Interrogating probabilistic inversion results for subsurface structural information

TL;DR

This paper demonstrates how interrogation theory can accurately quantify subsurface structures in geosciences, addressing biases in traditional inverse problem interpretations by incorporating full uncertainty estimates.

Contribution

It introduces the application of interrogation theory to geoscience inverse problems, showing its effectiveness in providing unbiased, accurate estimates of subsurface features.

Findings

Interrogation theory yields accurate estimates of subsurface structures.

Full nonlinear uncertainty assessments are crucial for reliable results.

Subjective interpretation of inverse solutions can be biased and less accurate.

Abstract

The goal of a scientific investigation is to find answers to specific questions. In geosciences this is typically achieved by solving an inference or inverse problem and interpreting the solution. However, the answer obtained is often biased because the solution to an inverse problem is nonunique and human interpretation is a biased process. Interrogation theory provides a systematic way to find optimal answers by considering their full uncertainty estimates, and by designing an objective function that defines desirable qualities in the answer. In this study we demonstrate interrogation theory by quantifying the size of a particular subsurface structure. The results show that interrogation theory provides an accurate estimate of the true answer, which cannot be obtained by direct, subjective interpretation of the solution mean and standard deviation. This demonstrates the value of interrogation theory. It also shows that fully nonlinear uncertainty assessments may be critical in order to address real-world scientific problems, which goes some way towards justifying their computational expense.