Three-dimensional mineral prospectivity mapping Considering structural restoration in the Dayingezhuang gold Deposit, eastern china

Authors:
raw_author_name: Jinli Wang
raw_affiliation_string: Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring Ministry of Education, School of Geosciences and Info-Physics, Central South University, Changsha 410083, China; Key Laboratory of Non-Ferrous Resources and Geological Hazard Detection, Changsha 410083, China
raw_author_name: Xiancheng Mao
raw_affiliation_string: Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring Ministry of Education, School of Geosciences and Info-Physics, Central South University, Changsha 410083, China; Key Laboratory of Non-Ferrous Resources and Geological Hazard Detection, Changsha 410083, China
raw_author_name: Zhankun Liu
raw_affiliation_string: Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring Ministry of Education, School of Geosciences and Info-Physics, Central South University, Changsha 410083, China; Key Laboratory of Non-Ferrous Resources and Geological Hazard Detection, Changsha 410083, China
raw_author_name: Hao Deng
raw_affiliation_string: Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring Ministry of Education, School of Geosciences and Info-Physics, Central South University, Changsha 410083, China; Key Laboratory of Non-Ferrous Resources and Geological Hazard Detection, Changsha 410083, China
raw_author_name: Jin Chen
raw_affiliation_string: Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring Ministry of Education, School of Geosciences and Info-Physics, Central South University, Changsha 410083, China; Key Laboratory of Non-Ferrous Resources and Geological Hazard Detection, Changsha 410083, China
raw_author_name: Yudong Chen
raw_affiliation_string: Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring Ministry of Education, School of Geosciences and Info-Physics, Central South University, Changsha 410083, China; Key Laboratory of Non-Ferrous Resources and Geological Hazard Detection, Changsha 410083, China

Abstract: In the era of big data and the rapid advancements in machine learning, particularly deep learning, three-dimensional mineral prospectivity mapping (3D MPM) has emerged as a powerful tool for accurately deciphering the complex nonlinear relationships between ore-controlling factors hidden within geoscientific datasets and known mineralization. However, post-mineralization structural deformations can significantly distort the structures and mineralization distribution. This distortion introduces a substantial amount of error into 3D MPM, thereby diminishing its effectiveness in mineral resource exploration. In response to this challenge, our study introduces an innovative 3D MPM approach incorporating structural restoration. This approach has been applied to the Dayingezhuang gold deposit, where the Zhaoping fault, experienced disruption due to the post-mineralization NW-trending Dayingezhuang fault. The existing 3D model of the Zhaoping fault was first constructed using the observation data. A 3D geometric transformation technique was then employed to restore the original structures and mineralization distribution during the mineralization period. Subsequently, morphological analysis and prospectivity modeling were performed using deep learning algorithms including the Bayesian decomposition model for regression and multilayer perceptron model for classification. The results indicated several crucial findings: (1) structural restoration effectively eliminated the spatial distortions (250 m to 500 m) within the Zhaoping fault and rectified the discontinuities in mineralization zones; (2) the post-mineralization ore-controlling features exhibited smaller dips and undulations, with the main mineralization concentrated in areas transitioning from steep to gentle dips (-10°/100 m to 2°/100 m); (3) the post-restoration regression and classification models showed higher accuracy and reliability compared to the pre-restoration models, with an improvement of approximately 27 % and 10 %, respectively. As per our research findings, structural restoration effectively eliminates distortions in both fault and mineralization induced by post-ore structural deformations. Consequently, this method significantly enhances the reliability of 3D MPM. In the context of structural deformations, it underscores the necessity and effectiveness of structural restoration in conducting 3D MPM on a deposit scale.