Natural source electromagnetic methods have the potential to recover rock property distributions from the surface to great depths. Unfortunately, results in complex 3D geo-electrical settings can be disappointing, especially where significant near-surface conductivity variations exist. In such settings, unconstrained inversion of magnetotelluric data is inexorably non-unique. We believe that: (1) correctly introduced information from seismic reflection can substantially improve MT inversion, (2) a cooperative inversion approach can be automated, and (3) massively parallel computing can make such a process viable. Nine inversion strategies including baseline unconstrained inversion and new automated/semiautomated cooperative inversion approaches are applied to industry-scale co-located 3D seismic and magnetotelluric data sets. These data sets were acquired in one of the Carlin gold deposit districts in north-central Nevada, USA. In our approach, seismic information feeds directly into the creation of sets of prior conductivity model and covariance coefficient distributions. We demonstrate how statistical analysis of the 123Surv Geophys (2016) 37:845-896 DOI 10.1007/s10712-016-9377-z distribution of selected seismic attributes can be used to automatically extract subvolumes that form the framework for prior model 3D conductivity distribution. Our cooperative inversion strategies result in detailed subsurface conductivity distributions that are consistent with seismic, electrical logs and geochemical analysis of cores. Such 3D conductivity distributions would be expected to provide clues to 3D velocity structures that could feed back into full seismic inversion for an iterative practical and truly cooperative inversion process. We anticipate that, with the aid of parallel computing, cooperative inversion of seismic and magnetotelluric data can be fully automated, and we hold confidence that significant and practical advances in this direction have been accomplished.

Seismic and electromagnetic methods are fundamental to Solid Earth research and subsurface exploration. Acquisition cost reduction is making dense 3D application of these methods accessible to a broad range of geo-scientists. However, the challenge of extracting geological meaning remains. We develop the concept of “textural domaining” for 3D seismic reflectivity data. Dip-steered seismic texture attributes are combined with unsupervised learning to generate sets of volume rendered images accompanied by a seismic texture reference diagram. These methods have the potential to reveal geological and geotechnical properties that would otherwise remain hidden. Analysis of seismic texture presents particular value in hard-rock settings where changes in velocity may be negligible across rock volumes exhibiting significant changes in rock mass texture. We demonstrate application and value of textural domaining with three industry-scale field examples. The first example links seismic texture to rock type along a 400 km long transect through central Australia. The second and third examples partition dense 3D seismic data based on texture for complex hard rock terrains in Nevada, USA and Kevitsa, Finland. Finally, we demonstrate application of domaining within texture guided cooperative inversion of 3D seismic reflectivity and magnetotelluric data to provide new perspectives on Solid Earth geology.

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