Work

Three dimensional inversion of mise-a-la-masse-based electric data using U-Net convolutional neural network

Year: 2024

Type: conference-paper

Abstract: Nonlinear inversion algorithms are away ill-pose for the limited and special data measurement, such as mise-a-la-masse-based electric method and borehole to surface electromagnetic method. It is natural to develop new inversion algorithms without calculating the the Jacobian matrix and its inverse matrix, especially for three dimensional inversion. Basing on U-Net fully convolutional neural network, a three dimensional inversion of mise-a-la-masse-based electric data is proposed in this paper. The random rotated spheroids are used to synthesize diverse geological models. A typical U-Net learning network is constructed with incorporating the Dice coefficient and the input data feedback to the trained network. Synthetic datasets testing validates the effectiveness of this proposed algorithm for mise-a-la-masse-based electric data.

Source: Journal of Physics Conference Series

DOI: 10.1088/1742-6596/2895/1/012049

Authors: Yuhang Meng, Jinghe Li, Zhanxiang He, Yujie Zhang

Institutions: Guilin University of Technology, Southern University of Science and Technology, Guangdong Provincial Key Laboratory of Geophysical High-resolution Imaging Technology, China University of Geosciences

Language: English

Topic: Geophysical and Geoelectrical Methods

Subfield: Geophysics

Field: Earth and Planetary Sciences

Domain: Physical Sciences

Open Access status: diamond

Funder: National Natural Science Foundation of China

Awards: G4661437033, G4887920270, G6431302662