The identification of gravity anomaly body based on the convolutional neural network
Yi-Chen WANG1,2,3, Lin-Tao LIU1,2, Hou-Ze XU1,2
1. Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China 2. State Key Laboratory of Geodesy and Earth’s Dynamics, Chinese Academy of Sciences, Wuhan 430077, China 3. Collage of Earth and Planetary Sciences,University of Chinese Academy of Sciences, Beijing 100049, China
This study combines the deep learning with the identification of gravity anomaly body. Based on the CNN (convolutional neural network) which has been gaining its use in the past several years in the field of image identification, the contour image of gravity signal is taken as the unidentified image, while the space parameters of the gravity anomaly body will be identified through CNN. In the training phase, a large number of the 3D anomaly bodies are generated with random variation of parameters, then the network is fed with parametric labels and the computed gravity contour images. The testing is performed with generated testing models to estimate the performance of the trained model. The trained CNN accuracy shows excellent accuracy in the identifications. Then the CNN model is tested with measured main gravity anomaly data of Kauring area in West Australia, and the identified parameters of the 3D anomaly body are compared with known results. It is shown that the generalization of CNN can handle identification of the measured gravity data.
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