RNN-based gradient prediction for solving magnetotelluric inverse problem

Joint inversion has drawn considerable attention due to the availability of multiple geophysical datasets, ever-increasing computational resources, development of advanced algorithms, and its ability to reduce inversion uncertainty. A key issue of joint inversion is to develop effective strategies to link different geophysical data in a unified mathematical framework, where the information obtained from different models can complement each other. In this paper, we propose a deep learning enhanced (DLE) joint inversion framework to simultaneously reconstruct different physical models by fusing different types of geophysical data. Traditionally, structure similarity constraints are pursued by joint inversion algorithms using manually crafted formulations (e.g. cross gradient). In this work, the constraint is constructed by a deep neural network (DNN) during the learning process. The framework is designed to combine the DNN and a traditional independent inversion workflow and improve the joint inversion result iteratively. The network can be easily extended to incorporate multi-physics without structural changes. Numerical experiments on the joint inversion of 2D DC resistivity data and seismic travel time are used to validate the proposed method. In addition, this learning-based framework demonstrates excellent generalization abilities when tested on datasets using different geological structures. It can also handle different sensing configurations and nonconforming discretization.

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Feature-based magnetotelluric inversion by variational autoencoder using a subdomain encoding scheme

Zhou

Guo

et al. 2023

GEOPHYSICS

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Magnetotelluric (MT) data inversion aims to reconstruct a subsurface resistivity model that minimizes the discrepancy between inverted and measured electromagnetic (EM) data. Conventional pixel-based minimum-structure inversion often yields a smoothed-out reconstruction with a relatively low resolution. A priori geophysical knowledge can be embedded into inversion and improve the reconstruction resolution through proper reparameterization. However, existing reparameterization approaches, such as model-based and parametric transform-based inversion, have limited ability to incorporate various a priori information. The effectiveness of existing deep generative model (DGM)-based inversion algorithms is still debatable when applied to scenarios with complex backgrounds. We propose a feature-based MT data inversion method based on variational autoencoder (VAE) with subdomain encoding scheme. Instead of encoding the entire domain of investigation (DoI), we adopt a 1D subdomain encoding scheme to encode the 1D resistivity-depth models using a single VAE. The latent variables for the 2D model are a combination of the latent variables for 1D models, and the encoded region of interest (RoI) can be flexibly determined. The latent variables of RoI and the pixels outside the RoI are simultaneously inverted using the gradient-descent method. The proposed 1D subdomain encoding scheme reduces the complexity and diversity of the dataset, and it can flexibly embed a priori knowledge with various uncertainties. Synthetic data inversion and inversion of the Southern Africa SAMTEX field data validate that the proposed method can effectively improve inversion accuracy and resolution.

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RNN-based gradient prediction for solving magnetotelluric inverse problem

Cited by 3 publications

References 8 publications

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A deep learning-enhanced framework for multiphysics joint inversion

Feature-based magnetotelluric inversion by variational autoencoder using a subdomain encoding scheme

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