A Neural Network Architecture Based on Attention Gate Mechanism for 3D Magnetotelluric Forward Modeling

• URL: http://arxiv.org/abs/2503.11408v1
• Date: Fri, 14 Mar 2025 13:48:25 GMT
• Title: A Neural Network Architecture Based on Attention Gate Mechanism for 3D Magnetotelluric Forward Modeling
• Authors: Xin Zhong, Weiwei Ling, Kejia Pan, Pinxia Wu, Jiajing Zhang, Zhiliang Zhan, Wenbo Xiao,
• Abstract summary: We propose a novel neural network architecture named MTAGU-Net, which integrates an attention gating mechanism for 3D MT forward modeling.<n>A dual-path attention gating module is designed based on forward response data images and embedded in the skip connections between the encoder and decoder.<n>A synthetic model generation method utilizing 3D Gaussian random field (GRF) accurately replicates the electrical structures of real-world geological scenarios.
• Score: 1.5862483908050367
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional three-dimensional magnetotelluric (MT) numerical forward modeling methods, such as the finite element method (FEM) and finite volume method (FVM), suffer from high computational costs and low efficiency due to limitations in mesh refinement and computational resources. We propose a novel neural network architecture named MTAGU-Net, which integrates an attention gating mechanism for 3D MT forward modeling. Specifically, a dual-path attention gating module is designed based on forward response data images and embedded in the skip connections between the encoder and decoder. This module enables the fusion of critical anomaly information from shallow feature maps during the decoding of deep feature maps, significantly enhancing the network's capability to extract features from anomalous regions. Furthermore, we introduce a synthetic model generation method utilizing 3D Gaussian random field (GRF), which accurately replicates the electrical structures of real-world geological scenarios with high fidelity. Numerical experiments demonstrate that MTAGU-Net outperforms conventional 3D U-Net in terms of convergence stability and prediction accuracy, with the structural similarity index (SSIM) of the forward response data consistently exceeding 0.98. Moreover, the network can accurately predict forward response data on previously unseen datasets models, demonstrating its strong generalization ability and validating the feasibility and effectiveness of this method in practical applications.

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