Computational Geoscience
Overview
As Earth Science enters the era of "Big Data," our department leverages high-performance computing and advanced mathematical frameworks to solve complex geological problems. This theme focuses on the development and application of cutting-edge algorithms to process, simulate, and invert geophysical data. By integrating traditional physics-based models with modern data science, we aim to gain deeper insights into the subsurface with unprecedented clarity and speed. Our research serves as a technological bridge, providing the computational tools necessary for modern resource exploration, hazard assessment, and fundamental planetary research.
Themes
AI/ML Applications in Geoscience: Leveraging artificial intelligence and machine learning to automate pattern recognition, classify geological facies, and predict subsurface properties from massive datasets.
Nonlinear Geophysical Signal Processing: Developing sophisticated techniques to extract meaningful information from complex, noisy, and non-linear data signals recorded by geophysical sensors.
Seismic Wave Simulation with Numerical Schemes: Utilizing advanced numerical methods to model how seismic energy travels through the Earth's heterogeneous interior.
Seismic Waveform Inversion: Implementing high-resolution imaging techniques that match observed waveforms with synthetic models to create detailed 3D maps of subsurface physical properties.
