Prismatic reflections in seismic data carry abundant information about subsurface steeply dipping structures, such as salt flanks or near-vertical faults, playing an important role in delineating these structures when effectively used. Conventional linear least-squares reverse time migration (L-LSRTM) fails to use prismatic waves due to the first-order Born approximation, resulting in a blurry image of steep interfaces. We propose a nonlinear LSRTM (NL-LSRTM) method to take advantage of prismatic waves for the detailed characterization of subsurface steeply dipping structures. Compared with current least-squares migration methods of prismatic waves, our NL-LSRTM is nonlinear and thus avoids the challenging extraction of prismatic waves or the prior knowledge of L-LSRTM result. The gradient of NL-LSRTM consists of the primary and prismatic imaging terms, which can accurately project both observed primary and prismatic waves into the image domain for the simultaneous depiction of near-horizontal and near-vertical structures. However, we find that the full Hessian based Newton normal equation has two similar terms, which prompts us to make further comparison between the Newton normal equation and the proposed NL-LSRTM. We demonstrate that the Newton normal equation is problematic when applied to the migration problem because the primary reflections in the seismic records will be wrongly projected into the image along the prismatic wavepath, resulting in an artifact-contaminated image. In contrast, the nonlinear data-fitting process included in the NL-LSRTM contributes to balancing the amplitudes of primary and prismatic imaging results, thus making NL-LSRTM produce superior images compared to the Newton normal equation. Several numerical tests validate the applicability and robustness of NL-LSRTM for the delineation of steeply dipping structures, and illustrate that the imaging results are much better than the conventional L-LSRTM.
{"title":"Nonlinear least-squares reverse time migration of prismatic waves for delineating steeply dipping structures","authors":"Zheng Wu, Yuzhu Liu, Jizhong Yang","doi":"10.1190/geo2022-0749.1","DOIUrl":"https://doi.org/10.1190/geo2022-0749.1","url":null,"abstract":"Prismatic reflections in seismic data carry abundant information about subsurface steeply dipping structures, such as salt flanks or near-vertical faults, playing an important role in delineating these structures when effectively used. Conventional linear least-squares reverse time migration (L-LSRTM) fails to use prismatic waves due to the first-order Born approximation, resulting in a blurry image of steep interfaces. We propose a nonlinear LSRTM (NL-LSRTM) method to take advantage of prismatic waves for the detailed characterization of subsurface steeply dipping structures. Compared with current least-squares migration methods of prismatic waves, our NL-LSRTM is nonlinear and thus avoids the challenging extraction of prismatic waves or the prior knowledge of L-LSRTM result. The gradient of NL-LSRTM consists of the primary and prismatic imaging terms, which can accurately project both observed primary and prismatic waves into the image domain for the simultaneous depiction of near-horizontal and near-vertical structures. However, we find that the full Hessian based Newton normal equation has two similar terms, which prompts us to make further comparison between the Newton normal equation and the proposed NL-LSRTM. We demonstrate that the Newton normal equation is problematic when applied to the migration problem because the primary reflections in the seismic records will be wrongly projected into the image along the prismatic wavepath, resulting in an artifact-contaminated image. In contrast, the nonlinear data-fitting process included in the NL-LSRTM contributes to balancing the amplitudes of primary and prismatic imaging results, thus making NL-LSRTM produce superior images compared to the Newton normal equation. Several numerical tests validate the applicability and robustness of NL-LSRTM for the delineation of steeply dipping structures, and illustrate that the imaging results are much better than the conventional L-LSRTM.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":"28 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136022863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gaoshan Guo, Stéphane Operto, Ali Gholami, Hossein S. Aghamiry
Extended-source full-waveform inversion (ES-FWI) first computes wavefields with data-driven source extensions such that the simulated data in inaccurate velocity models match the observed counterpart well enough to prevent cycle skipping. Then, the source extensions are minimized to update the model parameters toward the true medium. This two-step workflow is iterated until both data and sources are matched. It was recently shown that the source extensions are the least-squares solutions of the scattered data fitting problem. As a result, the source extensions are computed by propagating backward in time the deconvolved data residuals by the damped data-domain Hessian of the scattered data fitting problem. Estimating these weighted data residuals is the main computational bottleneck of time-domain ES-FWI. To mitigate this burden, we approximate the inverse data-domain Hessian by mono- and multi-dimensional matching filters with two simulations per source. We implement time-domain ES-FWI with the alternating-direction method of multiplier and total-variation regularization. Moreover, we apply ES-FWI with a multiscale approach involving frequency continuation and layer-stripping, with the latter being implemented with an offset-time dependent weighting operator. In this framework, we further regularize the inversions while mitigating their computational burden by matching the grid interval to the frequency bandwidth. Finally, the overall workflow combines ES-FWI and classical FWI during the early and late stages of the multiscale approach, respectively. We illustrate that the sensitivity of ES-FWI to the accuracy of the approximated inverse data-domain Hessian depends on the complexity of the targeted model, the data anatomy, and the accuracy of the starting model. In the case of the 2004 BP salt model, we demonstrate that the layer stripping is necessary when the inverse data-domain Hessian is approximated by a 2D Gabor matching filter and the starting model is crude, while this feature is not necessary with the Marmousi II model.
{"title":"Time-domain extended-source full-waveform inversion: algorithm and practical workflow","authors":"Gaoshan Guo, Stéphane Operto, Ali Gholami, Hossein S. Aghamiry","doi":"10.1190/geo2023-0055.1","DOIUrl":"https://doi.org/10.1190/geo2023-0055.1","url":null,"abstract":"Extended-source full-waveform inversion (ES-FWI) first computes wavefields with data-driven source extensions such that the simulated data in inaccurate velocity models match the observed counterpart well enough to prevent cycle skipping. Then, the source extensions are minimized to update the model parameters toward the true medium. This two-step workflow is iterated until both data and sources are matched. It was recently shown that the source extensions are the least-squares solutions of the scattered data fitting problem. As a result, the source extensions are computed by propagating backward in time the deconvolved data residuals by the damped data-domain Hessian of the scattered data fitting problem. Estimating these weighted data residuals is the main computational bottleneck of time-domain ES-FWI. To mitigate this burden, we approximate the inverse data-domain Hessian by mono- and multi-dimensional matching filters with two simulations per source. We implement time-domain ES-FWI with the alternating-direction method of multiplier and total-variation regularization. Moreover, we apply ES-FWI with a multiscale approach involving frequency continuation and layer-stripping, with the latter being implemented with an offset-time dependent weighting operator. In this framework, we further regularize the inversions while mitigating their computational burden by matching the grid interval to the frequency bandwidth. Finally, the overall workflow combines ES-FWI and classical FWI during the early and late stages of the multiscale approach, respectively. We illustrate that the sensitivity of ES-FWI to the accuracy of the approximated inverse data-domain Hessian depends on the complexity of the targeted model, the data anatomy, and the accuracy of the starting model. In the case of the 2004 BP salt model, we demonstrate that the layer stripping is necessary when the inverse data-domain Hessian is approximated by a 2D Gabor matching filter and the starting model is crude, while this feature is not necessary with the Marmousi II model.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":"38 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136022597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Peilin Yu, Yuyong Yang, Qiaomu Qi, Huailai Zhou, Yuanjun Wang
The development of natural fractures has a significant impact on underground reservoirs and leads to seismic anisotropy. Furthermore, the scale of natural fractures directly affects the oil and gas preservation, hydraulic fracture construction, and production development of shale reservoirs. Shear-wave anisotropy is a frequency dependent parameter and the change in shear-wave anisotropy with frequency is a function of the fracture scale. We propose an innovative method for predicting the fracture scale quantitatively using frequency-dependent shear-wave anisotropy. The quantitative relationship between different fracture scales and the frequency-dependent response of the shear-wave splitting (SWS) anisotropy can be obtained using a dynamic rock physics model. The frequency-dependent shear-wave anisotropy was calculated via SWS analysis in the frequency domain, after which this quantitative relationship and the calculated frequency-dependent response was used to establish an objective function for inversion of fracture scale at different depths using the least-squares algorithm. We synthesized data under ideal conditions, tested the proposed method, applied our method to field data, and found that the quantitative prediction method of the fracture scale yielded reasonable prediction results. The shear-wave anisotropy was calculated based on the SWS analysis from the horizontal components of the upgoing wavefields of the field vertical seismic profile. We compared the fracture scale calculated from logging data using the proposed method, and the results obtained indicated that this method can successfully predict the fracture scale quantitatively.
{"title":"Quantitative prediction of fracture scale based on frequency-dependent shear wave splitting","authors":"Peilin Yu, Yuyong Yang, Qiaomu Qi, Huailai Zhou, Yuanjun Wang","doi":"10.1190/geo2022-0652.1","DOIUrl":"https://doi.org/10.1190/geo2022-0652.1","url":null,"abstract":"The development of natural fractures has a significant impact on underground reservoirs and leads to seismic anisotropy. Furthermore, the scale of natural fractures directly affects the oil and gas preservation, hydraulic fracture construction, and production development of shale reservoirs. Shear-wave anisotropy is a frequency dependent parameter and the change in shear-wave anisotropy with frequency is a function of the fracture scale. We propose an innovative method for predicting the fracture scale quantitatively using frequency-dependent shear-wave anisotropy. The quantitative relationship between different fracture scales and the frequency-dependent response of the shear-wave splitting (SWS) anisotropy can be obtained using a dynamic rock physics model. The frequency-dependent shear-wave anisotropy was calculated via SWS analysis in the frequency domain, after which this quantitative relationship and the calculated frequency-dependent response was used to establish an objective function for inversion of fracture scale at different depths using the least-squares algorithm. We synthesized data under ideal conditions, tested the proposed method, applied our method to field data, and found that the quantitative prediction method of the fracture scale yielded reasonable prediction results. The shear-wave anisotropy was calculated based on the SWS analysis from the horizontal components of the upgoing wavefields of the field vertical seismic profile. We compared the fracture scale calculated from logging data using the proposed method, and the results obtained indicated that this method can successfully predict the fracture scale quantitatively.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":"4 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136022739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yikang Zheng, Yibo Wang, Jien Zhang, Ya Xu, Liang Zhao
A dense 2D seismic reflection survey was conducted at the Bayan Obo REE (rare-earth-elements)-Nb-Fe deposit in China to explore its geological features and mineral endowment. A composite processing workflow was established for prestack depth imaging of this dataset, which involved static correction to compensate for shifts caused by lateral inhomogeneities in near-surface layers and rugged topography. Standard methods were used to attenuate different types of noise and improve the signal-to-noise ratio of the data. A velocity model in the depth domain was obtained from refraction tomography and velocity analysis, and then used in migration. The preliminary interpretation of the final image revealed the mapping of mineralization at its true position in depth, indicating a relatively deep extension (∼1.5 km). The results demonstrate the potential of seismic imaging as a complementary exploration tool to gravity and electromagnetic methods for mineral exploration purposes in the Bayan Obo mining area, providing a high-resolution seismic image and allowing for depth characterization of the mineral deposits. Additionally, the images provide important information for guiding the selection of drilling borehole locations for deep exploration.
{"title":"Reflection seismic imaging of subsurface geological structures in the Bayan Obo mining area, China","authors":"Yikang Zheng, Yibo Wang, Jien Zhang, Ya Xu, Liang Zhao","doi":"10.1190/geo2023-0232.1","DOIUrl":"https://doi.org/10.1190/geo2023-0232.1","url":null,"abstract":"A dense 2D seismic reflection survey was conducted at the Bayan Obo REE (rare-earth-elements)-Nb-Fe deposit in China to explore its geological features and mineral endowment. A composite processing workflow was established for prestack depth imaging of this dataset, which involved static correction to compensate for shifts caused by lateral inhomogeneities in near-surface layers and rugged topography. Standard methods were used to attenuate different types of noise and improve the signal-to-noise ratio of the data. A velocity model in the depth domain was obtained from refraction tomography and velocity analysis, and then used in migration. The preliminary interpretation of the final image revealed the mapping of mineralization at its true position in depth, indicating a relatively deep extension (∼1.5 km). The results demonstrate the potential of seismic imaging as a complementary exploration tool to gravity and electromagnetic methods for mineral exploration purposes in the Bayan Obo mining area, providing a high-resolution seismic image and allowing for depth characterization of the mineral deposits. Additionally, the images provide important information for guiding the selection of drilling borehole locations for deep exploration.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":"42 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136377002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Efemena D. Emmanuel, Lee D. Slater, Kennedy O. Doro
Recent induced polarization studies suggest that the real part of surface conductivity ( σ ′ surf ) scales linearly with the imaginary conductivity ( σ ″ = σ ″ surf ) or normalized chargeability (Mn) for a range of soil types. The coefficients of this relationship l and l_Mn ( l = σ ″ / σ ′ surf or l_Mn = Mn/ σ ′ surf ) allow the separation of the surface and electrolytic conductivities from the bulk conductivity. However, the dependence of these constants on varying soil physicochemical properties, including under unsaturated conditions, is yet to be assessed. Using estimates of σ ′ surf from 18 undisturbed soil samples from a restored wetland and σ ″ measured over a frequency range of 0.01 Hz to 10 kHz, we compare the σ ′ surf and σ ″ with the laboratory measurements of soil properties. We calculate l and l_Mn for each soil sample and regress them against the soil properties. We find an apparent dependence of l on soil texture, bulk density, organic matter, and moisture contents, with coefficients of determination ( R 2 ) ranging from 0.5 to 0.65 at low frequencies (e.g., 1 Hz) but not at high frequencies (e.g., 936 Hz). This dependence of l on soil texture results from the insensitivity of σ ″ at low frequency to σ ′ surf and, by implication, to the soil properties controlling σ ′ surf . In contrast, l_Mn indicates no correlation with the soil properties because Mn is linearly correlated with σ ′ surf and correlated with the soil properties controlling σ ′ surf . Our results call for caution on the application of σ ″ at a single frequency as a proxy of σ ′ surf because σ ″ is not necessarily correlated with σ ′ surf across all soil types. Although using l_Mn derived from multifrequency measurements overcome this limitation, field acquisition of spectral information (e.g., up to 1000 Hz) remains a challenge.
{"title":"Exploring limitations in the induced polarization versus surface conductivity relationship in the case of wetland soils","authors":"Efemena D. Emmanuel, Lee D. Slater, Kennedy O. Doro","doi":"10.1190/geo2023-0345.1","DOIUrl":"https://doi.org/10.1190/geo2023-0345.1","url":null,"abstract":"Recent induced polarization studies suggest that the real part of surface conductivity ( σ ′ surf ) scales linearly with the imaginary conductivity ( σ ″ = σ ″ surf ) or normalized chargeability (Mn) for a range of soil types. The coefficients of this relationship l and l_Mn ( l = σ ″ / σ ′ surf or l_Mn = Mn/ σ ′ surf ) allow the separation of the surface and electrolytic conductivities from the bulk conductivity. However, the dependence of these constants on varying soil physicochemical properties, including under unsaturated conditions, is yet to be assessed. Using estimates of σ ′ surf from 18 undisturbed soil samples from a restored wetland and σ ″ measured over a frequency range of 0.01 Hz to 10 kHz, we compare the σ ′ surf and σ ″ with the laboratory measurements of soil properties. We calculate l and l_Mn for each soil sample and regress them against the soil properties. We find an apparent dependence of l on soil texture, bulk density, organic matter, and moisture contents, with coefficients of determination ( R 2 ) ranging from 0.5 to 0.65 at low frequencies (e.g., 1 Hz) but not at high frequencies (e.g., 936 Hz). This dependence of l on soil texture results from the insensitivity of σ ″ at low frequency to σ ′ surf and, by implication, to the soil properties controlling σ ′ surf . In contrast, l_Mn indicates no correlation with the soil properties because Mn is linearly correlated with σ ′ surf and correlated with the soil properties controlling σ ′ surf . Our results call for caution on the application of σ ″ at a single frequency as a proxy of σ ′ surf because σ ″ is not necessarily correlated with σ ′ surf across all soil types. Although using l_Mn derived from multifrequency measurements overcome this limitation, field acquisition of spectral information (e.g., up to 1000 Hz) remains a challenge.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":"33 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134909172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Utilizing multiple geophysical methods has become a prevailing approach in numerous geophysical applications to investigate subsurface structures and parameters. These multimethod-based exploration strategies have the potential to greatly diminish uncertainties and ambiguities encountered during geophysical data analysis and interpretation. One of the applications is the cooperative inversion of airborne magnetic and gravity gradient data for the interpretation of data obtained in mineral, oil and gas, and geothermal explorations. In this paper, a unified cooperative inversion framework is designed by combining the standard separate inversions with a deep neural network (DNN), which serves as the link between different types of data. A well-trained DNN takes the separately inverted susceptibility and density models as the inputs and provides improved models that will be used as the initial models of deterministic inversions. A two-round iteration strategy is adopted to guarantee the reasonability of the recovered models and overall efficiency of the inversion. In addition, this deep learning (DL)-based framework demonstrates excellent generalization abilities when tested on models that are entirely distinct from the training data sets. The framework can easily incorporate multi-physics without necessitating any structural changes to the network. Synthetic experiments validate that our DL-based method outperforms conventional separate inversions and cross-gradient-based joint inversion in view of the accuracy of the recovered models and inversion efficiency. Successful application to field data further verifies the effectiveness of our DL-based method.
{"title":"3D cooperative inversion of airborne magnetic and gravity gradient data using deep learning techniques","authors":"Yanyan Hu, Xiaolong Wei, Xuqing Wu, Jiajia Sun, Yueqin Huang, Jiefu Chen","doi":"10.1190/geo2023-0225.1","DOIUrl":"https://doi.org/10.1190/geo2023-0225.1","url":null,"abstract":"Utilizing multiple geophysical methods has become a prevailing approach in numerous geophysical applications to investigate subsurface structures and parameters. These multimethod-based exploration strategies have the potential to greatly diminish uncertainties and ambiguities encountered during geophysical data analysis and interpretation. One of the applications is the cooperative inversion of airborne magnetic and gravity gradient data for the interpretation of data obtained in mineral, oil and gas, and geothermal explorations. In this paper, a unified cooperative inversion framework is designed by combining the standard separate inversions with a deep neural network (DNN), which serves as the link between different types of data. A well-trained DNN takes the separately inverted susceptibility and density models as the inputs and provides improved models that will be used as the initial models of deterministic inversions. A two-round iteration strategy is adopted to guarantee the reasonability of the recovered models and overall efficiency of the inversion. In addition, this deep learning (DL)-based framework demonstrates excellent generalization abilities when tested on models that are entirely distinct from the training data sets. The framework can easily incorporate multi-physics without necessitating any structural changes to the network. Synthetic experiments validate that our DL-based method outperforms conventional separate inversions and cross-gradient-based joint inversion in view of the accuracy of the recovered models and inversion efficiency. Successful application to field data further verifies the effectiveness of our DL-based method.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135316130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yang Gao, Hao Li, Guofa Li, Pengpeng Wei, Huiqing Zhang
Seismic impedance inversion can obtain subsurface physical properties and plays an important role in hydrocarbon and mineral exploration. Due to the inaccurate and insufficient seismic data, the inverse problem is ill-posed as characterized by unreliability and non-uniqueness of solutions. Regularization techniques relying on certain prior information are often introduced to force the inverse problem to obtain stable results with predetermined characteristics. However, for complex geologic conditions, these methods are usually difficult to achieve satisfactory accuracy and resolution. We propose a deep-learning-based multichannel impedance inversion method, which flexibly incorporates prior information by training with numerous realistic structural 2D impedance models on the basis of features of field data. Our deep learning framework is supplemented by the attention mechanism and residual block to automatically learn more features and details from training data. We also introduce a new hybrid loss function that combines the ℓ 1 loss and Multi-scale Structural Similarity (MS-SSIM) loss to better enable the network to learn structural features. Synthetic and field examples demonstrate that the proposed method can effectively produce inversion results with high resolution, good lateral continuity, and enhanced structural features compared with traditional methods.
{"title":"Deep learning for high-resolution multichannel seismic impedance inversion","authors":"Yang Gao, Hao Li, Guofa Li, Pengpeng Wei, Huiqing Zhang","doi":"10.1190/geo2023-0096.1","DOIUrl":"https://doi.org/10.1190/geo2023-0096.1","url":null,"abstract":"Seismic impedance inversion can obtain subsurface physical properties and plays an important role in hydrocarbon and mineral exploration. Due to the inaccurate and insufficient seismic data, the inverse problem is ill-posed as characterized by unreliability and non-uniqueness of solutions. Regularization techniques relying on certain prior information are often introduced to force the inverse problem to obtain stable results with predetermined characteristics. However, for complex geologic conditions, these methods are usually difficult to achieve satisfactory accuracy and resolution. We propose a deep-learning-based multichannel impedance inversion method, which flexibly incorporates prior information by training with numerous realistic structural 2D impedance models on the basis of features of field data. Our deep learning framework is supplemented by the attention mechanism and residual block to automatically learn more features and details from training data. We also introduce a new hybrid loss function that combines the ℓ 1 loss and Multi-scale Structural Similarity (MS-SSIM) loss to better enable the network to learn structural features. Synthetic and field examples demonstrate that the proposed method can effectively produce inversion results with high resolution, good lateral continuity, and enhanced structural features compared with traditional methods.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":"31 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135267855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Maxime Gautier, Stéphanie Gautier, Rodolphe Cattin
Electrical Resistivity Tomography (ERT) is a widely used geophysical method for studying geological hazards, civil engineering, and environmental remediation. It provides information about the subsurfaces resistivity distribution by analyzing electrical data collected at the surface or in boreholes. However, interpreting ERT images can be complex due to the ambiguities in their resolution. To address this issue, we propose a post-processing method called PyMERRY (for Python iMprovement of Electrical Resistivity tomography ReliabilitY) to improve the reliability of ERT images. The PyMERRY code can be applied to any 2D resistivity model obtained from ERT inversion software. It computes a coverage mask that defines the domain well-constrained by both the data and the inversion process. It also evaluates the resistivity uncertainties in the ERT models. In addition to the sensitivity approaches, PyMERRY provides low and high resistivity values for all covered cells. Synthetic tests show that the approach is efficient and highlight the importance of resistivity contrasts, mesh selection, electrode spacing, and profile length in the reliability of ERT images. Compared to previous studies, using PyMERRY in south-central Bhutan allows a more accurate interpretation of ERT images. It confirms a high resistivity contrast across the Topographic Frontal Thrust and reveals the existence of small-scale variations.
电阻率层析成像(ERT)是一种广泛应用于地质灾害研究、土木工程和环境修复的地球物理方法。它通过分析在地面或钻孔中收集的电数据，提供有关地下电阻率分布的信息。然而，由于其分辨率的模糊性，解释ERT图像可能是复杂的。为了解决这个问题，我们提出了一种称为PyMERRY (Python iMprovement of Electrical电阻率层析成像可靠性)的后处理方法来提高ERT图像的可靠性。PyMERRY代码可以应用于ERT反演软件获得的任何二维电阻率模型。它计算一个覆盖掩码，该掩码定义了受数据和反演过程约束的域。并对ERT模型中电阻率的不确定性进行了评价。除了灵敏度方法外，PyMERRY还为所有覆盖的电池提供低电阻率和高电阻率值。综合测试表明，该方法是有效的，并突出了电阻率对比、网格选择、电极间距和剖面长度对ERT图像可靠性的重要性。与以前的研究相比，在不丹中南部使用PyMERRY可以更准确地解释ERT图像。它证实了地形前缘逆冲的高电阻率对比，并揭示了小尺度变化的存在。
{"title":"PyMERRY: a Python solution for improved interpretation of electrical resistivity tomography images","authors":"Maxime Gautier, Stéphanie Gautier, Rodolphe Cattin","doi":"10.1190/geo2023-0105.1","DOIUrl":"https://doi.org/10.1190/geo2023-0105.1","url":null,"abstract":"Electrical Resistivity Tomography (ERT) is a widely used geophysical method for studying geological hazards, civil engineering, and environmental remediation. It provides information about the subsurfaces resistivity distribution by analyzing electrical data collected at the surface or in boreholes. However, interpreting ERT images can be complex due to the ambiguities in their resolution. To address this issue, we propose a post-processing method called PyMERRY (for Python iMprovement of Electrical Resistivity tomography ReliabilitY) to improve the reliability of ERT images. The PyMERRY code can be applied to any 2D resistivity model obtained from ERT inversion software. It computes a coverage mask that defines the domain well-constrained by both the data and the inversion process. It also evaluates the resistivity uncertainties in the ERT models. In addition to the sensitivity approaches, PyMERRY provides low and high resistivity values for all covered cells. Synthetic tests show that the approach is efficient and highlight the importance of resistivity contrasts, mesh selection, electrode spacing, and profile length in the reliability of ERT images. Compared to previous studies, using PyMERRY in south-central Bhutan allows a more accurate interpretation of ERT images. It confirms a high resistivity contrast across the Topographic Frontal Thrust and reveals the existence of small-scale variations.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":"10 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135268075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Although it is possible to image important geological structures by assuming that the Earth's subsurface is a homogeneous and isotropic medium, there is inevitably a loss of information, especially in more complex geological media. Therefore, it is needed to include anisotropy in seismic imaging, particularly the most common in geophysics: the transversely isotropic medium. However, this also means a considerable increase in the computational cost of the reverse time migration (RTM). Thus, a new pseudo-acoustic wave equation for pure qP-wave in tilted transversely isotropic (TTI) media, which can also be efficiently implemented using the finite difference (FD) method with the unit vector method (UVM), is proposed, aiming to reduce the computational cost of the RTM. The proposed equation solved with fast Fourier transform is shown to be exact and faster for seismic migration than other equations found in the literature, but a greater efficiency is achievable by using FD to compute the second derivatives. Conversely, when solved with UVM, it is shown to be faster and kinematically accurate, whereas its dynamics are not accurately represented, as it is an acoustic approximation. Nevertheless, this new equation is tested on synthetic data, and its efficacy is demonstrated by modeling and migrating TTI data found in the literature.
{"title":"Simplified TTI pure qP-wave equation implemented in the space domain and applied for reverse time migration in tilted transversely isotropic media#xD;","authors":"Lucas S. Bitencourt, Reynam C. Pestana","doi":"10.1190/geo2022-0686.1","DOIUrl":"https://doi.org/10.1190/geo2022-0686.1","url":null,"abstract":"Although it is possible to image important geological structures by assuming that the Earth's subsurface is a homogeneous and isotropic medium, there is inevitably a loss of information, especially in more complex geological media. Therefore, it is needed to include anisotropy in seismic imaging, particularly the most common in geophysics: the transversely isotropic medium. However, this also means a considerable increase in the computational cost of the reverse time migration (RTM). Thus, a new pseudo-acoustic wave equation for pure qP-wave in tilted transversely isotropic (TTI) media, which can also be efficiently implemented using the finite difference (FD) method with the unit vector method (UVM), is proposed, aiming to reduce the computational cost of the RTM. The proposed equation solved with fast Fourier transform is shown to be exact and faster for seismic migration than other equations found in the literature, but a greater efficiency is achievable by using FD to compute the second derivatives. Conversely, when solved with UVM, it is shown to be faster and kinematically accurate, whereas its dynamics are not accurately represented, as it is an acoustic approximation. Nevertheless, this new equation is tested on synthetic data, and its efficacy is demonstrated by modeling and migrating TTI data found in the literature.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135268077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lei Lin, Hong Huang, Pengyun Zhang, Weichao Yan, Hao Wei, Hang Liu, Zhi Zhong
The properties of borehole formations, such as porosity, permeability, and water saturation, play a crucial role in characterizing and evaluating subsurface reservoirs. Although core sample experiments offer precise measurements, they are time-consuming and cost-intensive. An alternative method is to use logging data to construct an empirical model that predicts formation properties, which is widely studied due to its speed and affordability. Nevertheless, as the response of a logging point reflects its surrounding formation, conventional logging methods relying on point-to-point mapping perform poorly in complex reservoirs. Furthermore, the resolution of conventional logging is lower compared to imaging logging. To address these limitations, this study presents a novel approach to predicting formation properties based on a deep learning framework using heterogeneous well logging data. The proposed neural network framework takes short sequences of conventional logging data and windowed imaging logging data as inputs. The neural network applies 1-dimensional convolution to extract features from the conventional logging sequences and 2-dimensional convolution to extract features from the resistivity imaging data. Then these two feature vectors are fused and fed into a multi-layer fully connected neural network to predict formation properties. A case study of a carbonate reservoir demonstrates the proposed method delivers more accurate predictions of formation porosity, permeability, and water saturation than the point-to-point, sequence-to-point, and image-to-point prediction methods. Moreover, it is expected that the proposed paradigm will serve as a source of inspiration for forthcoming research endeavors aimed at enhancing the accuracy of predicting borehole formation properties in complex reservoirs.
{"title":"A deep learning framework for borehole formation properties prediction using heterogeneous well logging data: A case study of a carbonate reservoir in the Gaoshiti-Moxi area, Sichuan Basin, China","authors":"Lei Lin, Hong Huang, Pengyun Zhang, Weichao Yan, Hao Wei, Hang Liu, Zhi Zhong","doi":"10.1190/geo2023-0151.1","DOIUrl":"https://doi.org/10.1190/geo2023-0151.1","url":null,"abstract":"The properties of borehole formations, such as porosity, permeability, and water saturation, play a crucial role in characterizing and evaluating subsurface reservoirs. Although core sample experiments offer precise measurements, they are time-consuming and cost-intensive. An alternative method is to use logging data to construct an empirical model that predicts formation properties, which is widely studied due to its speed and affordability. Nevertheless, as the response of a logging point reflects its surrounding formation, conventional logging methods relying on point-to-point mapping perform poorly in complex reservoirs. Furthermore, the resolution of conventional logging is lower compared to imaging logging. To address these limitations, this study presents a novel approach to predicting formation properties based on a deep learning framework using heterogeneous well logging data. The proposed neural network framework takes short sequences of conventional logging data and windowed imaging logging data as inputs. The neural network applies 1-dimensional convolution to extract features from the conventional logging sequences and 2-dimensional convolution to extract features from the resistivity imaging data. Then these two feature vectors are fused and fed into a multi-layer fully connected neural network to predict formation properties. A case study of a carbonate reservoir demonstrates the proposed method delivers more accurate predictions of formation porosity, permeability, and water saturation than the point-to-point, sequence-to-point, and image-to-point prediction methods. Moreover, it is expected that the proposed paradigm will serve as a source of inspiration for forthcoming research endeavors aimed at enhancing the accuracy of predicting borehole formation properties in complex reservoirs.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135887905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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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