Computational geophysics

Program introduction

This program provides a foundation in computational methods for geophysical modeling, simulation, and interpretation, with an emphasis on seismic imaging and subsurface characterization. Training is meant to bridge physical theory and modern numerical implementation, not stop at one or the other.

Program scope

Numerical and algorithmic techniques used in contemporary geophysics: forward modeling, inverse problems, and large-scale data analysis. Emphasis is on reproducible workflows, computational efficiency, and results that stay physically interpretable.

Program topics

Numerical methods for geophysical modeling:

• Forward and inverse problems
• Computational seismic imaging
• Optimization and regularization techniques
• High-performance and large-scale computing concepts

Courses & teaching materials

Materials follow the program topics and support progressive learning from theory to applied computational workflows, using real geophysical datasets and reproducible implementations.

Aimed at advanced undergraduates, graduate students, and early-career researchers in geophysics, applied mathematics, and data science. Resources include lecture notes, slides, and exercises available for independent study or classroom use.

Target audience

Advanced undergraduate students, graduate researchers, computational geoscientists, and industry professionals looking to build or sharpen skills in numerical modeling, seismic inverse problems, and high-performance geophysical computing.