Missing Sonic Logs Generation for Gas Hydrate-Bearing Sediments via Hybrid Networks Combining Deep Learning With Rock Physics Modeling-Reference-Cited by-同舟云学术

Missing Sonic Logs Generation for Gas Hydrate-Bearing Sediments via Hybrid Networks Combining Deep Learning With Rock Physics Modeling

Published:2023 Issue: Volume:61 Page:1-15
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Li Zikun¹^ORCID,Xia Jialong¹,Liu Zhichao¹^ORCID,Lei Gang¹^ORCID,Lee Kyungbook²^ORCID,Ning Fulong¹^ORCID

Affiliation:

1. Faculty of Engineering and the National Center for International Research on Deep Earth Drilling and Resource Development, China University of Geosciences, Wuhan, China

2. Department of Geoenvironmental Sciences, Kongju National University, Gongju, Republic of Korea

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hubei Province

Guangdong Basic and Applied Basic Research Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10310238.pdf?arnumber=10310238

Reference109 articles.

1. What can machine learning do for seismic data processing? An interpolation application

2. Deep Relative Geologic Time: A Deep Learning Method for Simultaneously Interpreting 3‐D Seismic Horizons and Faults

3. Theory-guided hard constraint projection (HCP): A knowledge-based data-driven scientific machine learning method

4. Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows

5. Well Log Generation via Ensemble Long Short‐Term Memory (EnLSTM) Network

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