Advancing computational geophysics through physics-guided artificial intelligence, high-resolution imaging, and multi-scale subsurface characterization.
Focus: Serves as the institute's foundational methodology department. It bridges fundamental physical laws with artificial intelligence to construct robust, interpretable, and predictive subsurface models.
Scope: Development of Physics-Informed Neural Networks (PINNs), advanced multi-dimensional forward modeling, high-performance computing (HPC) deployment, and automated high-density dataset processing.
Focus: Drives the technological modernization of mineral prospecting. It optimizes resource detection by integrating physical geoscience constraints with AI-driven interpretation models.
Scope: Physics-guided AI architectures, joint inversion of multi-method data (magnetometry, gravimetry, and induced polarization), and advanced structural mapping for complex or remote terrains.
Focus: Focuses on structural dynamic imaging and risk management. It combines active and passive wave-propagation physics with computational inversion to monitor subsurface changes over time.
Scope: High-resolution seismic inversion, active and passive data processing, physics-based wavefield simulation, and automated forecasting for geological and environmental hazards.
Focus: Applies rigorous geophysical and physical principles to near-surface engineering, environmental protection, and shallow-depth site investigations.
Scope: Groundwater exploration, near-surface safety and geotechnical assessments, environmental hazard mitigation, and high-precision shallow-depth physical property mapping.
For technical questions regarding our methodologies, publications, or ongoing research themes (PINNs, DAS, Inversion).
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