Geophysical data analysis and inversion is a highly quantitative field that involves modeling, data processing, inversion, and visualization. In most cases, a geophysical experiment is conducted to collect data that are sensitive to a particular physical property of the earth. The data are processed and inverted to generate or test an earth model of the physical property in question. To better understand the earth's structure, different experiments are conducted using different imaging modalities. Usually the data of each experiment are inverted separately to generate a collection of earth models. An earth model that shares all physical attributes is usually called a common earth model.
Common earth models are very important in scientific and commercial applications because integrating all physical information allows earth scientists to better understand important geological and geophysical processes. Since it is understood that utilizing different modalities may improve inversion results, many algorithms rely on empirical constitutive relationships between the different physical models. However, such relations are typically site-dependent, inexact, and hard to obtain. This hinders the use of common earth models and the understanding that could be obtained from them.
This is an interdisciplinary research project to create a more systematic framework for joint inversion of multi-modal geophysical data. We pursue two different and complementary approaches: one based on statistics, and the other on geometry. While our methods have applicability across a wide spectrum of inversion modalities, we focus on the joint inversion of seismic and electromagnetic data. The methods we develop will have wider applicability beyond the joint seismic-electromagnetic inversion problems targeted here, and more generally beyond the geosciences in areas such as medical imaging.
