Deep Learning Inversion of Electrical Resistivity Data

TL;DR

This paper introduces ERSInvNet, a deep learning CNN-based approach for electrical resistivity data inversion, improving accuracy and speed over traditional methods by incorporating tier features, depth weighting, and smooth constraints.

Contribution

The paper proposes a novel CNN architecture, ERSInvNet, with tier feature maps and specialized loss functions for improved resistivity data inversion accuracy and efficiency.

Findings

ERSInvNet achieves superior inversion accuracy.

The method significantly reduces inference time.

Incorporating tier features and constraints enhances results.

Abstract

The inverse problem of electrical resistivity surveys (ERSs) is difficult because of its nonlinear and ill-posed nature. For this task, traditional linear inversion methods still face challenges such as suboptimal approximation and initial model selection. Inspired by the remarkable nonlinear mapping ability of deep learning approaches, in this article, we propose to build the mapping from apparent resistivity data (input) to resistivity model (output) directly by convolutional neural networks (CNNs). However, the vertically varying characteristic of patterns in the apparent resistivity data may cause ambiguity when using CNNs with the weight sharing and effective receptive field properties. To address the potential issue, we supply an additional tier feature map to CNNs to help those aware of the relationship between input and output. Based on the prevalent U-Net architecture, we design our network (ERSInvNet) that can be trained end-to-end and can reach a very fast inference speed during testing. We further introduce a depth weighting function and a smooth constraint into loss function to improve inversion accuracy for the deep region and suppress false anomalies. Six groups of experiments are considered to demonstrate the feasibility and efficiency of the proposed methods. According to the comprehensive qualitative analysis and quantitative comparison, ERSInvNet with tier feature map, smooth constraints, and depth weighting function together achieve the best performance.