{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/140020"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/140020","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"A Comparative Study of Machine Learning and Traditional Techniques for Grade Prediction and Grade-Tonnage Evaluation in a Small VMS Deposit","abstract":"Small-scale, high-grade volcanogenic massive sulfide (VMS) deposits present unique challenges for resource estimation due to their strong grade variability and complex geological structures. This thesis evaluates whether machine learning methods can improve grade prediction and tonnage estimation compared to traditional methods. A three-dimensional block model with 5 x 5 x 5 m resolution was constructed in Vulcan, and grade estimation was performed using Inverse Distance Weighting (IDW), Simple Kriging (SK), Ordinary Kriging (OK), and ensemble tree models. Traditional methods were assessed using cross-validation within Vulcan, while machine-learning models were evaluated using an independent train-test split. Approximately six million block centroids were exported for full model prediction to compare all methods directly. Machine learning models produced the highest accuracy in the test set but generated low-level noise predictions across sparsely informed areas. A filtering threshold of Au > 0.0001 g/t was applied to mitigate this effect and achieve geologically realistic tonnage estimates. Spatial block-model comparisons, residual analyses, and grade-tonnage curves showed distinct behaviors among methods. IDW yielded the highest tonnage at low cutoffs, Simple Kriging and Random Forest exhibited similar behavior in sparsely informed areas, and Ordinary Kriging consistently produced conservative tonnage estimates. After filtering, ensemble machine learning models provided improved grade discrimination and preserved localized high-grade zones more effectively than traditional methods. This study demonstrates that machine learning approaches can complement traditional methods and offer enhanced performance for small VMS deposits. The results highlight practical considerations for applying machine learning in early-stage resource evaluation and emphasize the need for domain-based modeling in later stages.","abstract_html":"Small-scale, high-grade volcanogenic massive sulfide (VMS) deposits present unique challenges for resource estimation due to their strong grade variability and complex geological structures. This thesis evaluates whether machine learning methods can improve grade prediction and tonnage estimation compared to traditional methods. A three-dimensional block model with 5 x 5 x 5 m resolution was constructed in Vulcan, and grade estimation was performed using Inverse Distance Weighting (IDW), Simple Kriging (SK), Ordinary Kriging (OK), and ensemble tree models. Traditional methods were assessed using cross-validation within Vulcan, while machine-learning models were evaluated using an independent train-test split. Approximately six million block centroids were exported for full model prediction to compare all methods directly. Machine learning models produced the highest accuracy in the test set but generated low-level noise predictions across sparsely informed areas. A filtering threshold of Au > 0.0001 g/t was applied to mitigate this effect and achieve geologically realistic tonnage estimates. Spatial block-model comparisons, residual analyses, and grade-tonnage curves showed distinct behaviors among methods. IDW yielded the highest tonnage at low cutoffs, Simple Kriging and Random Forest exhibited similar behavior in sparsely informed areas, and Ordinary Kriging consistently produced conservative tonnage estimates. After filtering, ensemble machine learning models provided improved grade discrimination and preserved localized high-grade zones more effectively than traditional methods. This study demonstrates that machine learning approaches can complement traditional methods and offer enhanced performance for small VMS deposits. The results highlight practical considerations for applying machine learning in early-stage resource evaluation and emphasize the need for domain-based modeling in later stages.","abstract_has_math":false,"creators":["Bag, Cemile Dilara"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Mining Engineering","degree_department":"Mining Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Westman, Erik Christian"],"committee_members":["Pandey, Rohit","Frieman, Ben M."],"year":2025,"date_issued":"2025-12-17","date_published":"2025-12-17","updated_at":"2026-07-22T22:20:32Z","subjects":["Machine learning; ore grade estimation; block modelling; grade-tonnage curve; VMS deposit; Random Forest; Gradient Boosting","IDW; Kriging"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:45468"],"render_values":[{"text":"vt_gsexam:45468","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/140020","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Westman, Erik Christian"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Pandey, Rohit","Frieman, Ben M."]},{"key":"dc:contributor.department","label":"Department","values":["Mining Engineering"]},{"key":"dc:creator","label":"Author","values":["Bag, Cemile Dilara"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-12-18T09:00:44Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-12-18T09:00:44Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-12-17"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mining Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Machine learning; ore grade estimation; block modelling; grade-tonnage curve; VMS deposit; Random Forest; Gradient Boosting","IDW; Kriging"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:45468"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/140020"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Small-scale, high-grade volcanogenic massive sulfide (VMS) deposits present unique challenges for resource estimation due to their strong grade variability and complex geological structures. This thesis evaluates whether machine learning methods can improve grade prediction and tonnage estimation compared to traditional methods. A three-dimensional block model with 5 x 5 x 5 m resolution was constructed in Vulcan, and grade estimation was performed using Inverse Distance Weighting (IDW), Simple Kriging (SK), Ordinary Kriging (OK), and ensemble tree models. Traditional methods were assessed using cross-validation within Vulcan, while machine-learning models were evaluated using an independent train-test split. Approximately six million block centroids were exported for full model prediction to compare all methods directly. Machine learning models produced the highest accuracy in the test set but generated low-level noise predictions across sparsely informed areas. A filtering threshold of Au > 0.0001 g/t was applied to mitigate this effect and achieve geologically realistic tonnage estimates. Spatial block-model comparisons, residual analyses, and grade-tonnage curves showed distinct behaviors among methods. IDW yielded the highest tonnage at low cutoffs, Simple Kriging and Random Forest exhibited similar behavior in sparsely informed areas, and Ordinary Kriging consistently produced conservative tonnage estimates. After filtering, ensemble machine learning models provided improved grade discrimination and preserved localized high-grade zones more effectively than traditional methods. This study demonstrates that machine learning approaches can complement traditional methods and offer enhanced performance for small VMS deposits. The results highlight practical considerations for applying machine learning in early-stage resource evaluation and emphasize the need for domain-based modeling in later stages."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Mineral deposits that contain gold and other valuable metals are becoming harder to find, especially the large deposits that historically supplied much of the world's production. As a result, smaller but high-grade deposits, such as those formed by ancient underwater volcanic activity, are becoming increasingly important. Estimating how much metal these deposits contain is a key step in planning a mine, but this is challenging when drillhole data are limited and the rocks vary greatly over short distances. This thesis explores whether modern computer-based approaches, known as machine-learning methods, can improve these estimates compared with traditional techniques used by geologists. Using a three-dimensional model of a gold-rich volcanic deposit, several estimation methods were tested and compared. Traditional methods rely mainly on distance and spatial patterns, while machine-learning methods learn relationships directly from the data. The results show that machine-learning methods can provide more accurate predictions where drilling information is available, and they can identify patterns that traditional methods may miss. However, they can also produce small, unrealistic grade values in areas far from drillholes. By filtering out these noise values, the predictions became more realistic and easier to compare with standard geological methods. Overall, this research shows that machine-learning techniques have strong potential to support early-stage evaluations of small, high-grade mineral deposits. When used carefully and combined with geological knowledge, they can help guide exploration decisions and improve the understanding of how much valuable material a deposit may contain."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["A Comparative Study of Machine Learning and Traditional Techniques for Grade Prediction and Grade-Tonnage Evaluation in a Small VMS Deposit"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Westman, Erik Christian"],"dc:contributor.committeemember":["Pandey, Rohit","Frieman, Ben M."],"dc:contributor.department":["Mining Engineering"],"dc:creator":["Bag, Cemile Dilara"],"dc:date.accessioned":["2025-12-18T09:00:44Z"],"dc:date.available":["2025-12-18T09:00:44Z"],"dc:date.issued":["2025-12-17"],"dc:description.abstract":["Small-scale, high-grade volcanogenic massive sulfide (VMS) deposits present unique challenges for resource estimation due to their strong grade variability and complex geological structures. This thesis evaluates whether machine learning methods can improve grade prediction and tonnage estimation compared to traditional methods. A three-dimensional block model with 5 x 5 x 5 m resolution was constructed in Vulcan, and grade estimation was performed using Inverse Distance Weighting (IDW), Simple Kriging (SK), Ordinary Kriging (OK), and ensemble tree models. Traditional methods were assessed using cross-validation within Vulcan, while machine-learning models were evaluated using an independent train-test split. Approximately six million block centroids were exported for full model prediction to compare all methods directly. Machine learning models produced the highest accuracy in the test set but generated low-level noise predictions across sparsely informed areas. A filtering threshold of Au > 0.0001 g/t was applied to mitigate this effect and achieve geologically realistic tonnage estimates. Spatial block-model comparisons, residual analyses, and grade-tonnage curves showed distinct behaviors among methods. IDW yielded the highest tonnage at low cutoffs, Simple Kriging and Random Forest exhibited similar behavior in sparsely informed areas, and Ordinary Kriging consistently produced conservative tonnage estimates. After filtering, ensemble machine learning models provided improved grade discrimination and preserved localized high-grade zones more effectively than traditional methods. This study demonstrates that machine learning approaches can complement traditional methods and offer enhanced performance for small VMS deposits. The results highlight practical considerations for applying machine learning in early-stage resource evaluation and emphasize the need for domain-based modeling in later stages."],"dc:description.abstractgeneral":["Mineral deposits that contain gold and other valuable metals are becoming harder to find, especially the large deposits that historically supplied much of the world's production. As a result, smaller but high-grade deposits, such as those formed by ancient underwater volcanic activity, are becoming increasingly important. Estimating how much metal these deposits contain is a key step in planning a mine, but this is challenging when drillhole data are limited and the rocks vary greatly over short distances. This thesis explores whether modern computer-based approaches, known as machine-learning methods, can improve these estimates compared with traditional techniques used by geologists. Using a three-dimensional model of a gold-rich volcanic deposit, several estimation methods were tested and compared. Traditional methods rely mainly on distance and spatial patterns, while machine-learning methods learn relationships directly from the data. The results show that machine-learning methods can provide more accurate predictions where drilling information is available, and they can identify patterns that traditional methods may miss. However, they can also produce small, unrealistic grade values in areas far from drillholes. By filtering out these noise values, the predictions became more realistic and easier to compare with standard geological methods. Overall, this research shows that machine-learning techniques have strong potential to support early-stage evaluations of small, high-grade mineral deposits. When used carefully and combined with geological knowledge, they can help guide exploration decisions and improve the understanding of how much valuable material a deposit may contain."],"dc:description.degree":["Master of Science"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:45468"],"dc:identifier.uri":["https://hdl.handle.net/10919/140020"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Machine learning; ore grade estimation; block modelling; grade-tonnage curve; VMS deposit; Random Forest; Gradient Boosting","IDW; Kriging"],"dc:title":["A Comparative Study of Machine Learning and Traditional Techniques for Grade Prediction and Grade-Tonnage Evaluation in a Small VMS Deposit"],"dc:type":["Thesis"],"thesis:degree_discipline":["Mining Engineering"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:20:32Z"}