Linear geophysical inversion with seismic image-guided kriging
Year: 2026
Type: conference-abstract
Abstract: Linear geophysical inversions, such as the mapping of surface gravity anomaly to subsurface density anomaly, are a cornerstone of geophysical practice. However, the high degree of ill-posedness of such problems makes interpretation of the resulting subsurface models challenging. I develop a methodology to combine surface potential field measurements, direct subsurface sampling through scout boreholes, and seismic reflection imaging, to generate consistent subsurface anomaly models of significantly greater accuracy and interpretability. The method relies on non-stationary, anisotropic Gaussian process priors, with the anisotropy derived from image processing of seismic reflection data. Cast into the language of geostatistics, the method could be referred to as seismic image-guided kriging. We validate the method on a synthetic example cropped from the Marmousi2 seismic benchmark, and find that the use of the image-guided prior resulted in a 39% improvement in correlation with the unknown true density anomaly compared to use of a stationary prior.
Source: ASEG Extended Abstracts
Author: Jack Muir
Institution: Australian National University
Language: English
Subfield: Geophysics
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7223087272