Subsurface Lithological Characterization Via Machine Learning-assisted Electrical Resistivity and SPT-N Modeling: A Case Study from Sabah, Malaysia DOI
Mbuotidem David Dick, Andy Anderson Bery, Adedibu Sunny Akingboye

et al.

Earth Systems and Environment, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 5, 2024

Language: Английский

Application of High-Resolution Aeromagnetic Mapping for Subsurface Investigation to Enhance Infrastructure Planning for Sustainable Smart City Development within Kwara State University, Malete DOI

Oluwafemi Abdulmujeeb Oluyemoh,

Gabriel Efomeh Omolaiye, Jimoh Ajadi

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: May 16, 2025

Abstract The successful development of sustainable smart cities requires a thorough understanding subsurface conditions to guide infrastructural planning and minimize construction risks. This study applies high-resolution aeromagnetic mapping for rapid investigation within Kwara State University, Malete, enhance data-driven decision-making city development. Aeromagnetic datasets were processed through Total Magnetic Intensity (TMI), Reduction-to-Equator (RTE), Regional-Residual separation, First Second Vertical Derivatives, Horizontal Derivative, Tilt Analytical Signal, Upward Continuation, Euler Deconvolution techniques reveal the magnetic signatures structural framework subsurface. interpretation maps revealed significant southwest-northeast trending fracture zones that are identify as weak source groundwater potential zones, while high in northwest area toward northern part shows competent higher geologic structures critical foundation stability heavy infrastructures project like Smart City Deeper more stable basement delineated towards northwestern some eastern parts area, which makes these areas suitable infrastructure, structurally complex shallower centre southwest require careful geotechnical assessment. emphasises importance integrating geophysical investigations into ensure sustainable, resilient efficient university environments similar regions.

Language: Английский

Citations

0

Subsurface Lithological Characterization Via Machine Learning-assisted Electrical Resistivity and SPT-N Modeling: A Case Study from Sabah, Malaysia DOI
Mbuotidem David Dick, Andy Anderson Bery, Adedibu Sunny Akingboye

et al.

Earth Systems and Environment, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 5, 2024

Language: Английский

Citations

0