Application of Sample-Compressed Neural Network and Adaptive-Clustering Algorithm for Magnetotelluric Inverse Modeling

In this letter, two machine learning algorithms are improved, including a sample-compressed neural network algorithm for magnetotelluric (MT) inversion and an adaptive-clustering analysis algorithm for boundary demarcation. MT is widely used in deep geological structure exploration; however, data pr...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters Vol. 18; no. 9; pp. 1540 - 1544
Main Authors: Liu, Weiqiang, Lu, Qingtian, Yang, Liangyong, Lin, Pinrong, Wang, Zhihui
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.09.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive algorithms
Adaptive sampling
Algorithms
Artificial neural networks (ANNs)
Automation
Cluster analysis
Clustering
Clustering algorithms
clustering methods
Data analysis
Data processing
Discrete cosine transforms
Geological structures
Geology
geophysical image processing
geophysics
Image coding
Learning algorithms
Machine learning
Machine learning algorithms
Neural networks
Optimization
Training
Underground structures
ISSN:1545-598X, 1558-0571
Online Access:Get full text
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Summary:In this letter, two machine learning algorithms are improved, including a sample-compressed neural network algorithm for magnetotelluric (MT) inversion and an adaptive-clustering analysis algorithm for boundary demarcation. MT is widely used in deep geological structure exploration; however, data processing and interpretation still need to be further improved. Inverting the underground electrical structure model from the surface electromagnetic response is a highly nonlinear optimization problem. Common quasi-linear algorithms rely on the initial model and are easy to converge to a local minimum. In addition, demarcating the boundary and attributes of the abnormal bodies according to the inversion results is often manual, inefficient, and haphazard. The validity of the above two machine learning methods is proved by using the simulated data and the actual data. The new algorithms can improve the efficiency and automation of MT data inversion imaging.
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ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2020.3005796