Guoqiang Xue, Pengfei Lv, Weiying Chen, Xiaochun Li, Ya Xu, Xin Wu, Jian Wang, Yonggang Zhao, Xianhua Li
Bayan Obo is the largest rare earth element (REE) deposit in the world. The occurrence of REE is closely related to the dolomite in this area. Dolomite serves both as the mother rock of REE mineralization and the ore body. How to accurately locate and characterize dolomite is the key to determine the distribution of REE and estimate its reserves. A large amount of geophysical work has been conducted in this area, including a dense seismic array, various electromagnetic methods, gravity and aeromagnetic surveys, as well as numerous petrophysical property measurements. To fully leverage the results obtained by these geophysical methods and develop and understanding of the physical property structure, a multi-source geophysical data fusion technology was proposed. First, various physical property profiles obtained from inversion on the same profile are converted into images with identical resolution and dimension. Then, an image adaptive feature extraction technique based on transfer learning is used to extract features of different scales from multi-source images. Subsequently, the fusion image is reconstructed based on the local nearest neighbor weighted average feature fusion rule to obtain the final fusion result. This aids in identifying the spatial appearance pattern of the target for detection. Given the physical characteristics of the mineralized dolomite, which has high density, high resistivity and high magnetic susceptibility, its location and shape can be defined in the fusion image. The results indicate that the occurrence depth of dolomite can extend up to 1500 meters, and the dolomite has a southward tilt as one of its primary structural characteristics. The predicted range of dolomite distribution is consistent with the formation range revealed by drilling, making it a reliable basis for predicting the distribution of rare earth ore bodies.
{"title":"Determining the location of the Bayan Obo REE mineralization body by the transfer learning method","authors":"Guoqiang Xue, Pengfei Lv, Weiying Chen, Xiaochun Li, Ya Xu, Xin Wu, Jian Wang, Yonggang Zhao, Xianhua Li","doi":"10.1190/geo2023-0212.1","DOIUrl":"https://doi.org/10.1190/geo2023-0212.1","url":null,"abstract":"Bayan Obo is the largest rare earth element (REE) deposit in the world. The occurrence of REE is closely related to the dolomite in this area. Dolomite serves both as the mother rock of REE mineralization and the ore body. How to accurately locate and characterize dolomite is the key to determine the distribution of REE and estimate its reserves. A large amount of geophysical work has been conducted in this area, including a dense seismic array, various electromagnetic methods, gravity and aeromagnetic surveys, as well as numerous petrophysical property measurements. To fully leverage the results obtained by these geophysical methods and develop and understanding of the physical property structure, a multi-source geophysical data fusion technology was proposed. First, various physical property profiles obtained from inversion on the same profile are converted into images with identical resolution and dimension. Then, an image adaptive feature extraction technique based on transfer learning is used to extract features of different scales from multi-source images. Subsequently, the fusion image is reconstructed based on the local nearest neighbor weighted average feature fusion rule to obtain the final fusion result. This aids in identifying the spatial appearance pattern of the target for detection. Given the physical characteristics of the mineralized dolomite, which has high density, high resistivity and high magnetic susceptibility, its location and shape can be defined in the fusion image. The results indicate that the occurrence depth of dolomite can extend up to 1500 meters, and the dolomite has a southward tilt as one of its primary structural characteristics. The predicted range of dolomite distribution is consistent with the formation range revealed by drilling, making it a reliable basis for predicting the distribution of rare earth ore bodies.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135800938","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}
Attenuation always exists when seismic waves propagate in underground anelastic media, especially in hydrocarbon-bearing reservoirs. Quality factor Q or attenuation factor 1/ Q can be used to quantify the seismic wave attenuation and has become an important hydrocarbon indicator. The relationship between the plane-wave reflection coefficient ( R plane ) in anelastic media and P- and S-wave quality factors has been widely used in the plane-wave seismic inversion to estimate the quality factors. The R plane provides an adequate approximation for the deeper subsurface. However, for the shallow subsurface and anelastic wavefields excited by point sources, the R plane is inaccurate and its meaning involves some fundamental difficulties. In view of this, a Q-dependent P-P spherical-wave reflection coefficient ( R sph ) in anelastic media is used here. Considering that having too many parameters to be inverted will lead to unstable and inaccurate inversion results, we further derive an approximate anelastic R sph and anelastic spherical-wave impedance ( Z sph ), which are frequency dependent and are the functions of P- and S-wave velocities, density, and P-wave minimum quality factor ( Q pm ). Finally, a complex spherical-wave seismic inversion approach in anelastic media for the P-wave minimum quality factor is developed. Using the Bayesian inversion approach and complex convolution model, we first estimate the multilayer Z sph from the complex seismic traces with different frequencies and incidence angles. Based on the inverted angle- and frequency-dependent Z sph , the P- and S-wave velocities, density, and P-wave minimum quality factor are further estimated using a nonlinear inversion tool. Synthetic examples verify the feasibility and robustness of the complex spherical-wave seismic inversion approach in anelastic media. In the shallow subsurface, the spherical-wave inversion is superior to plane-wave inversion. A field example further demonstrates the accuracy and great potential of our approach in hydrocarbon-bearing reservoir prediction.
{"title":"Complex Spherical-wave Seismic Inversion in Anelastic Media for the P-wave Minimum Quality Factor","authors":"Guangsen Cheng, Chuanlin He, Zhaoyun Zong, Zhanyuan Liang, Xingyao Yin, Xiaoyu Zhang","doi":"10.1190/geo2023-0102.1","DOIUrl":"https://doi.org/10.1190/geo2023-0102.1","url":null,"abstract":"Attenuation always exists when seismic waves propagate in underground anelastic media, especially in hydrocarbon-bearing reservoirs. Quality factor Q or attenuation factor 1/ Q can be used to quantify the seismic wave attenuation and has become an important hydrocarbon indicator. The relationship between the plane-wave reflection coefficient ( R plane ) in anelastic media and P- and S-wave quality factors has been widely used in the plane-wave seismic inversion to estimate the quality factors. The R plane provides an adequate approximation for the deeper subsurface. However, for the shallow subsurface and anelastic wavefields excited by point sources, the R plane is inaccurate and its meaning involves some fundamental difficulties. In view of this, a Q-dependent P-P spherical-wave reflection coefficient ( R sph ) in anelastic media is used here. Considering that having too many parameters to be inverted will lead to unstable and inaccurate inversion results, we further derive an approximate anelastic R sph and anelastic spherical-wave impedance ( Z sph ), which are frequency dependent and are the functions of P- and S-wave velocities, density, and P-wave minimum quality factor ( Q pm ). Finally, a complex spherical-wave seismic inversion approach in anelastic media for the P-wave minimum quality factor is developed. Using the Bayesian inversion approach and complex convolution model, we first estimate the multilayer Z sph from the complex seismic traces with different frequencies and incidence angles. Based on the inverted angle- and frequency-dependent Z sph , the P- and S-wave velocities, density, and P-wave minimum quality factor are further estimated using a nonlinear inversion tool. Synthetic examples verify the feasibility and robustness of the complex spherical-wave seismic inversion approach in anelastic media. In the shallow subsurface, the spherical-wave inversion is superior to plane-wave inversion. A field example further demonstrates the accuracy and great potential of our approach in hydrocarbon-bearing reservoir prediction.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135766030","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}
Tuning effects, induced by the interference between scattering waves at the top and bottom interfaces, characterize the dependence of thin-layer seismic responses on wave frequencies, thin-layer thicknesses, and medium elastic properties. The characteristics of tuning effects are frequently used to infer thin-layer properties. We analyze the tuning effects of a thin triclinic layer between two varying triclinic half-spaces. Exact thin-layer reflection and transmission (R/T) coefficients are developed to characterize the pre-stack thin-layer tuning effects of P-, S1-, and S2-waves. The thin-layer R/T coefficient approximations are proposed to build concise relationships between tuning effect characteristics and medium parameters. The relationships give insights when estimating thin-layer properties from interpreting tuning effect characteristics. As inferred from the approximations, the tuning effect of a thin triclinic layer is composed of two fundamental tuning effects making sense for two particular thin-layer models of which one has identical enclosing half-spaces and the other has identical elastic parameter discontinuities at the bottom and top interfaces. The combined influences of wave frequencies, thin-layer thicknesses, and incidence angles on the two fundamental tuning effects can be assessed by a unique factor for each wave mode. For a general thin triclinic layer, this factor characterizes the periodic variations of reflection amplitudes versus wave frequencies. The maximum and minimum thin-layer reflection amplitudes are determined by the reflectivities at the top and bottom interfaces. With wave frequencies or thin-layer thicknesses increasing from zero, thin-layer reflections have smaller or larger amplitudes when the two single-interface reflectivities have equal or opposite polarities, respectively. We develop a method to evaluate the sensitivity of thin-layer reflection amplitudes to thin-layer elastic parameters. The sensitivity is higher when the two single-interface reflectivities have opposite polarities compared to the equal-polarity case. Numerical tests are used to demonstrate the proposed approximation accuracy and the characteristics of tuning effects.
{"title":"Tuning effects of a thin layer with triclinic anisotropy","authors":"Song Jin, Xiangyun Hu, Xuelei Li, Alexey Stovas","doi":"10.1190/geo2022-0551.1","DOIUrl":"https://doi.org/10.1190/geo2022-0551.1","url":null,"abstract":"Tuning effects, induced by the interference between scattering waves at the top and bottom interfaces, characterize the dependence of thin-layer seismic responses on wave frequencies, thin-layer thicknesses, and medium elastic properties. The characteristics of tuning effects are frequently used to infer thin-layer properties. We analyze the tuning effects of a thin triclinic layer between two varying triclinic half-spaces. Exact thin-layer reflection and transmission (R/T) coefficients are developed to characterize the pre-stack thin-layer tuning effects of P-, S1-, and S2-waves. The thin-layer R/T coefficient approximations are proposed to build concise relationships between tuning effect characteristics and medium parameters. The relationships give insights when estimating thin-layer properties from interpreting tuning effect characteristics. As inferred from the approximations, the tuning effect of a thin triclinic layer is composed of two fundamental tuning effects making sense for two particular thin-layer models of which one has identical enclosing half-spaces and the other has identical elastic parameter discontinuities at the bottom and top interfaces. The combined influences of wave frequencies, thin-layer thicknesses, and incidence angles on the two fundamental tuning effects can be assessed by a unique factor for each wave mode. For a general thin triclinic layer, this factor characterizes the periodic variations of reflection amplitudes versus wave frequencies. The maximum and minimum thin-layer reflection amplitudes are determined by the reflectivities at the top and bottom interfaces. With wave frequencies or thin-layer thicknesses increasing from zero, thin-layer reflections have smaller or larger amplitudes when the two single-interface reflectivities have equal or opposite polarities, respectively. We develop a method to evaluate the sensitivity of thin-layer reflection amplitudes to thin-layer elastic parameters. The sensitivity is higher when the two single-interface reflectivities have opposite polarities compared to the equal-polarity case. Numerical tests are used to demonstrate the proposed approximation accuracy and the characteristics of tuning effects.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135765916","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}
Southwestern Hokkaido, Japan, is characterized by numerous Quaternary volcanoes and geothermal areas. At the same time, the region hosts various critical infrastructures, and there is a need to assess the impact of volcanic hazards on them. Geophysics could provide scientific clues for the hazard assessment by elucidating the abundance of subsurface magma. To clarify the resistivity structure from the crust to uppermost mantle of the Toya caldera, a representative Quaternary volcanic area, a wideband magnetotellurics survey of 117 points over land, sea, and lake areas, as well as 3D inversion was conducted. In combination with petrological and seismological findings, quantitative interpretation of the inverted model found that conductive bodies in the uppermost mantle (14–68 Ωm) suggest the presence of melts (0.25 vol%–3.4 vol%) or fluids (0.068 vol%–0.45 vol%). An extremely conductive body (<10 Ωm) at a depth of approximately 3–14 km in the eastern geothermal area could be interpreted as a hydrothermal reservoir; below this body, the conductive column (1.8–15 Ωm), rising from the uppermost mantle, suggested fluid upwelling. In contrast, high resistivity (>100 Ωm) beneath Usu Volcano, the center of active volcanism, suggested that no mobile magma was present. A columnar-shaped region of slightly low resistivity (44 Ωm at minimum) was observed below the Toya caldera, which was inferred as cooling magma, or an altered or heated upper crust attributed to past magma intrusion. A resistivity structure observed below the volcanic edifice was considered to reflect the steady state of the dormant volcanic system in this area, and there was likely no large amount of melt that would be deemed imminent for a caldera-forming eruption. This information could be a valuable scientific contribution to the volcanic hazard risk assessments currently being conducted in Japan.
{"title":"THREE-DIMENSIONAL RESISTIVITY STRUCTURE IN TOYA CALDERA REGION, SOUTHWEST HOKKAIDO, JAPAN - CONSTRAINTS ON MAGMATIC AND GEOTHERMAL ACTIVITIES","authors":"Shogo Komori, Shinichi Takakura, Yuji Mitsuhata, Toshiyuki Yokota, Toshihiro Uchida, Masahiko Makino, Yosuke Kato, Kazuya Yamamoto","doi":"10.1190/geo2022-0558.1","DOIUrl":"https://doi.org/10.1190/geo2022-0558.1","url":null,"abstract":"Southwestern Hokkaido, Japan, is characterized by numerous Quaternary volcanoes and geothermal areas. At the same time, the region hosts various critical infrastructures, and there is a need to assess the impact of volcanic hazards on them. Geophysics could provide scientific clues for the hazard assessment by elucidating the abundance of subsurface magma. To clarify the resistivity structure from the crust to uppermost mantle of the Toya caldera, a representative Quaternary volcanic area, a wideband magnetotellurics survey of 117 points over land, sea, and lake areas, as well as 3D inversion was conducted. In combination with petrological and seismological findings, quantitative interpretation of the inverted model found that conductive bodies in the uppermost mantle (14–68 Ωm) suggest the presence of melts (0.25 vol%–3.4 vol%) or fluids (0.068 vol%–0.45 vol%). An extremely conductive body (<10 Ωm) at a depth of approximately 3–14 km in the eastern geothermal area could be interpreted as a hydrothermal reservoir; below this body, the conductive column (1.8–15 Ωm), rising from the uppermost mantle, suggested fluid upwelling. In contrast, high resistivity (>100 Ωm) beneath Usu Volcano, the center of active volcanism, suggested that no mobile magma was present. A columnar-shaped region of slightly low resistivity (44 Ωm at minimum) was observed below the Toya caldera, which was inferred as cooling magma, or an altered or heated upper crust attributed to past magma intrusion. A resistivity structure observed below the volcanic edifice was considered to reflect the steady state of the dormant volcanic system in this area, and there was likely no large amount of melt that would be deemed imminent for a caldera-forming eruption. This information could be a valuable scientific contribution to the volcanic hazard risk assessments currently being conducted in Japan.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135801189","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}
Prestack time migration (PSTM), a commonly used tool for seismic imaging, has been widely applied in 3D seismic data processing. However, the conventional PSTM algorithms use only one effective velocity parameter (i.e., rms velocity) for each imaging point, which may not be accurate when stronger lateral variations occur in seismic velocities. In this paper, we introduce a new parameter called the velocity variation factor that considers velocity variations in inhomogeneous media to improve PSTM. This new parameter, together with the rms velocity, describes the propagation Green function at an imaging point with two effective parameters rather than one effective parameter as in conventional PSTMs. This provides a more accurate traveltime calculation for the wave propagating through media with moderate lateral velocity variation. Unlike the conventional bending-ray PSTM, the additional effective parameter is fully independent of the rms velocities. We estimate the two effective parameters at each imaging point by flattening the neighboring image gathers with a global optimization algorithm. The objective function is built at each imaging point using a selective cross-correlation based time shift, which can quantitatively describe the slight bending of events in the local migrated gathers regardless of the quality of the gathers. We estimate the two effective parameters using the very fast simulated annealing (VFSA) algorithm and multiscale approach, thus avoiding the local minimum caused by the noises in the migrated gathers. We apply the proposed two-parameter PSTM to a real 3D land dataset to demonstrate its industrial applicability. A comparison of the new imaging result with the conventional prestack depth migration (PSDM) is also presented.
{"title":"Improving prestack time migration by introducing a new velocity-related parameter: Parameter picking and 3D real data application","authors":"Xu Jincheng, Jianfeng Zhang","doi":"10.1190/geo2023-0319.1","DOIUrl":"https://doi.org/10.1190/geo2023-0319.1","url":null,"abstract":"Prestack time migration (PSTM), a commonly used tool for seismic imaging, has been widely applied in 3D seismic data processing. However, the conventional PSTM algorithms use only one effective velocity parameter (i.e., rms velocity) for each imaging point, which may not be accurate when stronger lateral variations occur in seismic velocities. In this paper, we introduce a new parameter called the velocity variation factor that considers velocity variations in inhomogeneous media to improve PSTM. This new parameter, together with the rms velocity, describes the propagation Green function at an imaging point with two effective parameters rather than one effective parameter as in conventional PSTMs. This provides a more accurate traveltime calculation for the wave propagating through media with moderate lateral velocity variation. Unlike the conventional bending-ray PSTM, the additional effective parameter is fully independent of the rms velocities. We estimate the two effective parameters at each imaging point by flattening the neighboring image gathers with a global optimization algorithm. The objective function is built at each imaging point using a selective cross-correlation based time shift, which can quantitatively describe the slight bending of events in the local migrated gathers regardless of the quality of the gathers. We estimate the two effective parameters using the very fast simulated annealing (VFSA) algorithm and multiscale approach, thus avoiding the local minimum caused by the noises in the migrated gathers. We apply the proposed two-parameter PSTM to a real 3D land dataset to demonstrate its industrial applicability. A comparison of the new imaging result with the conventional prestack depth migration (PSDM) is also presented.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135767337","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}
Shear wave velocity (Vs) is a vital parameter for various petrophysical, geophysical, and geomechanical applications in subsurface characterization. However, obtaining shear sonic log is often challenging since it often costs extra budget and time to acquire. Conventional methods for predicting Vs often rely on empirical relationships and rock physics models. However, these models often fall short in accuracy due to their inability to account for the complex nonlinear factors affecting the relationship between Vs and other parameters. We propose a physics-guided machine learning approach to predict shear sonic log using the various physical parameters (e.g. natural gamma ray, P-wave velocity, density, resistivity) that can be routinely obtained from standard logging suites. Three types of rock physical constraints including the mudrock line, empirical P- and S- wave velocity relationship and multi-parameter regression from the logging data, are combined with three physical guidance strategies including constructing physics-guided pseudo labels, physics-guided loss function and transfer learning, to blind test four wells based on one training well in a clastic reservoir. Compared to pure supervised ML, all the model that incorporates physical constraints significantly improves prediction accuracy and generalization performance, demonstrating the importance of incorporating first-order physical laws into data-driven network training. The multi-parameter regression relationship combined with the strategy of constructing physics-guided pseudo labels gives the best prediction performance, with the average root mean square error (RMSE) of the blind test dropping by 47%.
{"title":"Rock Physics guided machine learning for shear sonic log prediction","authors":"Luanxiao Zhao, Jingyu Liu, Minghui Xu, Zhenyu Zhu, Yuanyuan Chen, Jianhua Geng","doi":"10.1190/geo2023-0152.1","DOIUrl":"https://doi.org/10.1190/geo2023-0152.1","url":null,"abstract":"Shear wave velocity (Vs) is a vital parameter for various petrophysical, geophysical, and geomechanical applications in subsurface characterization. However, obtaining shear sonic log is often challenging since it often costs extra budget and time to acquire. Conventional methods for predicting Vs often rely on empirical relationships and rock physics models. However, these models often fall short in accuracy due to their inability to account for the complex nonlinear factors affecting the relationship between Vs and other parameters. We propose a physics-guided machine learning approach to predict shear sonic log using the various physical parameters (e.g. natural gamma ray, P-wave velocity, density, resistivity) that can be routinely obtained from standard logging suites. Three types of rock physical constraints including the mudrock line, empirical P- and S- wave velocity relationship and multi-parameter regression from the logging data, are combined with three physical guidance strategies including constructing physics-guided pseudo labels, physics-guided loss function and transfer learning, to blind test four wells based on one training well in a clastic reservoir. Compared to pure supervised ML, all the model that incorporates physical constraints significantly improves prediction accuracy and generalization performance, demonstrating the importance of incorporating first-order physical laws into data-driven network training. The multi-parameter regression relationship combined with the strategy of constructing physics-guided pseudo labels gives the best prediction performance, with the average root mean square error (RMSE) of the blind test dropping by 47%.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136210413","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}
Jinji Li, Scott D. Keating, Kristopher A. Innanen, Roman Shor, Nasser Kazemi
Full-waveform inversion (FWI), as an optimization-based approach to estimating subsurface models, is limited by incomplete acquisition and illumination of the subsurface. The incorporation of additional data from new and independent raypaths should be expected to result in significant increase in the accuracy of FWI models. In principle, seismic-while-drilling (SWD) technology can supply these additional raypaths; however, it introduces a new suite of unknowns, namely precise source locations (i.e., drilling path), source signature, and radiation characteristics. A new FWI algorithm is formulated in which the source radiation patterns and positions join the velocity and density values of the grid cells as unknowns to be determined. Several numerical inversion experiments are then conducted with different source settings using a synthetic model. The SWD sources are supplemented by explosive sources and multicomponent receivers at the surface, simulating a conventional surface acquisition geometry. The subsurface model and SWD source properties are recovered and analyzed. The analysis is suggestive that SWD involvement can enhance the accuracy of FWI models, with varying degrees of enhancement depending on factors such as trajectory inclination, source density, and drill path extension. The impact of SWD-FWI over standard FWI is reduced when low-frequency data are missing, but improvements over the models constructed with no subsurface sources remain. This formulation permits general source information, such as position and moment tensor components, to be independently obtained. This inversion scheme may lead to a range of potential applications for which medium properties and source information are required.
{"title":"Simultaneous waveform inversion of seismic-while-drilling data for P-wave velocity, density, and source parameters","authors":"Jinji Li, Scott D. Keating, Kristopher A. Innanen, Roman Shor, Nasser Kazemi","doi":"10.1190/geo2023-0101.1","DOIUrl":"https://doi.org/10.1190/geo2023-0101.1","url":null,"abstract":"Full-waveform inversion (FWI), as an optimization-based approach to estimating subsurface models, is limited by incomplete acquisition and illumination of the subsurface. The incorporation of additional data from new and independent raypaths should be expected to result in significant increase in the accuracy of FWI models. In principle, seismic-while-drilling (SWD) technology can supply these additional raypaths; however, it introduces a new suite of unknowns, namely precise source locations (i.e., drilling path), source signature, and radiation characteristics. A new FWI algorithm is formulated in which the source radiation patterns and positions join the velocity and density values of the grid cells as unknowns to be determined. Several numerical inversion experiments are then conducted with different source settings using a synthetic model. The SWD sources are supplemented by explosive sources and multicomponent receivers at the surface, simulating a conventional surface acquisition geometry. The subsurface model and SWD source properties are recovered and analyzed. The analysis is suggestive that SWD involvement can enhance the accuracy of FWI models, with varying degrees of enhancement depending on factors such as trajectory inclination, source density, and drill path extension. The impact of SWD-FWI over standard FWI is reduced when low-frequency data are missing, but improvements over the models constructed with no subsurface sources remain. This formulation permits general source information, such as position and moment tensor components, to be independently obtained. This inversion scheme may lead to a range of potential applications for which medium properties and source information are required.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136254879","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}
Seismic data interpolation plays a crucial role in obtaining dense and regularly sampled data, contributing to improve the quality of seismic data in seismic exploration. Sparsity-promoting methods utilize a two-step iteration to gradually recover missing traces, by exploiting the sparsity representation of seismic data in transform domains, such as Fourier, wavelet, and curvelet transform, within the framework of the projection onto convex sets (POCS). In the first step, the missing traces are restored by applying the thresholding shrinkage to the transform coefficients. In the second step, the observed data is inserted into the updated result. However, this method relies on a preselected transform and lacks the capability to adaptively capture sparse representations. Additionally, determining the optimal threshold parameters can pose difficulties. These limitations yield unsatisfactory reconstruction results. To address this issue, we propose a novel approach called Sparse Prior-Based Seismic Interpolation Network (SP-Net) that combines the sparsity-promoting method with a deep neural network. Unlike traditional end-to-end networks, our proposed neural network integrates the widely-used POCS method into its architecture, enabling automatic learning of the sparse transform and threshold parameters from the training dataset. By combining the merits of the sparsity-promoting techniques and data-driven deep learning approaches, SP-Net achieves enhanced adaptability and more accurate interpolation results. Through experiments conducted on synthetic and field seismic data, we demonstrate the effectiveness of our proposed method.
{"title":"SP-Net: A Sparse Prior-Based Deep Network for Seismic Data Interpolation","authors":"Mengyi Wu, Lihua Fu, Wenqian Fang, Jiajia Cao","doi":"10.1190/geo2022-0262.1","DOIUrl":"https://doi.org/10.1190/geo2022-0262.1","url":null,"abstract":"Seismic data interpolation plays a crucial role in obtaining dense and regularly sampled data, contributing to improve the quality of seismic data in seismic exploration. Sparsity-promoting methods utilize a two-step iteration to gradually recover missing traces, by exploiting the sparsity representation of seismic data in transform domains, such as Fourier, wavelet, and curvelet transform, within the framework of the projection onto convex sets (POCS). In the first step, the missing traces are restored by applying the thresholding shrinkage to the transform coefficients. In the second step, the observed data is inserted into the updated result. However, this method relies on a preselected transform and lacks the capability to adaptively capture sparse representations. Additionally, determining the optimal threshold parameters can pose difficulties. These limitations yield unsatisfactory reconstruction results. To address this issue, we propose a novel approach called Sparse Prior-Based Seismic Interpolation Network (SP-Net) that combines the sparsity-promoting method with a deep neural network. Unlike traditional end-to-end networks, our proposed neural network integrates the widely-used POCS method into its architecture, enabling automatic learning of the sparse transform and threshold parameters from the training dataset. By combining the merits of the sparsity-promoting techniques and data-driven deep learning approaches, SP-Net achieves enhanced adaptability and more accurate interpolation results. Through experiments conducted on synthetic and field seismic data, we demonstrate the effectiveness of our proposed method.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136294611","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}
Joanna H.W. Kho, Max A. Meju, Roger V. Miller, Ahmad Shahir Saleh
Understanding the deep structural controls on reservoir distribution and overburden heterogeneity is important for near-field exploration and green-field development of the Miocene to Holocene carbonate buildups in Central Luconia province in offshore Borneo, Malaysia. Data from 59 stations along a ~180 km-long controlled-source electromagnetic (CSEM) and magnetotelluric (MT) survey line with three segments recorded using different CSEM transmitter-towing directions were available. We applied three-dimensional (3D) anisotropic resistivity inversion with crossgradient constraint and verified the resulting models using resistivity logs from nearby wells and the acoustic basement interpreted from seismic data. Our anisotropic resistivity models reveal a fragmented carbonate-rich zone atop a segmented basement comprising electrically resistive horsts coinciding with the northerly Mega-platform, Central and Southern Field Highs separated by steep conductive zones coinciding with the West, East and Southeast Troughs previously interpreted from seismic data. The structural highs correlate with the spatial distribution of the known carbonate buildups implying a genetic link. The carbonate bodies are overlain and underlain by persistent layers (C2 and C3) of low resistivity and high anisotropy which we interpret as indicating compressional deformation or detachment zones. Overburden layer C2 (whose base coincides with the top of a key sedimentary package, Cycle V in seismic data) is thinnest over the East Trough and is discontinuous at the eastern margin of the West Trough, which are locations where drilled wells did not find hydrocarbons implying that the seal rocks are inefficient or breached at those localities. We used these observations to refine the existing seismic-based interpretation of carbonate play-types along our transect showing how 3D joint CSEM-MT imaging can potentially contribute to derisking or optimizing future exploration and/or development work in this province.
{"title":"Deep structural controls on the distribution of carbonate reservoirs and overburden heterogeneity in Central Luconia province, offshore Borneo revealed by 3D anisotropic inversion of regional CSEM-MT profile data","authors":"Joanna H.W. Kho, Max A. Meju, Roger V. Miller, Ahmad Shahir Saleh","doi":"10.1190/geo2023-0178.1","DOIUrl":"https://doi.org/10.1190/geo2023-0178.1","url":null,"abstract":"Understanding the deep structural controls on reservoir distribution and overburden heterogeneity is important for near-field exploration and green-field development of the Miocene to Holocene carbonate buildups in Central Luconia province in offshore Borneo, Malaysia. Data from 59 stations along a ~180 km-long controlled-source electromagnetic (CSEM) and magnetotelluric (MT) survey line with three segments recorded using different CSEM transmitter-towing directions were available. We applied three-dimensional (3D) anisotropic resistivity inversion with crossgradient constraint and verified the resulting models using resistivity logs from nearby wells and the acoustic basement interpreted from seismic data. Our anisotropic resistivity models reveal a fragmented carbonate-rich zone atop a segmented basement comprising electrically resistive horsts coinciding with the northerly Mega-platform, Central and Southern Field Highs separated by steep conductive zones coinciding with the West, East and Southeast Troughs previously interpreted from seismic data. The structural highs correlate with the spatial distribution of the known carbonate buildups implying a genetic link. The carbonate bodies are overlain and underlain by persistent layers (C2 and C3) of low resistivity and high anisotropy which we interpret as indicating compressional deformation or detachment zones. Overburden layer C2 (whose base coincides with the top of a key sedimentary package, Cycle V in seismic data) is thinnest over the East Trough and is discontinuous at the eastern margin of the West Trough, which are locations where drilled wells did not find hydrocarbons implying that the seal rocks are inefficient or breached at those localities. We used these observations to refine the existing seismic-based interpretation of carbonate play-types along our transect showing how 3D joint CSEM-MT imaging can potentially contribute to derisking or optimizing future exploration and/or development work in this province.","PeriodicalId":55102,"journal":{"name":"Geophysics","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136353266","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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