AUTHOR=Liu Yao , Cheng Jianyuan , Lü Qingtian , Liu Zaibin , Lu Jingjin , Fan Zhenyu , Zhang Lianzhi TITLE=Deep learning for geological mapping in the overburden area JOURNAL=Frontiers in Earth Science VOLUME=12 YEAR=2024 URL=https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2024.1407173 DOI=10.3389/feart.2024.1407173 ISSN=2296-6463 ABSTRACT=

This paper aims to achieve bedrock geologic mapping in the overburden area using big data, distributed computing, and deep learning techniques. First, the satellite Bouguer gravity anomaly with a resolution of 2′×2′ in the range of E66°-E96°, N40°-N55° and 1:5000000 Asia-European geological map are used to design a dataset for bedrock prediction. Then, starting from the gravity anomaly formula in the spherical coordinate system, we deduce the non-linear functional between rock density ρ and rock mineral composition m, content p, buried depth h, diagenesis time t and other variables. We analyze the feasibility of using deep neural network to approximate the above nonlinear generalization. The problem of solving deep neural network parameters is transformed into a non-convex optimization problem. We give an iterative, gradient descent-based solution algorithm for the non-convex optimization problem. Utilizing neural architecture search (NAS) and human-designed approach, we propose a geological-geophysical mapping network (GGMNet). The dataset for the network consists of both gravity anomaly and a priori geological information. The network has fast convergence speed and stable iteration during the training process. It also has better performance than a single neural network search or human-designed architectures, with the mean pixel accuracy (MAP) = 63.1% and the frequency weighted intersection over union (FWIoU) = 42.88. Finally, the GGMNet is used to predict the rock distribution of the Junggar Basin.