Version 1
: Received: 3 April 2024 / Approved: 4 April 2024 / Online: 4 April 2024 (17:47:38 CEST)
How to cite:
Akingboye, A. S.; Bery, A. A.; Aminu, M. B.; Dick, M. D.; Bala, G. A.; Ale, T. O. Supervised Statistical Optimization of Interfaced Geophysical–Geotechnical Datasets for Holistic Surface–Subsurface Modeling. Preprints2024, 2024040390. https://doi.org/10.20944/preprints202404.0390.v1
Akingboye, A. S.; Bery, A. A.; Aminu, M. B.; Dick, M. D.; Bala, G. A.; Ale, T. O. Supervised Statistical Optimization of Interfaced Geophysical–Geotechnical Datasets for Holistic Surface–Subsurface Modeling. Preprints 2024, 2024040390. https://doi.org/10.20944/preprints202404.0390.v1
Akingboye, A. S.; Bery, A. A.; Aminu, M. B.; Dick, M. D.; Bala, G. A.; Ale, T. O. Supervised Statistical Optimization of Interfaced Geophysical–Geotechnical Datasets for Holistic Surface–Subsurface Modeling. Preprints2024, 2024040390. https://doi.org/10.20944/preprints202404.0390.v1
APA Style
Akingboye, A. S., Bery, A. A., Aminu, M. B., Dick, M. D., Bala, G. A., & Ale, T. O. (2024). Supervised Statistical Optimization of Interfaced Geophysical–Geotechnical Datasets for Holistic Surface–Subsurface Modeling. Preprints. https://doi.org/10.20944/preprints202404.0390.v1
Chicago/Turabian Style
Akingboye, A. S., Gabriel Abraham Bala and Temitayo Olamide Ale. 2024 "Supervised Statistical Optimization of Interfaced Geophysical–Geotechnical Datasets for Holistic Surface–Subsurface Modeling" Preprints. https://doi.org/10.20944/preprints202404.0390.v1
Abstract
Surface–subsurface soil-rock modeling is crucial for infrastructure design and groundwater yield optimization, especially in the complex crystalline basement terrains of Penang Island, Malaysia. This study conducted large-scale characterization of soil-rock profiles using ERT, SRT, rock quality designation, and soil penetration test (SPT N-values) data, optimized through regression modeling. The approach effectively identified soil-rock structures and their suitability to prevent structural failures and enhance groundwater productivity. Correlations between borehole lithologic logs and velocity–resistivity models revealed distinct soil compositions across the study area. The results highlighted thick, saturated, and loose silty to sandy bodies in the eastern to northern sections, contrasting with sandy compositions and penetrative fractures in the southern part. The study also found good correlations between rock mass quality and N-values for different soil types. Depths of intra-bedrock weathered/fractured units varied between 12 and >35 m. This has significant implications for determining optimal foundation depths in the study area. Groundwater productivity was associated with intra-bedrock planes of weakness at depths exceeding 40 m. Overall, this study developed empirical relations between geophysical and geotechnical parameters for wet tropical granitic terrains, filling critical gaps in understanding subsurface characteristics.
Environmental and Earth Sciences, Geophysics and Geology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.