Explainable Convolutional Neural Networks Driven Knowledge Mining for Seismic Facies Classification-Reference-Cited by-同舟云学术

Explainable Convolutional Neural Networks Driven Knowledge Mining for Seismic Facies Classification

Published:2023 Issue: Volume:61 Page:1-18
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

You Jiachun¹^ORCID,Zhao Jinquan¹,Huang Xingguo²^ORCID,Zhang Gulan³^ORCID,Chen Anqing⁴,Hou Mingcai⁴,Cao Junxing¹

Affiliation:

1. College of Geophysics, Chengdu University of Technology, Chengdu, China

2. College of Instrumentation and Electrical Engineering, Jilin University, Changchun, China

3. School of Geoscience and Technology, Southwest Petroleum University, Chengdu, China

4. Key Laboratory of Deep-Time Geography and Environment Reconstruction and Applications, MNR & Institute of Sedimentary Geology, Chengdu University of Technology, Sichuan, Chengdu, China

Funder

National Natural Science Foundation of China

Sichuan Science and Technology Program

China National Petroleum Corporation (CNPC) Innovation Found

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10136737.pdf?arnumber=10136737

Reference47 articles.

1. Seismic facies analysis based on speech recognition feature parameters

2. "Why Should I Trust You?"

3. Application of three-dimensional seismic texture analysis with special reference to deep-marine facies discrimination and interpretation: Offshore Angola, west Africa

4. Combining Deep Learning With Physics Based Features in Explosion‐Earthquake Discrimination

5. Latent space modeling of seismic data: An overview

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