Model-Based Synthetic Geoelectric Sampling for Magnetotelluric Inversion With Deep Neural Networks

Neural networks (NNs) are efficient tools for rapidly obtaining geoelectric models to solve magnetotelluric (MT) inversion problems. Training an NN with strong predictive power requires numerous training samples to prevent underfitting. To reduce the computational burden of generating a large number...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2022, Vol.60, p.1-14
Main Authors: Li, Ruiheng, Yu, Nian, Wang, Xuben, Liu, Yang, Cai, Zhikun, Wang, Enci
Format: Article
Language:English
Subjects:
Artificial neural networks
Computer applications
Computing costs
Conductivity
Cost analysis
Data imbalance
Data models
deep neural network (DNN)
Distribution
Geoelectricity
Geological mapping
inversion
Learning algorithms
Machine learning
magnetotelluric (MT)
Mathematical model
Neural networks
Neurons
Predictive models
Training
training sample
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Summary:Neural networks (NNs) are efficient tools for rapidly obtaining geoelectric models to solve magnetotelluric (MT) inversion problems. Training an NN with strong predictive power requires numerous training samples to prevent underfitting. To reduce the computational burden of generating a large number of training samples, this work analyzes the influence of the sample features and distribution on the training effect for an NN and proposes an efficient method of sample generation. This innovative method consists of three steps: 1) geoelectrically simplifying the features; 2) removing unnecessary features on the basis of realistic geological characteristics; and 3) mapping the samples to a higher-dimensional space. Numerical examples based on simple stratified models show that the number of samples can be reduced to below one-millionth of the original number while improving the predictive effect of the NN. The performance and effectiveness for processing more complex structures are verified by the inversion results obtained for a public data set, COPROD2. We conclude that this advanced method can generate high-quality training samples at a greatly reduced computational cost. The analysis of the sample features and distribution not only advances the state of research on the use of machine learning in geophysical inversion but also is a forward-looking study on the mechanisms of underfitting, tracing the source of these phenomena back to the training samples used.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2020.3043661