Linear geophysical inversion with seismic image-guided kriging
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.
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BibTeX
@misc{muir2026linear,
title = {{Linear geophysical inversion with seismic image-guided kriging}},
author = {Jack Broderick Muir},
howpublished = {EarthArXiv},
year = {2026},
month = sep,
doi = {10.31223/x5xf7x},
url = {https://eartharxiv.org/repository/view/14851/},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - UNPB
TI - Linear geophysical inversion with seismic image-guided kriging
AU - Jack Broderick Muir
PY - 2026
DA - 2026/09/07
PB - EarthArXiv
DO - 10.31223/x5xf7x
UR - https://eartharxiv.org/repository/view/14851/
AB - 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.
ER -