As the world's population continues to grow and economies develop, there will be a commensurate need for additional energy supply that is sustainable and that can be acquired while respecting the environment. The Middle East, home to some of the world's largest oil and gas reserves, is one of the most important regions globally for energy production and exports. Countries such as Saudi Arabia and the United Arab Emirates are key players in the global oil market, while Qatar is a major exporter of liquified natural gas. Fossil fuels will continue to play a major role in the region's — and the world's — energy mix for the foreseeable future, but there is growing interest in developing renewable energy sources such as solar, wind power, and geothermal.
{"title":"President's Page: Energy transition offers bright future for geoscientists","authors":"M. Badri","doi":"10.1190/tle42060386.1","DOIUrl":"https://doi.org/10.1190/tle42060386.1","url":null,"abstract":"As the world's population continues to grow and economies develop, there will be a commensurate need for additional energy supply that is sustainable and that can be acquired while respecting the environment. The Middle East, home to some of the world's largest oil and gas reserves, is one of the most important regions globally for energy production and exports. Countries such as Saudi Arabia and the United Arab Emirates are key players in the global oil market, while Qatar is a major exporter of liquified natural gas. Fossil fuels will continue to play a major role in the region's — and the world's — energy mix for the foreseeable future, but there is growing interest in developing renewable energy sources such as solar, wind power, and geothermal.","PeriodicalId":35661,"journal":{"name":"Leading Edge","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41814362","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}
J. Richardson, Kun Fu, Shaoming Lu, B. Bai, Xin Cheng, D. Vígh
Full-waveform inversion (FWI) has been widely used on 3D data sets to build detailed velocity models over the past 10 years. Most of these projects used pressure data and an acoustic approximation, with the assumption that the field data were dominated with P-waves. This approach of FWI can determine parameters related to the acoustic wave equation. It focuses on updating velocities by minimizing the misfit between observed and model data. Acoustic FWI, using the pressure component of collected data, has shown tremendous potential in simple geologic settings. Successful FWI projects, using wide-azimuth streamer, ocean bottom, and land survey geometries, have convinced the oil industry to pursue the next step by involving more physical properties. However, questions remain on how far we can properly describe field data with the acoustic approximation and at what point we need to switch to a much more expensive elastic wave equation implementation. In a complicated geologic region such as the Gulf of Mexico (GoM), the seismic wavefield can be complex and elastic FWI is needed to achieve a better velocity model, even when using mostly pressure data alone. We demonstrate the application of elastic FWI on sparse-node ocean-bottom-node data from the GoM and show comparisons to the acoustic solution. The comparisons demonstrate the benefits of the elastic FWI implementation when applied to image steeply dipping Miocene sands beneath a complex salt canopy, despite the increased computational expense. Furthermore, we demonstrate that when elastic FWI is applied to sufficiently high frequencies, the FWI-derived reflectivity product and velocity model are reliable interpretation products.
{"title":"Elastic full-waveform inversion on Caesar-Tonga — Case study","authors":"J. Richardson, Kun Fu, Shaoming Lu, B. Bai, Xin Cheng, D. Vígh","doi":"10.1190/tle42060414.1","DOIUrl":"https://doi.org/10.1190/tle42060414.1","url":null,"abstract":"Full-waveform inversion (FWI) has been widely used on 3D data sets to build detailed velocity models over the past 10 years. Most of these projects used pressure data and an acoustic approximation, with the assumption that the field data were dominated with P-waves. This approach of FWI can determine parameters related to the acoustic wave equation. It focuses on updating velocities by minimizing the misfit between observed and model data. Acoustic FWI, using the pressure component of collected data, has shown tremendous potential in simple geologic settings. Successful FWI projects, using wide-azimuth streamer, ocean bottom, and land survey geometries, have convinced the oil industry to pursue the next step by involving more physical properties. However, questions remain on how far we can properly describe field data with the acoustic approximation and at what point we need to switch to a much more expensive elastic wave equation implementation. In a complicated geologic region such as the Gulf of Mexico (GoM), the seismic wavefield can be complex and elastic FWI is needed to achieve a better velocity model, even when using mostly pressure data alone. We demonstrate the application of elastic FWI on sparse-node ocean-bottom-node data from the GoM and show comparisons to the acoustic solution. The comparisons demonstrate the benefits of the elastic FWI implementation when applied to image steeply dipping Miocene sands beneath a complex salt canopy, despite the increased computational expense. Furthermore, we demonstrate that when elastic FWI is applied to sufficiently high frequencies, the FWI-derived reflectivity product and velocity model are reliable interpretation products.","PeriodicalId":35661,"journal":{"name":"Leading Edge","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46785624","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}
Hui Liu, F. Rollins, K. Pratt, Elizabeth Da Silva, Nathalie Mootoo, Tongning Yang, D. Ren, Fei Gao, J. Mei
The Gulf of Mexico (GoM) is one of the most prolific oil and gas producing provinces in the world. The Mad Dog Field, like many large deepwater fields in the GoM, is subsalt. The geometric complexity of the overlying salt causes extremely variable image quality of the strata beneath the salt. Improving the seismic image has been critical for field development, and a tremendous amount of effort has been expended over the years to solve this problem. Over the past two decades, data acquisition has evolved from narrow-azimuth towed streamer to wide-azimuth streamer, and finally to ocean-bottom nodes. Processing methods such as using different anisotropic velocity models of increasing complexity, exhaustive iterations of salt modeling, acoustic full-waveform inversion, and most recently elastic full-waveform inversion have been applied. Dozens of wells have been drilled at Mad Dog guided by the resulting seismic images, and many acquisition and processing learnings have been acquired and implemented over this period to optimize the imaging. This paper explores the techniques that have caused major uplift to subsalt imaging and some techniques that were of only minor impact, while giving a glimpse into the imaging history of one of the GoM's giant fields.
{"title":"Solving Mad Dog subsalt imaging in two decades: From WATS to OBN to elastic FWI","authors":"Hui Liu, F. Rollins, K. Pratt, Elizabeth Da Silva, Nathalie Mootoo, Tongning Yang, D. Ren, Fei Gao, J. Mei","doi":"10.1190/tle42060398.1","DOIUrl":"https://doi.org/10.1190/tle42060398.1","url":null,"abstract":"The Gulf of Mexico (GoM) is one of the most prolific oil and gas producing provinces in the world. The Mad Dog Field, like many large deepwater fields in the GoM, is subsalt. The geometric complexity of the overlying salt causes extremely variable image quality of the strata beneath the salt. Improving the seismic image has been critical for field development, and a tremendous amount of effort has been expended over the years to solve this problem. Over the past two decades, data acquisition has evolved from narrow-azimuth towed streamer to wide-azimuth streamer, and finally to ocean-bottom nodes. Processing methods such as using different anisotropic velocity models of increasing complexity, exhaustive iterations of salt modeling, acoustic full-waveform inversion, and most recently elastic full-waveform inversion have been applied. Dozens of wells have been drilled at Mad Dog guided by the resulting seismic images, and many acquisition and processing learnings have been acquired and implemented over this period to optimize the imaging. This paper explores the techniques that have caused major uplift to subsalt imaging and some techniques that were of only minor impact, while giving a glimpse into the imaging history of one of the GoM's giant fields.","PeriodicalId":35661,"journal":{"name":"Leading Edge","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44170861","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}
Cyril D. Boateng discusses his SEG field camp, “Investigating the slave trade in southeastern Ghana using integrated geophysical techniques.” He explains the concept behind “the archaeology of slavery” and describes the various geophysical investigations used across four communities. This conversation highlights the significant value that geophysics brings to a problem. It shows how SEG field camps are an invaluable tool for building the next generation of scientists and providing humanitarian benefits.
{"title":"Seismic Soundoff: Uncovering the hidden history of Ghana","authors":"A. Geary","doi":"10.1190/tle42060444.1","DOIUrl":"https://doi.org/10.1190/tle42060444.1","url":null,"abstract":"Cyril D. Boateng discusses his SEG field camp, “Investigating the slave trade in southeastern Ghana using integrated geophysical techniques.” He explains the concept behind “the archaeology of slavery” and describes the various geophysical investigations used across four communities. This conversation highlights the significant value that geophysics brings to a problem. It shows how SEG field camps are an invaluable tool for building the next generation of scientists and providing humanitarian benefits.","PeriodicalId":35661,"journal":{"name":"Leading Edge","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44812741","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 Azerbaijan State Oil and Industry University (ASOIU) Student Chapter recently organized a career fair. The event brought together students, alumni, and professionals from Baku and nearby cities to explore various career options in the energy industry and gain valuable insights into the current job market.
{"title":"Student Zone: Student chapter highlights opportunities by hosting a career fair","authors":"Javid Aliyev","doi":"10.1190/tle42060435.1","DOIUrl":"https://doi.org/10.1190/tle42060435.1","url":null,"abstract":"The SEG Azerbaijan State Oil and Industry University (ASOIU) Student Chapter recently organized a career fair. The event brought together students, alumni, and professionals from Baku and nearby cities to explore various career options in the energy industry and gain valuable insights into the current job market.","PeriodicalId":35661,"journal":{"name":"Leading Edge","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43868726","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}
Samuel I Buist, Li Jiang, Obi Egbue, Daniel Tebo, Luis Lopez, Zhiyuan Wei, A. Hao, Chi Chen
The Atlantis Field has gone through more than two decades of continuous seismic imaging efforts, during which time many innovative technologies were incubated, the most recent one being the successful application of full-waveform inversion (FWI) in salt environments. This technique led to a significant improvement in the subsalt image. However, imaging challenges remain for the Atlantis reservoirs, primarily due to the complex overburden salt geometries and the highly compartmentalized reservoir. Even with an improved velocity model from FWI, the conventional reverse time migration (RTM) images still suffer from illumination issues and contain strong migration swings that hinder the subsalt imaging and subsequent interpretations. Furthermore, early versions of FWI employed an acoustic assumption, leading to visible salt halos at the salt boundaries in the velocity model, which adversely impacted the reservoir imaging. In the last 12 months, elastic time-lag FWI (TLFWI) and FWI-derived reflectivity (FDR) imaging using long-offset ocean-bottom node data have minimized these imaging issues at Atlantis, providing another step change in subsalt understanding. Although the 3D RTM images using the elastic FWI velocity model are similar overall to their acoustic counterparts, the 4D time-lapse RTM images at Atlantis show noticeable improvements. Furthermore, FDR images derived from elastic FWI velocities show obvious benefits over the acoustic ones. With a more accurate modeling engine that allows for better match between synthetic and real data, FDR imaging shows improved illumination, higher signal-to-noise ratio, and better reservoir details over acoustic FDR imaging. This recent advancement in using elastic TLFWI has had immediate positive effects in facilitating the Atlantis Field's current and future development.
{"title":"Atlantis — 20 years of seismic innovation finally removes the shroud of mystery","authors":"Samuel I Buist, Li Jiang, Obi Egbue, Daniel Tebo, Luis Lopez, Zhiyuan Wei, A. Hao, Chi Chen","doi":"10.1190/tle42060406.1","DOIUrl":"https://doi.org/10.1190/tle42060406.1","url":null,"abstract":"The Atlantis Field has gone through more than two decades of continuous seismic imaging efforts, during which time many innovative technologies were incubated, the most recent one being the successful application of full-waveform inversion (FWI) in salt environments. This technique led to a significant improvement in the subsalt image. However, imaging challenges remain for the Atlantis reservoirs, primarily due to the complex overburden salt geometries and the highly compartmentalized reservoir. Even with an improved velocity model from FWI, the conventional reverse time migration (RTM) images still suffer from illumination issues and contain strong migration swings that hinder the subsalt imaging and subsequent interpretations. Furthermore, early versions of FWI employed an acoustic assumption, leading to visible salt halos at the salt boundaries in the velocity model, which adversely impacted the reservoir imaging. In the last 12 months, elastic time-lag FWI (TLFWI) and FWI-derived reflectivity (FDR) imaging using long-offset ocean-bottom node data have minimized these imaging issues at Atlantis, providing another step change in subsalt understanding. Although the 3D RTM images using the elastic FWI velocity model are similar overall to their acoustic counterparts, the 4D time-lapse RTM images at Atlantis show noticeable improvements. Furthermore, FDR images derived from elastic FWI velocities show obvious benefits over the acoustic ones. With a more accurate modeling engine that allows for better match between synthetic and real data, FDR imaging shows improved illumination, higher signal-to-noise ratio, and better reservoir details over acoustic FDR imaging. This recent advancement in using elastic TLFWI has had immediate positive effects in facilitating the Atlantis Field's current and future development.","PeriodicalId":35661,"journal":{"name":"Leading Edge","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43330545","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}
Cable Warren, Sribharath Kainkaryam, Ben Lasscock, Altay Sansal, Sanath Govindarajan, A. Valenciano
Interpreting salt bodies in the Gulf of Mexico (GoM) can be complex due to various factors affecting the accuracy of automated techniques. Variability of salt structures, seismic acquisition parameters, and imaging algorithms can impact the resulting seismic image. These differences can result in variations in seismic resolution and texture, making it challenging to develop automated interpretation techniques that are accurate and reliable for identifying salt bodies in the GoM. However, using seismic images with similar acquisition parameters and processing methods minimizes these differences and makes machine-learning (ML) models applicable. Utilizing nine data sets from the eastern GoM, a nine-fold cross-validation technique was applied to measure the generalization performance of the ML model. This method involves using one data set as the test set and the remaining eight for training and repeating the process for all subsets. We further applied an ensemble of the nine models to predict salt on a new unseen survey in Green Canyon. The study aimed to illustrate how salt variability and morphology in the GoM can impact the ability of the ML algorithm to predict salt bodies on unseen data.
{"title":"Toward generalized models for machine-learning-assisted salt interpretation in the Gulf of Mexico","authors":"Cable Warren, Sribharath Kainkaryam, Ben Lasscock, Altay Sansal, Sanath Govindarajan, A. Valenciano","doi":"10.1190/tle42060390.1","DOIUrl":"https://doi.org/10.1190/tle42060390.1","url":null,"abstract":"Interpreting salt bodies in the Gulf of Mexico (GoM) can be complex due to various factors affecting the accuracy of automated techniques. Variability of salt structures, seismic acquisition parameters, and imaging algorithms can impact the resulting seismic image. These differences can result in variations in seismic resolution and texture, making it challenging to develop automated interpretation techniques that are accurate and reliable for identifying salt bodies in the GoM. However, using seismic images with similar acquisition parameters and processing methods minimizes these differences and makes machine-learning (ML) models applicable. Utilizing nine data sets from the eastern GoM, a nine-fold cross-validation technique was applied to measure the generalization performance of the ML model. This method involves using one data set as the test set and the remaining eight for training and repeating the process for all subsets. We further applied an ensemble of the nine models to predict salt on a new unseen survey in Green Canyon. The study aimed to illustrate how salt variability and morphology in the GoM can impact the ability of the ML algorithm to predict salt bodies on unseen data.","PeriodicalId":35661,"journal":{"name":"Leading Edge","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48968225","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}
{"title":"In memory of Louise Pellerin","authors":"R. Knight, D. Alumbaugh","doi":"10.1190/tle42060438.1","DOIUrl":"https://doi.org/10.1190/tle42060438.1","url":null,"abstract":"Louise Pellerin","PeriodicalId":35661,"journal":{"name":"Leading Edge","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45932051","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}
Since the first offshore well was drilled there in 1938, the Gulf of Mexico (GoM) has been one of the world's most exciting and prolific oil-producing basins. Evolving seismic and production technology has kept it an active and vibrant basin for almost a century. The original offshore seismic used short streamers and dynamite sources; these evolved to ever-longer streamers, greater numbers of channels, and much more environmentally sensitive air guns to acquire huge amounts of 2D data. The Miocene trends closer to shore were largely developed with this, and bright spot technology in the 1960s and 1970s guided and allowed the rapid development of the prolific Plio-Pleistocene gas fields near the edge of the shelf.
{"title":"Introduction to this special section: Regional focus: Gulf of Mexico","authors":"F. Rollins, M. Vyas, G. Hennenfent","doi":"10.1190/tle42060388.1","DOIUrl":"https://doi.org/10.1190/tle42060388.1","url":null,"abstract":"Since the first offshore well was drilled there in 1938, the Gulf of Mexico (GoM) has been one of the world's most exciting and prolific oil-producing basins. Evolving seismic and production technology has kept it an active and vibrant basin for almost a century. The original offshore seismic used short streamers and dynamite sources; these evolved to ever-longer streamers, greater numbers of channels, and much more environmentally sensitive air guns to acquire huge amounts of 2D data. The Miocene trends closer to shore were largely developed with this, and bright spot technology in the 1960s and 1970s guided and allowed the rapid development of the prolific Plio-Pleistocene gas fields near the edge of the shelf.","PeriodicalId":35661,"journal":{"name":"Leading Edge","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47512426","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}
In earth science, integrating noninvasive continuous data streams with discrete invasive measurements remains an open challenge. We address such a problem — that of predicting whole-core mineralogy using discrete measurements with the help of machine learning. Our targets are sparsely sampled mineralogy from X-ray diffraction, and features are continually sampled elemental oxides from X-ray fluorescence. Both data sets are acquired on a core cut from a Mississippian-age mixed siliciclastic-carbonate formation in the U.S. midcontinent. The novelty lies in predicting multiple classes of output targets from input features in a small multidimensional data setting. Our workflow has three salient aspects. First, it shows how single-output models are more effective in relating selective target-feature subsets than using a multi-output model for simultaneously relating the entire target-feature set. Specifically, we adopt a competitive ensemble strategy comprising three classes of regression algorithms — elastic net (linear regression), XGBoost (tree-based), and feedforward neural networks (nonlinear regression). Second, it shows that feature selection and engineering, when done using statistical relationships within the data set and domain knowledge, can significantly improve target predictability. Third, it incorporates k-fold cross-validation and grid-search-based parameter tuning to predict targets within 4%–6% accuracy using 40% training data. Results open doors to generating a wealth of information in energy, environmental, and climate sciences where remotely sensed data are inexpensive and abundant but physical sampling may be limited due to analytic, logistic, or economic issues.
{"title":"Integration of sparse and continuous data sets using machine learning for core mineralogy interpretation","authors":"M. Nawal, B. Shekar, P. Jaiswal","doi":"10.1190/tle42060421.1","DOIUrl":"https://doi.org/10.1190/tle42060421.1","url":null,"abstract":"In earth science, integrating noninvasive continuous data streams with discrete invasive measurements remains an open challenge. We address such a problem — that of predicting whole-core mineralogy using discrete measurements with the help of machine learning. Our targets are sparsely sampled mineralogy from X-ray diffraction, and features are continually sampled elemental oxides from X-ray fluorescence. Both data sets are acquired on a core cut from a Mississippian-age mixed siliciclastic-carbonate formation in the U.S. midcontinent. The novelty lies in predicting multiple classes of output targets from input features in a small multidimensional data setting. Our workflow has three salient aspects. First, it shows how single-output models are more effective in relating selective target-feature subsets than using a multi-output model for simultaneously relating the entire target-feature set. Specifically, we adopt a competitive ensemble strategy comprising three classes of regression algorithms — elastic net (linear regression), XGBoost (tree-based), and feedforward neural networks (nonlinear regression). Second, it shows that feature selection and engineering, when done using statistical relationships within the data set and domain knowledge, can significantly improve target predictability. Third, it incorporates k-fold cross-validation and grid-search-based parameter tuning to predict targets within 4%–6% accuracy using 40% training data. Results open doors to generating a wealth of information in energy, environmental, and climate sciences where remotely sensed data are inexpensive and abundant but physical sampling may be limited due to analytic, logistic, or economic issues.","PeriodicalId":35661,"journal":{"name":"Leading Edge","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44399223","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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