Cross-gradients joint inversion of disparate ...

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XVI International Conference on Computational Methods in Water Resources (CMWR-XVI) Ingeniørhuset

Cross-gradients joint inversion of disparate geophysical data for improved subsurface characterization: multiple-physics field examples
Paper
Author:Luis Gallardo <lgallard@cicese.mx> (Earth Science Division, CICESE, Mexico)
Presenter:Luis Gallardo <lgallard@cicese.mx> (Earth Science Division, CICESE, Mexico)
Date: 2006-06-18     Track: Special Sessions     Session: Hydrogeophysical data fusion
DOI:10.4122/1.1000000516
DOI:10.4122/1.1000000517

The characterization and monitoring of hydrogeological and other complex subsurface processes requires a detailed knowledge of several properties of the composing rocks and fluids. Whilst some of these properties can be measured directly, other properties have to be estimated by indirect measurements such as geophysical data. However, it is not uncommon that the geophysical data yield models of limited accuracy that may not contribute significantly to our understanding of the subsurface processes. Interestingly, the distribution of apparently uncorrelated physical properties seems to be controlled by common subsurface attributes that, when taken into account, can improve the accuracy and meaning of the otherwise independent geophysical models. For instance, a highly porous rock that is saturated with water can be sharply defined by a combined low seismic velocity-low electrical resistivity area. However, such a correlation can not be generalised to different environments. An outstanding feature of the subsurface that is common to the geophysical data is the geometrical distribution of the physical properties which can be measured by the physical property changes. This condition of commonality can be incorporated in the process of estimation to obtain meaningful and more reliable subsurface models in a process of joint inversion. In this work I define the joint inversion of disparate geophysical data as the search of those models that satisfy their respective geophysical data in a least squares sense and are geometrically similar. I quantify the geometrical similarity with the null values of the cross-products of the gradients of two physical properties and pose solutions for these objective functions that account for multiple-physics models (P- and S-wave velocities as well as DC- and MT- resistivity). I present several test and field examples that show the improvements attained in parameter accuracy and geometrical resemblance as well as their implications for petrophysical and structural associations for several near-surface field sites.