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Physics-driven self-supervised learning system for seismic velocity inversion Geophysics (IF 3.3) Pub Date : 2023-01-31 Bin Liu, Peng Jiang, Qingyang Wang, Yuxiao Ren, Senlin Yang, Anthony G. Cohn
Seismic velocity inversion plays a vital role in various applied seismology processes. A series of deep learning methods have been developed that rely purely on manually provided labels for supervision; however, their performances depend heavily on using large training data sets with corresponding velocity models. Because no physical laws are used in the training phase, it is usually challenging to
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Average QP and QS estimation in marine sediments using a dense receiver array Geophysics (IF 3.3) Pub Date : 2023-01-31 Robin André Rørstadbotnen, Martin Landrø
A new spectral ratio method has been used to compute average P- and S-wave quality factors, QP and QS, for the sedimentary sequence below the “V”-shaped Oseberg permanent reservoir monitoring (PRM) system. Quality factors are important for a more accurate characterization of the subsurface and to obtain additional information on the physical processes within the earth, such as fluid content and partial
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Temperature-dependent modal analysis of the InSight lander on Mars Geophysics (IF 3.3) Pub Date : 2023-01-31 Lei Zhang, Fei Gao, Zai Liu, Peng Cao, Jinhai Zhang
The first high-performance seismometer on Mars, deployed by InSight, has been working for nearly two Martian years. However, the recorded seismic data are substantially affected by the natural frequencies of the lander. To analyze the effect of the natural frequencies of the lander on specific components of the seismic data, we have built a simplified finite-element model of the lander to conduct modal
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This issue of Geophysics Geophysics (IF 3.3) Pub Date : 2023-01-27
In this article, the Editor of Geophysics provides an overview of all technical articles in this issue of the journal.
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Extracting small time-lapse traveltime changes in a reservoir using primaries and internal multiples after Marchenko-based target zone isolation Geophysics (IF 3.3) Pub Date : 2023-01-27 Johno van IJsseldijk, Joost van der Neut, Jan Thorbecke, Kees Wapenaar
Geophysical monitoring of subsurface reservoirs relies on detecting small changes in the seismic response between a baseline and monitor study. However, internal multiples, related to the over- and underburden, can obstruct the view of the target response, hence complicating the time-lapse analysis. To retrieve a response that is free from the over- and underburden effects, the data-driven Marchenko
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Extracting mud invasion information using borehole radar — A numerical study Geophysics (IF 3.3) Pub Date : 2023-01-27 Feng Zhou, Iraklis Giannakis, Antonios Giannopoulos, Klaus Holliger, Evert Slob
In hydrocarbon drilling, mud filtrate penetrates permeable formations and alters the pore fluid characteristics in the immediate vicinity of the borehole. Typically, the prevailing in situ pore fluids are displaced by the invading mud filtrate, which leads to gradually changing distributions of the fluid and electrical properties. Understanding this invasion process is crucial for the interpretation
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Hilbert transform of unequally sampled data: Application to dispersion relations in magnetotellurics Geophysics (IF 3.3) Pub Date : 2023-01-23 Ahmet Tuğrul Başokur
The real and imaginary parts of the Fourier transform of a causal signal can be estimated from one another using the Hilbert transform. The numerical computation can be carried out by a fast Fourier transform or the convolution of the data with an appropriate Hilbert kernel. However, magnetotelluric (MT) data are unequally sampled because logarithmic frequency axes are conventionally used. A method
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Interpretation of well logs and core data via Bayesian inversion Geophysics (IF 3.3) Pub Date : 2023-01-23 Tianqi Deng, Joaquín Ambía, Carlos Torres-Verdín
Estimating in situ petrophysical and compositional properties of rocks (e.g., porosity, mineralogy, and fluid saturation) from well logs and core measurements is critical for the evaluation of subsurface fluid resources. Traditional multimineral analysis of well logs is susceptible to abnormal borehole and geometric conditions, such as shoulder-bed effects and tool/borehole-related biases. Moreover
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Finite-difference modeling of 3D frequency-domain elastic wave equation using an affine mixed-grid method Geophysics (IF 3.3) Pub Date : 2023-01-23 Shu-Li Dong, Jing-Bo Chen
In seismic frequency-domain finite-difference modeling, the numerical accuracy depends on how mass and partial differential terms are discretized in the acoustic/elastic wave equation. For the mass term(s), a combination of consistent and lumped mass methods is widely accepted. For the partial differential terms, there exist many discretization technologies, such as the average-derivative method (ADM)
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Separating seismic diffractions by an improved Cook-distance singular value decomposition method Geophysics (IF 3.3) Pub Date : 2023-01-16 Zongnan Chen, Jingtao Zhao, Suping Peng, Tongjie Sheng
When seismic waves encounter abrupt points of stratum or lithology in the process of propagation, such as fault edges, pinch-out points, or the protrusion of unconformity surfaces, the Snell law will break down and many diffractions will be generated. However, the diffraction information is typically masked by strong reflections; thus, separating diffractions is one key issue for diffraction imaging
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Unpaired training: Optimize the seismic data denoising model without paired training data Geophysics (IF 3.3) Pub Date : 2023-01-16 Haitao Ma, He Ba, Yue Li, Yuxing Zhao, Ning Wu
With the development of seismic exploration technology, distributed acoustic sensing (DAS) has recently received attention in geophysics. However, owing to the complexity of the layout techniques in the DAS systems, and the unknown or harsh exploration environment, seismic data acquired by this technique usually contain the noise of diverse components, which increases the difficulty in subsequent data
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Decoupled wave equation and forward modeling of qP wave in VTI media with the new acoustic approximation Geophysics (IF 3.3) Pub Date : 2023-01-11 Kai Liang, Danping Cao, Shangrao Sun, Xingyao Yin
Acoustic approximation has received wide attention in modeling and inversion for the anisotropic wave equation to avoid high computational cost and parameter trade-off in seismic inversion. However, it also is limited by the stability condition, such as the instability for the qP-wave equation in transversely isotropic media with ε<δ. We have developed a new approach that decoupled wave equation and
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Target-oriented waveform inversion based on Marchenko redatumed data Geophysics (IF 3.3) Pub Date : 2023-01-11 Yuzhao Lin, Huaishan Liu
Target-oriented inversion (TOI) can resolve subsurface reservoir parameters based on surface waveform data. Moreover, virtual data at the datum level and a precise local forward operator are required to accurately perform inversion in the target area. The Marchenko redatuming, a state-of-the-art seismic redatuming method, is used to obtain the virtual data of the target area. Local forward operators
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Ensemble empirical mode decomposition and stacking model for filtering borehole distributed acoustic sensing records Geophysics (IF 3.3) Pub Date : 2023-01-10 Yi Zhao, Zhicheng Zhong, Yue Li, Dan Shao, Yongpeng Wu
We have evaluated the ensemble empirical mode decomposition (EEMD) and stacking model for borehole seismic-data denoising. The borehole records collected by distributed acoustic sensing (DAS) technology have multitype noise contamination, and it is difficult to attenuate these noises while recovering the seismic waves well. We first perform EEMD on the seismic data to obtain the signal-to-noise modal
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Data-assimilated time-lapse visco-acoustic full-waveform inversion: Theory and application for injected CO2 plume monitoring Geophysics (IF 3.3) Pub Date : 2023-01-09 Chao Huang, Tieyuan Zhu, Guangchi Xing
Continuous seismic monitoring for quantifying CO2 plume migration and detection of any potential leakages in the subsurface is essential for the security of long-term anthropogenic carbon dioxide geologic storage. Traditional time-lapse full-waveform inversion (TLFWI) methods aim to map the CO2 distribution by estimating seismic velocity changes, but recent studies find that CO2-induced attenuation
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Three-parameter prestack nonlinear inversion constrained by gradient structure similarity Geophysics (IF 3.3) Pub Date : 2023-01-10 Zhiqiang Wang, Jinghuai Gao, Haixia Zhao, Xiaolong Zhao
Estimating the elastic parameters from prestack inversion is of great importance for reservoir characterization. Conventional three-parameter prestack inversion methods rely heavily on well logs, and it is difficult to obtain reliable inversion results in situations with limited numbers of wells. Alternatively, we have developed a joint inversion strategy, integrating the advantages of post- and prestack
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Fault detection using a convolutional neural network trained with point-spread function-convolution-based samples Geophysics (IF 3.3) Pub Date : 2023-01-06 Jiankun Jing, Zhe Yan, Zheng Zhang, Hanming Gu, Bingkai Han
Automatic fault detection in seismic images using deep learning-based methods has attracted great interest in recent years. Detecting faults by deep learning methods is generally considered a supervised learning task, which requires numerous diverse training samples and corresponding accurate labels. Because field data with faults labeled by experienced interpreters are difficult to acquire, training
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Residual learning with feedback for strong random noise attenuation in seismic data Geophysics (IF 3.3) Pub Date : 2023-01-06 Zhangquan Liao, Yong Li, Yingtian Liu, Yifan Yang, Yiming Zhang
In random seismic noise attenuation, when the noise energy is higher than or close to a signal, it is difficult to distinguish the signal from the noise. This random noise is relatively strong compared to the signal and is called strong random noise. We have developed a deep learning framework to recover the signal from the strong random noise. The framework is based on a residual learning network
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Porosity and permeability prediction using a transformer and periodic long short-term network Geophysics (IF 3.3) Pub Date : 2023-01-05 Liuqing Yang, Sergey Fomel, Shoudong Wang, Xiaohong Chen, Wei Chen, Omar M. Saad, Yangkang Chen
Effective reservoir parameter prediction is important for subsurface characterization and understanding fluid migration. However, conventional methods for obtaining porosity and permeability are based on either core measurements or mathematical/petrophysical modeling, which are expensive or inefficient. In this study, we develop a reliable and low-cost deep learning (DL) framework for reservoir permeability
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Joint elastic reverse time migration of towed streamer and sparse ocean-bottom seismic node hybrid data Geophysics (IF 3.3) Pub Date : 2023-01-05 Pengfei Yu, Mingzhi Chu, Yunxia Xu, Baojin Zhang, Jianhua Geng
Compared to 1C towed-streamer (TS) seismic exploration, 4C ocean-bottom seismic (OBS) node exploration has great advantages in complex structure imaging, lithology, and fluid identification using elastic waves. However, sparse spatial sampling of OBS node surveys highlights the problems of the imaging acquisition footprint, poor phase continuity, and low signal-to-noise ratio (S/N) with conventional
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Regularization of anisotropic full-waveform inversion with multiple parameters by adversarial neural networks Geophysics (IF 3.3) Pub Date : 2023-01-05 Jiashun Yao, Michael Warner, Yanghua Wang
The anisotropic full-waveform inversion (FWI) is a seismic inverse problem for multiple parameters, which aims to simultaneously reconstruct the vertical velocity and the anisotropic parameters of the earth’s subsurface. This multiparameter inverse problem suffers from two issues. First, the objective function of the data fitting is less sensitive to the anisotropic parameters. Second, the crosstalk
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Hierarchical transfer learning for deep learning velocity model building Geophysics (IF 3.3) Pub Date : 2023-01-05 Jérome Simon, Gabriel Fabien-Ouellet, Erwan Gloaguen, Ishan Khurjekar
Deep learning is a promising approach to velocity model building because it has the potential of processing large seismic surveys with minimal resources. By leveraging large quantities of model-gather pairs, neural networks (NNs) can automatically map data to the model space, directly providing a solution to the inverse problem. Such mapping requires big data, which proves prohibitive for 2D and 3D
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Imaging near-surface S-wave velocity and attenuation models by full-waveform inversion with distributed acoustic sensing-recorded surface waves Geophysics (IF 3.3) Pub Date : 2023-01-04 Wenyong Pan, Luping Qu, Kristopher A. Innanen, Jan Dettmer, Marie Macquet, Donald Lawton, Yanfei Wang
Distributed acoustic sensing (DAS) technology is, increasingly, the seismic acquisition mode of choice for its high spatial sampling rate, low cost, and nonintrusive deployability. It is being widely evaluated as an enabler of seismic monitoring for CO2 sequestration in building subsurface time-lapse images and in characterizing near-surface environments. To advance this evaluation, field seismic surveys
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Joint inversion with petrophysical constraints using indicator functions and the extended alternating direction method of multipliers Geophysics (IF 3.3) Pub Date : 2023-01-04 Ke Wang, Dikun Yang
Joint inversions often need to construct an objective function with multiple complex constraining terms, which usually increase the computational cost, take a long time to converge, and often are nondifferentiable. We have developed a joint inversion framework that implements petrophysical constraints by indicator functions and solves the optimization problem using the alternating direction method
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Pressure-dependent joint elastic-electrical properties of calcite-cemented artificial sandstones Geophysics (IF 3.3) Pub Date : 2023-01-04 Tongcheng Han, Pan Wang, Li-Yun Fu
Understanding the correlations between the elastic and electrical properties of various types of rocks is the key to the successful joint interpretation of seismic and electromagnetic survey data to provide petrophysical parameters to better assess the subsurface earth. However, the pressure-dependent joint elastic-electrical properties of calcite-cemented sandstones remain poorly understood, even
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Time-lapse seismic imaging using shot gathers with nonrepeatable source wavelets Geophysics (IF 3.3) Pub Date : 2023-01-04 Xin Fu, Kristopher A. Innanen
In time-lapse seismic applications, the signal produced by changes in the properties of subsurface rocks is generally obscured by noise associated with imperfect repeatability between surveys. A particularly important obstacle in the formation of time-lapse difference images is variation in the effective source wavelet between baseline and monitoring data sets. However, the partially separable influence
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Full-waveform inversion of a Brazilian presalt ocean-bottom node data set using a concentric circle source geometry Geophysics (IF 3.3) Pub Date : 2023-01-04 Edwin Fagua Duarte, Danyelle da Silva, Pedro Tiago Carvalho, João Medeiros de Araújo, Jorge L Lopez
We have designed a workflow to apply full-waveform inversion (FWI) to a new type of acquisition recorded on ocean-bottom nodes (OBNs) in the Brazilian presalt area. The data set consists of three large-radius concentric circles of seismic source points recorded on OBN placed within the area of the circles. This geometry provides full azimuth distribution and benefits from long offsets in which the
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Magnetotelluric data denoising method combining two deep-learning-based models Geophysics (IF 3.3) Pub Date : 2023-01-04 Jin Li, Yecheng Liu, Jingtian Tang, Yiqun Peng, Xian Zhang, Yong Li
The magnetotelluric (MT) data collected in an ore-concentration area are extremely vulnerable to all kinds of noise pollution. However, separating real MT signals from strong noise is still a difficult problem, and the noise in MT data is quite distinct from clean data in morphological features. By performing the signal-noise identification and data prediction, we develop a deep learning method to
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Reflection and diffraction separation in the dip-angle common-image gathers using convolutional neural network Geophysics (IF 3.3) Pub Date : 2022-12-28 Jiaxing Sun, Jidong Yang, Zhenchun Li, Jianping Huang, Jie Xu, Subin Zhuang
In exploration seismology, reflections have been extensively used for imaging and inversion to detect hydrocarbon and mine resources, which are generated from subsurface continuous impedance interfaces. When the interface is not continuous and its size reduces to less than half-wavelength, reflected wave becomes diffraction. Reflections and diffractions can be used to image subsurface targets, and
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A synthetic denoising algorithm for full-waveform induced polarization based on deep learning Geophysics (IF 3.3) Pub Date : 2022-12-27 Weiqiang Liu, Qingtian Lü, Rujun Chen, Pinrong Lin, Jiayong Yan, Kun Zhang, Regean Pumulo Pitiya
Induced polarization (IP) is a widely used geophysical exploration technique. Continuous random noise is one of the most prevalent interferences that can seriously contaminate the IP signal and distort the apparent electrical characteristics. We develop a noise separation algorithm based on deep learning to overcome this issue. The standard IP signals are first produced by combining the Cole-Cole model
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Improving sparse representation with deep learning: A workflow for separating strong background interference Geophysics (IF 3.3) Pub Date : 2022-12-27 Dawei Liu, Wei Wang, Xiaokai Wang, Zhensheng Shi, Mauricio D. Sacchi, Wenchao Chen
Revealing hidden reservoirs that are severely shielded by strong background interference (SBI) is critical to subsequent refined interpretation. To enhance the characterization of these reservoirs, current interpretation workflows merge multiple attribute information, necessitating intensive human expertise. As an alternative, we regard SBI suppression as a signal separation problem and develop a workflow
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MEANet: Magnitude estimation via physics-based features time series, an attention mechanism, and neural networks Geophysics (IF 3.3) Pub Date : 2022-12-28 Jindong Song, Jingbao Zhu, Shanyou Li
The traditional magnitude estimation method, which establishes a linear relationship between a single warning parameter and the magnitude, exhibits considerable scatter and underestimation. In addition, the extraction of features from raw waveforms by a deep learning network is a black box. To provide a more robust magnitude estimation and to construct a deep learning network with an interpretable
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Vertical transversely isotropic elastic least-squares reverse time migration based on elastic wavefield vector decomposition Geophysics (IF 3.3) Pub Date : 2022-12-28 Ke Chen, Lu Liu, Lele Zhang, Yang Zhao
Anisotropic elastic reverse time migration (RTM) is a promising technique for imaging complex oil and gas reservoirs. However, the migrated images often suffer from low spatial resolution, migration artifacts, wave-mode crosstalk, and unbalanced amplitude response. Conventional vertical transversely isotropic elastic least-squares reverse time migration (VTI-elastic LSRTM) defines stiffness parameter
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Implications of static low-frequency model on seismic geomechanics inversion Geophysics (IF 3.3) Pub Date : 2022-12-28 Javad Sharifi
I have developed a novel insight into the differences between static and dynamic moduli and their effects on the performance of seismic geomechanics inversion. This achievement is obtained from triaxial deformation tests and ultrasonic measurements on core plugs and reveals that the static Young’s modulus deviates from the dynamic one in porous media, especially in particular ranges of depth and pressure
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A deep learning-enhanced framework for multiphysics joint inversion Geophysics (IF 3.3) Pub Date : 2022-12-27 Yanyan Hu, Xiaolong Wei, Xuqing Wu, Jiajia Sun, Jiuping Chen, Yueqin Huang, Jiefu Chen
Joint inversion has drawn considerable attention due to the availability of multiple geophysical data sets, ever-increasing computational resources, the development of advanced algorithms, and its ability to reduce inversion uncertainty. A key issue of joint inversion is to develop effective strategies to link different geophysical data in a unified mathematical framework, in which the information
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Deep-learning missing well-log prediction via long short-term memory network with attention-period mechanism Geophysics (IF 3.3) Pub Date : 2022-12-23 Liuqing Yang, Shoudong Wang, Xiaohong Chen, Wei Chen, Omar M. Saad, Yangkang Chen
Underground reservoir information can be obtained through well-log interpretation. However, some logs might be missing due to various reasons, such as instrument failure. A deep-learning-based method that combines a convolutional layer and a long short-term memory (LSTM) layer is proposed to estimate the missing logs without the expensive relogging. The convolutional layer is used to extract the depth-series
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Weighted-average time-lapse seismic full-waveform inversion Geophysics (IF 3.3) Pub Date : 2022-12-27 Amir Mardan, Bernard Giroux, Gabriel Fabien-Ouellet
As seismic data can contain information over a large spatial area and are sensitive to changes in the properties of the subsurface, seismic imaging has become the standard geophysical monitoring method for many applications such as carbon capture and storage and reservoir monitoring. The availability of practical tools such as full-waveform inversion (FWI) makes time-lapse seismic FWI a promising method
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Diffraction imaging of discontinuities using migrated dip-angle gathers Geophysics (IF 3.3) Pub Date : 2022-12-27 Peng Lin, Suping Peng, Yang Xiang, Chuangjian Li, Xiaoqin Cui
Diffractions produced by subsurface geologic features are an effective means of imaging small-scale discontinuities at high resolution. We have developed a novel diffraction imaging approach that combines the geometric mode decomposition (GMD) algorithm and the least-squares Gaussian distribution fitting (LSGDF) technique. In the dip-angle domain, reflections tend to look like smiles and are mainly
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Two-stage broad learning inversion framework for shear-wave velocity estimation Geophysics (IF 3.3) Pub Date : 2022-12-23 Xiao-Hui Yang, Peng Han, Zhentao Yang, Xiaofei Chen
Shear-wave (S-wave) velocity is considered an essential parameter for the study of the earth, and Rayleigh wave inversion has been widely accepted and used to determine it. Given high-quality measured dispersion curves, the inversion performance depends on the applied optimization algorithm inside the inversion process. We propose a novel inversion framework to promote efficient and accurate inversion
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Separation and imaging of seismic diffractions using geometric mode decomposition Geophysics (IF 3.3) Pub Date : 2022-12-23 Peng Lin, Suping Peng, Chuangjian Li, Yang Xiang, Xiaoqin Cui
Seismic diffractions from small-scale discontinuities or inhomogeneities carry key geologic information and can provide high-resolution images of these objects. Because diffractions characterized by weak energy are easily masked by strong reflections, diffraction-enhancement processing is essential before subwavelength information detection. Therefore, a novel diffraction-separation method is developed
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Low-frequency ambient ocean-bottom node surface-wave seismology: A Gulf of Mexico case history Geophysics (IF 3.3) Pub Date : 2022-12-23 Aaron J. Girard, Jeffrey Shragge, Bjorn Olofsson
Long-time marine seismic recordings are becoming more common with the increased use of ocean-bottom nodes (OBNs), which can measure ambient seismic energy at frequencies lower than the typical minimum values in active-source compressed air-gun surveys. Interferometric processing on long-time ambient multicomponent data allows for the extraction of low-frequency (sub-2.0 Hz) responses in virtual source
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Reduction of normal-moveout stretch using nonstationary scaling transformation in time-frequency domain Geophysics (IF 3.3) Pub Date : 2022-12-23 M. Javad Khoshnavaz, Amin Roshandel Kahoo, Mehrdad Soleimani Monfared, Hamid Reza Siahkoohi
The normal-moveout (NMO) stretch causes decrease in the dominant frequency of seismic wavelet after conventional NMO correction and severely damages the quality of the stacked data for shallower reflectors at far offsets. Muting, which is commonly used to handle this problem, reduces seismic fold and negatively affects results of the amplitude-variation-with-offset analysis within the stretched area
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A two-step singular spectrum analysis method for robust low-rank approximation of seismic data Geophysics (IF 3.3) Pub Date : 2022-12-23 Weilin Huang
The singular spectrum analysis (SSA) method can detect the low-rank structure of data and therefore has become a powerful tool in seismic data processing and analysis. In particular, the SSA method can effectively suppress seismic random noise according to the different behaviors of coherent signal and random noise in the singular spectrum. However, there has been much research and experimentation
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Multiscale recurrent-guided denoising network for distributed acoustic sensing-vertical seismic profile background noise attenuation Geophysics (IF 3.3) Pub Date : 2022-12-21 Ming Cheng, Shaoping Lu, Xintong Dong, Tie Zhong
In recent years, distributed optical fiber acoustic sensing (DAS) has emerged as a novel seismic acquisition technique. Compared with conventional hydrophones and geophones of microelectromechanical systems, DAS has an advantage in terms of acquisition geometry, such as low-cost and high-density observations. However, the collected DAS records always suffer from various types of noise, which poses
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Simultaneous source separation by shot collocation and strength variation Geophysics (IF 3.3) Pub Date : 2022-12-21 Toan Dao, Chao Zhang, Mirko van der Baan, Martin Landrø
Simultaneous shooting offers opportunities for significant cost savings in seismic data acquisitions. The most common strategy uses random delay shots where source separation is achieved during the processing stage, thereby doubling source densities. We have determined that the creation of a collocated source survey, where shots are repeated simultaneously at multiple positions, is a viable alternative
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Joint physics-based and data-driven time-lapse seismic inversion: Mitigating data scarcity Geophysics (IF 3.3) Pub Date : 2022-12-21 Yanhua Liu, Shihang Feng, Ilya Tsvankin, David Alumbaugh, Youzuo Lin
In carbon capture and sequestration, developing rapid and effective imaging techniques is crucial for real-time monitoring of the spatial and temporal dynamics of CO2 propagation during and after injection. With continuing improvements in computational power and data storage, data-driven techniques based on machine learning (ML) have been effectively applied to seismic inverse problems. In particular
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Unsupervised deep learning for 3D interpolation of highly incomplete data Geophysics (IF 3.3) Pub Date : 2022-12-13 Omar M. Saad, Sergey Fomel, Raymond Abma, Yangkang Chen
We propose to denoise and reconstruct the 3D seismic data simultaneously using an unsupervised deep learning (DL) framework, which does not require any prior information about the seismic data and is free of labels. We use an iterative process to reconstruct the 3D highly incomplete seismic data. For each iteration, we use the DL framework to denoise the 3D seismic data and initially reconstruct the
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Analysis of the viscoelasticity in coal based on the fractal theory Geophysics (IF 3.3) Pub Date : 2022-12-13 TaiLang Zhao, GuanGui Zou, SuPing Peng, Hu Zeng, Fei Gong, YaJun Yin
Coal is a complex viscoelastic porous medium with fractal characteristics at different scales. To model the macroscale structure of coal, a fractal viscoelastic model is established, and the P-wave velocity dispersion and attenuation characteristics are discussed based on the complex modulus derived from this model. The numerical simulation results indicate that the fractional order α and relaxation
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Transformations of borehole magnetic data in the frequency domain and estimation of the total magnetization direction: A case study from the Mengku iron-ore deposit, Northwest China Geophysics (IF 3.3) Pub Date : 2022-12-13 Guoli Li, Shuang Liu, Ke Shi, Henglei Zhang, Boxin Zuo, Dan Zhu, Xiangyun Hu
Borehole magnetic prospecting measures the three components of the magnetic field and is sensitive to the vertical depth of the magnetic source, which plays an important role in deep mineral exploration. Magnetic field transformations in the frequency domain constitute common and important processing for ground and airborne data but are rarely applied to borehole magnetic data. Here, we deduce component
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Deep physics-aware stochastic seismic inversion Geophysics (IF 3.3) Pub Date : 2022-12-13 Paula Yamada Bürkle, Leonardo Azevedo, Marley Vellasco
Seismic inversion allows the prediction of subsurface properties from seismic reflection data and is a key step in reservoir modeling and characterization. With the generalization of machine learning in geophysics, deep learning methods have been proposed as efficient seismic inversion methods. However, most of these methods lack a probabilistic approach to deal with the uncertainties inherent in the
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Calculation of the stable Poynting vector using the first-order temporal derivative of the seismic wavefield Geophysics (IF 3.3) Pub Date : 2022-12-13 Zhiyuan Li, Jiquan Wang, Xiaona Ma, Runjie Wang
The Poynting vector is a powerful tool for calculating the propagation directions of a seismic wavefield, and it has a wide range of applications in reverse time migration. However, an instability issue commonly arises while calculating the Poynting vector. The Poynting vector is a product of the temporal and spatial derivatives of the wavefield. The two derivatives are equal to zero at the local extrema
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Wave propagation in thermo-poroelasticity: A finite-element approach Geophysics (IF 3.3) Pub Date : 2022-12-06 Juan Enrique Santos, José Mario Carcion, Gabriela Beatriz Savioli, Jing Ba
We have developed continuous and discrete-time finite-element (FE) methods to solve an initial boundary-value problem for the thermo-poroelasticity wave equation based on the combined Biot/Lord-Shulman (LS) theories to describe the porous and thermal effects, respectively. In particular, the LS model, which includes a Maxwell-Vernotte-Cattaneo relaxation term, leads to a hyperbolic heat equation, thus
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Uncertainty quantification of geologic model parameters in 3D gravity inversion by Hessian-informed Markov chain Monte Carlo Geophysics (IF 3.3) Pub Date : 2022-12-06 Zhouji Liang, Florian Wellmann, Omar Ghattas
Geologic modeling has been widely adopted to investigate underground structures. However, modeling processes inevitably have uncertainties due to scarcity of data, measurement errors, and simplification of the modeling method. Recent developments in geomodeling methods have introduced a Bayesian framework to constrain the model uncertainties by considering the additional geophysical data in the modeling
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Improving shallow and deep seismic-while-drilling with a downhole pilot in a desert environment Geophysics (IF 3.3) Pub Date : 2022-12-05 Ilya Silvestrov, Andrey Bakulin, Ali Aldawood, Emad Hemyari, Anton Egorov
Processing seismic data from drillbit-generated vibrations requires a reliable source signature for correlation and deconvolution purposes. Recently, a land field trial has been conducted in a desert environment. A memory-based downhole vibration accelerometer has been used together with a more conventional top-drive sensor to continuously record the pilot signal from 590 to 8600 ft (180–2621 m). Past
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On singularity points in elastic orthorhombic media Geophysics (IF 3.3) Pub Date : 2022-12-05 Alexey Stovas, Yuriy Roganov, Vyacheslav Roganov
The analysis of singularity points in anisotropic models of low symmetry is very important for seismic modeling and seismic data processing. For ray tracing, the group velocity vector dramatically changes the orientation in the vicinity of the singularity point. When evaluating seismic amplitudes, the relative geometric spreading of seismic wave tends to zero in the singularity point. Regarding seismic
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Reservoir multiparameter prediction method based on deep learning for CO2 geologic storage Geophysics (IF 3.3) Pub Date : 2022-12-05 Dong Li, Suping Peng, Yinling Guo, Yongxu Lu, Xiaoqin Cui, Wenfeng Du
Time-lapse seismic difference refers to the comprehensive response of fluid saturation, pore pressure, and porosity. However, the contribution of different parameters to the seismic response is difficult to distinguish. The high-precision prediction of these reservoir parameters is of great significance in CO2 geologic storage and oil and gas development. Therefore, a simultaneous time-lapse reservoir
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Numerical modeling of the permeability in Bentheim sandstone under confining pressure Geophysics (IF 3.3) Pub Date : 2022-12-05 Mirko Siegert, Marcel Gurris, Maxim Lebedev, Erik H. Saenger
A new model for the determination of the permeability in sandstones under confining pressure is presented. Building on the concepts of digital rock physics, a numerical model is derived from a 3D tomographic scan. The pressure-dependent behavior is mimicked by adding an artificial flow resistance to the pore throats. All permeability simulations are performed using an in-house finite-volume code. In
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Hierarchical wave-mode separation in the poroelastic medium using eigenform analysis Geophysics (IF 3.3) Pub Date : 2022-12-01 Yiwei Tian, Jidong Yang, Zhenchun Li, Jianping Huang, Shanyuan Qin
In the elastic medium, the scalar and vector P- and S-waves decomposition has been extensively studied and some strategies can be extended to the poroelastic medium to extract P- and S-wavefields. However, there are three propagation modes in the poroelastic medium in Biot’s theory, namely, a fast P wave, a slow P wave, and an S wave. Because the propagation characteristic of a slow P wave is different
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Depth-domain angle and depth variant seismic wavelets extraction for prestack seismic inversion Geophysics (IF 3.3) Pub Date : 2022-12-01 Rui Zhang, Zhiwen Deng
Many prestack depth migration methods have been developed and widely used to generate depth-domain seismic images, resulting in a need for depth-domain prestack seismic inversion of the subsurface elastic properties for reservoir characterization. Time-domain inversion techniques often are used after the depth-domain data set is transformed to the time domain. We provide a new technique for directly
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On the estimation of reflectivity in reverse time migration: Implementational forms of the inverse-scattering imaging condition Geophysics (IF 3.3) Pub Date : 2022-12-01 Natiê A. Albano, Jessé C. Costa, Jörg Schleicher, Amélia Novais
The inverse-scattering imaging condition (ISIC) for reverse time migration (RTM) aims at recovering amplitudes proportional to seismic reflectivity. It has been derived as the high-frequency asymptotic inverse of Born modeling, which justifies its being called a true-amplitude imaging condition. It involves the temporal and spatial derivatives of the up- and downgoing wavefields, in this way generalizing