Interrogating Subsurface Structures Using Probabilistic Tomography: An Example Assessing the Volume of Irish Sea Basins. Issue 4 (21st April 2022) - Zhao, Xuebin; Curtis, Andrew; Zhang, Xin
Abstract: The ultimate goal of a scientific investigation is usually to find answers to specific, often low‐dimensional questions: what is the size of a subsurface body? Does a hypothesized subsurface feature exist? Existing information is reviewed, an experiment is designed and performed to acquire new data, and the most likely answer is estimated. Typically the answer is interpreted from geological and geophysical data or models, but is biased because only one particular forward function is considered, one inversion method is applied, and because human interpretation is a biased process. Interrogation theory provides a systematic way to answer specific questions by combining forward, design, inverse, and decision theories. The optimal answer is made more robust since it balances multiple possible forward models, inverse algorithms and model parametrizations, probabilistically. In a synthetic test, we evaluate the area of a low‐velocity anomaly by interrogating Bayesian tomographic results. By combining the effect of four inversion algorithms, the optimal answer is very close to the true answer, even on a coarsely gridded parametrization. In a field data test, we evaluate the volume of the East Irish Sea basins using probabilistic 3D shear wave speed depth inversion results. This example shows that interrogation theory provides a useful way to answer realistic questions about the Earth. A key revelation is that while the majority of computation may be spent solving inverse problems, much of the skill and effort involved in answering questions may be spent defining and calculating target function values in a clear and unbiased manner. Plain Language Summary: This paper shows how to answer specific questions about the subsurface using probabilistic tomography. Usually tomographic methods are used to estimate images of the subsurface; the "best" images are then interpreted to answer questions of interest. This work shows that by setting up a formal target function that allows any image to be interpreted automatically, many samples of possible subsurface models can be translated into probabilistic answers to the questions, from which a least‐biased answer can be constructed. In the real‐data examples presented here the subsurface shape of a sedimentary basin is determined automatically, and a least‐biased estimate of its volume is constructed. This method is shown to give accurate answers about high resolution structures even given only low resolution tomographic images; this suggests that the probabilistic results compensate for the lack of resolution. Key Points: We use interrogation theory to answer specific questions about the subsurface using probabilistic tomography results In a synthetic example, the method estimates the area of a low velocity anomaly accurately, even given coarsely gridded tomographic images We apply the method to a real data set and evaluate the volume of the East Irish Sea sedimentary basins using 3D depth inversion results