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20 pages, 8294 KiB

Open AccessArticle

A Reconstructed Method of Acoustic Logging Data and Its Application in Seismic Lithological Inversion for Uranium Reservoir

by Zhangqing Sun, Songlin Yang, Fengjiao Zhang, Jipu Lu, Ruihu Wang, Xiyang Ou, Anguai Lei, Fuxing Han, Wenpan Cen, Da Wei and Mingchen Liu

Remote Sens. 2023, 15(5), 1260; https://doi.org/10.3390/rs15051260 - 24 Feb 2023

Cited by 1 | Viewed by 1377

Abstract

As a sedimentary mineral, most sandstone type uranium deposits are formed in petroliferous basins. Therefore, we can fully tap the residual economic value of historical logging and 3D seismic data measured for oil and gas to search for sandstone type uranium deposits. However, [...] Read more.

As a sedimentary mineral, most sandstone type uranium deposits are formed in petroliferous basins. Therefore, we can fully tap the residual economic value of historical logging and 3D seismic data measured for oil and gas to search for sandstone type uranium deposits. However, a large amount of acoustic logging data are missing in the target stratum of the uranium reservoir in that it is not the main stratum of oil and gas. A reconstructed method of acoustic logging data based on clustering analysis and with the low-frequency compensation of deterministic inversion is proposed to solve this problem. Secondly, we can use these logging data with seismic data to obtain the 3D inversion data volume representing the sand body of the uranium reservoir based on seismic lithological inversion. Then, we can also delimit the 3D spatial range of sandstone type uranium deposits in petroliferous basins based on the calibration of uranium anomaly and sub-body detection. Finally, a 3D field data example is given to test and analyze the effectiveness of the above research schemes. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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20 pages, 13102 KiB

Open AccessArticle

Acoustic Impedance Inversion from Seismic Imaging Profiles Using Self Attention U-Net

by Liurong Tao, Haoran Ren and Zhiwei Gu

Remote Sens. 2023, 15(4), 891; https://doi.org/10.3390/rs15040891 - 6 Feb 2023

Cited by 8 | Viewed by 3071

Abstract

Seismic impedance inversion is a vital way of geological interpretation and reservoir investigation from a geophysical perspective. However, it is inevitably an ill-posed problem due to the noise or the band-limited characteristic of seismic data. Artificial neural network have been used to solve [...] Read more.

Seismic impedance inversion is a vital way of geological interpretation and reservoir investigation from a geophysical perspective. However, it is inevitably an ill-posed problem due to the noise or the band-limited characteristic of seismic data. Artificial neural network have been used to solve nonlinear inverse problems in recent years. This research obtained an acoustic impedance profile by feeding seismic profile and background impedance into a well-trained self-attention U-Net. The U-Net got convergence by appropriate iteration, and the output predicted the impedance profiles in the test. To value the quality of predicted profiles from different perspectives, e.g., correlation, regression, and similarity, we used four kinds of indexes. At the same time, our results were predicted by conventional methods (e.g., deconvolution with recursive inversion, and TV regularization) and a 1D neural network was calculated in contrast. Self-attention U-Net showed to be robust to noise and does not require prior knowledge. Furthermore, spatial continuity is also better than deconvolution, regularization, and 1D deep learning methods in contrast. The U-Net in this paper is a type of full convolutional neural network, so there are no limits to the shape of the input. Based on this, a large impedance profile can be predicted by U-Net, which is trained by a patchy training dataset. In addition, this paper applied the proposed method to the field data obtained by the Ceduna survey without any label. The predictions prove that this well-trained network could be generalized from synthetic data to field data. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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19 pages, 35999 KiB

Open AccessArticle

A Compact High-Order Finite-Difference Method with Optimized Coefficients for 2D Acoustic Wave Equation

by Liang Chen, Jianping Huang, Li-Yun Fu, Weiting Peng, Cheng Song and Jiale Han

Remote Sens. 2023, 15(3), 604; https://doi.org/10.3390/rs15030604 - 19 Jan 2023

Cited by 5 | Viewed by 2482

Abstract

High-precision finite difference (FD) wavefield simulation is one of the key steps for the successful implementation of full-waveform inversion and reverse time migration. Most explicit FD schemes for solving seismic wave equations are not compact, which leads to difficulty and low efficiency in [...] Read more.

High-precision finite difference (FD) wavefield simulation is one of the key steps for the successful implementation of full-waveform inversion and reverse time migration. Most explicit FD schemes for solving seismic wave equations are not compact, which leads to difficulty and low efficiency in boundary condition treatment. Firstly, we review a family of tridiagonal compact FD (CFD) schemes of various orders and derive the corresponding optimization schemes by minimizing the error between the true and numerical wavenumber. Then, the optimized CFD (OCFD) schemes and a second-order central FD scheme are used to approximate the spatial and temporal derivatives of the 2D acoustic wave equation, respectively. The accuracy curves display that the CFD schemes are superior to the central FD schemes of the same order, and the OCFD schemes outperform the CFD schemes in certain wavenumber ranges. The dispersion analysis and a homogeneous model test indicate that increasing the upper limit of the integral function helps to reduce the spatial error but is not conducive to ensuring temporal accuracy. Furthermore, we examine the accuracy of the OCFD schemes in the wavefield modeling of complex structures using a Marmousi model. The results demonstrate that the OCFD4 schemes are capable of providing a more accurate wavefield than the CFD4 scheme when the upper limit of the integral function is 0.5π and 0.75π. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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19 pages, 17085 KiB

Open AccessArticle

Automatic Horizon Picking Using Multiple Seismic Attributes and Markov Decision Process

by Chengliang Wu, Bo Feng, Xiaonan Song, Huazhong Wang, Rongwei Xu and Shen Sheng

Remote Sens. 2023, 15(3), 552; https://doi.org/10.3390/rs15030552 - 17 Jan 2023

Cited by 5 | Viewed by 2455

Abstract

Picking the reflection horizon is an important step in velocity inversion and seismic interpretation. Manual picking is time-consuming and no longer suitable for current large-scale seismic data processing. Automatic algorithms using different seismic attributes such as instantaneous phase or dip attributes have been [...] Read more.

Picking the reflection horizon is an important step in velocity inversion and seismic interpretation. Manual picking is time-consuming and no longer suitable for current large-scale seismic data processing. Automatic algorithms using different seismic attributes such as instantaneous phase or dip attributes have been proposed. However, the computed attributes are usually inaccurate near discontinuities. The waveforms in the horizontal direction often change dramatically, which makes it difficult to track a horizon using the similarity of attributes. In this paper, we propose a novel method for automatic horizon picking using multiple seismic attributes and the Markov decision process (MDP). For the design of the MDP model, the decision time and state are defined as the horizontal and vertical spatial position on a seismic image, respectively. The reward function is defined in multi-dimensional feature attribute space. Multiple attributes can highlight different aspects of a seismic image and therefore overcome the limitations of the single-attribute MDP through the cross-constraint of multiple attributes. The optimal decision is made by searching the largest state value function in the reward function space. By considering cumulative reward, the lateral continuity of a seismic image can be effectively considered, and the impacts of abnormal waveform changes or bad traces in local areas for automatic horizon picking can be effectively avoided. An effective implementation scheme is designed for picking multiple reflection horizons. The proposed method has been successfully tested on both synthetic and field data. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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18 pages, 10009 KiB

Open AccessArticle

Adaptive Feature Map-Guided Well-Log Interpolation

by Lingqian Wang, Hui Zhou and Hanming Chen

Remote Sens. 2023, 15(2), 459; https://doi.org/10.3390/rs15020459 - 12 Jan 2023

Cited by 1 | Viewed by 1699

Abstract

As an irreplaceable quantitative interpretation method, prestack seismic inversion enables the effective estimation of subsurface elastic parameters for reservoir prediction. However, for the model-driven prestack seismic inversion, the band-limited characteristics and noise interference of observed seismic data result in its high dependence on [...] Read more.

As an irreplaceable quantitative interpretation method, prestack seismic inversion enables the effective estimation of subsurface elastic parameters for reservoir prediction. However, for the model-driven prestack seismic inversion, the band-limited characteristics and noise interference of observed seismic data result in its high dependence on the initial models. This suggests that reasonable initial models act as a supplement to reliable variation trends in formation and can reduce the non-uniqueness of inversion results. In this article, we introduce a well-log interpolation method with a feature map-guided non-local means algorithm, which is for establishing high-fidelity initial models used for prestack seismic inversion. First, we briefly review the basic theory of general model-driven prestack seismic inversion. Then, we use dictionary learning to split the poststack seismic record into patches, and represent them with sparse vectors, instead of directly using seismic record. The advantage of dictionary learning is that it can adaptively extract useful signals from noisy observed data and provide fine structures by sparse reconstruction. Therefore, the proposed feature extraction method can improve the noise immunity and reliability of the well-log interpolation. More accurate initial models are pre-constructed efficiently by our feature extraction method, which improves the reliability of prestack seismic inversion results. Two kinds of observed seismic data are used, including the poststack seismic record for well-log interpolation and prestack seismic data used for inversion. Synthetic and field data tests both demonstrate the favorable performance of the proposed well-log interpolation method. In summary, a novel and convenient initial model building approach is provided, which contributes to seismic exploration and geologic modeling. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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19 pages, 84702 KiB

Open AccessArticle

Outlier Denoising Using a Novel Statistics-Based Mask Strategy for Compressive Sensing

by Weiqi Wang, Jidong Yang, Jianping Huang, Zhenchun Li and Miaomiao Sun

Remote Sens. 2023, 15(2), 447; https://doi.org/10.3390/rs15020447 - 11 Jan 2023

Cited by 2 | Viewed by 1746

Abstract

Denoising is always an important step in seismic processing, in order to produce high-quality data for subsequent imaging and inversion. Different types of noise can be suppressed using targeted denoising methods. For outlier noise with singular amplitudes, many classical denoising methods suffer from [...] Read more.

Denoising is always an important step in seismic processing, in order to produce high-quality data for subsequent imaging and inversion. Different types of noise can be suppressed using targeted denoising methods. For outlier noise with singular amplitudes, many classical denoising methods suffer from signal leakage. To mitigate this issue, we developed a statistics-based mask method and incorporated it into the compressive sensing (CS) framework, in order to remove outlier noise. A statistical analysis for seismic data amplitudes was first used to identify the locations of traces containing outlier noise. Then, the outlier trace locations were compared with a mask matrix generated by jitter sampling, and we replaced the sampled traces of the jitter mask that had the outlier noise with their nearby unsampled traces. The optimized sampling matrix enabled us to effectively identify and remove outliers. This optimized mask strategy converts an outlier denoising problem into a data reconstruction problem. Finally, a sparsely constrained inverse problem was solved using a soft-threshold iteration solver to recover signals at the null locations. The feasibility and adaptability of the proposed method were demonstrated through numerical experiments for synthetic and field data. The results showed that the proposed method outperformed the conventional f-x deconvolution and median filter method, and could accurately suppress outlier noise and recover missed expected signals. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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17 pages, 6680 KiB

Open AccessArticle

Supervirtual Refraction Interferometry in the Radon Domain

by Yizhe Su, Deli Wang, Bin Hu, Xiangbo Gong and Junming Zhang

Remote Sens. 2023, 15(2), 384; https://doi.org/10.3390/rs15020384 - 8 Jan 2023

Cited by 3 | Viewed by 1611

Abstract

Accurate picking of seismic first arrivals is very important for first arrival travel time tomography, but the first arrivals appearing at far offsets are often more difficult to pick accurately due to the low signal-to-noise ratio (SNR). The conventional supervirtual refraction interferometry (SVI) [...] Read more.

Accurate picking of seismic first arrivals is very important for first arrival travel time tomography, but the first arrivals appearing at far offsets are often more difficult to pick accurately due to the low signal-to-noise ratio (SNR). The conventional supervirtual refraction interferometry (SVI) method can improve the SNR of first arrivals to a certain extent; however, it is not suitable for seismic data that interfered by strong noise. In order to better process the first arrivals at far offsets with serious noise interference, we propose a modified method, in which SVI implemented in the Radon domain (RDSVI) due to the cross-correlation in the Radon domain have a better effect. According to the kinematic characteristics of first arrival refractions, SVI is performed in the linear Radon domain. Both synthetic data and field data demonstrate the proposed method can enhance the effective signal and attenuate the strong noise simultaneously, so as to significantly improve the SNR of the first arrival data. Meanwhile, the RDSVI method is tested on the first arrival data with missing traces, which proves that this method can overcome the influence of abnormal traces and is suitable for the reconstruction of sparsely sampled seismic data. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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16 pages, 13758 KiB

Open AccessArticle

Pore and Microfracture Characterization in Tight Gas Sandstone Reservoirs with a New Rock-Physics-Based Seismic Attribute

by Zhiqi Guo, Xiaoying Qin and Cai Liu

Remote Sens. 2023, 15(2), 289; https://doi.org/10.3390/rs15020289 - 4 Jan 2023

Cited by 5 | Viewed by 1896

Abstract

Pores and microfractures provide storage spaces and migration pathways for gas accumulation in tight sandstones with low porosity and permeability, acting as one of the controlling factors of gas production. The development of a rational rock physics model is essential for better understanding [...] Read more.

Pores and microfractures provide storage spaces and migration pathways for gas accumulation in tight sandstones with low porosity and permeability, acting as one of the controlling factors of gas production. The development of a rational rock physics model is essential for better understanding the elastic responses of tight sandstone with complex pore structures. Accordingly, seismic characterization of pores and microfractures based on the rock physics model provides valuable information in predicting high-quality tight gas sandstone reservoirs. This paper proposes a rock-physics-based approach to compute the pore–microfracture indicator (PMI) from elastic properties for pore structure evaluation in tight sandstones. The PMI is achieved based on the axis rotation of the elastic parameter space using well-log data. The rotation angle is determined by finding the maximum correlation between the linearized combination of the elastic parameters and the introduced factor associated with total porosity and microfracture porosity. The microfracture porosity is then estimated with an inversion scheme based on the double-porosity model. Finally, the optimized rotation angle is employed to compute the PMI with seismic data. The obtained results are of great benefit in predicting the permeable zones, providing valuable information for sweet spot characterization in tight gas sandstone reservoirs. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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12 pages, 12117 KiB

Open AccessCommunication

Generating Paired Seismic Training Data with Cycle-Consistent Adversarial Networks

by Zheng Zhang, Zhe Yan, Jiankun Jing, Hanming Gu and Haiying Li

Remote Sens. 2023, 15(1), 265; https://doi.org/10.3390/rs15010265 - 2 Jan 2023

Cited by 6 | Viewed by 2319

Abstract

Deep-learning-based seismic data interpretation has received extensive attention and focus in recent years. Research has shown that training data play a key role in the process of intelligent seismic interpretation. At present, the main methods used to obtain training data are synthesizing seismic [...] Read more.

Deep-learning-based seismic data interpretation has received extensive attention and focus in recent years. Research has shown that training data play a key role in the process of intelligent seismic interpretation. At present, the main methods used to obtain training data are synthesizing seismic data and manually labeling the real data. However, synthetic data have certain feature differences from real data, and the manual labeling of data is time-consuming and subjective. These factors limit the application of deep learning algorithms in seismic data interpretation. To obtain realistic seismic training data, we propose label-to-data networks based on cycle-consistent adversarial networks in this work. These networks take random labels and unlabeled real seismic data as input and generate synthetic seismic data that match the random labels and have similar features to the real seismic data. Quantitative analysis of the generated data demonstrate the effectiveness of the proposed methods. Meanwhile, test results on different data indicate that the generated data are reliable and can be applied for seismic fault detection. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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17 pages, 4012 KiB

Open AccessArticle

Magnetic Anomaly Characteristics and Magnetic Basement Structure in Earthquake-Affected Changning Area of Southern Sichuan Basin, China: A New Perspective from Land-Based Stations

by Chao Dong, Bin Chen and Can Wang

Remote Sens. 2023, 15(1), 23; https://doi.org/10.3390/rs15010023 - 21 Dec 2022

Cited by 2 | Viewed by 2428

Abstract

The Changning area is located in the southern Sichuan basin and the western Yangtze Plate and is the most abundant shale gas exploration area in China. In recent years, Changning has experienced frequent earthquakes with moderate magnitudes, attracting extensive interest. To investigate the [...] Read more.

The Changning area is located in the southern Sichuan basin and the western Yangtze Plate and is the most abundant shale gas exploration area in China. In recent years, Changning has experienced frequent earthquakes with moderate magnitudes, attracting extensive interest. To investigate the magnetic characteristics in Changning, 952 land-based stations were employed to establish a magnetic anomaly model with a resolution of 2 km, and the subsurface magnetic basement structure was obtained by an iterative algorithm in the Fourier domain. The magnetic anomaly model shows significant distinctions between the northern salt mine area and the southern shale gas area. The magnetic basement includes the crystalline basement and the Sinian sedimentary rock metamorphic basement, which has strong magnetism. The large intracratonic rift that developed in the Sinian–Early Cambrian plays an important role in the evolution of Changning, which also impacts magnetic anomalies and the magnetic basement structure. Finally, by comparing the seismic wave velocity ratio structure, the deeper magnetic basement that corresponds to the higher seismic wave velocity ratio can be explained. This article implies that magnetic anomalies and magnetic basement depth have a certain correlation with earthquakes in Changning, and it provides a geodynamic reference for Changning and the southern Sichuan basin. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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23 pages, 11416 KiB

Open AccessArticle

High-Dimensional Seismic Data Reconstruction Based on Linear Radon Transform–Constrained Tensor CANDECOM/PARAFAC Decomposition

by Zhiyuan Ouyang, Liqi Zhang, Huazhong Wang and Kai Yang

Remote Sens. 2022, 14(24), 6275; https://doi.org/10.3390/rs14246275 - 11 Dec 2022

Cited by 2 | Viewed by 1638

Abstract

Random noise and missing seismic traces are common in field seismic data, which seriously affects the subsequent seismic processing flow. The complete noise-free high-dimensional seismic dataset in the frequency–space (f-x) domain under the local linear assumption are regarded as a low-rank tensor, and [...] Read more.

Random noise and missing seismic traces are common in field seismic data, which seriously affects the subsequent seismic processing flow. The complete noise-free high-dimensional seismic dataset in the frequency–space (f-x) domain under the local linear assumption are regarded as a low-rank tensor, and each high dimensional seismic dataset containing only one linear event is a rank-1 tensor. The tensor CANDECOM/PARAFAC decomposition (CPD) method estimates complete noise-free seismic signals by characterizing high-dimensional seismic signals as the sum of several rank-1 tensors. In order to improve the stability and effect of the tensor CPD algorithm, this paper proposes a linear Radon transform–constrained tensor CPD method (RCPD) by using the sparsity of factor matrix in the Radon domain after high-dimensional seismic signal tensor CPD and uses alternating direction multiplier method (ADMM) to solve the established optimization problem. This proposed method is an essential realization of the high-dimensional linear Radon transform, and the results of synthetic and field data reconstruction prove the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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22 pages, 12206 KiB

Open AccessArticle

Time-Lapse Cross-Well Monitoring of CO₂ Sequestration Using Coda Wave Interferometry

by Zhuo Xu, Fengjiao Zhang, Christopher Juhlin, Xiangbo Gong, Liguo Han, Calin Cosma and Stefan Lueth

Remote Sens. 2022, 14(24), 6194; https://doi.org/10.3390/rs14246194 - 7 Dec 2022

Viewed by 1552

Abstract

In this study, we explored the capability of coda wave interferometry (CWI) for monitoring CO₂ storage by estimating the seismic velocity changes caused by CO₂ injection. Given that the CWI method is highly efficient, the primary aim of this study was [...] Read more.

In this study, we explored the capability of coda wave interferometry (CWI) for monitoring CO₂ storage by estimating the seismic velocity changes caused by CO₂ injection. Given that the CWI method is highly efficient, the primary aim of this study was to provide a quick detection tool for the long-term monitoring of CO₂ storage safety. In particular, we looked at monitoring with a cross-well geometry. We also expected that CWI could help to reduce the inversion errors of existing methods. Time-lapse upgoing waves and downgoing waves from two-component datasets were utilized to efficiently monitor the area between the wells and provide a quick indication of possible CO₂ leakage. The resulting mean velocity changes versus the depth indicated the depth where velocity changes occurred. Combining the upgoing and downgoing wavefields provided a more specific indication of the depth range for changes. The calculated velocity changes were determined using the time shift between the time-lapse wavefields caused by CO₂ injection/leakage. Hence, the resulting velocity changes were closely related to the ratio of propagation path length through the CO₂ injection/leakage layer over the length of the entire travel path. The results indicated that the noise level and repeatability of the time-lapse datasets significantly influenced the results generated using CWI. Therefore, denoising and time-lapse processing were very important for improving the detectability of any change. Applying CWI to time-lapse cross-well surveys can be an effective tool for monitoring CO₂ in the subsurface at a relatively low computational cost. As a highly efficient monitoring method, it is sensitive to changes in the seismic response caused by velocity changes in the subsurface and provides additional constraints on the inversion results from conventional travel time tomography and full waveform inversion. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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20 pages, 12301 KiB

Open AccessArticle

Least-Squares Reverse-Time Migration of Water-Bottom-Related Multiples

by Yanbao Zhang, Yike Liu and Jia Yi

Remote Sens. 2022, 14(23), 5979; https://doi.org/10.3390/rs14235979 - 25 Nov 2022

Cited by 2 | Viewed by 1434

Abstract

Reverse-time migration of multiples can generate imaging results with wider illumination, higher folds, and broader wavenumber spectra than the conventional migration of primaries. However, the results of the migration of multiples retain heavy crosstalks generated by interactions between unrelated multiples, thereby seriously degrading [...] Read more.

Reverse-time migration of multiples can generate imaging results with wider illumination, higher folds, and broader wavenumber spectra than the conventional migration of primaries. However, the results of the migration of multiples retain heavy crosstalks generated by interactions between unrelated multiples, thereby seriously degrading imaging qualities. To eliminate such crosstalks, we propose a least-squares optimized algorithm of multiples. In this method, different-order water-column multiples and water-bottom-related multiples are extracted using multiple decomposition strategies before migration procedures. The proposed method treats the nth-order water-column multiples as virtual sources for Born modeling to produce the predicted (n+1)th-order water-bottom-related multiples. In each iteration, the gradients are calculated by crosscorrelating the forward-propagated nth-order water-column multiples with the backward-propagated seismic residuals between the observed and predicted (n+1)th-order water-bottom-related multiples. The developed approach is referred to as the least-squares reverse-time migration of water-bottom-related multiples (LSRTM-WM). Numerical experiments on a layered model and the Pluto 1.5 model demonstrate that LSRTM-WM can significantly remove crosstalks and considerably improve spatial resolution. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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12 pages, 5102 KiB

Open AccessArticle

Frequency-Wavenumber Domain Elastic Full Waveform Inversion with a Multistage Phase Correction

by Yong Hu, Li-Yun Fu, Qingqing Li, Wubing Deng and Liguo Han

Remote Sens. 2022, 14(23), 5916; https://doi.org/10.3390/rs14235916 - 22 Nov 2022

Cited by 2 | Viewed by 1894

Abstract

Elastic full waveform inversion (EFWI) is essential for obtaining high-resolution multi-parameter models. However, the conventional EFWI may suffer from severe cycle skipping without the low-frequency components in elastic seismic data. To solve this problem, we propose a multistage phase correction-based elastic full waveform [...] Read more.

Elastic full waveform inversion (EFWI) is essential for obtaining high-resolution multi-parameter models. However, the conventional EFWI may suffer from severe cycle skipping without the low-frequency components in elastic seismic data. To solve this problem, we propose a multistage phase correction-based elastic full waveform inversion method in the frequency-wavenumber domain, which we call PC-EFWI for short. Specifically, the seismic data are first split using 2-D sliding windows; for each window, the seismic data are then transformed into the frequency-wavenumber domain for PC-EFWI misfit. In addition, we introduced a phase correction factor in the PC-EFWI misfit. In this way, it is possible to reduce phase differences between measured and synthetic data to mitigate cycle skipping by adjusting the phase correction factor in different scales. Numerical examples with the 2-D Marmousi model demonstrate that the frequency-wavenumber domain PC-EFWI with multistage strategy is an excellent way to reduce the risk of EFWI cycle skipping and build satisfactory start models for the conventional EFWI. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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24 pages, 2827 KiB

Open AccessArticle

Frequency-Domain Q-Compensated Reverse Time Migration Using a Stabilization Scheme

by Xiong Ma, Hao Li, Zhixian Gui, Xiaobo Peng and Guofa Li

Remote Sens. 2022, 14(22), 5850; https://doi.org/10.3390/rs14225850 - 18 Nov 2022

Cited by 3 | Viewed by 1673

Abstract

Seismic attenuation occurs during seismic wave propagation in a viscous medium, which will result in a poor image of subsurface structures. The attenuation compensation by directly amplifying the extrapolated wavefields may suffer from numerical instability because of the exponential compensation for seismic wavefields. [...] Read more.

Seismic attenuation occurs during seismic wave propagation in a viscous medium, which will result in a poor image of subsurface structures. The attenuation compensation by directly amplifying the extrapolated wavefields may suffer from numerical instability because of the exponential compensation for seismic wavefields. To alleviate this issue, we have developed a stabilized frequency-domain Q-compensated reverse time migration (FQ-RTM). In the algorithm, we use a stabilized attenuation compensation operator, which includes both the stabilized amplitude compensation operator and the dispersion correction operator, for the seismic wavefield extrapolation. The dispersion correction operator is calculated based on the frequency-domain dispersion-only viscoacoustic wave equation, while the amplitude compensation operator is derived via a stabilized division of two propagation wavefields (the dispersion-only wavefield and the viscoacoustic wavefield). Benefiting from the stabilization scheme in the amplitude compensation, the amplification of the seismic noises is suppressed. The frequency-domain cross-correlation imaging condition is exploited to obtain the compensated image. The layered model experiments demonstrate the effectiveness and great compensation performance of our method. The BP gas model examples further verify its feasibility and stability. The field data applications indicate the practicability of the proposed method. The comparison between the acoustic and compensated results confirms that the proposed method is able to compensate for the seismic attenuation while suppressing the amplification of the high-frequency seismic noise. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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19 pages, 5214 KiB

Open AccessArticle

An Automatic Velocity Analysis Method for Seismic Data-Containing Multiples

by Junming Zhang, Deli Wang, Bin Hu and Xiangbo Gong

Remote Sens. 2022, 14(21), 5428; https://doi.org/10.3390/rs14215428 - 28 Oct 2022

Cited by 3 | Viewed by 2110

Abstract

Normal moveout (NMO)-based velocity analysis can provide macro velocity models for prestack data processing and seismic attribute inversion. Datasets with an increasing size require conventional velocity analysis to be transformed to a more automatic mode. The sensitivity to multiple reflections limits the wide [...] Read more.

Normal moveout (NMO)-based velocity analysis can provide macro velocity models for prestack data processing and seismic attribute inversion. Datasets with an increasing size require conventional velocity analysis to be transformed to a more automatic mode. The sensitivity to multiple reflections limits the wide application of automatic velocity analysis. Thus, we propose an automatic velocity analysis method for seismic data-containing multiples to overcome the limit of multiple interference. The core idea of the proposed algorithm is to utilize a multi-attribute analysis system to transform the multiple attenuation problem to a multiple identification problem. To solve the identification problem, we introduce the local similarity to attribute the predicted multiples and build a quantitative attribute called multiple similarity. Considering robustness and accuracy, we select two supplementary attributes based on velocity and amplitude difference, i.e., velocity variation with depth and amplitude level. Then we utilize the technique for order preference by similarity to ideal solution (TOPSIS) to balance weights for different attributes in automatic velocity analysis. An RGB system is adopted for multi-attributes fusion in velocity spectra for visualization and quality control. Using both synthetic and field examples to evaluate the effectiveness of the proposed method for data-containing multiples, the results demonstrate the excellent performance in the accuracy of the extracted velocity model. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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20 pages, 12373 KiB

Open AccessArticle

A New Seismic Inversion Scheme Using Fluid Dispersion Attribute for Direct Gas Identification in Tight Sandstone Reservoirs

by Zhiqi Guo, Danyu Zhao and Cai Liu

Remote Sens. 2022, 14(21), 5326; https://doi.org/10.3390/rs14215326 - 25 Oct 2022

Cited by 10 | Viewed by 1862

Abstract

Sufficient gas accumulation is an essential factor that controls the effective development of tight sandstone gas reservoirs that are generally characterized by low porosity and permeability. Seismic methods are important for predicting potential gas areas in tight sandstones. However, the complex relationships between [...] Read more.

Sufficient gas accumulation is an essential factor that controls the effective development of tight sandstone gas reservoirs that are generally characterized by low porosity and permeability. Seismic methods are important for predicting potential gas areas in tight sandstones. However, the complex relationships between rock physical properties and gas saturation make gas enrichment estimation with seismic methods challenging. Nonetheless, seismic velocity dispersion using a wave-induced fluid flow mechanism can enable gas identification by utilizing the associated dispersion attributes. This paper proposes a method for improved gas identification using a new fluid dispersion attribute obtained by incorporating the decoupled fluid-solid seismic amplitude variation with offset representation into the frequency-dependent inversion scheme. Numerical analyses and synthetic data tests confirmed the enhanced sensitivity of the fluid dispersion attribute to gas saturation compared to the conventionally used compressional wave velocity dispersion attribute. Field data applications further validated the ability of the proposed fluid dispersion attribute to improve gas prediction in tight sandstone reservoirs. The results of the measurements enable rational interpretation of the geological significance of assessments of reservoir properties from gas-producing wellbores. The proposed fluid dispersion attribute is a reliable indicator for gas prediction and represents a useful tool for characterizing tight sandstone reservoirs. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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15 pages, 32688 KiB

Open AccessArticle

Structure-Preserving Random Noise Attenuation Method for Seismic Data Based on a Flexible Attention CNN

by Wenda Li, Tianqi Wu and Hong Liu

Remote Sens. 2022, 14(20), 5240; https://doi.org/10.3390/rs14205240 - 20 Oct 2022

Cited by 2 | Viewed by 2144

Abstract

The noise attenuation of seismic data is an indispensable part of seismic data processing, directly impacting the following inversion and imaging. This paper focuses on two bottlenecks in the AI-based denoising method of seismic data: the destruction of structural information of seismic data [...] Read more.

The noise attenuation of seismic data is an indispensable part of seismic data processing, directly impacting the following inversion and imaging. This paper focuses on two bottlenecks in the AI-based denoising method of seismic data: the destruction of structural information of seismic data and the inferior generalizability. We propose a flexible attention-CNN (FACNN) and realized the denoising work of seismic data. This paper’s main work and advantages were concentrated on the following three aspects: (i) We propose attention gates (AGs), which progressively suppressed features in irrelevant background parts and improved the denoising performance. (ii) We added a noise level map M as an additional channel, making a single CNN model expected to inherit the flexibility of handling noise models with different parameters, even spatially variant noises. (iii) We propose a mixed loss function based on MS_SSIM to improve the performance of FACNN further. Adding the noise level map can improve the network’s generalization ability, and adding the attention structure with the mixed loss function can better protect the structural information of the seismic data. The numerical tests showed that our method has better generalization and can better protect the details of seismic events. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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18 pages, 4045 KiB

Open AccessArticle

An Envelope Travel-Time Objective Function for Reducing Source–Velocity Trade-Offs in Wave-Equation Tomography

by Wenyong Pan, Ning Ma and Yanfei Wang

Remote Sens. 2022, 14(20), 5223; https://doi.org/10.3390/rs14205223 - 19 Oct 2022

Cited by 1 | Viewed by 2167

Abstract

In conventional cross-correlation (CC)-based wave-equation travel-time tomography, wrong source wavelets can result in inaccurate velocity inversion results, which is known as the source–velocity trade-off. In this study, an envelope travel-time objective function is developed for wave-equation tomography to alleviate the non-uniqueness and uncertainty [...] Read more.

In conventional cross-correlation (CC)-based wave-equation travel-time tomography, wrong source wavelets can result in inaccurate velocity inversion results, which is known as the source–velocity trade-off. In this study, an envelope travel-time objective function is developed for wave-equation tomography to alleviate the non-uniqueness and uncertainty due to wrong source wavelets. The envelope of a seismic signal helps reduce the waveform fluctuations/distortions caused by variations of the source time function. We show that for two seismic signals generated with different source wavelets, the travel-time shift calculated by cross-correlation of their envelopes is more accurate compared to that obtained by directly cross-correlating their waveforms. Then, the CC-based envelope travel-time (ET) objective function is introduced for wave-equation tomography. A new adjoint source has also been derived to calculate the sensitivity kernels. In the numerical inversion experiments, a synthetic example with cross-well survey is first given to show that compared to the traditional CC travel-time objective function, the ET objective function is relatively insensitive to source wavelet variations and can reconstruct the elastic velocity structures more reliably. Finally, the effectiveness and advantages of our method are verified by inversion of early arrivals in a practical seismic survey for recovering near-surface velocity structures. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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13 pages, 6662 KiB

Open AccessArticle

Accurately Stable Q-Compensated Reverse-Time Migration Scheme for Heterogeneous Viscoelastic Media

by Ning Wang, Ying Shi and Hui Zhou

Remote Sens. 2022, 14(19), 4782; https://doi.org/10.3390/rs14194782 - 24 Sep 2022

Cited by 10 | Viewed by 1923

Abstract

The development of multi-component seismic acquisition technology creates new possibilities for the high-precision imaging of complex media. Compared to the scalar acoustic wave equation, the elastic wave equation takes the information of P-waves, S-waves, and converted waves into account simultaneously, enabling accurate description [...] Read more.

The development of multi-component seismic acquisition technology creates new possibilities for the high-precision imaging of complex media. Compared to the scalar acoustic wave equation, the elastic wave equation takes the information of P-waves, S-waves, and converted waves into account simultaneously, enabling accurate description of actual seismic propagation. However, inherent attenuation is one of the important factors that restricts multi-component high-precision migration imaging. Its influence is mainly reflected in the following three ways: first, the attenuation of the amplitude energy makes the deep structure display unclear; second, phase distortion introduces errors to the positioning of underground structures; and third, the loss of high frequency components reduces imaging resolution. Therefore, it is crucial to fully consider the absorption and attenuation characteristics of the real Earth during seismic modeling and imaging. This paper aims to develop an accurate attenuation compensation reverse-time migration scheme for complex heterogeneous viscoelastic media. We first utilize a novel viscoelastic wave equation with decoupled fractional Laplacians to depict the Earth’s attenuation behavior. Then, an adaptive stable attenuation compensation operator is developed to realize high-precision attenuation compensation imaging. Several synthetic and field data analyses verify the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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22 pages, 13708 KiB

Open AccessArticle

Joint Inversion of 3D Gravity and Magnetic Data under Undulating Terrain Based on Combined Hexahedral Grid

by Haoyuan He, Tonglin Li and Rongzhe Zhang

Remote Sens. 2022, 14(18), 4651; https://doi.org/10.3390/rs14184651 - 17 Sep 2022

Cited by 5 | Viewed by 2356

Abstract

As an effective underground imaging method, the joint inversion of the gravity and magnetic data has an important application in the comprehensive interpretation of mineral exploration, and unstructured modeling is the key to accurately solving its topographic problem. However, the traditional tetrahedral grid [...] Read more.

As an effective underground imaging method, the joint inversion of the gravity and magnetic data has an important application in the comprehensive interpretation of mineral exploration, and unstructured modeling is the key to accurately solving its topographic problem. However, the traditional tetrahedral grid can only impose the gradient-based constraints approximately, owing to its poor arrangement regularity. To address the difficulty of applying a cross-gradient constraint in an unstructured grid, we propose a joint inversion based on a combined hexahedral grid, which regularly divides the shallow part into curved hexahedrons and the deep part into regular hexahedrons. Instead of a cross-gradient in the spatial sense, we construct a geometric sense “cross-gradient” for a structural constraint to reduce the influence of approximation. In addition, we further correct the traditional sensitivity-based weighting function according to element volume, to make it suitable for an unstructured grid. Model tests indicate that the new grid can impose the cross-gradient constraint more strongly, and the proposed correction can effectively solve the false anomaly caused by the element volume difference. Finally, we apply our method to the measured data from a mining area in Huzhong, Heilongjiang Province, China, and successfully invert out the specific location of a known skarn deposit, which further proves its practicability. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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18 pages, 8284 KiB

Open AccessArticle

Deep Learning with Adaptive Attention for Seismic Velocity Inversion

by Fangda Li, Zhenwei Guo, Xinpeng Pan, Jianxin Liu, Yanyi Wang and Dawei Gao

Remote Sens. 2022, 14(15), 3810; https://doi.org/10.3390/rs14153810 - 7 Aug 2022

Cited by 9 | Viewed by 3530

Abstract

The subsurface velocity model is crucial for high-resolution seismic imaging. Although full-waveform inversion (FWI) is a high-accuracy velocity inversion method, it inevitably suffers from challenging problems, including human interference, strong nonuniqueness, and high computing costs. As an efficient and accurate nonlinear algorithm, deep [...] Read more.

The subsurface velocity model is crucial for high-resolution seismic imaging. Although full-waveform inversion (FWI) is a high-accuracy velocity inversion method, it inevitably suffers from challenging problems, including human interference, strong nonuniqueness, and high computing costs. As an efficient and accurate nonlinear algorithm, deep learning (DL) has been used to estimate velocity models. However, conventional DL is insufficient to characterize detailed structures and retrieve complex velocity models. To address the aforementioned problems, we propose a hybrid network (AG-ResUnet) involving fully convolutional layers, attention mechanism, and residual unit to estimate velocity models from common source point (CSP) gathers. Specifically, the attention mechanism extracts the boundary information, which serves as a structural constraint in network training. We introduce the structural similarity index (SSIM) to the loss function, which minimizes the misfit between predicted velocity and ground truth. Compared with FWI and other networks, AG-ResUnet is more effective and efficient. Experiments on transfer learning and noisy data inversion demonstrate that AG-ResUnet makes a generalized and robust velocity prediction with rich structural details. The synthetic examples demonstrate that our method can improve seismic velocity inversion, contributing to guiding the imaging of geological structures. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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19 pages, 4727 KiB

Open AccessArticle

A High-Precision Elastic Reverse-Time Migration for Complex Geologic Structure Imaging in Applied Geophysics

by Jinwei Fang, Ying Shi, Hui Zhou, Hanming Chen, Qingchen Zhang and Ning Wang

Remote Sens. 2022, 14(15), 3542; https://doi.org/10.3390/rs14153542 - 24 Jul 2022

Cited by 5 | Viewed by 1903

Abstract

High-precision elastic reverse-time migration (ERTM) imaging has always been one of the trends in the development of geophysics. However, current wavefield simulations using time-domain finite-difference (FD) approaches in ERTM have second-order temporal accuracy, resulting in travel time changes and waveform distortion in wavefield [...] Read more.

High-precision elastic reverse-time migration (ERTM) imaging has always been one of the trends in the development of geophysics. However, current wavefield simulations using time-domain finite-difference (FD) approaches in ERTM have second-order temporal accuracy, resulting in travel time changes and waveform distortion in wavefield propagation with large time steps, i.e., temporal dispersion. Errors caused by the temporal dispersion can lead to erroneous imaging locations and out-of-focus diffraction events. A new ERTM and its workflow are established here using temporal and spatial high-order FD accuracy wavefields and the vector-based imaging condition. Our method computes elastic vector-based wavefields by solving a P- and S-wave decomposition form of a quasi-stress–velocity equation. An advanced finite-difference scheme is employed in the wavefield solution to achieve simulation with temporal fourth-order accuracy and spatial arbitrary even-order accuracy. The normalized dot-product imaging condition of the source and receiver P/S wavefields is then applied to generate high-quality images. The elastic wavefield simulation and ERTM numerical examples presented here reveal that the anti-dispersion workflow can improve modeling and imaging accuracy. In addition, the field data application shows that our method can achieve reasonable and reliable ERTM images. This method can integrate the most advanced imaging techniques into this computational framework to improve imaging accuracy. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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27 pages, 10052 KiB

Open AccessArticle

Wavefield Decomposition of Ocean-Bottom Multicomponent Seismic Data with Composite Calibration Filters

by Mingzhi Chu and Pengfei Yu

Remote Sens. 2022, 14(13), 3121; https://doi.org/10.3390/rs14133121 - 29 Jun 2022

Cited by 1 | Viewed by 1944

Abstract

Downgoing/upgoing P/S-wave decomposition of ocean-bottom seismic (OBS) multicomponent data can help suppress the water-layer multiples and cross-talks between P- and S-waves, and therefore plays an important role in seismic migration and construction of P- and S-wave velocity models. We proposed novel composite calibration [...] Read more.

Downgoing/upgoing P/S-wave decomposition of ocean-bottom seismic (OBS) multicomponent data can help suppress the water-layer multiples and cross-talks between P- and S-waves, and therefore plays an important role in seismic migration and construction of P- and S-wave velocity models. We proposed novel composite calibration filters by introducing an additional dimension to the calibration of the particle velocity components, extending the wave-equation-based adaptive decomposition method. We also modified the existing workflow by jointly using primary reflections at near-to-medium offsets and ocean-bottom refractions at far offsets in the calibration optimization. The decomposition scheme with the novel calibration filters yielded satisfactory results in a deep-water OBS field data decomposition example. Expected decomposition effects, such as the enhancement of primary reflections and the attenuation of water-layer multiple events, can be observed in the decomposed upgoing wavefields. An experiment illustrated the effectiveness of composite calibration filters that compensated for unexpected velocity errors along the offset dimension. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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16 pages, 7466 KiB

Open AccessArticle

Crustal Electrical Structure of the Ganzi Fault on the Eastern Tibetan Plateau: Implications for the Role of Fluids in Earthquakes

by Yuanzhi Cheng, Yanlong Kong, Zhongxing Wang, Yonghui Huang and Xiangyun Hu

Remote Sens. 2022, 14(13), 2990; https://doi.org/10.3390/rs14132990 - 22 Jun 2022

Cited by 2 | Viewed by 2002

Abstract

The initiation and evolution of seismic activity in intraplate regions are controlled by heterogeneous stress and highly fractured rocks within the rock mass triggered by fluid migration. In this study, we imaged the electrical structure of the crust beneath the Ganzi fault using [...] Read more.

The initiation and evolution of seismic activity in intraplate regions are controlled by heterogeneous stress and highly fractured rocks within the rock mass triggered by fluid migration. In this study, we imaged the electrical structure of the crust beneath the Ganzi fault using a three-dimensional magnetotelluric inversion technique, which is host to an assemblage of resistive and conductive features extending into the lower crust. It presents a near-vertical low-resistance zone that cuts through the brittle ductile transition zone, extends to the lower crust, and acts as a pathway for fluid migration from the crustal flow to the upper crustal depths. Conductors in the upper and lower crust are associated with saline fluids and 7% to 16% partial melting, respectively. The relationship between the earthquake epicenter and the surrounding electrical structure suggests that the intraplate seismicity is triggered by overpressure fluids, which are dependent on fluid volume changes generated by the decompression dehydration of partially molten material during upwelling and native fluid within the crustal flow. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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15 pages, 10542 KiB

Open AccessTechnical Note

Strong-Scattering Multiparameter Reconstruction Based on Elastic Direct Envelope Inversion and Full-Waveform Inversion with Anisotropic Total Variation Constraint

by Pan Zhang, Ru-Shan Wu, Liguo Han and Yixiu Zhou

Remote Sens. 2023, 15(3), 746; https://doi.org/10.3390/rs15030746 - 27 Jan 2023

Cited by 1 | Viewed by 1637

Abstract

Strong-scattering medium can usually form a good sealing medium for oil and gas resources. However, conventional elastic full-waveform inversion (EFWI) methods are difficult to build reliable velocity models under the condition of lacking low-frequency information. The elastic direct envelope inversion (EDEI) method has [...] Read more.

Strong-scattering medium can usually form a good sealing medium for oil and gas resources. However, conventional elastic full-waveform inversion (EFWI) methods are difficult to build reliable velocity models under the condition of lacking low-frequency information. The elastic direct envelope inversion (EDEI) method has been proven to be able to model large-scale Vp and Vs structures of strong-scattering media. The successive use of EDEI and EFWI can obtain fine structures of the strong scatterers and their shielding areas. However, the inversion effects of inner velocity and bottom boundaries of strong scatterers by the existing methods need to be improved. In this paper, we propose the elastic direct envelope inversion with anisotropic total variation constraint (EDEI-ATV). The anisotropic total variation (ATV) constraint has the advantage of making the velocity more uniform inside the layer and sharper on boundaries, which can be used to improve the inversion results of EDEI. During the iterations, the ATV constraint is directly applied to the update of Vp and Vs, and the alternately iterative algorithm can achieve good results. After obtaining reliable large-scale Vp and Vs structures, the EFWI with anisotropic total variation constraint (EFWI-ATV) is performed to obtain high-precision Vp and Vs structures. Numerical examples verify the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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13 pages, 4006 KiB

Open AccessTechnical Note

First-Break Picking of Large-Offset Seismic Data Based on CNNs with Weighted Data

by Yuchen Yin, Liguo Han, Pan Zhang, Zhanwu Lu and Xujia Shang

Remote Sens. 2023, 15(2), 356; https://doi.org/10.3390/rs15020356 - 6 Jan 2023

Cited by 5 | Viewed by 2305

Abstract

Deep reflection seismic data are usually accompanied by large-offset data, and the accurate and rapid identification of the first arrivals of seismic records plays an important role in eliminating the effects of topography and other factors that increase with the increasing offsets. In [...] Read more.

Deep reflection seismic data are usually accompanied by large-offset data, and the accurate and rapid identification of the first arrivals of seismic records plays an important role in eliminating the effects of topography and other factors that increase with the increasing offsets. In this paper, we propose a method based on convolutional neural networks (CNNs) that can accurately identify the first arrivals of large-offset seismic data. A time window for linear dynamic correction was established to convert the raw seismic data into rectangular images so as to reduce the amount of invalid sample data and improve the training efficiency. In order to enhance the prediction effect of the far-offset first arrivals, we propose the strategy of adjusting the weight of the far-offset data to increase the weight of the far-offset data in the training dataset and, thus, to improve the first arrival accuracy. The manually picked first arrivals are used as labels and the input to the CNNs for training, and the full-offset first arrivals are the output. The travel time tomography velocity is modeled and compared based on the first arrivals obtained through manual picking, industrial software automatic picking, and CNN prediction. The results show that the application of CNNs to large-offset seismic datasets can help researchers to obtain the first arrivals at different offsets, while the inclusion of far-offset weights can effectively improve the modeling depth of the tomography inversion, and the accuracy of the results is high. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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14 pages, 7448 KiB

Open AccessTechnical Note

Sparse Inversion for the Iterative Marchenko Scheme of Irregularly Sampled Data

by Jingwen Zeng and Liguo Han

Remote Sens. 2023, 15(2), 322; https://doi.org/10.3390/rs15020322 - 5 Jan 2023

Cited by 1 | Viewed by 1235

Abstract

The Marchenko method is a data-driven way that makes it possible to calculate Green’s functions from virtual points in the subsurface using the reflection data at the surface and requiring only a macro velocity model. This method requires collocated sources and receivers. However, [...] Read more.

The Marchenko method is a data-driven way that makes it possible to calculate Green’s functions from virtual points in the subsurface using the reflection data at the surface and requiring only a macro velocity model. This method requires collocated sources and receivers. However, in practice, irregular sampling of sources or receivers will cause gaps and distortions in the obtained focusing functions and Green’s functions. To solve this problem, this paper proposes to integrate a sparse inversion into the iterative Marchenko scheme. Specifically, we add sparsity constraints to the Marchenko equations and apply the sparse inversion during the iterative process. To reduce the strict requirements on acquisition geometries, our work deals with the situation in which the sources are subsampled where the integrations are carried out over the receivers, while the existing point spread function method solves the situation where the receivers are subsampled. We make a step to handle both situations at the same time by integrating this method with our work because of the same iterative framework. Our new method is applied to a two-dimensional numerical example with irregularly sampled data. The result shows that it can effectively fill gaps in the obtained focusing functions and Green’s functions in the Marchenko method. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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15 pages, 5048 KiB

Open AccessTechnical Note

Data-Driven Seismic Impedance Inversion Based on Multi-Scale Strategy

by Guang Zhu, Xiaohong Chen, Jingye Li and Kangkang Guo

Remote Sens. 2022, 14(23), 6056; https://doi.org/10.3390/rs14236056 - 29 Nov 2022

Cited by 1 | Viewed by 1993

Abstract

Seismic impedance inversion is one of the most commonly used techniques for reservoir characterization. High accuracy and high resolution seismic impedance is a prerequisite for subsequent reservoir interpretation. The data-driven approach offers the opportunity for accurate impedance prediction by establishing a nonlinear mapping [...] Read more.

Seismic impedance inversion is one of the most commonly used techniques for reservoir characterization. High accuracy and high resolution seismic impedance is a prerequisite for subsequent reservoir interpretation. The data-driven approach offers the opportunity for accurate impedance prediction by establishing a nonlinear mapping between seismic data and impedance. However, existing data-driven methods take the raw seismic data directly as input, making it difficult for the network to learn high frequency weak signal information and resulting in low resolution inversion results. In order to mitigate the above issues, a data-driven seismic impedance inversion method based on multi-scale strategy is proposed. The method first obtains seismic data at different scales using frequency division techniques and do normalization on the extracted multi-scale data to ensure the consistency of the seismic signal energy in different frequency bands. The multi-scale seismic data will then be fed into the network, which helps the network to learn the high frequency information features more easily, and ultimately obtain higher resolution inversion results. We use the most commonly used convolutional neural network (CNN) as an example to demonstrate that the proposed multi-scale data-driven seismic impedance inversion method can improve the resolution of the inversion results. In addition, since the above seismic impedance inversion method is executed trace-by-trace, the f-x prediction filtering technique is introduced to improve the lateral continuity of the inversion results and obtain more geologically reliable impedance profiles. The validity of the proposed method is verified in the application of synthetic model data as well as an actual data set. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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13 pages, 5011 KiB

Open AccessTechnical Note

Weak Signal Enhancement for Passive Seismic Data Reconstruction Based on Deep Learning

by Binghui Zhao, Liguo Han, Pan Zhang and Yuchen Yin

Remote Sens. 2022, 14(21), 5318; https://doi.org/10.3390/rs14215318 - 24 Oct 2022

Cited by 3 | Viewed by 1875

Abstract

In conventional passive seismic exploration, it is often necessary to make a long-period seismic record. On the one hand, the passive seismic records with long period allowed us to screen several good passive seismic records with long period for seismic interferometry reconstruction and [...] Read more.

In conventional passive seismic exploration, it is often necessary to make a long-period seismic record. On the one hand, the passive seismic records with long period allowed us to screen several good passive seismic records with long period for seismic interferometry reconstruction and perform piecewise stacking on them. On the other hand, a sufficiently long recording time can help us avoid noise interference generated by nonpassive sources during the recording process, such as animal activities, construction operations, industrial electrical interference, etc. Compared with the passive seismic records with short period, the passive seismic records with long period can obtain higher signal-to-noise ratio after seismic interferometry reconstruction. However, they also cause huge consumptions of manpower, material resources, and time. Based on this, this paper proposes a seismic interferometry reconstruction method using passive signals of short-period recordings. Based on deep learning technology, the effective information is extracted and enhanced, the strong coherent noise after reconstruction is suppressed and weakened, the SNR of reconstructed recording is improved, and the effective information is mined. It can effectively reduce the time of passive seismic recording required for acquisition and improve acquisition efficiency. In addition, it also has a certain monitoring effect on real-time changes in underground structures. Full article

(This article belongs to the Special Issue Geophysical Data Processing in Remote Sensing Imagery)

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