Shear Wave Velocity in Geoscience: Applications, Energy-Efficient Estimation Methods, and Challenges

Mitra Khalilidermani, Dariusz Knez () and Mohammad Ahmad Mahmoudi Zamani
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Mitra Khalilidermani: Department of Drilling and Geoengineering, Faculty of Drilling, Oil, and Gas, AGH University of Krakow, 30-059 Krakow, Poland
Dariusz Knez: Department of Drilling and Geoengineering, Faculty of Drilling, Oil, and Gas, AGH University of Krakow, 30-059 Krakow, Poland
Mohammad Ahmad Mahmoudi Zamani: Iranian Mining and Industry Organization, Ahvaz, Iran

Energies, 2025, vol. 18, issue 13, 1-28

Abstract: Shear wave velocity (V s ) is a key geomechanical variable in subsurface exploration, essential for hydrocarbon reservoirs, geothermal reserves, aquifers, and emerging use cases, like carbon capture and storage (CCS), offshore geohazard assessment, and deep Earth exploration. Despite its broad significance, no comprehensive multidisciplinary review has evaluated the latest applications, estimation methods, and challenges in V s prediction. This study provides a critical review of these aspects, focusing on energy-efficient prediction techniques, including geophysical surveys, remote sensing, and artificial intelligence (AI). AI-driven models, particularly machine learning (ML) and deep learning (DL), have demonstrated superior accuracy by capturing complex subsurface relationships and integrating diverse datasets. While AI offers automation and reduces reliance on extensive field data, challenges remain, including data availability, model interpretability, and generalization across geological settings. Findings indicate that integrating AI with geophysical and remote sensing methods has the potential to enhance V s prediction, providing a cost-effective and sustainable alternative to conventional approaches. Additionally, key challenges in V s estimation are identified, with recommendations for future research. This review offers valuable insights for geoscientists and engineers in petroleum engineering, mining, geophysics, geology, hydrogeology, and geotechnics.

Keywords: artificial intelligence; machine learning; remote sensing; geophysical surveys; seismic exploration; wave propagation; subsurface energy resources; reservoir characterization; geotechnical engineering (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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