Electrical and seismic refraction methods: Fundamental concepts, current trends, and emerging machine learning prospects
Abstract This comprehensive review examines electrical and seismic refraction methods, emphasizing their advanced applications in electrical resistivity tomography (ERT) and seismic refraction tomography (SRT). These techniques are crucial for understanding surface–subsurface crustal dynamics, offer...
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Springer
2025-07-01
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| Series: | Discover Geoscience |
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| Online Access: | https://doi.org/10.1007/s44288-025-00169-8 |
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| author | Adedibu Sunny Akingboye |
| author_facet | Adedibu Sunny Akingboye |
| author_sort | Adedibu Sunny Akingboye |
| collection | DOAJ |
| description | Abstract This comprehensive review examines electrical and seismic refraction methods, emphasizing their advanced applications in electrical resistivity tomography (ERT) and seismic refraction tomography (SRT). These techniques are crucial for understanding surface–subsurface crustal dynamics, offering critical insights into resistivity and velocity structures for geological and geohazard assessments. The review also explores the induced polarization (IP) and self-potential (SP) methods as complementary approaches. Despite their proven utility, electrical and seismic refraction approaches face limitations arising from lithological heterogeneities, complex geological settings, and inherent data uncertainties. These challenges highlight the need for multidisciplinary strategies, including methodological innovations and integrative data frameworks. Recently, machine learning (ML) techniques have been increasingly applied to these geophysical methods, particularly joint ERT and SRT analyses, optimizing nonlinear inversion processes and improving the interpretation of complex subsurface conditions. The case studies presented in this review evaluate how supervised and unsupervised ML techniques enhance ERT and SRT by improving data interpretation, refining inversion accuracy, automating lithological differentiation, and predicting seismic velocity from resistivity data. The findings emphasize the growing significance of integrating traditional geophysical methods with data-driven approaches to improve subsurface modeling. Continued innovations in ERT and SRT methodologies, along with emerging computational tools and ML applications, will further enhance their effectiveness in geological, hydrological, environmental, and hazard assessments. |
| format | Article |
| id | doaj-art-a3d200f0be104c58a168f3e4668ce6cf |
| institution | Kabale University |
| issn | 2948-1589 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Geoscience |
| spelling | doaj-art-a3d200f0be104c58a168f3e4668ce6cf2025-08-20T03:42:37ZengSpringerDiscover Geoscience2948-15892025-07-013114210.1007/s44288-025-00169-8Electrical and seismic refraction methods: Fundamental concepts, current trends, and emerging machine learning prospectsAdedibu Sunny Akingboye0Department of Earth Sciences, Adekunle Ajasin UniversityAbstract This comprehensive review examines electrical and seismic refraction methods, emphasizing their advanced applications in electrical resistivity tomography (ERT) and seismic refraction tomography (SRT). These techniques are crucial for understanding surface–subsurface crustal dynamics, offering critical insights into resistivity and velocity structures for geological and geohazard assessments. The review also explores the induced polarization (IP) and self-potential (SP) methods as complementary approaches. Despite their proven utility, electrical and seismic refraction approaches face limitations arising from lithological heterogeneities, complex geological settings, and inherent data uncertainties. These challenges highlight the need for multidisciplinary strategies, including methodological innovations and integrative data frameworks. Recently, machine learning (ML) techniques have been increasingly applied to these geophysical methods, particularly joint ERT and SRT analyses, optimizing nonlinear inversion processes and improving the interpretation of complex subsurface conditions. The case studies presented in this review evaluate how supervised and unsupervised ML techniques enhance ERT and SRT by improving data interpretation, refining inversion accuracy, automating lithological differentiation, and predicting seismic velocity from resistivity data. The findings emphasize the growing significance of integrating traditional geophysical methods with data-driven approaches to improve subsurface modeling. Continued innovations in ERT and SRT methodologies, along with emerging computational tools and ML applications, will further enhance their effectiveness in geological, hydrological, environmental, and hazard assessments.https://doi.org/10.1007/s44288-025-00169-8Electrical resistivity tomography (ERT)Induced polarization (IP)Self-potential (SP) methodSeismic refraction tomography (SRT)Machine learningSurface–subsurface modeling |
| spellingShingle | Adedibu Sunny Akingboye Electrical and seismic refraction methods: Fundamental concepts, current trends, and emerging machine learning prospects Discover Geoscience Electrical resistivity tomography (ERT) Induced polarization (IP) Self-potential (SP) method Seismic refraction tomography (SRT) Machine learning Surface–subsurface modeling |
| title | Electrical and seismic refraction methods: Fundamental concepts, current trends, and emerging machine learning prospects |
| title_full | Electrical and seismic refraction methods: Fundamental concepts, current trends, and emerging machine learning prospects |
| title_fullStr | Electrical and seismic refraction methods: Fundamental concepts, current trends, and emerging machine learning prospects |
| title_full_unstemmed | Electrical and seismic refraction methods: Fundamental concepts, current trends, and emerging machine learning prospects |
| title_short | Electrical and seismic refraction methods: Fundamental concepts, current trends, and emerging machine learning prospects |
| title_sort | electrical and seismic refraction methods fundamental concepts current trends and emerging machine learning prospects |
| topic | Electrical resistivity tomography (ERT) Induced polarization (IP) Self-potential (SP) method Seismic refraction tomography (SRT) Machine learning Surface–subsurface modeling |
| url | https://doi.org/10.1007/s44288-025-00169-8 |
| work_keys_str_mv | AT adedibusunnyakingboye electricalandseismicrefractionmethodsfundamentalconceptscurrenttrendsandemergingmachinelearningprospects |