Historical exploration for economic nickel (Ni) mineralization has often targeted magmatic sulfide deposits in extensional settings. However, convergent-margin-hosted Alaskan-type complexes represent a potentially underexplored source of Ni. Case studies of the geophysical responses associated with two magmatic Ni deposits (one is typical, and one is associated with an Alaskan-type complex) are presented, and the results are compared. Data were assessed from historical and newly acquired airborne geophysical surveys that were collected over the Mayville property in southeast Manitoba and the Turnagain property in northern British Columbia. The properties were explored by Mustang Minerals Corporation and Giga Metals Corporation, respectively. Airborne electromagnetic (EM) and magnetic data were utilized to compare the two properties and the mineralized zones. The review showed that the Mayville magmatic sulfide deposit was directly detectible with EM methods, and the passive and active-source methods were complementary to one another. The EM data did not directly detect the Turnagain Alaskan-type deposit, but the magnetics data proved to be successful in defining the geologic framework. Implications for future targeting and exploration for economic Ni mineralization are considered.
{"title":"A comparison of airborne geophysical data over two magmatic nickel deposits","authors":"Blake Cross, Hannah Peterson","doi":"10.1190/tle42040237.1","DOIUrl":"https://doi.org/10.1190/tle42040237.1","url":null,"abstract":"Historical exploration for economic nickel (Ni) mineralization has often targeted magmatic sulfide deposits in extensional settings. However, convergent-margin-hosted Alaskan-type complexes represent a potentially underexplored source of Ni. Case studies of the geophysical responses associated with two magmatic Ni deposits (one is typical, and one is associated with an Alaskan-type complex) are presented, and the results are compared. Data were assessed from historical and newly acquired airborne geophysical surveys that were collected over the Mayville property in southeast Manitoba and the Turnagain property in northern British Columbia. The properties were explored by Mustang Minerals Corporation and Giga Metals Corporation, respectively. Airborne electromagnetic (EM) and magnetic data were utilized to compare the two properties and the mineralized zones. The review showed that the Mayville magmatic sulfide deposit was directly detectible with EM methods, and the passive and active-source methods were complementary to one another. The EM data did not directly detect the Turnagain Alaskan-type deposit, but the magnetics data proved to be successful in defining the geologic framework. Implications for future targeting and exploration for economic Ni mineralization are considered.","PeriodicalId":35661,"journal":{"name":"Leading Edge","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42718709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Time-lapse (4D) seismic inversion is the leading method to quantitatively monitor fluid-flow dynamics in the subsurface, with applications ranging from enhanced oil recovery to subsurface CO2 storage. The process of inverting 4D seismic data for reservoir properties is a notoriously ill-posed inverse problem due to the band-limited and noisy nature of seismic data and inaccuracies in the repeatability of 4D acquisition surveys. Consequently, ad-hoc regularization strategies are essential for the 4D seismic inverse problem to obtain geologically meaningful subsurface models and associated 4D changes. Motivated by recent advances in the field of convex optimization, we propose a joint inversion-segmentation algorithm for 4D seismic inversion that integrates total variation and segmentation priors as a way to counteract missing frequencies and present noise in 4D seismic data. The proposed inversion framework is designed for poststack seismic data and applied to a pair of seismic volumes from the open Sleipner 4D seismic data set. Our method has three main advantages over state-of-the-art least-squares inversion methods. First, it produces high-resolution baseline and monitor acoustic models. Second, it mitigates nonrepeatable noise and better highlights real 4D changes by leveraging similarities between multiple data. Finally, it provides a volumetric classification of the acoustic impedance 4D difference model (4D changes) based on user-defined classes (i.e., percentages of speedup or slowdown in the subsurface). Such advantages may enable more robust stratigraphic/structural and quantitative 4D seismic interpretation and provide more accurate inputs for dynamic reservoir simulations. Alongside presenting our novel inversion method, we introduce a streamlined data preprocessing sequence for the 4D Sleipner poststack seismic data set that includes time-shift estimation and well-to-seismic tie. Finally, we provide insights into the open-source framework for large-scale optimization that we used to implement the proposed algorithm in an efficient and scalable manner.
{"title":"Seeing through the CO2 plume: Joint inversion-segmentation of the Sleipner 4D seismic data set","authors":"J. Romero, N. Luiken, M. Ravasi","doi":"10.1190/tle42070457.1","DOIUrl":"https://doi.org/10.1190/tle42070457.1","url":null,"abstract":"Time-lapse (4D) seismic inversion is the leading method to quantitatively monitor fluid-flow dynamics in the subsurface, with applications ranging from enhanced oil recovery to subsurface CO2 storage. The process of inverting 4D seismic data for reservoir properties is a notoriously ill-posed inverse problem due to the band-limited and noisy nature of seismic data and inaccuracies in the repeatability of 4D acquisition surveys. Consequently, ad-hoc regularization strategies are essential for the 4D seismic inverse problem to obtain geologically meaningful subsurface models and associated 4D changes. Motivated by recent advances in the field of convex optimization, we propose a joint inversion-segmentation algorithm for 4D seismic inversion that integrates total variation and segmentation priors as a way to counteract missing frequencies and present noise in 4D seismic data. The proposed inversion framework is designed for poststack seismic data and applied to a pair of seismic volumes from the open Sleipner 4D seismic data set. Our method has three main advantages over state-of-the-art least-squares inversion methods. First, it produces high-resolution baseline and monitor acoustic models. Second, it mitigates nonrepeatable noise and better highlights real 4D changes by leveraging similarities between multiple data. Finally, it provides a volumetric classification of the acoustic impedance 4D difference model (4D changes) based on user-defined classes (i.e., percentages of speedup or slowdown in the subsurface). Such advantages may enable more robust stratigraphic/structural and quantitative 4D seismic interpretation and provide more accurate inputs for dynamic reservoir simulations. Alongside presenting our novel inversion method, we introduce a streamlined data preprocessing sequence for the 4D Sleipner poststack seismic data set that includes time-shift estimation and well-to-seismic tie. Finally, we provide insights into the open-source framework for large-scale optimization that we used to implement the proposed algorithm in an efficient and scalable manner.","PeriodicalId":35661,"journal":{"name":"Leading Edge","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48359666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The National Geophysical Research Institute (NGRI) Geophysical Society is an SEG student chapter located in Hyderabad, India. Members of the student chapter have enjoyed giving back to our alma-mater universities and community in a variety of ways.
{"title":"Student Zone: Student chapter gives back to alma-mater universities and the local community","authors":"Shaik Nasif Ahmed, Saqib Zia, P. Patro","doi":"10.1190/tle42030222.1","DOIUrl":"https://doi.org/10.1190/tle42030222.1","url":null,"abstract":"The National Geophysical Research Institute (NGRI) Geophysical Society is an SEG student chapter located in Hyderabad, India. Members of the student chapter have enjoyed giving back to our alma-mater universities and community in a variety of ways.","PeriodicalId":35661,"journal":{"name":"Leading Edge","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41805536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The estimation of the parameters of a mathematical model by data-fitting procedures goes back more than 200 years. Mathematics historians seem to agree to credit C. F. Gauss for the introduction of this idea ( Gauss, 2011 ). In the field of exploration geophysics, Lailly (1983) and Tarantola (1984) were the first to propose the use of data-fitting techniques to estimate model parameters that control the propagation of waves in the subsurface. The concept of full-waveform inversion (FWI) was born.
{"title":"Introduction to this special section: Full-waveform inversion","authors":"F. Perrone, N. Grobbe","doi":"10.1190/tle42030152.1","DOIUrl":"https://doi.org/10.1190/tle42030152.1","url":null,"abstract":"The estimation of the parameters of a mathematical model by data-fitting procedures goes back more than 200 years. Mathematics historians seem to agree to credit C. F. Gauss for the introduction of this idea ( Gauss, 2011 ). In the field of exploration geophysics, Lailly (1983) and Tarantola (1984) were the first to propose the use of data-fitting techniques to estimate model parameters that control the propagation of waves in the subsurface. The concept of full-waveform inversion (FWI) was born.","PeriodicalId":35661,"journal":{"name":"Leading Edge","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47506657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bin Gong, Tianjiang Li, King Sim Lee, Zhaobo Meng, Kai Zhang, A. Bibolova, Z. Katrenov
Seismic image deterioration is a major problem for structures under complex overburdens. In areas under major faults and salt bodies, it is common to observe amplitude washout zones, poor structural definition, and relatively strong coherent and incoherent seismic noise. We developed a data-driven workflow of Q least-squares migration to mitigate these problems by enhancing the horizontal and vertical resolutions of seismic images. The workflow includes accurate velocity model building with joint full-waveform inversion and tomography to maximize the stacking power of P-wave primaries, Q tomography that balances weak-amplitude washout zones and minimizes swing noises, and least-squares migration with the aid of a constraint map that yields higher-resolution structural images. Its application on a wide-azimuth data set from the Tengiz oil field demonstrates the effective mitigation of fault shadow issues with an overall improvement of image quality. In addition, the uplift in focusing of diffracted energies shows promising improvements in enhancing seismic mega-amplitude events. Thus, the proposed method greatly increases the fidelity of seismic attribute analysis when compared with conventional vintage processing.
{"title":"Application of Q-FWI-tomography and least-squares migration to improve seismic resolution in Tengiz oil field","authors":"Bin Gong, Tianjiang Li, King Sim Lee, Zhaobo Meng, Kai Zhang, A. Bibolova, Z. Katrenov","doi":"10.1190/tle42030156.1","DOIUrl":"https://doi.org/10.1190/tle42030156.1","url":null,"abstract":"Seismic image deterioration is a major problem for structures under complex overburdens. In areas under major faults and salt bodies, it is common to observe amplitude washout zones, poor structural definition, and relatively strong coherent and incoherent seismic noise. We developed a data-driven workflow of Q least-squares migration to mitigate these problems by enhancing the horizontal and vertical resolutions of seismic images. The workflow includes accurate velocity model building with joint full-waveform inversion and tomography to maximize the stacking power of P-wave primaries, Q tomography that balances weak-amplitude washout zones and minimizes swing noises, and least-squares migration with the aid of a constraint map that yields higher-resolution structural images. Its application on a wide-azimuth data set from the Tengiz oil field demonstrates the effective mitigation of fault shadow issues with an overall improvement of image quality. In addition, the uplift in focusing of diffracted energies shows promising improvements in enhancing seismic mega-amplitude events. Thus, the proposed method greatly increases the fidelity of seismic attribute analysis when compared with conventional vintage processing.","PeriodicalId":35661,"journal":{"name":"Leading Edge","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44168457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cristina Reta-Tang, J. Sheng, Faqi Liu, A. V. Cantú, A. C. Vargas
Velocity model building and imaging for land surveys are often challenging due to near-surface complexity contaminating the reflection signal. Incorporating full-waveform inversion (FWI) in the velocity model building workflow for land surveys offers benefits not achieved with traditional model building tools, but it also brings difficulties. We have developed an effective model building workflow for land seismic data that incorporates dynamic matching FWI (DMFWI). DMFWI employs an objective function that uses multidimensional local windowed crosscorrelations between the dynamically matched version of observed and synthetic data. Dynamic matching de-emphasizes the impact of amplitudes, allowing the algorithm to focus on using kinematic information for velocity updates. The proposed workflow produces a geologically plausible and consistent model for data acquired with limited offsets. Refraction and reflection tomography may also be included in the workflow. The workflow is applied to onshore surveys in Mexico. Despite challenges of the near-surface geology and limitations of the acquisition parameters in the study areas, the proposed model building workflow successfully derives a high-resolution velocity model that significantly improves the migrated depth image.
{"title":"Application of full-waveform inversion to land data: Case studies in onshore Mexico","authors":"Cristina Reta-Tang, J. Sheng, Faqi Liu, A. V. Cantú, A. C. Vargas","doi":"10.1190/tle42030190.1","DOIUrl":"https://doi.org/10.1190/tle42030190.1","url":null,"abstract":"Velocity model building and imaging for land surveys are often challenging due to near-surface complexity contaminating the reflection signal. Incorporating full-waveform inversion (FWI) in the velocity model building workflow for land surveys offers benefits not achieved with traditional model building tools, but it also brings difficulties. We have developed an effective model building workflow for land seismic data that incorporates dynamic matching FWI (DMFWI). DMFWI employs an objective function that uses multidimensional local windowed crosscorrelations between the dynamically matched version of observed and synthetic data. Dynamic matching de-emphasizes the impact of amplitudes, allowing the algorithm to focus on using kinematic information for velocity updates. The proposed workflow produces a geologically plausible and consistent model for data acquired with limited offsets. Refraction and reflection tomography may also be included in the workflow. The workflow is applied to onshore surveys in Mexico. Despite challenges of the near-surface geology and limitations of the acquisition parameters in the study areas, the proposed model building workflow successfully derives a high-resolution velocity model that significantly improves the migrated depth image.","PeriodicalId":35661,"journal":{"name":"Leading Edge","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42162890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The SEG Advanced Modeling (SEAM) Arid benchmark model was designed to simulate an extremely heterogeneous low-velocity near surface (NS), which is typical of desert environments and typically not well characterized or imaged. Imaging of land seismic data is highly sensitive to errors in the NS velocity model. Vertical seismic profiling (VSP) partly alleviates the impact of the NS as the receivers are located at depth in the borehole. Deep learning (DL) offers a flexible optimization framework for full-waveform inversion (FWI), often outperforming typically used optimization methods. We investigate the quality of images that can be obtained from SEAM Arid VSP data by acoustic mini-batch reverse time migration (RTM) and full-waveform imaging. First, we focus on the effects of seismic vibrator and receiver array positioning and imperfect knowledge of the NS model when inverting 2D acoustic data. FWI imaging expectedly and consistently outperforms RTM in our tests. We find that the acquisition density is critical for RTM imaging and less so for FWI, while NS model accuracy is critical for FWI and has less effect on RTM imaging. Distributed acoustic sensing along the full length of the well provides noticeable improvement over a limited aperture array of geophones in imaging deep targets in both RTM and FWI imaging scenarios. Finally, we compare DL-based FWI imaging with inverse scattering RTM using the upgoing wavefield from the original SEAM data. Use of significantly more realistic 3D elastic physics for the simulated data generation and simple 2D acoustic inversion engine makes our inverse problem more realistic. We observe that FWI imaging in this case produces an image with fewer artifacts.
{"title":"Acquisition and near-surface impacts on VSP mini-batch FWI and RTM imaging in desert environment","authors":"V. Kazei, Hong Liang, A. Aldawood","doi":"10.1190/tle42030165.1","DOIUrl":"https://doi.org/10.1190/tle42030165.1","url":null,"abstract":"The SEG Advanced Modeling (SEAM) Arid benchmark model was designed to simulate an extremely heterogeneous low-velocity near surface (NS), which is typical of desert environments and typically not well characterized or imaged. Imaging of land seismic data is highly sensitive to errors in the NS velocity model. Vertical seismic profiling (VSP) partly alleviates the impact of the NS as the receivers are located at depth in the borehole. Deep learning (DL) offers a flexible optimization framework for full-waveform inversion (FWI), often outperforming typically used optimization methods. We investigate the quality of images that can be obtained from SEAM Arid VSP data by acoustic mini-batch reverse time migration (RTM) and full-waveform imaging. First, we focus on the effects of seismic vibrator and receiver array positioning and imperfect knowledge of the NS model when inverting 2D acoustic data. FWI imaging expectedly and consistently outperforms RTM in our tests. We find that the acquisition density is critical for RTM imaging and less so for FWI, while NS model accuracy is critical for FWI and has less effect on RTM imaging. Distributed acoustic sensing along the full length of the well provides noticeable improvement over a limited aperture array of geophones in imaging deep targets in both RTM and FWI imaging scenarios. Finally, we compare DL-based FWI imaging with inverse scattering RTM using the upgoing wavefield from the original SEAM data. Use of significantly more realistic 3D elastic physics for the simulated data generation and simple 2D acoustic inversion engine makes our inverse problem more realistic. We observe that FWI imaging in this case produces an image with fewer artifacts.","PeriodicalId":35661,"journal":{"name":"Leading Edge","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41682188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Warner, T. Nangoo, A. Umpleby, N. Shah, C. Manuel, D. Bevc, M. Merino
Conventional seismic velocity model building in complicated salt-affected areas requires the explicit identification of salt boundaries in migrated images and typically involves testing of possible subsurface scenarios through multiple generations. The resulting velocity models are slow to generate and may contain interpreter-driven features that are difficult to verify. We show that it is possible to build a full final velocity model using advanced forms of full-waveform inversion applied directly to raw field data, starting from a model that contains only a simple 1D compaction trend. This approach rapidly generates the final velocity model and migrates processed reflection data at least as accurately as conventionally generated models. We demonstrate this methodology using an ocean-bottom-node data set acquired in deep water over Walker Ridge in the Gulf of Mexico. Our approach does not require exceptionally long offsets or the deployment of special low-frequency sources. We restrict the inversion so it does not use significant energy below 3 Hz or offsets longer than 14 km. We use three advanced forms of waveform inversion to recover the final model. The first is adaptive waveform inversion to proceed from models that begin far from the true model. The second is nonlinear reflection waveform inversion to recover subsalt velocity structure from reflections and their long-period multiples. The third is constrained waveform inversion to produce salt- and sediment-like velocity floods without explicitly identifying salt boundaries or velocities. In combination, these three algorithms successively improve the velocity model so it fully predicts the raw field data and accurately migrates primary reflections, though explicit migration forms no part of the workflow. Thus, model building via waveform inversion is able to proceed from field data to the final model in just a few weeks. It entirely avoids the many cycles of model rebuilding that may otherwise be required.
{"title":"Automated salt model building: From compaction trend to final velocity model using waveform inversion","authors":"M. Warner, T. Nangoo, A. Umpleby, N. Shah, C. Manuel, D. Bevc, M. Merino","doi":"10.1190/tle42030196.1","DOIUrl":"https://doi.org/10.1190/tle42030196.1","url":null,"abstract":"Conventional seismic velocity model building in complicated salt-affected areas requires the explicit identification of salt boundaries in migrated images and typically involves testing of possible subsurface scenarios through multiple generations. The resulting velocity models are slow to generate and may contain interpreter-driven features that are difficult to verify. We show that it is possible to build a full final velocity model using advanced forms of full-waveform inversion applied directly to raw field data, starting from a model that contains only a simple 1D compaction trend. This approach rapidly generates the final velocity model and migrates processed reflection data at least as accurately as conventionally generated models. We demonstrate this methodology using an ocean-bottom-node data set acquired in deep water over Walker Ridge in the Gulf of Mexico. Our approach does not require exceptionally long offsets or the deployment of special low-frequency sources. We restrict the inversion so it does not use significant energy below 3 Hz or offsets longer than 14 km. We use three advanced forms of waveform inversion to recover the final model. The first is adaptive waveform inversion to proceed from models that begin far from the true model. The second is nonlinear reflection waveform inversion to recover subsalt velocity structure from reflections and their long-period multiples. The third is constrained waveform inversion to produce salt- and sediment-like velocity floods without explicitly identifying salt boundaries or velocities. In combination, these three algorithms successively improve the velocity model so it fully predicts the raw field data and accurately migrates primary reflections, though explicit migration forms no part of the workflow. Thus, model building via waveform inversion is able to proceed from field data to the final model in just a few weeks. It entirely avoids the many cycles of model rebuilding that may otherwise be required.","PeriodicalId":35661,"journal":{"name":"Leading Edge","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47429753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhigang Zhang, Zedong Wu, Zhiyuan Wei, J. Mei, Rongxin Huang, Ping Wang
Full-waveform inversion (FWI) has become the centerpiece of velocity model building (VMB) in seismic data processing in recent years. It has proven capable of significantly improving the velocity model and, thus, the migration image for different acquisition types and geologic settings, including complex environments such as salt. With the advent of FWI imaging, the scope of FWI applications has extended further from VMB into the imaging landscape. However, current FWI applications in the industry prevalently employ the acoustic approximation. One common problem of acoustic FWI (A-FWI) is the apparent salt halos at the salt-sediment interface in the resulting FWI velocity and FWI image, the presence of which hinders direct interpretation and imaging focusing around salt bodies. With synthetic and field data examples, we demonstrate that this salt halo is caused mainly by the large mismatch between the elastic recorded data and the acoustic modeled data, particularly at middle to long offsets. To overcome limitations imposed by acoustic assumptions, we developed an elastic FWI (E-FWI) algorithm that combines an elastic modeling engine with the time-lag cost function, which we call elastic time-lag FWI (E-TLFWI). With a more accurate modeling engine, E-TLFWI significantly reduces the salt halo observed in its acoustic counterpart. However, we also observe that the images migrated using the A-FWI and E-FWI velocity models remain similar overall, with some slight improvements around and beneath salt boundaries, particularly near steep salt flanks, as a result of the reduced salt halo. By contrast, FWI images derived from E-TLFWI show considerable benefits over those from acoustic time-lag FWI, such as improved event focusing, better structural continuity, and higher signal-to-noise ratio. The sharpened salt boundaries and enhanced quality of the FWI images reveal the significant value of E-FWI and provide the justification for its greatly increased cost.
{"title":"Enhancing salt model resolution and subsalt imaging with elastic FWI","authors":"Zhigang Zhang, Zedong Wu, Zhiyuan Wei, J. Mei, Rongxin Huang, Ping Wang","doi":"10.1190/tle42030207.1","DOIUrl":"https://doi.org/10.1190/tle42030207.1","url":null,"abstract":"Full-waveform inversion (FWI) has become the centerpiece of velocity model building (VMB) in seismic data processing in recent years. It has proven capable of significantly improving the velocity model and, thus, the migration image for different acquisition types and geologic settings, including complex environments such as salt. With the advent of FWI imaging, the scope of FWI applications has extended further from VMB into the imaging landscape. However, current FWI applications in the industry prevalently employ the acoustic approximation. One common problem of acoustic FWI (A-FWI) is the apparent salt halos at the salt-sediment interface in the resulting FWI velocity and FWI image, the presence of which hinders direct interpretation and imaging focusing around salt bodies. With synthetic and field data examples, we demonstrate that this salt halo is caused mainly by the large mismatch between the elastic recorded data and the acoustic modeled data, particularly at middle to long offsets. To overcome limitations imposed by acoustic assumptions, we developed an elastic FWI (E-FWI) algorithm that combines an elastic modeling engine with the time-lag cost function, which we call elastic time-lag FWI (E-TLFWI). With a more accurate modeling engine, E-TLFWI significantly reduces the salt halo observed in its acoustic counterpart. However, we also observe that the images migrated using the A-FWI and E-FWI velocity models remain similar overall, with some slight improvements around and beneath salt boundaries, particularly near steep salt flanks, as a result of the reduced salt halo. By contrast, FWI images derived from E-TLFWI show considerable benefits over those from acoustic time-lag FWI, such as improved event focusing, better structural continuity, and higher signal-to-noise ratio. The sharpened salt boundaries and enhanced quality of the FWI images reveal the significant value of E-FWI and provide the justification for its greatly increased cost.","PeriodicalId":35661,"journal":{"name":"Leading Edge","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46087614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Serguei Goussev discusses his new book, Gravity and Magnetic Encyclopedic Dictionary. Goussev describes his motivation for compiling the resource, the unique format he created, a few of his favorite terms, and what he hopes the book will achieve.
{"title":"Seismic Soundoff: Why you need the Gravity and Magnetic Encyclopedic Dictionary","authors":"A. Geary","doi":"10.1190/tle42030228.1","DOIUrl":"https://doi.org/10.1190/tle42030228.1","url":null,"abstract":"Serguei Goussev discusses his new book, Gravity and Magnetic Encyclopedic Dictionary. Goussev describes his motivation for compiling the resource, the unique format he created, a few of his favorite terms, and what he hopes the book will achieve.","PeriodicalId":35661,"journal":{"name":"Leading Edge","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48577102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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