model, even in complete unseen during the training data, like the Marmousi model. When used as the initial model for FWI, the models estimated by Deep-Tomography can also improve substantially the final results obtained with FWI. [ABSTRACT FROM AUTHOR]

Diffractions comprise the seismic response of subsurface geological discontinuities and thus can provide detailed geological information during 3-D seismic exploration. The separation of weak diffractions from specular reflections is challenging, especially when the diffractions and reflections have similar kinematical characteristics in the 3-D pre-stack case. Conventional separation methods often estimate the local slope based on optimization or the Hilbert transform, which directly depends on the distribution behaviour of seismic events and may lead to the aliasing effects of the hyperbolic reflected and diffracted slopes in the shot domain. In this study, a different method is employed: local slopes are parametrized and constrained to the normal moveout velocity and ray parameter, which can distinguish reflections and diffractions in the shot domain and enhance the stability and accuracy of local slopes. Interestingly, when the receiver line and the geological edge are coplanar, the corresponding edge diffractions and reflections exhibit extremely similar behaviour, rendering them indistinguishable. Considering this phenomenon, a 3-D pre-stack diffraction separation strategy is proposed based on the estimation of the local slopes in two orthogonal directions. Thus, the accurate local slope can be used for plane-wave destruction when separating diffractions in the 3-D pre-stack domain. Synthetic and field data applications demonstrate that the proposed separation strategy is effective and can obtain high-quality diffraction wavefields for detecting the subsurface discontinuous structure. [ABSTRACT FROM AUTHOR]

Passive source location estimation technology plays an extremely important role in microseismic monitoring. However, the energy of microseismic signals is generally weak and prone to be interrupted as noise, which will affect the detection and location accuracy of microseismic events. Besides, the existing wave-equation-based source location estimation methods are not suitable for strongly heterogeneous strata, such as faults, salt bodies, fractures filled with proppant and other fluids, etc. These complex geological structures often trigger scattered waves. Thus, based on scattering theory, a high-resolution microseismic source location estimation method with a new grouping imaging condition is proposed by implementing a contrast source numerical simulation method in the process of wavefield backpropagation. First, the contrast source algorithm is used to perform wavefield extrapolation by solving the Helmholtz equation in the frequency domain, which can provide more comprehensive, effective information to get high-precision locations. Moreover, an optimized grouping imaging condition is proposed, which can improve the imaging resolution and computational efficiency. The effectiveness of the method we propose is illustrated using numerical examples. Compared with traditional source location estimation methods, including with low signal-to-noise ratio or multisource location estimation, this method results in strong energy focusing achieved in an effecient manner. [ABSTRACT FROM AUTHOR]

Summary: Data recorded by distributed acoustic sensing (DAS) along an optical fibre sample the spatial and temporal properties of seismic wavefields at high spatial density. Often leading to massive amount of data when collected for seismic monitoring along many kilometre long cables. The spatially coherent signals from weak seismic arrivals within the data are often obscured by incoherent noise. We present a flexible and computationally efficient filtering technique, which makes use of the dense spatial and temporal sampling of the data and that can handle the large amount of data. The presented adaptive frequency–wavenumber filter suppresses the incoherent seismic noise while amplifying the coherent wavefield. We analyse the response of the filter in time and spectral domain, and we demonstrate its performance on a noisy data set that was recorded in a vertical borehole observatory showing active and passive seismic phase arrivals. Lastly, we present a performant open-source software implementation enabling real-time filtering of large DAS data sets. [ABSTRACT FROM AUTHOR]

SUMMARY: Higher frequency receiver function (RF) analysis based on dense nodal arrays has been widely used for imaging crustal structures. However, the scattered Rayleigh waves generated by the steep topography including mountain ranges and basin-range junction zones, have become a significant interference that can lead to false structures in RF images. In this study, we propose a novel method to remove scattered Rayleigh waves from RF profiles by using a high-resolution linear Radon transform. Based on the difference in the apparent velocity of Rayleigh and converted waves at interfaces, we construct a scheme to design an optimal filter mask. Synthetic and observed data show that this method can be an effective tool to remove high-amplitude Rayleigh waves and preserve low-amplitude converted waves almost harmlessly. Modelling tests also show that it is suitable for non-uniform station spacing, white noise and models that include dipping interfaces. [ABSTRACT FROM AUTHOR]

While distributed acoustic sensing (DAS) has been demonstrated to have great potential in seismology, DAS data often have much higher levels of stochastic and coherent noise (e.g. instrument noise, traffic vibrations) than data collected by traditional seismometers. The linearly, densely spaced nature of DAS arrays presents a suite of opportunities for more innovative processing techniques that can be used to address this issue. One way to take advantage of DAS's array architecture is through the use of curvelets. Curvelets have a non-uniform scaling property that makes them an excellent tool for representing images with discontinuities along piecewise, twice continuously differentiable curves. This anisotropic scaling property makes curvelets an ideal processing tool for DAS data, for which the measured wavefield can be represented as an image composed of curved features. Here, we use the curvelet frame as a tool for the manipulation of DAS signal and demonstrate how this manipulation can improve our ability to identify important features in DAS data sets. We use the curvelet representation to partition the measured wavefield using DAS data collected near Ridgecrest, CA, following the 2019 M w7.1 Ridgecrest earthquake. Here, we isolate the earthquake-induced wavefield from coherent and stochastic noise using the curvelet frame in an effort to improve the results of template matching of the Ridgecrest aftershock sequence. We show that our wavefield-partitioning technique facilitates the identification of over 30 per cent more aftershocks and greatly reduces the magnitude of diurnal depressions in the aftershock catalogue due to cultural noise. [ABSTRACT FROM AUTHOR]

Iterative least-squares reverse time migration (LSRTM) is the state-of-the-art linearized waveform inversion method to obtain quantitative subsurface parameters. The main drawback of such an iterative imaging scheme is the significant computational expense of many modelling/adjoint cycles through iterations. In the context of the extended domain, an interesting alternative to LSRTM is the asymptotic linearized direct waveform inversion, providing quantitative results with only a single iteration. This approach was first proposed for constant-density acoustics and recently extended to the variable-density case. The former is based on the application of the asymptotic inverse Born operator, whereas the latter has two more extra steps: building an angle-dependent response of the asymptotic inverse Born operator and then solving a weighted least-squares approach for simultaneous inversion of two acoustic parameters. To examine the importance of accounting for density variations, we compare constant- and variable-density linearized direct waveform inversion techniques applied to a marine real data set from the Eastern Nankai Trough, offshore Japan. The inversion results confirm the efficiency of the asymptotic linearized direct waveform inversion in estimating quantitative parameters within a single iteration. The variable-density direct inversion yields subsurface images that (1) exhibit a superior resolution and (2) better reconstruct the field data than does the constant-density approach, even if the data set does not contain large enough surface offset to fully decompose velocity and density perturbations. [ABSTRACT FROM AUTHOR]

In an ideal case, the time-lapse differences in 4-D seismic data should only reflect the changes of the subsurface geology. Practically, however, undesirable discrepancies are generated because of various reasons. Therefore, proper time-lapse processing techniques are required to improve the repeatability of time-lapse seismic data and to capture accurate seismic information to analyse target changes. In this study, we propose a machine learning-based time-lapse seismic data processing method improving repeatability. A training data construction method, training strategy and machine learning network architecture based on a convolutional autoencoder are proposed. Uniform manifold approximation and projection are applied to the training and target data to analyse the features corresponding to each data point. When the feature distribution of the training data is different from the target data, we implement data augmentation to enhance the diversity of the training data. The method is verified through numerical experiments using both synthetic and field time-lapse seismic data, and the results are analysed with several methods, including a comparison of repeatability metrics. From the results of the numerical experiments, we can conclude that the proposed convolutional autoencoder can enhance the repeatability of the time-lapse seismic data and increase the accuracy of observed variations in seismic signals generated from target changes. [ABSTRACT FROM AUTHOR]

Structural seismic interpretation and quantitative characterization are historically intertwined processes. The latter provides estimates of the properties of the subsurface, which can be used to aid structural interpretation alongside the original seismic data and a number of other seismic attributes. In this work, we redefine this process as an inverse problem which tries to jointly estimate subsurface properties (i.e. acoustic impedance) and a piece-wise segmented representation of the subsurface based on user-defined macroclasses. By inverting for the quantities simultaneously, the inversion is primed with prior knowledge about the regions of interest, whilst at the same time it constrains this belief with the actual seismic measurements. As the proposed functional is separable in the two quantities, these are optimized in an alternating fashion, where each subproblem is solved using the Primal-Dual algorithm. Subsequently, the final segmented model is used as input to an ad hoc workflow that extracts the perimeter of the detected shapes and produces a structural framework (i.e. seismic horizons) consistent with the estimated subsurface properties. The effectiveness of the proposed method is illustrated through numerical examples on synthetic and field data sets. [ABSTRACT FROM AUTHOR]

Subsurface velocity model building is a crucial step for seismic imaging. It is a challenging problem for conventional methods such as full-waveform inversion (FWI) and wave equation migration velocity analysis (WEMVA), due to the highly nonlinear relationship between subsurface velocity values and seismic responses. In addition, traditional FWI and WEMVA methods are often computationally expensive. In this paper, we propose to apply a deep learning technique to construct subsurface velocity models automatically from common-image gather (CIG) volumes. In our method, pairs of synthetic velocity models and CIG volumes are generated to train a convolutional neural network. Our proposed network achieves promising results on different synthetic data sets. The training performance of several commonly used loss functions is also studied. [ABSTRACT FROM AUTHOR]

As a data-driven approach, the performance of deep learning models depends largely on the quantity and quality of the training data sets, which greatly limits the application of deep learning to tasks with small data sets. Unfortunately, sometimes we need to use limited small data sets to complete our tasks, such as distributed acoustic sensing (DAS) data denoising. However, using a small data set to train the network may cause overfitting, resulting in poor network generalization. To solve this problem, we propose an approach based on the combination of a generative adversarial network and a deep convolutional neural network. First, we used a small noise data set to train a generative adversarial network to generate synthetic noise samples, and then used these synthetic noise samples to augment the noise data set. Next, we used the augmented noise data set and the signal data set obtained through forward modelling to construct a synthetic training set. Finally, a denoising network based on a convolutional neural network was trained on the constructed synthetic training set. Experimental results show that the augmented data set can effectively improve the denoising performance and generalization ability of the network, and the denoising network trained on the augmented data set can more effectively reduce various kinds of noise in the DAS data. [ABSTRACT FROM AUTHOR]

We test a fully non-linear method to solve Bayesian seismic tomographic problems using data consisting of observed traveltimes of first-arriving waves. Rather than using Monte Carlo methods to sample the posterior probability distribution that embodies the solution of the tomographic inverse problem, we use variational inference. Variational methods solve the Bayesian inference problem under an optimization framework by seeking the best approximation to the posterior distribution from a family of distributions, while still providing fully probabilistic results. We introduce a new variational method for geophysics—normalizing flows. The method models the posterior distribution by using a series of invertible and differentiable transforms—the flows. By optimizing the parameters of these transforms the flows are designed to convert a simple and analytically known probability distribution into a good approximation of the posterior distribution. Numerical examples show that normalizing flows can provide an accurate tomographic result including full uncertainty information while significantly decreasing the computational cost compared to Monte Carlo and other variational methods. In addition, this method provides analytic solutions for the posterior distribution rather than an ensemble of posterior samples. This opens the possibility that subsequent calculations that use the posterior distribution might be performed analytically. [ABSTRACT FROM AUTHOR]

Train traffic has been recognized as a powerful noise source for subsurface imaging and monitoring due to its properties of being strong, economic and repeatable. Compared with the traditional train, the high-speed train (HST) runs faster and generates vibrations that also manifest as periodical signals but with a more striking feature of sharp equidistant spectral lines in the frequency spectrum. Like the vibrations generated by the traditional train, the HST-induced vibrations can be utilized after the retrieval of interstation impulse responses using seismic interferometry. However, little attention has been paid to the characteristics of HST-induced vibrations during seismic interferometry. The aim of this study is to investigate the effects of train traffic characteristics on the seismic interferometry method. We first present an alternative derivation of seismic interferometry that is not based on the assumption of uncorrelation between sources at different locations. The derivation is valid for scalar waves in 3-D and for elastic surface waves. After analysing the effects of several HST-related factors, including HST moving direction, HST carriage number, rail structure, HST speed and the stacking number of HST records, on the seismic interferometry, we find that the seismic interferometry using HST traffic noise generates crosstalk that can be effectively attenuated by stacking the interferometric results of several HSTs with slightly different speeds. This finding has been validated by the seismic interferometry of both synthetic and field HST traffic noise. We further demonstrate that the retrieved surface waves can be used to estimate near-surface velocities. [ABSTRACT FROM AUTHOR]

Seismic attenuation measurements, especially those obtained from coda decay analysis, are becoming a key data source for the characterization of the heterogeneous Earth due to their sensitivity to small-scale heterogeneities. However, the relation between the scattering attenuation measured from coda waves and physical rock properties is still unclear. The goal of this study is to identify the main petrophysical and mineralogical factors controlling coda attenuation in volcanic rocks at the laboratory scale, as a necessary step before modelling seismic waves in real volcanic media. Coda wave attenuation was estimated from ultrasonic S -wave waveforms. To quantify the heterogeneity of the rocks and link them with this attenuation parameter, we performed several categorizations of the pore and grain systems of volcanic samples. Considering that seismic attenuation in rock samples can be modelled using the framework of wave propagation in random media, a statistical analysis of shear wave velocity fluctuations was performed: this analysis gives correlation lengths ranging from 0.09 to 1.20 mm, which represents the length scale of heterogeneity in the samples. The individual evaluation of the pore space and mineral content revealed that the pores of the samples (characterized by large vesicles) have a bigger effect than the grains on the heterogeneity level. We have developed a framework where intrinsic properties of the host rocks drive seismic attenuation by correlating the petro-mineralogical characteristics obtained from image data processing and analysis, with the coda attenuation measured at ultrasonic frequencies. There is conclusive evidence that porosity alone is not the primary controller of coda attenuation: it is also changed by the alteration level (i.e. oxidation, coating of the vesicles, secondary minerals) and the size of grains and pores. Among all the parameters analysed, it appears that the pore space topology is the main contributor to scattering attenuation in the volcanic samples. [ABSTRACT FROM AUTHOR]

Detection of seismic events at or below the noise level is enabled by the use of dense arrays of receivers and corresponding advances in data analysis methods. It is not only important to detect tectonic events, but also events from man-made, non-earthquake sources and events that originate from coupling between the solid Earth and the atmosphere. In urban environments with high ambient noise levels the effectiveness of event detection methods is unclear, particularly when deployment restrictions result in an irregular receiver array geometry. Here, we deploy a dense nodal array for 1 month in the highly populated city state of Singapore. We develop a new detection method based on image processing that we call spectrogram stacking, which detects anomalous, coherent spectral energy across the array. It simultaneously detects multiple classes of signal with differing spectral content and aids event classification, so it is particularly useful for signal exploration when signal characteristics are unknown. Our approach detects more local events compared to the traditional short-term average over long-term average and waveform similarity methods, while all methods detect similar numbers of teleseismic and regional earthquakes. Local events are principally man-made non-earthquake sources, with several events from the same location exhibiting repeating waveforms. The closest earthquake occurs in peninsular Malaysia, in an area where no earthquakes have previously been detected. We also detect ground motion over a wide frequency range from discrete thunder events that show complex coupling between acoustic and elastic wavefield propagation. We suggest that care should be taken deciphering local high-frequency tectonic events in areas prone to thunder storms. [ABSTRACT FROM AUTHOR]

The Northern Chilean subduction zone is characterized by long-term subduction erosion with very little sediment input at the trench and the lack of an accretionary prism. Here, multichannel seismic reflection (MCS) data were acquired as part of the CINCA (Crustal Investigations off- and onshore Nazca Plate/Central Andes) project in 1995. These lines cover among others the central part of the M W 8.1 Iquique earthquake rupture zone before the earthquake occurred on 1 April 2014. We have re-processed one of the lines crossing the updip parts of this earthquake at 19°40′S, close to its hypocentre. After careful data processing and data enhancement, we applied a coherency-based pre-stack depth migration algorithm, yielding a detailed depth image. The resulting depth image shows the subduction interface prior to the Iquique megathrust earthquake down to a depth of approximately 16 km and gives detailed insight into the characteristics of the seismogenic coupling zone. We found significantly varying interplate reflectivity along the plate interface which we interpret to be caused by the comparably strong reflectivity of subducted fluid-rich sediments within the grabens and half-grabens that are predominant in this area due to the subduction-related bending of the oceanic plate. No evidence was found for a subducted seamount associated to the Iquique Ridge along the slab interface at this latitude as interpreted earlier from the same data set. By comparing relocated fore- and aftershock seismicity of the Iquique earthquake with the resulting depth image, we can divide the continental wedge into two domains. First, a frontal unit beneath the lower slope with several eastward dipping back-rotated splay faults but no seismicity in the upper plate as well as along the plate interface. Secondly, a landward unit beneath the middle slope with differing reflectivity that shows significant seismicity in the upper plate as well as along the plate interface. Both units are separated by a large eastward dipping mega splay fault, the root zone of which shows diffuse seismicity, both in the upper plate and at the interface. The identification of a well-defined nearly aseismic frontal unit sheds new light on the interplate locking beneath the lower continental slope and its controls. [ABSTRACT FROM AUTHOR]

Geophysical imaging using the inversion procedure is a powerful tool for the exploration of the Earth's subsurface. However, the interpretation of inverted images can sometimes be difficult, due to the inherent limitations of existing inversion algorithms, which produce smoothed sections. In order to improve and automate the processing and interpretation of inverted geophysical models, we propose an approach inspired from data mining. We selected an algorithm known as DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to perform clustering of inverted geophysical sections. The methodology relies on the automatic sorting and clustering of data. DBSCAN detects clusters in the inverted electrical resistivity values, with no prior knowledge of the number of clusters. This algorithm has the advantage of being defined by only two parameters: the neighbourhood of a point in the data space, and the minimum number of data points in this neighbourhood. We propose an objective procedure for the determination of these two parameters. The proof of concept described here is applied to simulated ERT (electrical resistivity tomography) sections, for the following three cases: two layers with a step, two layers with a rebound, and two layers with an anomaly embedded in the upper layer. To validate this approach, sensitivity studies were carried out on both of the above parameters, as well as to assess the influence of noise on the algorithm's performance. Finally, this methodology was tested on real field data. DBSCAN detects clusters in the inverted electrical resistivity models, and the former are then associated with various types of earth materials, thus allowing the structure of the prospected area to be determined. The proposed data-mining algorithm is shown to be effective, and to improve the interpretation of the inverted ERT sections. This new approach has considerable potential, as it can be applied to any geophysical data represented in the form of sections or maps. [ABSTRACT FROM AUTHOR]

The largest earthquake recorded in South Korea occurred in Gyeongju on 2016 September 12. Since then, understanding the geological structures near the Gyeongju earthquake's epicentre has been demanded. As a preliminary step to understand the geological structures around the epicentre, a 3-D audio-magnetotelluric (AMT) survey was conducted. These 3-D AMT data were imaged through data processing and inversion. However, because the AMT data acquired in Gyeongju were very noisy, conventional imaging procedures did not yield reliable results. To obtain a reliable model inverted from such noisy data, we propose various imaging strategies: an additional data processing technique using the Nyquist diagram after conventional data processing and several inversion strategies related to the selection of data, the weighting of the data, the constraints on the model parameter vector and the Lagrange multiplier used for the regularization. By applying the additional data processing step and several inversion strategies, we were able to successfully invert the noisy field data. The inversion results verify that the data-screening procedure applying the Nyquist diagram remarkably improves the results compared with those obtained using only conventional data processing. The imaging strategies proposed in this case study can be used to image noisy MT/AMT field data for other regions. [ABSTRACT FROM AUTHOR]

Conventional seismic source inversion estimates the earthquake rupture process on an assumed fault plane that is determined a priori. It has been a difficult challenge to obtain the fault geometry together with the rupture process by seismic source inversion because of the nonlinearity of the inversion technique. In this study, we propose an inversion method to estimate the fault geometry and the rupture process of an earthquake from teleseismic P waveform data, through an elaboration of our previously published finite-fault inversion analysis (Shimizu et al. 2020). That method differs from conventional methods by representing slip on a fault plane with five basis double-couple components, expressed by potency density tensors, instead of two double-couple components compatible with the fault direction. Because the slip direction obtained from the potency density tensors should be compatible with the fault direction, we can obtain the fault geometry consistent with the rupture process. In practice we rely on an iterative process, first assuming a flat fault plane and then updating the fault geometry by using the information included in the obtained potency density tensors. In constructing a non-planar model-fault surface, we assume for simplicity that the fault direction changes only in either the strike or the dip direction. After checking the validity of the proposed method through synthetic tests, we applied it to the M W 7.7 2013 Balochistan, Pakistan, and M W 7.9 2015 Gorkha, Nepal, earthquakes, which occurred along geometrically complex fault systems. The modelled fault for the Balochistan earthquake is a curved strike-slip fault convex to the south-east, which is consistent with the observed surface ruptures. The modelled fault for the Gorkha earthquake is a reverse fault with a ramp-flat-ramp structure, which is also consistent with the fault geometry derived from geodetic and geological data. These results exhibit that the proposed method works well for constraining fault geometry of an earthquake. [ABSTRACT FROM AUTHOR]

Exsolved iron oxides in silicate minerals can be nearly ideal palaeomagnetic recorders, due to their single-domain-like behaviour and the protection from chemical alteration by their surrounding silicate host. Because their geometry is crystallographically controlled by the host silicate, these exsolutions possess a shape preferred orientation that is ultimately controlled by the mineral fabric of the silicates. This leads to potentially significant anisotropic acquisition of remanence, which necessitates correction to make accurate interpretations in palaeodirectional and palaeointensity studies. Here, we investigate the magnetic shape anisotropy carried by magnetite exsolutions in pyroxene single crystals, and in pyroxene-bearing rocks based on torque measurements and rotational hysteresis data. Image analysis is used to characterize the orientation distribution of oxides, from which the observed anisotropy can be modelled. Both the high-field torque signal and corresponding models contain components of higher order, which cannot be accurately described by second-order tensors usually used to describe magnetic fabrics. Conversely, low-field anisotropy data do not show this complexity and can be adequately described with second-order tensors. Hence, magnetic anisotropy of silicate-hosted exsolutions is field-dependent and this should be taken into account when interpreting isolated ferromagnetic fabrics, and in anisotropy corrections. [ABSTRACT FROM AUTHOR]