Tutorial: Inversion for Geologists
Channel: Software Underground
Date: 2021-04-20

Seogi Kang

Materials for the tutorial are available at:
- Slides: http://bit.ly/transform-2021-slides
- Jupyter Notebooks: https://curvenote.com/@swung/inversion-for-geologists-transform-2021
- GitHub: https://github.com/simpeg/transform-2021-simpeg
- Slack-channel: t21-tue-inversion-for-geologists

Latest developments in sensor technology (e.g., satellite, airborne) provide us a large amount of high-quality data, which contains important information about the subsurface properties and processes. The size and complexity of geophysical data that we need to deal with is exponentially increasing. As geoscientists, therefore, data-driven problem solving is an essential skill to explore the flooding of data and extract meaningful information from them. In this lecture, I focus on constructing a mental model for data-driven problem solving with tangible examples in mining and groundwater applications. The methodology that I am using for the data-driven problem solving in the lecture is an inversion technique, which has been a core framework for geophysicists to image distribution of subsurface physical properties (e.g., seismic velocity, density, electric resistivity, magnetic susceptibility). I start with some important concepts about how we transmit energy into Earth’s subsurface, and measure derived signals including information about the subsurface. Then, I explain how we can formulate a problem in a way which we can drive a solution from the measured data and prior knowledge about the problem.