[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128435-en":3,"doc-seo-128435-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128435,962084925290,"Ophelia","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Using Generative Adversarial Networks to Translate Microresistivity Image Logs of Carbonates Into Synthetic Core Images With Accurate Dunham Textures","Core images offer high-resolution texture and fabric information for sedimentary rocks, but core availability is often restricted by cost and poor recovery, especially in friable carbonate sequences. This study uses Generative Adversarial Networks with supervised image-to-image translation to generate realistic synthetic core images from Formation MicroScanner (FMS) microresistivity logs. Ten model variants were trained and evaluated using quantitative imaging metrics versus ground-truth cores, and geologist classification results. The best pix2pixHD configuration reduces error and substantially improves Dunham texture classification from 14% on FMS logs to 73% on synthetic cores, remaining robust across datasets, wells, and carbonate lithologies.","RESEARCH ARTICLE  \n10.1029/2024JH000533  \nKey Points:  \n• Generative Adversarial Networks were used to convert formation microscanner image logs into realistic synthetic core images  \n• Classification accuracy by geologists increases by five folds on our synthetic core images compared to formation microscanner image logs  \n• The method demonstrates robustness across diverse data sets, wells and carbonate lithologies  \nCorrespondence to:  \nC. M. John,  \n[cedric.john@qmul.ac.uk](cedric.john@qmul.ac.uk)  \nCitation:  \nBaharuddin, S., & John, C. M. (2025) . Using generative adversarial networks to translate microresistivity image logs of carbonates into synthetic core images with accurate Dunham textures. Journal of Geophysical Research: Machine Learning and Computation, 2, e2024JH000533 .  \n[https://doi.org/10.1029/2024JH000533](https://doi.org/10.1029/2024JH000533)  \nReceived 26 NOV 2024 Accepted 17 FEB 2025  \nAuthor Contributions:  \nConceptualization: Cédric M. John  \nData curation: Saira Baharuddin, Cédric M. John  \nFormal analysis: Saira Baharuddin, Cédric M. John  \nFunding acquisition: Saira Baharuddin  \nInvestigation: Saira Baharuddin  \nMethodology: Saira Baharuddin  \nProject administration: Cédric M. John  \nSoftware: Saira Baharuddin  \nSupervision: Cédric M. John  \nValidation: Saira Baharuddin  \nVisualization: Saira Baharuddin Writing – original draft:  \nSaira Baharuddin  \nWriting – review & editing: Cédric  \nM. John  \n© 2025. The Author(s) . Journal of Geophysical Research: Machine Learning and Computation published by Wiley Periodicals LLC on behalf of American Geophysical Union.  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \nUsing Generative Adversarial Networks to Translate Microresistivity Image Logs of Carbonates Into Synthetic Core Images With Accurate Dunham Textures  \nSaira Baharuddin1 and Cédric M. John1,2   \n1Department of Earth Science and Engineering, Imperial College, London, UK, 2Now at Digital Environment Research Institute (DERI), Queen Mary University of London, London, UK  \nAbstract Core images provide a high‐resolution data set of the textures and fabrics of sedimentary rocks, but their availability is often limited by cost and/or poor core recovery. An alternative solution is to utilize advanced high‐resolution micro‐resistivity images acquired through wireline logging, such as the Formation MicroScanner (FMS) . But interpreting FMS image logs requires specialized knowledge that not all geologists possess. In this study, we explore the potential of Generative Adversarial Networks (GANs) in generating realistic core images from FMS logs using supervised image‐to‐image translation models. We trained a total of 10 models, testing various combinations of FMS data input formats, image processing methods, GAN architecture, and training hyperparameters. The supervised pix2pixHD model trained using concatenated FMS pad images with a batch size of 4 produces the most realistic core images: these have low Root Mean Square Error, high Peak Signal to Noise Ratio and high Structural Similarity Index Method values when compared to the ground truth core images. Our results also stress the importance of a diversified data set to reduce bias and enhance the applicability of the trained models to other wells and fields. Blind testing with geologists shows that classification accuracy for Dunham textures increases from 14% on FMS image logs to 73% on synthetic core images. The approach proposed here has thus the potential to transform the field of subsurface characterization by bridging the gap between the limited availability of core samples and the need for comprehensive geological facies classification.  \nPlain Language Summary This study focuses on creating realistic images of rock cores using advanced computer models. Rock cores are essential for understanding ","cbCaihUKUAWFyqPz","https://ap.wps.com/l/cbCaihUKUAWFyqPz","pdf",5669719,1,19,"English","en",105,"# Key Points\n## Method and model training\n## Evaluation metrics and geologist classification\n# Introduction\n## Core acquisition challenges\n## Wireline logging and FMS image logs","[{\"question\":\"Why are synthetic core images needed in carbonate studies?\",\"answer\":\"Core images are crucial for sedimentary texture and fabric analysis, but obtaining cores can be costly and time-consuming, and carbonate core recovery is often low. This limits continuity of geological records and hinders facies interpretation.\"},{\"question\":\"How does the proposed method generate synthetic core images from FMS logs?\",\"answer\":\"The study trains supervised image-to-image translation models using Generative Adversarial Networks to map Formation MicroScanner (FMS) microresistivity image logs into realistic synthetic core images. Ten model configurations are tested, including different inputs, processing choices, GAN architectures, and hyperparameters.\"},{\"question\":\"What performance improvement is reported for geologist classification of Dunham textures?\",\"answer\":\"Blind testing with geologists shows Dunham texture classification accuracy increases from 14% using FMS image logs to 73% using synthetic core images.\"}]","Using Generative Adversarial Networks to Translate Microresistivity Image Logs of Carbonates Into Synthetic Core Images With Accurate Dunham Textures | PDF",1785947681,48,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"using-generative-adversarial-networks-to-translate-microresistivity-image-logs-of-carbonates-into-synthetic-core-images-with-accurate-dunham-textures","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/using-generative-adversarial-networks-to-translate-microresistivity-image-logs-of-carbonates-into-synthetic-core-images-with-accurate-dunham-textures/128435/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are synthetic core images needed in carbonate studies?","Question",{"text":75,"@type":76},"Core images are crucial for sedimentary texture and fabric analysis, but obtaining cores can be costly and time-consuming, and carbonate core recovery is often low. This limits continuity of geological records and hinders facies interpretation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method generate synthetic core images from FMS logs?",{"text":80,"@type":76},"The study trains supervised image-to-image translation models using Generative Adversarial Networks to map Formation MicroScanner (FMS) microresistivity image logs into realistic synthetic core images. Ten model configurations are tested, including different inputs, processing choices, GAN architectures, and hyperparameters.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance improvement is reported for geologist classification of Dunham textures?",{"text":84,"@type":76},"Blind testing with geologists shows Dunham texture classification accuracy increases from 14% using FMS image logs to 73% using synthetic core images.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]