Semiautomatic and Automatic Cooperative Inversion of Seismic and Magnetotelluric Data

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 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

Structural Controls on Slope Failure Within the Western Santa Barbara Channel Based on 2‐D and 3‐D Seismic Imaging

Abstract The Santa Barbara Channel, offshore California, contains several submarine landslides and ample evidence for incipient failure. This region hosts active thrust and reverse faults that accommodate several mm/yr of convergence, yet the relationships between tectonic deformation and slope failure remain unclear. We present 3‐D and 2‐D multichannel seismic reflection (MCS) data sets, multibeam bathymetry, and chronostratigraphic constraints to investigate the controls on slope failure. Splay faulting along the North Channel Deformation Trend (NCDT) coincides with a distinct zone of compressional uplift and onlapping of steeply dipping Quaternary strata. The NCDT is spatially correlated with seafloor fissures, and 3‐D seismic analyses reveal an intricate system of en echelon reverse faults that offset sediments younger than ~25 ka. Localized uplift zones are located between faults, one of which underlies the Gaviota landslide headscarp. We observe a direct relationship between slope failure and along‐strike variations in the tectonostratigraphic framework. Based on geophysical properties at Ocean Drilling Program (ODP) Site 893, we predict a trend in compaction and porosity reduction in the basin that drives pore fluids up‐dip, toward the zone of onlap above the NCDT, thus reducing slope stability. This interplay between tectonic, sedimentary, and fluid‐flow processes along the NCDT has created a confluence of preconditioning factors, with Gaviota and Goleta landslides being distinguished from the surrounding slopes by their position above the NCDT. The distribution of seafloor fissures suggests sections of the slope remain unstable and are prone to future landsliding. These results provide insights into the processes and 3‐D feedbacks that lead to slope instability along other convergent margins

Undistorting the past: new techniques for orthorectification of archaeological aerial frame imagery

Archaeologists using airborne data can encounter a large variety of frame images in the course of their work. These range from vertical aerial photographs acquired with very expensive calibrated optics to oblique images from hand-held, uncalibrated cameras and even photographs shot with compact cameras from an array of unmanned airborne solutions. Additionally, imagery can be recorded in one or more spectral bands of the complete optical electromagnetic spectrum. However, these aerial images are rather useless from an archaeological standpoint as long as they are not interpreted in detail. Furthermore, the relevant archaeological information interpreted from these images has to be mapped and compared with information from other sources. To this end, the imagery must be accurately georeferenced, and the many geometrical distortions induced by the optics, the terrain and the camera tilt should be corrected. This chapter focuses on several types of archaeological airborne frame imagery, the distortion factors that are influencing these two-dimensional still images and the necessary steps to compute orthophotographs from them. Rather than detailing the conventional photogrammetric orthorectification workflows, this chapter mainly centres on the use of computer vision-based solutions such as structure from motion (SfM) and dense multi-view stereo (MVS). In addition to a theoretical underpinning of the working principles and algorithmic steps included in both SfM and MVS, real-world imagery originating from traditional and more advanced airborne imaging platforms will be used to illustrate the possibilities of such a computer vision-based approach: the variety of imagery that can be dealt with, how (accurately) these images can be transformed into map-like orthophotographs and how these results can aid in the documentation of archaeological resources at a variety of spatial scales. Moreover, the case studies detailed in this chapter will also prove that this approach might move beyond current restrictions of conventional photogrammetry due to its applicability to datasets that were previously thought to be unsuitable for convenient georeferencing