{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/140547"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/140547","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Integrating Machine Learning Techniques with Measurement-While-Drilling Data for Subsurface Characterization in Open-Pit Mines","abstract":"Measurement-While-Drilling (MWD) systems generate continuous drilling data that reflect subsurface conditions in real time. With the increasing availability of this data, there is a growing opportunity to use data-driven methods to support geological interpretation and geotechnical risk assessment in mining. However, the complexity and variability of drilling signals require analytical workflows that go beyond traditional threshold-based interpretation. This thesis integrates machine learning techniques with MWD data to improve subsurface characterization in two open-pit mines, each influenced by different operational and geological conditions. The first research component focuses on identifying zones of disturbed or weakened ground by detecting drilling behavior indicative of voids and compromised rock mass conditions in a mine affected by historic underground workings. The second component applies a structured data preparation and analysis workflow to develop predictive models for lithology and penetration rate in a separate open-pit operation, demonstrating how MWD data can support geological classification and drilling performance evaluation. Across both studies, the research highlights the importance of exploratory data analysis (EDA), feature engineering, and appropriate model selection. The results show that machine learning offers a scalable and effective way to extract meaningful information from MWD data, enhancing both geotechnical hazard detection and geological modeling. These findings demonstrate the value of integrating modern data science methods into mining workflows, contributing to safer and more informed operational decision-making.","abstract_html":"Measurement-While-Drilling (MWD) systems generate continuous drilling data that reflect subsurface conditions in real time. With the increasing availability of this data, there is a growing opportunity to use data-driven methods to support geological interpretation and geotechnical risk assessment in mining. However, the complexity and variability of drilling signals require analytical workflows that go beyond traditional threshold-based interpretation. This thesis integrates machine learning techniques with MWD data to improve subsurface characterization in two open-pit mines, each influenced by different operational and geological conditions. The first research component focuses on identifying zones of disturbed or weakened ground by detecting drilling behavior indicative of voids and compromised rock mass conditions in a mine affected by historic underground workings. The second component applies a structured data preparation and analysis workflow to develop predictive models for lithology and penetration rate in a separate open-pit operation, demonstrating how MWD data can support geological classification and drilling performance evaluation. Across both studies, the research highlights the importance of exploratory data analysis (EDA), feature engineering, and appropriate model selection. The results show that machine learning offers a scalable and effective way to extract meaningful information from MWD data, enhancing both geotechnical hazard detection and geological modeling. These findings demonstrate the value of integrating modern data science methods into mining workflows, contributing to safer and more informed operational decision-making.","abstract_has_math":false,"creators":["Addy, Jesse"],"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":["Ripepi, Nino S.","Pandey, Rohit"],"year":2025,"date_issued":"2025-12-22","date_published":"2025-12-22","updated_at":"2026-07-22T22:18:47Z","subjects":["Measurement-While-Drilling (MWD); exploratory data analysis (EDA); supervised learning; unsupervised learning; void detection; lithology; penetration rate; open-pit mining."],"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:45446"],"render_values":[{"text":"vt_gsexam:45446","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/140547","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":["Ripepi, Nino S.","Pandey, Rohit"]},{"key":"dc:contributor.department","label":"Department","values":["Mining Engineering"]},{"key":"dc:creator","label":"Author","values":["Addy, Jesse"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-12-23T09:01:04Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-12-23T09:01:04Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-12-22"]},{"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":["Measurement-While-Drilling (MWD); exploratory data analysis (EDA); supervised learning; unsupervised learning; void detection; lithology; penetration rate; open-pit mining."]}]},{"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:45446"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/140547"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Measurement-While-Drilling (MWD) systems generate continuous drilling data that reflect subsurface conditions in real time. With the increasing availability of this data, there is a growing opportunity to use data-driven methods to support geological interpretation and geotechnical risk assessment in mining. However, the complexity and variability of drilling signals require analytical workflows that go beyond traditional threshold-based interpretation. This thesis integrates machine learning techniques with MWD data to improve subsurface characterization in two open-pit mines, each influenced by different operational and geological conditions. The first research component focuses on identifying zones of disturbed or weakened ground by detecting drilling behavior indicative of voids and compromised rock mass conditions in a mine affected by historic underground workings. The second component applies a structured data preparation and analysis workflow to develop predictive models for lithology and penetration rate in a separate open-pit operation, demonstrating how MWD data can support geological classification and drilling performance evaluation. Across both studies, the research highlights the importance of exploratory data analysis (EDA), feature engineering, and appropriate model selection. The results show that machine learning offers a scalable and effective way to extract meaningful information from MWD data, enhancing both geotechnical hazard detection and geological modeling. These findings demonstrate the value of integrating modern data science methods into mining workflows, contributing to safer and more informed operational decision-making."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Modern mines rely on drilling to understand what lies beneath the surface, but the data collected during drilling is often underused. This thesis explores how information recorded automatically during drilling known as Measurement-While-Drilling (MWD) data can be used to better understand ground conditions and make mining safer and more efficient. Using data from two open-pit mines, this research applies computer-based learning methods to interpret drilling behavior. In the first case, the goal was to find hidden voids and weak areas underground, which can lead to unsafe working conditions if left undetected. In the second case, the same type of drilling data was used to identify different rock types and predict how fast the drill would advance, helping with planning and decision-making. By combining careful data analysis with modern machine learning techniques, this work shows that drilling data can reveal far more than just depth or hardness. It can help predict geological conditions, highlight safety risks, and reduce uncertainty without slowing down mining operations. This research demonstrates that mines can gain valuable insight simply by making better use of the data they already collect."]},{"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":["Integrating Machine Learning Techniques with Measurement-While-Drilling Data for Subsurface Characterization in Open-Pit Mines"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Westman, Erik Christian"],"dc:contributor.committeemember":["Ripepi, Nino S.","Pandey, Rohit"],"dc:contributor.department":["Mining Engineering"],"dc:creator":["Addy, Jesse"],"dc:date.accessioned":["2025-12-23T09:01:04Z"],"dc:date.available":["2025-12-23T09:01:04Z"],"dc:date.issued":["2025-12-22"],"dc:description.abstract":["Measurement-While-Drilling (MWD) systems generate continuous drilling data that reflect subsurface conditions in real time. With the increasing availability of this data, there is a growing opportunity to use data-driven methods to support geological interpretation and geotechnical risk assessment in mining. However, the complexity and variability of drilling signals require analytical workflows that go beyond traditional threshold-based interpretation. This thesis integrates machine learning techniques with MWD data to improve subsurface characterization in two open-pit mines, each influenced by different operational and geological conditions. The first research component focuses on identifying zones of disturbed or weakened ground by detecting drilling behavior indicative of voids and compromised rock mass conditions in a mine affected by historic underground workings. The second component applies a structured data preparation and analysis workflow to develop predictive models for lithology and penetration rate in a separate open-pit operation, demonstrating how MWD data can support geological classification and drilling performance evaluation. Across both studies, the research highlights the importance of exploratory data analysis (EDA), feature engineering, and appropriate model selection. The results show that machine learning offers a scalable and effective way to extract meaningful information from MWD data, enhancing both geotechnical hazard detection and geological modeling. These findings demonstrate the value of integrating modern data science methods into mining workflows, contributing to safer and more informed operational decision-making."],"dc:description.abstractgeneral":["Modern mines rely on drilling to understand what lies beneath the surface, but the data collected during drilling is often underused. This thesis explores how information recorded automatically during drilling known as Measurement-While-Drilling (MWD) data can be used to better understand ground conditions and make mining safer and more efficient. Using data from two open-pit mines, this research applies computer-based learning methods to interpret drilling behavior. In the first case, the goal was to find hidden voids and weak areas underground, which can lead to unsafe working conditions if left undetected. In the second case, the same type of drilling data was used to identify different rock types and predict how fast the drill would advance, helping with planning and decision-making. By combining careful data analysis with modern machine learning techniques, this work shows that drilling data can reveal far more than just depth or hardness. It can help predict geological conditions, highlight safety risks, and reduce uncertainty without slowing down mining operations. This research demonstrates that mines can gain valuable insight simply by making better use of the data they already collect."],"dc:description.degree":["Master of Science"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:45446"],"dc:identifier.uri":["https://hdl.handle.net/10919/140547"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Measurement-While-Drilling (MWD); exploratory data analysis (EDA); supervised learning; unsupervised learning; void detection; lithology; penetration rate; open-pit mining."],"dc:title":["Integrating Machine Learning Techniques with Measurement-While-Drilling Data for Subsurface Characterization in Open-Pit Mines"],"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":"2026-07-22T22:18:47Z"}