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

Kumar, A; Singh, UK; Pradhan, B

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

Kumar, A Singh, UK Pradhan, B

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Geoscience and Remote Sensing Letters, 2024, 21, pp. 1-5
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Kumar, A
dc.contributor.author	Singh, UK
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.date.accessioned	2024-08-07T05:30:14Z
dc.date.available	2024-08-07T05:30:14Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Geoscience and Remote Sensing Letters, 2024, 21, pp. 1-5
dc.identifier.issn	1545-598X
dc.identifier.issn	1558-0571
dc.identifier.uri	http://hdl.handle.net/10453/180320
dc.description.abstract	Recent advancements in deep learning (DL) have demonstrated potential for interpreting ground-penetrating radar (GPR) data, which is crucial for near surface geophysical investigations. However, the complexity of DL models and the challenge of interpreting their decision-making processes remain significant obstacles. This study addresses these challenges by applying explainable AI (XAI) techniques - specifically, local interpretable model-agnostic explanations (LIMEs) and gradient-weighted class activation mapping (Grad-CAM) - to elucidate the DL-based inversion process for 2-D GPR data. Our novel approach marks the first application of these XAI techniques in the context of GPR data analysis for subsurface utility mapping, revealing critical features and hierarchical feature extraction processes that drive the model's predictions. By offering detailed insights into the model's internal operations, this research not only enhances the interpretability of DL models in geophysical applications but also establishes a new standard for incorporating XAI in subsurface utility detection, paving the way for more accurate, reliable, and understandable DL applications in geophysical studies.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Geoscience and Remote Sensing Letters
dc.relation.isbasedon	10.1109/LGRS.2024.3400934
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 0909 Geomatic Engineering
dc.subject.classification	Geological & Geomatics Engineering
dc.subject.classification	3704 Geoinformatics
dc.subject.classification	3709 Physical geography and environmental geoscience
dc.subject.classification	4013 Geomatic engineering
dc.title	Enhancing Interpretability in Deep Learning-Based Inversion of 2-D Ground Penetrating Radar Data: An Explainable AI (XAI) Strategy
dc.type	Journal Article
utslib.citation.volume	21
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0909 Geomatic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	closed_access	*
dc.date.updated	2024-08-07T05:29:58Z
pubs.publication-status	Published
pubs.volume	21

Abstract:

Recent advancements in deep learning (DL) have demonstrated potential for interpreting ground-penetrating radar (GPR) data, which is crucial for near surface geophysical investigations. However, the complexity of DL models and the challenge of interpreting their decision-making processes remain significant obstacles. This study addresses these challenges by applying explainable AI (XAI) techniques - specifically, local interpretable model-agnostic explanations (LIMEs) and gradient-weighted class activation mapping (Grad-CAM) - to elucidate the DL-based inversion process for 2-D GPR data. Our novel approach marks the first application of these XAI techniques in the context of GPR data analysis for subsurface utility mapping, revealing critical features and hierarchical feature extraction processes that drive the model's predictions. By offering detailed insights into the model's internal operations, this research not only enhances the interpretability of DL models in geophysical applications but also establishes a new standard for incorporating XAI in subsurface utility detection, paving the way for more accurate, reliable, and understandable DL applications in geophysical studies.

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http://hdl.handle.net/10453/180320