Deep Learning in Geophysics: Current Status, Challenges, and Future Directions
Author:
Affiliation:
1. Energy & Resource Engineering, Chonnam National University, Gwangju, Korea
Publisher
The Korean Society of Mineral and Energy Resources Engineers
Link
https://www.jksmer.or.kr/articles/pdf/doi/10.32390/ksmer.2024.61.1.061.pdf
Reference102 articles.
1. An adaptive linear-mode decomposition for effective separation of linear and nonlinear seismic events, ground roll, and random noise
2. A convolutional neural network approach to electrical resistivity tomography
3. Probabilistic inversions of electrical resistivity tomography data with a machine learning‐based forward operator
4. Semi-supervised learning for acoustic impedance inversion
5. Analysis of Deep CNN-based Ground Penetrating Radar (GPR) Image Classification Process using Explainable AI
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