[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290544-105":59,"doc-detail-290544-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","making-sense-of-multi-attribute-machine-learning-gaining-seismic-interpretation-insights-with-shap-for-unsupervised-clustering","Making Sense of Multi-Attribute Machine Learning - Gaining Seismic Interpretation Insights with SHAP for Unsupervised Clustering","","Seismic multi-attribute analysis uses unsupervised machine learning to reveal patterns across multiple attribute dimensions and summarize information for appraisal of geological objects behind different seismic signal expressions. Traditionally, unsupervised outputs such as volumes and maps act mainly as leads, while interpretation still requires revisiting original attribute volumes. The lack of interpretability leaves model reasoning opaque to interpreters. This thesis integrates SHAP explainability with an unsupervised workflow by using Gaussian Mixture Model cluster membership probability distributions as a proxy confidence metric.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/making-sense-of-multi-attribute-machine-learning-gaining-seismic-interpretation-insights-with-shap-for-unsupervised-clustering/290544/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/making-sense-of-multi-attribute-machine-learning-gaining-seismic-interpretation-insights-with-shap-for-unsupervised-clustering/290544.png","ImageObject",300,407,{"name":92,"@type":93},"Noah","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-07","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",17,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the thesis address in unsupervised seismic multi-attribute analysis?","Question",{"text":113,"@type":114},"Unsupervised learning can identify patterns, but its interpretability layer is missing, making internal reasoning opaque to the seismic interpreter. Conventional workflows still require returning to original attribute volumes for interpretation.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does the study adapt SHAP for an unsupervised clustering workflow?",{"text":118,"@type":114},"It uses probability distributions from a Gaussian Mixture Model as a proxy metric for cluster membership confidence. This provides the attribution target needed to apply SHAP in an unsupervised setting.",{"name":120,"@type":111,"acceptedAnswer":121},"What does the integration reveal about seismic attribute contributions?",{"text":122,"@type":114},"The SHAP analysis shows how individual seismic attributes contribute to each cluster assignment, producing geophysically meaningful information alongside appraised clusters.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290544,1789641986,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":21,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144,"preview_page_count":4},8796095462418,"https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780","UNIVERSITY OF OKLAHOMA  \nGRADUATE COLLEGE  \nMAKING SENSE OF MULTI-ATTRIBUTE MACHINE LEARNING: GAINING SEISMIC INTERPRETATION INSIGHTS WITH SHAP FOR UNSUPERVISED  \nCLUSTERING  \nA THESIS  \nSUBMITTED TO THE GRADUATE FACULTY  \nin partial fulfillment of the requirements for the  \nDegree of  \nMaster of Science  \nBy HILMI PUTRA  \nNorman, Oklahoma  \nMAKING SENSE OF MULTI-ATTRIBUTE MACHINE LEARNING: GAINING SEISMIC INTERPRETATION INSIGHTS WITH SHAP FOR UNSUPERVISED  \nCLUSTERING  \nA THESIS APPROVED FOR THE  \nSCHOOL OF GEOSCIENCES  \nBY THE COMMITTEE CONSISTING OF  \nDr. Heather Bedle, Chair  \nDr. Hao Hu  \nDr. David Lubo-Robles  \nABSTRACT  \nIn seismic multi-attribute analysis, unsupervised machine learning helps identify patterns inherent in the data across the multiple attribute dimensions. It summarizes the information intuitively for appraisal of the underlying geological objects responsible for the different seismic signal expressions. Conventionally, though, unsupervised machine learning products—in the form of volumes and maps—serves mainly as leads for the seismic interpreter, while applying further seismic attribute theory for interpretation still requires going back to the original input attribute volumes. This points to a deeper challenge: unsupervised methods inherently lack an interpretability layer, leaving the model's internal reasoning opaque to the interpreter.  \nExplainability frameworks such as SHapley Additive exPlanations (SHAP) have gained traction in supervised learning, where a well-defined output metric—typically a prediction probability or classification score—enables the attribution of input feature contributions. Without an equivalent metric, unsupervised models have largely been outside the scope of SHAP-based interpretability. To bridge this gap, this study utilizes probability distributions from a Gaussian Mixture Model (GMM) as a proxy metric for cluster membership confidence, effectively applying SHAP to an unsupervised multi-attribute machine learning workflow.  \nThis integration enables analysis into how individual seismic attributes contribute to each cluster assignment. Which means each appraised cluster is then accompanied by geophysically meaningful information that highlights the key signal expressions and attributes characterizing it. This added interpretability works to equip the geoscientist with data-driven quantitative means to support their interpretation, while simultaneously grounding the machine learning results back to seismic attribute theory.  \nIn this study, we found that the SHAP graphical descriptions align with seismic attribute characterization of geological targets in the Taranaki Basin, showing how the cluster is expressed in each attribute space. The graphical descriptions enhance the role of the unsupervised machine learning workflows in seismic multi-attribute analysis and provide intuition-driven mechanisms for quality control and trust in machine learning products.  \nTABLE OF CONTENTS  \nABSTRACT................................................................................................................................... iv  \nTABLE OF CONTENTS ................................................................................................................ v  \n[TABLE OF FIGURES ................................................................................................................... vi](TABLE OF FIGURES ................................................................................................................... vi)  \n[1 Introduction...............................](1 Introduction...............................).............................................................................................. 1  \n1.1 Data ................................................................................................................................. 3  \n1.2 Geological Target ...............................................................................................","cbCaikARc9oUC5h3","https://ap.wps.com/l/cbCaikARc9oUC5h3","pdf",12326476,"English","# Abstract\n# 1 Introduction\n## 1.1 Data\n## 1.2 Geological Target\n# 2 Methodology\n## 2.1 Seismic Attributes\n## 2.2 Gaussian Mixture Models (GMM)\n## 2.3 SHapley Additive exPlanations (SHAP)\n# 3 Results and Discussion\n## 3.1 GMM\n## 3.2 SHAP Analysis\n# 4 Conclusion\n## 4.1 Limitations","[{\"question\":\"What problem does the thesis address in unsupervised seismic multi-attribute analysis?\",\"answer\":\"Unsupervised learning can identify patterns, but its interpretability layer is missing, making internal reasoning opaque to the seismic interpreter. Conventional workflows still require returning to original attribute volumes for interpretation.\"},{\"question\":\"How does the study adapt SHAP for an unsupervised clustering workflow?\",\"answer\":\"It uses probability distributions from a Gaussian Mixture Model as a proxy metric for cluster membership confidence. This provides the attribution target needed to apply SHAP in an unsupervised setting.\"},{\"question\":\"What does the integration reveal about seismic attribute contributions?\",\"answer\":\"The SHAP analysis shows how individual seismic attributes contribute to each cluster assignment, producing geophysically meaningful information alongside appraised clusters.\"}]","Making Sense of Multi-Attribute Machine Learning - Gaining Seismic Interpretation Insights with SHAP for Unsupervised Clustering | PDF",176]