Integrated Geophysical Modelling and Inversion of Anisotropic Subsurface Geometro-Mechanical Properties Using Multiphysics Data Assimilation

Project Abstract

Integrated Geophysical Modelling and Inversion of Anisotropic Subsurface Geometro-Mechanical Properties Using Multiphysics Data Assimilation develops a unified framework to quantify the anisotropic geometro-mechanical properties of subsurface formations by fusing multiple geophysical datasets through a physically consistent multiphysics data assimilation approach. The core objective is to reconstruct spatially varying tensors of elastic, hydraulic, poroelastic, and geometrical properties that govern wave propagation, stiffness, anisotropy of permeability, and the mechanical response of rock masses under in-situ stress conditions. The methodology integrates cross-domain physics, including anisotropic seismic wave propagation, electrical resistivity tomography, ground-penetrating radar, and controlled-source magnetotellurics, with laboratory-derived constitutive relations to constrain inversion and mitigate nonuniqueness. A Bayesian ensemble Kalman filter and adjoint-based optimization scheme are employed to assimilate time-lapse and multi-parameter observations, enabling simultaneous estimation of anisotropy axes, velocity models, pore-fluid pressures, and geomechanical parameters such as Young’s modulus, shear modulus, and Poisson’s ratio as direction-dependent fields. The forward model leverages a coupled poroelastic-elastodynamic formulation that accounts for stress-induced anisotropy, crack orientation, and inelastic behavior, while stabilization strategies address ill-posedness from sparse data and heterogeneous sampling. Synthetic benchmarks demonstrate the framework’s ability to recover complex anisotropic behavior in layered and fractured media, with quantifiable uncertainty quantification that highlights parameter correlations and identifiability limits. The study advances inversion strategies by incorporating multi-fidelity surrogates, model reduction, and physics-informed priors derived from rock-physics relationships to accelerate convergence and improve robustness in field-scale deployments. Case studies are designed around challenging environments such as fault zones, fractured carbonate reservoirs, and tailings dams, where anisotropic geometro-mechanical properties critically influence stability, stimulation planning, and resource recovery. The integration of multiphysics data not only enhances resolution of anisotropic fields but also enables real-time monitoring of subsurface response to external stimuli, including hydraulic fracturing, reservoir depletion, and seismically induced stress changes. Sensitivity analyses identify dominant data modalities and acquisition geometries necessary to constrain key parameters, informing optimal survey design. The research delivers a scalable computational toolkit with open-source components for forward modeling, data assimilation, and uncertainty quantification, accompanied by guidelines for practitioners to interpret anisotropic parameter estimates in engineering and hazard assessment. Overall, the work contributes to a more predictive understanding of subsurface geomechanical behavior under anisotropic conditions and provides a rigorous, data-driven pathway for integrated geophysical characterization in exploration and geotechnical applications.

Project Overview

What This Project Is About

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This project looks at how scientists use data from different geophysical methods to understand what lies beneath the earth's surface, focusing on how rock properties vary with direction (anisotropy) and how these properties influence measurements like seismic waves and magnetic signals. It combines simple ideas from physics, geology, and data analysis to create a clearer picture of subsurface structure and behavior.

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The Problem It Addresses

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Geophysical data often comes from multiple sources that tell different stories about the same area. When rocks behave differently in different directions, interpreting data becomes difficult. This project seeks a unified approach to combine multiple datasets to better estimate subsurface properties and reduce ambiguity in the results.

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Objectives of the Project

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1. Explain in plain terms what geophysical data can tell us about the subsurface.
2. Introduce anisotropy and why direction matters for rock properties.
3. Demonstrate a simple method to integrate different data types.
4. Develop a basic workflow for estimating subsurface properties from data.
5. Assess the reliability and limits of the estimates with easy-to-understand metrics.

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What You Will Do Step by Step

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1. Review basic geophysics concepts and define key terms in simple language.
2. Collect or simulate basic multi-method data sets (e.g., simple seismic and magnetic signals).
3. Combine data using a straightforward, non-technical data fusion approach.
4. Estimate subsurface properties, highlighting anisotropy effects.
5. Test the results with basic checks to see how sensitive the estimates are to data changes.
6. Prepare visuals that clearly show the subsurface model and uncertainties.

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Expected Outcome

Students will deliver a simple, transparent workflow for integrating multiple geophysical data to infer direction-dependent rock properties, with clear explanations of assumptions, limitations, and potential real-world uses in exploration or hazard assessment.