Physics-guided deep learning-based inversion for airborne electromagnetic data
Author:
Affiliation:
1. Department of Geophysics, School of Earth and Space Sciences, Peking University , Beijing 100871 , China
2. Hebei Hongshan National Observatory on Thick Sediments and Seismic Hazards, Peking University , Beijing 100871 , China
Abstract
Funder
National Natural Science Foundation of China
China Postdoctoral Science Foundation
Publisher
Oxford University Press (OUP)
Link
https://academic.oup.com/gji/advance-article-pdf/doi/10.1093/gji/ggae244/58537171/ggae244.pdf
Reference46 articles.
1. An overview of a highly versatile forward and stable inverse algorithm for airborne, ground-based and borehole electromagnetic and electric data;Auken;Explor. Geophys.,2015
2. Quasi-)real-time inversion of airborne time-domain electromagnetic data via artificial neural network;Bai;Remote. Sens.,2020
3. Probabilistic categorical groundwater salinity mapping from airborne electromagnetic data adjacent to California's Lost Hills and Belridge Oil Fields;Ball;Water Resour. Res.,2020
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