A novel machine learning approach for interpolating seismic velocity and electrical resistivity models for early-stage soil-rock assessment
Author:
Funder
Malaysian Ministry of Higher Education
Universiti Sains Malaysia for the Short-Term Grant
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s12145-024-01303-9.pdf
Reference91 articles.
1. Akingboye AS (2024) Geophysical research on the interplays between soil–rock variability and hydrogeological structures: a case study. Quarterly J Eng Geology Hydrogeo, 57(1). https://doi.org/10.1144/qjegh2023-100
2. Akingboye AS, Bery AA (2021). Performance evaluation of copper and stainless-steel electrodes in electrical tomographic imaging. J Phys Sci 32(3), 13–29. https://doi.org/10.21315/jps2021.32.3.2
3. Akingboye AS, Bery AA (2022a) Development of novel velocity-resistivity geostatistical relationships for tropical granitic environments. 83rd EAGE Annual Conference & Exhibition, 5, 1–5. https://doi.org/10.3997/2214-4609.202210929
4. Akingboye AS, Bery AA (2022b) Characteristics and rippability conditions of near-surface lithologic units (Penang Island, Malaysia) derived from multimethod geotomographic models and geostatistics. J Appli Geophy 204(March):104723. https://doi.org/10.1016/j.jappgeo.2022.104723
5. Akingboye AS, Bery AA (2023) Development of novel velocity–resistivity relationships for granitic terrains based on complex collocated geotomographic modeling and supervised statistical analysis. Acta Geophysica 71(6):2675–2698. https://doi.org/10.1007/s11600-023-01049-w
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