Interrogating Tomographic Uncertainties for Subsurface Structural Information

Abstract

<p>The ultimate goal of a geophysical investigation is usually to find answers to scientific (often low-dimensional) questions: how large is a subsurface body? How deeply does lithosphere subduct? Does a certain subsurface feature exist? Background research reviews existing information, an experiment is designed and performed to acquire new data, and the most likely answer is estimated. Typically the answer is interpreted from geophysical inversions, but is usually biased because only one particular forward function (model-data relationship) is considered, one inversion method is used, and because human interpretation is a biased process. <strong><em>Interrogation theory </em></strong>provides a systematic way to answer specific questions. Answers balance information from multiple forward models, inverse methods and model parametrizations probabilistically, and optimal answers are found using decision theory.</p><p>Two examples illustrate interrogation of the Earth’s subsurface. In a synthetic test, we estimate the cross-sectional area of a subsurface low velocity anomaly by interrogating Bayesian probabilistic tomographic maps. By combining the results of four different nonlinear inversion algorithms, the optimal answer is very close to the true answer. In a field data application, we evaluate the extent of the Irish Sea Sedimentary Basin based on the uncertainties in velocity structure derived from Love wave tomography. This example shows that the computational expense of estimating uncertainties adds explicit value to answers.</p><p>This study demonstrates that interrogation theory answers realistic questions about the Earth’s subsurface. The same theory can be used to solve different types of scientific problem - experimental design, interpreting models, expert elicitation and risk estimation - and can be applied in any field of science. One of its most important contributions is to show that fully nonlinear estimates of uncertainty are critical for decision-making in real-world geoscientific problems, potentially justifying their computational expense.</p><p> </p>