How Can We Make Better Decisions About What Lies Underground?
Geophysical data offer incomplete clues about the subsurface. Bayesian inversion turns them into plausible scenarios and shows which uncertainties matter for real-world decisions.
Decisions often rely on what lies underground, from drilling locations to carbon dioxide storage safety. However, the Earth's subsurface remains hidden from direct observation. To infer its physical properties, scientists employ geophysical inverse modeling, a computationally intensive technique that infers key properties from indirect measurements like seismic waves or gravity readings.
While powerful, this method suffers from high dimensionality and noisy, incomplete data, making it challenging to draw reliable conclusions from the models produced. To address these issues, scientists are turning to Bayesian inference methods, which incorporate prior geological knowledge with observed data to systematically quantify uncertainty.
A recent article in Reviews of Geophysics examines recent advances in Bayesian methods for modeling the Earth's subsurface from indirect geophysical data, exploring how these techniques can help scientists evaluate a range of plausible underground models, identify poorly constrained regions, and make more informed decisions.
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