Special Session Proposal for ISMLG 2023
Image Analysis and Machine Learning for Geomechanics
Budi Zhao, University College Dublin
Eleni Stavropoulou, EPFL (Swiss Federal Institute of Technology Lausanne)
The rising interest and demand for energy geo-structures and energy storage systems require a fundamental understanding of the hydro-chemo-thermo-mechanically coupled processes in soils and rocks for thermal piles, nuclear waste disposal, gas storage and caprock integrity, etc. Advanced imaging techniques provide essential insights into the behaviour of geomaterials under various applied conditions, including hydraulic pressure, chemical reaction, temperature, and mechanical loading. For example, X-ray micro-tomography is a non-destructive 3D imaging tool for the in-situ multi-physical characterisation of geomaterials. Other imaging techniques include digital cameras, neutron tomography, X-ray diffraction, and magnetic resonance imaging.
Image analysis and machine learning apply widely in our daily life, from face recognition to self-driving cars. In geomechanics, machine learning is becoming increasingly popular to enhance the quantitative information that image analysis can provide through phase segmentation, fracture recognition, particle tracking, etc. This session aims to attract high-quality contributions that use image analysis and machine learning in geomechanics.
The relevant topics include but are not limited to
Dissolution and precipitation
Freezing and thawing
Fines migration and clogging
Microbially induced calcite precipitation