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Transient Electromagnetic Machine Learning Inversion Based on Pseudo Wave Field Data
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AI
DOI：10.1109/TGRS.2022.3187021.png)
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摘要
En 中文
Machine learning inversion (ML-inversion) has been widely used in the interpretation of geophysical data. However, because the transient electromagnetic (TEM) is not sensitive to the response of the small-size or large-depth abnormal body, there are some problems, such as poor imaging accuracy and low resolution when directly processing raw TEM data with machine learning (ML) method. To solve this problem, we propose to use the pseudo seismic wavelet (PSW) data obtained from the TEM wave field transformation to perform TEM ML-inversion. Simulation and example application verify that compared with the TEM data, the PSW data has higher recognition sensitivity to the electrical changes of underground anomalies, and the ML-inversion based on the PSW data can significantly improve the TEM imaging accuracy. Our study demonstrates that the PSW data can be used to achieve TEM ML-inversion, which may provide a new way for the further combination of electromagnetic data processing and ML technology.
Keyword:
Mathematical models
Conductivity
Electromagnetics
Geology
Data models
Training
Sensitivity
Machine learning inversion (ML-inversion)
pseudo seismic wavelet (PSW)
transient electromagnetic (TEM)
wave field transformation
期刊
IF:
8.6
论文数:
2.2W
被引数:
10.7W
机构
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