Deep learning for geological mapping in the overburden area.

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'x2' 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 descentbased 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. [ABSTRACT FROM AUTHOR]