Abstract
Identifying near-surface lithological conditions is crucial for investigations such as building foundations, engineering projects, and groundwater resources, among others. Geotechnical drilling has limitations in collecting data from precise locations. Therefore, combining two geophysical techniques with machine learning (ML) algorithms for subsurface characterization yields better outcomes. Consequently, this novel approach was employed for the interpolation of SRT–ERT models and to develop the relationships between them for the geological terrain of the Kabota-Tawau area of Sabah, Malaysia. Two survey lines were established within a geologically favorable area of interest to evaluate and enhance the understanding of the study area’s near-surface lithologic units. The resistivity and seismic P-wave velocity (Vp) techniques were utilized to acquire the field data, after which the resulting models were interpolated. To improve subsurface lithological differentiation, the K-means clustering and simple linear regression algorithms were utilized to analyze the interpolated resistivity and Vp datasets. Via this approach, the area’s subsurface lithologies were identified as the clayey silt topsoil, along with weathered units characterized by stiff to very stiff clayey/silty material, very stiff to hard clayey/silty material, and hard to very hard clayey/silty unit. The developed velocity-resistivity empirical relation exhibits a practical prediction success rate exceeding 86% with high positive correlations, making it statistically significant and accurate in characterizing underlying geological variations. These findings underscore the efficacy of both ML approaches in accurately identifying distinct subsurface geological variations.
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Acknowledgments
The authors would like to express gratitude and appreciation to individuals who have contributed to the completion of this paper. Special appreciation to the Editor and the reviewers for their valuable comments and recommendations, which have greatly contributed to improving the quality of our paper. The authors express their gratitude to Universiti Sains Malaysia, staff, and technologists at the School of Physics (Geophysics Units), as well as the postgraduate students and TETfund, Nigeria for their valuable assistance in the process of field data acquisition and manuscript development.
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The authors express their gratitude to the Malaysian Ministry of Higher Education (MoHE) for providing financial support via the Fundamental Research Grant Scheme (203/PFIZIK/6712108), as well as to Universiti Sains Malaysia for the GOT Incentives (R502-KR-GOT001-0000000157-K134), which have enabled the funding of this research endeavors.
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• Mbuotidem Dick: Conceptualization, Methodology, Data – curation & validation, Writing – review & editing, and Resources
• Dr. Andy Bery: Conceptualization, Methodology, Data – curation & validation, Writing – review & editing, Resources, Funding, and Supervision
• Nsidibe Okanna: Methodology and Review Writing
• Kufre Richard: Writing – review and Resources
• Yasir Bashir: Writing – review & editing, and Resources
• Adedebu Akingboye: Review writing, Proofreading, Validation of data, Supervision and Methodology and interpretation
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Dick, M.D., Bery, A.A., Okonna, N.N. et al. A novel machine learning approach for interpolating seismic velocity and electrical resistivity models for early-stage soil-rock assessment. Earth Sci Inform (2024). https://doi.org/10.1007/s12145-024-01303-9
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DOI: https://doi.org/10.1007/s12145-024-01303-9