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Andy Anderson Bery on Muck Rack

Andy Anderson Bery

As seen in: Nature, IAEME Publications
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Articles

Improving subsurface characterization in tropical granitic terrains using machine learning and deep learning-enhanced resistivity–chargeability modeling

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By Adedibu Sunny Akingboye, Andy Anderson Bery, Hui Tang, Ayokunle Olalekan Ige, Mbuotidem David Dick, Obinna Chigoziem Akakuru, Gabriel Abraham Bala
| Nature Verified
Abstract Accurately characterizing subsurface lithological heterogeneity in tropical granitic terrains remains challenging because conventional electrical resistivity tomography (ERT) and induced polarization (IP) inversions often smooth abrupt boundaries, obscure weathered interfaces, and underrepresent clay–moisture contrasts.
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State-of-the-Art Review of Artificial Intelligence in Environmental Geophysics and Geotechnical Engineering

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By Adedibu Sunny Akingboye, Andy Anderson Bery, Mbuotidem David Dick
| Preprints.org
You are already at the latest version Artificial intelligence (AI) is transforming environmental geophysics and geotechnical engineering (EGGE), shifting practice from empirical and deterministic workflows toward data-rich, physics-consistent, and decision-oriented subsurface intelligence.
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A Machine Learning-Driven Geophysical-Geotechnical Approach for Improved Engineering Site Assessment[v1]

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By Mbuotidem David Dick, Andy Anderson Bery, Adedibu Sunny Akingboye, Mfoniso Aka
| Preprints.org
Submitted: 10 February 2025 You are already at the latest version Subsurface geological formations are vital for validating deep engineering design assumptions, particularly in weathered terrains where unstable ground conditions pose risks. Geophysical investigations often face challenges due to inverse problem uncertainties and inadequate subsurface data.
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