Enhancing Interpretability in Deep Learning-Based Inversion of 2-D Ground Penetrating Radar Data: An Explainable AI (XAI) Strategy-Reference-Cited by-同舟云学术

Enhancing Interpretability in Deep Learning-Based Inversion of 2-D Ground Penetrating Radar Data: An Explainable AI (XAI) Strategy

Published:2024 Issue: Volume:21 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Kumar Abhishek¹^ORCID,Singh Upendra K.²^ORCID,Pradhan Biswajeet³^ORCID

Affiliation:

1. Center for Exploration Geophysics (CEG), Curtin University, ARRC/CSIRO Building, Kensington, WA, Australia

2. Department of Applied Geophysics, Indian Institute of Technology (ISM), Dhanbad, Jharkhand, India

3. CAMGIS, School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, NSW, Australia

Funder

Center for Advanced Modeling and Geospatial Information Systems (CAMGISs), Faculty of Engineering and Information Technology (IT), University of Technology Sydney

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/8859/10365397/10530263.pdf?arnumber=10530263

Reference16 articles.

1. A review of Ground Penetrating Radar application in civil engineering: A 30-year journey from Locating and Testing to Imaging and Diagnosis

2. A Review of GPR Application on Transport Infrastructures: Troubleshooting and Best Practices

3. Enhancing subsurface contamination assessment via ensemble prediction of ground electrical property: A Colorado AMD-impacted wetland case study

4. Direct Velocity Inversion of Ground Penetrating Radar Data Using GPRNet

5. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

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