{"title":"Correction to: Mapping of geological structures and sediment thickness from analysis of aeromagnetic data over the Obudu Basement Complex of Nigeria","authors":"","doi":"10.1093/jge/gxae043","DOIUrl":"https://doi.org/10.1093/jge/gxae043","url":null,"abstract":"","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140661614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yang Zhao, Xin Wu, Weiying Chen, Junjie Xue, Jinjing Shi
The short-offset transient electromagnetic (SOTEM) method carries out survey in the near source region, the strong signal makes it suitable for deep detection with high precision. When the underground structure is complex, three-dimensional (3D) inversion of SOTEM data is necessary to meet the need of high-precision detection. Currently, difficulties faced by the conventional 3D inversion methods include high computational complexity, and the influence of the initial model. Deep learning (DL), as a completely nonlinear algorithm, can predict the underground structure from the measured data. DL is completely data-driven, does not use traditional misfit optimization methods. In this study, an efficient way is proposed to conduct 3D inversion for SOTEM data, which trains a 3D U-Net based on massive data to establish a mapping from SOTEM data to geoelectric models. After the training is completed, input the new SOTEM data into the trained network, and the corresponding geoelectric model can be obtained. Although the training is a time-consuming process, prediction for new data can be completed in seconds. The inversion results for simulated data indicate that the 3D U-Net has good generalization performance and anti-noise ability. The inversion performance of 3D U-Net on the double-anomaly model has improved by 51.1% compared to 3D fully convolutional network (FCN). The inversion results of the 3D U-Net on the field data successfully delineated the aquiferous collapse column. The inversion results for simulated and field data demonstrate that the proposed method can achieve accurate 3D inversion for large volume of data while greatly saving computational time.
{"title":"Three-dimensional inversion for short-offset transient electromagnetic data based on 3D U-Net","authors":"Yang Zhao, Xin Wu, Weiying Chen, Junjie Xue, Jinjing Shi","doi":"10.1093/jge/gxae046","DOIUrl":"https://doi.org/10.1093/jge/gxae046","url":null,"abstract":"\u0000 The short-offset transient electromagnetic (SOTEM) method carries out survey in the near source region, the strong signal makes it suitable for deep detection with high precision. When the underground structure is complex, three-dimensional (3D) inversion of SOTEM data is necessary to meet the need of high-precision detection. Currently, difficulties faced by the conventional 3D inversion methods include high computational complexity, and the influence of the initial model. Deep learning (DL), as a completely nonlinear algorithm, can predict the underground structure from the measured data. DL is completely data-driven, does not use traditional misfit optimization methods. In this study, an efficient way is proposed to conduct 3D inversion for SOTEM data, which trains a 3D U-Net based on massive data to establish a mapping from SOTEM data to geoelectric models. After the training is completed, input the new SOTEM data into the trained network, and the corresponding geoelectric model can be obtained. Although the training is a time-consuming process, prediction for new data can be completed in seconds. The inversion results for simulated data indicate that the 3D U-Net has good generalization performance and anti-noise ability. The inversion performance of 3D U-Net on the double-anomaly model has improved by 51.1% compared to 3D fully convolutional network (FCN). The inversion results of the 3D U-Net on the field data successfully delineated the aquiferous collapse column. The inversion results for simulated and field data demonstrate that the proposed method can achieve accurate 3D inversion for large volume of data while greatly saving computational time.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140673832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nonlinear beamforming (NLBF) has emerged as a highly effective technology for enhancing seismic data quality. The crux of NLBF's success lies in its ability to robustly estimate local traveltime operators directly from input data, a process that entails solving millions or even billions of nonlinear optimization problems per input gather. Among the solvers utilized for estimating these operators is the 2 + 2 + 1 method, for which we have previously introduced algorithmic implementations on both the CPU and GPU platforms. In this paper, we present an efficiency-improved GPU algorithm for the 2 + 2 + 1 method, particularly beneficial when dealing with small data apertures in NLBF. Our enhanced GPU algorithm brings significant improvements in computation efficiency through several strategic measures, which include leveraging Horner's method to minimize the mathematical overhead of traveltime calculation, implementing a GPU-friendly data reduction algorithm to exploit GPU computational power, and optimizing shared GPU memory usage as the primary workspace whenever feasible. To demonstrate the tangible efficiency enhancement achieved by our new GPU algorithm, via two illustrative examples, we compare its performance with that of our previous implementation.
{"title":"An efficiency-improved GPU algorithm for the 2 + 2 + 1 method in nonlinear beamforming","authors":"Yimin Sun, I. Silvestrov, A. Bakulin","doi":"10.1093/jge/gxae050","DOIUrl":"https://doi.org/10.1093/jge/gxae050","url":null,"abstract":"\u0000 Nonlinear beamforming (NLBF) has emerged as a highly effective technology for enhancing seismic data quality. The crux of NLBF's success lies in its ability to robustly estimate local traveltime operators directly from input data, a process that entails solving millions or even billions of nonlinear optimization problems per input gather. Among the solvers utilized for estimating these operators is the 2 + 2 + 1 method, for which we have previously introduced algorithmic implementations on both the CPU and GPU platforms. In this paper, we present an efficiency-improved GPU algorithm for the 2 + 2 + 1 method, particularly beneficial when dealing with small data apertures in NLBF. Our enhanced GPU algorithm brings significant improvements in computation efficiency through several strategic measures, which include leveraging Horner's method to minimize the mathematical overhead of traveltime calculation, implementing a GPU-friendly data reduction algorithm to exploit GPU computational power, and optimizing shared GPU memory usage as the primary workspace whenever feasible. To demonstrate the tangible efficiency enhancement achieved by our new GPU algorithm, via two illustrative examples, we compare its performance with that of our previous implementation.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140672729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bo Yu, Ying Shi, Yukun Tian, Hui Zhou, Zhanqing Yu, Yuanpeng Zhang, Weihong Wang
Shale oil reservoir emerges as a significant unconventional energy source, commonly predicted by anisotropic seismic inversion. Considering the intricate nature of shale oil reservoirs, it becomes imperative to consider uncertainties during anisotropic inversion. An effective approach to address this involves stochastic inversion, specifically the anisotropic Bayesian linearized inversion (ABLI), which characterizes statistical and spatial correlations of subsurface parameters through a crucial multivariate correlation matrix constructed through geostatistics. However, an inevitable challenge in stochastic inversion arises from interference during the calibration of statistical and spatial correlations of subsurface parameters. This challenge becomes particularly pronounced in anisotropic inversion, heightened by the multitude of involved model parameters. Existing decorrelation approaches primarily address statistical correlation, neglecting the impact of spatial correlation. To tackle this issue, a novel multi-parameter decoupling strategy is proposed, formulating decoupling anisotropic Bayesian linearized inversion (D-ABLI). D-ABLI introduces an advanced decorrelation approach, and uses principal component analysis (PCA) to simultaneously eliminate impact of statistical and spatial correlations on ABLI. The decoupling enhances the inversion accuracy of model parameters in ABLI, particularly for density and anisotropic parameters. The theoretical underpinnings of the decoupling strategy are demonstrated to be reasonable, and the effectiveness of D-ABLI is proved through a theoretical data test and a field data test regarding shale oil reservoirs. The D-ABLI results offer the capability to estimate fracture density accurately and unveil the distribution of shale oil.
{"title":"Anisotropic Bayesian linearized stochastic seismic inversion with multi-parameter decoupling","authors":"Bo Yu, Ying Shi, Yukun Tian, Hui Zhou, Zhanqing Yu, Yuanpeng Zhang, Weihong Wang","doi":"10.1093/jge/gxae049","DOIUrl":"https://doi.org/10.1093/jge/gxae049","url":null,"abstract":"\u0000 Shale oil reservoir emerges as a significant unconventional energy source, commonly predicted by anisotropic seismic inversion. Considering the intricate nature of shale oil reservoirs, it becomes imperative to consider uncertainties during anisotropic inversion. An effective approach to address this involves stochastic inversion, specifically the anisotropic Bayesian linearized inversion (ABLI), which characterizes statistical and spatial correlations of subsurface parameters through a crucial multivariate correlation matrix constructed through geostatistics. However, an inevitable challenge in stochastic inversion arises from interference during the calibration of statistical and spatial correlations of subsurface parameters. This challenge becomes particularly pronounced in anisotropic inversion, heightened by the multitude of involved model parameters. Existing decorrelation approaches primarily address statistical correlation, neglecting the impact of spatial correlation. To tackle this issue, a novel multi-parameter decoupling strategy is proposed, formulating decoupling anisotropic Bayesian linearized inversion (D-ABLI). D-ABLI introduces an advanced decorrelation approach, and uses principal component analysis (PCA) to simultaneously eliminate impact of statistical and spatial correlations on ABLI. The decoupling enhances the inversion accuracy of model parameters in ABLI, particularly for density and anisotropic parameters. The theoretical underpinnings of the decoupling strategy are demonstrated to be reasonable, and the effectiveness of D-ABLI is proved through a theoretical data test and a field data test regarding shale oil reservoirs. The D-ABLI results offer the capability to estimate fracture density accurately and unveil the distribution of shale oil.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140684700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The limitations of the coverage range and density of transmission wave often result in less-than-ideal results in traveltime tomography. In contrast, joint transmission-reflection traveltime tomography can not only recover deep structures that transmission tomography cannot detect but also optimize its inversion results. In this article, we perform joint tomography on borehole seismic (VSP, RVSP and crosswell seismic) data to obtain near-wellbore structures. In the forward part, we solve the factored equation by the discontinuous Galerkin (DG) method to calculate the transmission/reflection traveltime. Due to the large wavefront curvature near the source point, the traveltime errors generated by the numerical simulation will propagate from the source to all the calculation domains. According to the factorization principle, the equation solution is decomposed into two parts to solve the point-source singularity. To further improve the accuracy of solving traveltime, we use the DG method to solve the factored eikonal equation with additive factors (the factored DG method), obtaining second-order accuracy solution. The adjoint-state method is employed in the inversion section to calculate the gradient of the misfit function. And we use the traveltime difference observed inside the model to define the misfit function, which is more suitable for borehole seismic and avoids the influence of surface normal vectors on gradients. Numerical tests applied on models indicate that the joint tomography method has the potential to accurately inverse the seismic structure information near the well and recover the deep underground structure.
{"title":"DG-based joint transmission-reflection traveltime tomography and its application of borehole seismic","authors":"Xin Chen, Zhaolin Zhu, Danping Cao","doi":"10.1093/jge/gxae044","DOIUrl":"https://doi.org/10.1093/jge/gxae044","url":null,"abstract":"\u0000 The limitations of the coverage range and density of transmission wave often result in less-than-ideal results in traveltime tomography. In contrast, joint transmission-reflection traveltime tomography can not only recover deep structures that transmission tomography cannot detect but also optimize its inversion results. In this article, we perform joint tomography on borehole seismic (VSP, RVSP and crosswell seismic) data to obtain near-wellbore structures. In the forward part, we solve the factored equation by the discontinuous Galerkin (DG) method to calculate the transmission/reflection traveltime. Due to the large wavefront curvature near the source point, the traveltime errors generated by the numerical simulation will propagate from the source to all the calculation domains. According to the factorization principle, the equation solution is decomposed into two parts to solve the point-source singularity. To further improve the accuracy of solving traveltime, we use the DG method to solve the factored eikonal equation with additive factors (the factored DG method), obtaining second-order accuracy solution. The adjoint-state method is employed in the inversion section to calculate the gradient of the misfit function. And we use the traveltime difference observed inside the model to define the misfit function, which is more suitable for borehole seismic and avoids the influence of surface normal vectors on gradients. Numerical tests applied on models indicate that the joint tomography method has the potential to accurately inverse the seismic structure information near the well and recover the deep underground structure.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140710804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yutao Liu, Yuquan Wu, Gang Li, Aqeel Abbas, Taikun Shi
To process magnetic anomaly data, appropriate parameters for field separation, denoising, and Euler deconvolution have to be manually selected. The traditional workflow is inefficient and cannot fulfill the rapid detection of submarine cables due to the complex processing and manual parameter tuning. This study presents an end-to-end deep learning approach for the identification and positioning of submarine cables based on magnetic anomalies. The proposed approach effectively establishes a direct mapping correlation between the magnetic field data and the position of the submarine cable. Synthetic tests suggest that our method has a better performance in terms of positioning accuracy than the conventional Euler method. Our results for the field data are comparable to those obtained using conventional techniques. Furthermore, the proposed method achieves an optimal solution by employing clustering technique and selecting the solution with the maximum confidence, which avoids spurious solutions associated with traditional methods. The proposed method can directly determine the position of the submarine cables using the raw magnetic field data. Contrary to the traditional processing workflow, field separation and denoising are not necessary in this novel approach, resulting in higher processing efficiency and a simpler processing process.
{"title":"Submarine cable detection using an end-to-end neural network-based magnetic data inversion","authors":"Yutao Liu, Yuquan Wu, Gang Li, Aqeel Abbas, Taikun Shi","doi":"10.1093/jge/gxae045","DOIUrl":"https://doi.org/10.1093/jge/gxae045","url":null,"abstract":"\u0000 To process magnetic anomaly data, appropriate parameters for field separation, denoising, and Euler deconvolution have to be manually selected. The traditional workflow is inefficient and cannot fulfill the rapid detection of submarine cables due to the complex processing and manual parameter tuning. This study presents an end-to-end deep learning approach for the identification and positioning of submarine cables based on magnetic anomalies. The proposed approach effectively establishes a direct mapping correlation between the magnetic field data and the position of the submarine cable. Synthetic tests suggest that our method has a better performance in terms of positioning accuracy than the conventional Euler method. Our results for the field data are comparable to those obtained using conventional techniques. Furthermore, the proposed method achieves an optimal solution by employing clustering technique and selecting the solution with the maximum confidence, which avoids spurious solutions associated with traditional methods. The proposed method can directly determine the position of the submarine cables using the raw magnetic field data. Contrary to the traditional processing workflow, field separation and denoising are not necessary in this novel approach, resulting in higher processing efficiency and a simpler processing process.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140710133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hydraulic tortuosity is a crucial parameter affecting the movement of fluid in porous media. Currently, researchers have used different methods to construct porous media models and studied the variation of hydraulic tortuosity with porosity. In this paper, we use Monte Carlo random particle, Quartet structure generation set (QSGS), and CT-scan reconstruction to construct porous media models with different porosity. The finite element method is used to simulate the fluid passing through the models. The effectiveness of the QSGS algorithm in constructing porous media is verified. The hydraulic tortuosity of the three types of models is computed using the streamline length ratio method, and its variation law with porosity is explored. The results show that the change law of the three models is consistent. The law of power function change is satisfied between the two for all models, which means the increase in porosity causes a decrease in tortuosity. Different models are constructed to explore the effect of tortuosity on permeability. An increase in tortuosity results in a smaller permeability when other conditions are equal. This paper aims to provide effective methods for constructing porous media models and a reference for studying hydraulic tortuosity.
{"title":"Hydraulic tortuosity of porous media: Comparison of different modeling methods","authors":"Yuming Zhu, Wenzheng Yue","doi":"10.1093/jge/gxae039","DOIUrl":"https://doi.org/10.1093/jge/gxae039","url":null,"abstract":"\u0000 Hydraulic tortuosity is a crucial parameter affecting the movement of fluid in porous media. Currently, researchers have used different methods to construct porous media models and studied the variation of hydraulic tortuosity with porosity. In this paper, we use Monte Carlo random particle, Quartet structure generation set (QSGS), and CT-scan reconstruction to construct porous media models with different porosity. The finite element method is used to simulate the fluid passing through the models. The effectiveness of the QSGS algorithm in constructing porous media is verified. The hydraulic tortuosity of the three types of models is computed using the streamline length ratio method, and its variation law with porosity is explored. The results show that the change law of the three models is consistent. The law of power function change is satisfied between the two for all models, which means the increase in porosity causes a decrease in tortuosity. Different models are constructed to explore the effect of tortuosity on permeability. An increase in tortuosity results in a smaller permeability when other conditions are equal. This paper aims to provide effective methods for constructing porous media models and a reference for studying hydraulic tortuosity.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140714896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The shallow river channel sandstone reservoirs of Jurassic in western Sichuan Basin are rich in natural gas. Gas-bearing sweet-spot has a ‘sausage like’ distribution feature, with complex gas water distribution. Analysis of seismic data at different angles show that gas-bearing reservoir formation has different seismic attenuation features from the water-bearing formations, and such differences have a certain correlation with gas production. Accordingly, a gas-bearing detection technology based on angle-dependent seismic attenuation feature is proposed. Firstly, matching pursuit time-frequency analysis method is used to extract high-resolution time-frequency spectrum from seismic data at different incidence angles. Then, the angle-dependent seismic attenuation attribute is estimated using the extracted time-frequency spectrum. Finally, the attribute is combined with the inverted impedance for gas-bearing detection. With the advantages of lower uncertainty and less affected by reservoir porosity, the application results of developed method have high coincidence rate with the drilled wells, and the drilling wells deployed based on the detection results have achieved high production.
{"title":"Angle-dependent seismic attenuation based gas-bearing detection of sandstone river channel reservoir in western Sichuan Basin","authors":"Zhentao Sun, Xingyao Yin, Yongzhen Ji","doi":"10.1093/jge/gxae042","DOIUrl":"https://doi.org/10.1093/jge/gxae042","url":null,"abstract":"\u0000 The shallow river channel sandstone reservoirs of Jurassic in western Sichuan Basin are rich in natural gas. Gas-bearing sweet-spot has a ‘sausage like’ distribution feature, with complex gas water distribution. Analysis of seismic data at different angles show that gas-bearing reservoir formation has different seismic attenuation features from the water-bearing formations, and such differences have a certain correlation with gas production. Accordingly, a gas-bearing detection technology based on angle-dependent seismic attenuation feature is proposed. Firstly, matching pursuit time-frequency analysis method is used to extract high-resolution time-frequency spectrum from seismic data at different incidence angles. Then, the angle-dependent seismic attenuation attribute is estimated using the extracted time-frequency spectrum. Finally, the attribute is combined with the inverted impedance for gas-bearing detection. With the advantages of lower uncertainty and less affected by reservoir porosity, the application results of developed method have high coincidence rate with the drilled wells, and the drilling wells deployed based on the detection results have achieved high production.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140727205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yuanyuan Li, T. Alkhalifah, Jianping Huang, Zhenchun Li
It remains challenging for elastic full waveform inversion (EFWI) to characterize the elastic properties of a target reservoir deep beneath complex overburden media. This can be attributed to two factors. 1) The complex wavefield distortions arising from the overburden obscure the target reflection properties, resulting in a target zone with limited energy illumination. 2) High-resolution EFWI for the whole inversion domain is computationally expensive. To overcome these challenges, instead of directly inverting for the subsurface model using surface seismic data, we develop a target-oriented elastic waveform inversion scheme. We first retrieve the elastic reflection response to the target zone of interest by projecting seismic data from surface to the datum level. Then, we can apply high-resolution EFWI on the target zone by using the retrieved elastic reflection data. To better handle the complex overburden, which may include anisotropy and salt bodies, we make full use of the prior estimate of the overburden in the redatuming process to obtain a reliable reflection response to the target zone. In the numerical examples, we use the SEAM model with an anisotropic overburden and a salt body model to demonstrate the feasibility and effectiveness of the proposed method and analyze the influence of anisotropy and high-contrast salt in the overburden on the redatuming and inversion results, respectively.
{"title":"Characterizing the target of interest underlying a complex overburden with target-oriented elastic waveform inversion","authors":"Yuanyuan Li, T. Alkhalifah, Jianping Huang, Zhenchun Li","doi":"10.1093/jge/gxae038","DOIUrl":"https://doi.org/10.1093/jge/gxae038","url":null,"abstract":"\u0000 It remains challenging for elastic full waveform inversion (EFWI) to characterize the elastic properties of a target reservoir deep beneath complex overburden media. This can be attributed to two factors. 1) The complex wavefield distortions arising from the overburden obscure the target reflection properties, resulting in a target zone with limited energy illumination. 2) High-resolution EFWI for the whole inversion domain is computationally expensive. To overcome these challenges, instead of directly inverting for the subsurface model using surface seismic data, we develop a target-oriented elastic waveform inversion scheme. We first retrieve the elastic reflection response to the target zone of interest by projecting seismic data from surface to the datum level. Then, we can apply high-resolution EFWI on the target zone by using the retrieved elastic reflection data. To better handle the complex overburden, which may include anisotropy and salt bodies, we make full use of the prior estimate of the overburden in the redatuming process to obtain a reliable reflection response to the target zone. In the numerical examples, we use the SEAM model with an anisotropic overburden and a salt body model to demonstrate the feasibility and effectiveness of the proposed method and analyze the influence of anisotropy and high-contrast salt in the overburden on the redatuming and inversion results, respectively.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140740136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Qingquan Zhi, Xingchun Wang, Junjie Wu, Xiu Li, Xiaohong Deng
Transient electromagnetic surveys are commonly conducted to map the distribution of resistivity, a key physical property in mineral exploration and other geological prospecting problems. However, the responses obtained in regions associated with chargeable minerals are always distorted by the induced polarization effects. In this study, the distorted responses are initially simulated in the frequency domain employing the Cole-Cole complex resistivity model and subsequently converted into the time domain through a time-frequency transformation method. A uniform half-space model is employed to validate the algorithm and illustrate the distortion characteristics of the responses in polarizable formations. A three-layer model is designed to estimate the misinterpretation of slightly complicated models. An actual misinterpretation is demonstrated by field responses containing induced polarization effects collected in the Wulong gold mine. The results show that the distortions under different geoelectrical conditions are consistent, enhancing the responses in the early stage and counteracting the responses in the late stage. The strong induced polarizable effects distort the responses by causing explicit sign reversals, whereas the weak induced polarizable effects only distort the decay rate of the responses. These distortions are prone to causing misinterpretations and resulting in excessively intricate geological structures.
{"title":"The distortion and misinterpretation of TEM responses caused by IP effect","authors":"Qingquan Zhi, Xingchun Wang, Junjie Wu, Xiu Li, Xiaohong Deng","doi":"10.1093/jge/gxae040","DOIUrl":"https://doi.org/10.1093/jge/gxae040","url":null,"abstract":"\u0000 Transient electromagnetic surveys are commonly conducted to map the distribution of resistivity, a key physical property in mineral exploration and other geological prospecting problems. However, the responses obtained in regions associated with chargeable minerals are always distorted by the induced polarization effects. In this study, the distorted responses are initially simulated in the frequency domain employing the Cole-Cole complex resistivity model and subsequently converted into the time domain through a time-frequency transformation method. A uniform half-space model is employed to validate the algorithm and illustrate the distortion characteristics of the responses in polarizable formations. A three-layer model is designed to estimate the misinterpretation of slightly complicated models. An actual misinterpretation is demonstrated by field responses containing induced polarization effects collected in the Wulong gold mine. The results show that the distortions under different geoelectrical conditions are consistent, enhancing the responses in the early stage and counteracting the responses in the late stage. The strong induced polarizable effects distort the responses by causing explicit sign reversals, whereas the weak induced polarizable effects only distort the decay rate of the responses. These distortions are prone to causing misinterpretations and resulting in excessively intricate geological structures.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140374848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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