ADASYN-CatBoost method for lithology identification with imbalanced well-logging data: A case study of Zhaoxian Gold deposit in northwest Jiaodong peninsula, China

PreviousNext No AccessSEG 2021 Workshop: 4th International Workshop on Mathematical Geophysics: Traditional & Learning, Virtual, 17–19 December 2021ADASYN-CatBoost method for lithology identification with imbalanced well-logging data: A case study of Zhaoxian Gold deposit in northwest Jiaodong peninsula, ChinaAuthors: Zhuowei YiYanhong ZouZhuowei YiCentral South UniversitySearch for more papers by this author and Yanhong ZouCentral South UniversitySearch for more papers by this authorhttps://doi.org/10.1190/iwmg2021-38.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract Accurate and rapid identification of underlying rock mass in the coverage area is of great significance for geological prospecting. Various machine learning methods have been applied to identify lithology from well logs in oil and gas fields. However, due to the non-equilibrium sample in mineral deposits because of various lithology distribution under complex geological conditions, it is necessary to investigate the availability of lithology identification using such machine learning algorithms. In this paper, we propose an application framework using adaptive synthetic (ADASYN) sampling approach and CatBoost algorithm to identify lithology from well logs in a mineral deposit. Firstly, the ADASYN algorithm is used to process the imbalanced logging sample data, adaptively generating new samples according to the weighted distributions of minority data samples. Then the CatBoost classifier was built via the procedure of grid search and cross-validation to optimize the hyperparameters. Finally, as a case of a set of well logs of Zhaoxian gold deposit in northwest Jiaodong, an optimized CatBoost classifier was built to identify ten lithological classes in the study area. The results demonstrate that CatBoost with ADASYN oversampling has good classification performance in lithology identification, with a precision of 98.21 %, a recall of 98.20 %and an F1-score of 98.20 %. The superior performance of the CatBoost classifier was validated in comparison with the GBDT and LightGBM. The validity of the classification results of the model is verified by comparing with the core lithology. The proposed method provides an effective technical means for the rapid identification of complex lithology in mineral deposits. Keywords: logging, algorithm, resistivity, spontaneous potential, machine learningPermalink: https://doi.org/10.1190/iwmg2021-38.1FiguresReferencesRelatedDetails SEG 2021 Workshop: 4th International Workshop on Mathematical Geophysics: Traditional & Learning, Virtual, 17–19 December 2021ISSN (online):2159-6832Copyright: 2022 Pages: 193 publication data© 2022 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 24 Feb 2022 CITATION INFORMATION Zhuowei Yi and Yanhong Zou, (2022), "ADASYN-CatBoost method for lithology identification with imbalanced well-logging data: A case study of Zhaoxian Gold deposit in northwest Jiaodong peninsula, China," SEG Global Meeting Abstracts : 147-151. https://doi.org/10.1190/iwmg2021-38.1 Plain-Language Summary Keywordsloggingalgorithmresistivityspontaneous potentialmachine learningPDF DownloadLoading ...