Accelerating Hydraulic Fracture Imaging by Deep Transfer Learning-Reference-Cited by-同舟云学术

Accelerating Hydraulic Fracture Imaging by Deep Transfer Learning

Published:2022-07 Issue:7 Volume:70 Page:6117-6121
ISSN:0018-926X
Container-title:IEEE Transactions on Antennas and Propagation
language:
Short-container-title:IEEE Trans. Antennas Propagat.

Author:

Zhang Runren¹^ORCID,Sun Qingtao¹^ORCID,Mao Yiqian¹^ORCID,Cui Liangze¹^ORCID,Jia Yongze¹^ORCID,Huang Wei-Feng¹^ORCID,Ahmadian Mohsen²^ORCID,Liu Qing Huo¹^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA

2. Bureau of Economic Geology, The University of Texas at Austin, Austin, TX, USA

Funder

U.S. Department of Energy, National Energy Technology Laboratory

Advanced Energy Consortium with ExxonMobil, Repsol, Total, and Shell as members

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

https://ieeexplore.ieee.org/ielam/8/9843191/9743614-aam.pdf

Reference26 articles.

1. Hydraulic Fracture Scattering Simulation by Thin Dielectric Structure-Based Higher Order Surface Integral Equations

2. Validation of the Utility of the Contrast-Agent-Assisted Electromagnetic Tomography Method for Precise Imaging of a Hydraulically Induced Fracture Network

3. Imaging Hydraulic Fractures Under Energized Steel Casing by Convolutional Neural Networks

4. Deep learning prior models from seismic images for full-waveform inversion

5. Deep-learning tomography

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3. Quantitative Diffraction Tomography for Weak Scatterers Based on Aliasing Modification of the Multifrequency Spatial Spectrum;IEEE Transactions on Geoscience and Remote Sensing;2023

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