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To estimate variation of stress state and sediment consolidation in the Nankai plate subduction zone off southwest Japan, we measured the P-wave to S-wave velocity ratio (VP/VS) and S-wave splitting along the seismic line extending from the trench to the seismogenic zone. For this purpose, we used active-source seismic data recorded by multicomponent ocean bottom seismometers (OBS). Because it is difficult to identify the PS-converted reflection waveforms for each of the geological boundaries in this deep offshore region, we focused on the more easily identified PPS-refracted waveforms that register the conversion of the up-going P-waves to S-waves at the igneous crust surface. We estimated the average VP/VS ratio within the sedimentary section by using the time lag between the P-refracted waves and PPS-converted waves. This VP/VS ratio changes abruptly at the trough axis (i.e., the deformation front of the accretionary prism) arguably because of compaction associated with the accretion process. We observed relatively high VP/VS around the seismogenic megasplay fault, which may partially indicate the abnormal pore pressure and intensive fractures associated with the fault. To estimate the stress-induced fracture orientation and stress magnitude, we computed the fast S-wave polarization direction and estimated S-wave velocity anisotropy by applying the crosscorrelation method to the PPS-converted waves. To improve signal-to-noise ratio of the waveform for S-wave splitting analysis, we stacked PPS-converted waveforms on receiver gather. These anisotropic characteristics change at the seismogenic megasplay fault: the fast polarization direction is nearly parallel to the subduction direction seaward of the megasplay fault and is perpendicular to the subduction direction landward of the megasplay fault. This velocity anisotropy is especially strong around the megasplay fault. These results imply that the preferred fracture orientation, as well as the principal stress orientation, is oblique to the direction of plate subduction near the megasplay fault.

The key paradigm of digital rock physics (DRP) "image and compute" implies imaging and digitizing the pore space and mineral matrix of natural rock and then numerically simulating various physical processes in this digital object to obtain such macroscopic rock properties as permeability, electrical conductivity, and elastic moduli. The steps of this process include image acquisition, image processing (noise reduction, smoothing, and segmentation); setting up the numerical experiment (object size and resolution as well as the boundary conditions); and numerically solving the field equations. Finally, we need to interpret the solution thus obtained in terms of the desired macroscopic properties. For each of these DRP steps, there is more than one method and implementation. Our goal is to explore and record the variability of the computed effective properties as a function of using different tools and workflows. Such benchmarking is the topic of the two present companion papers. Here, in the first part, we introduce four 3D microstructures, a segmented Fontainebleau sandstone sample (porosity 0.147), a gray-scale Berea sample; a gray-scale Grosmont carbonate sample; and a numerically constructed pack of solid spheres (porosity 0.343). Segmentation of the gray-scale images by three independent teams reveals the uncertainty of this process: the segmented porosity range is between 0.184 and 0.209 for Berea and between 0.195 and 0.271 for the carbonate. The implications of the uncertainty associated with image segmentation are explored in a second paper.

This is the second and final part of our digital rock physics (DRP) benchmarking study. We use segmented 3-D images (one for Fontainebleau, three for Berea, three for a carbonate, and one for a sphere pack) to directly compute the absolute permeability, the electrical resistivity, and elastic moduli. The numerical methods tested include a finite-element solver (elastic moduli and electrical conductivity), two finite-difference solvers (elastic moduli and electrical conductivity), a Fourier-based Lippmann-Schwinger solver (elastic moduli), a lattice-Boltzmann solver (hydraulic permeability), and the explicit-jump method (hydraulic permeability and electrical conductivity). The set-ups for these numerical experiments, including the boundary conditions and the total model size, varied as well. The results thus produced vary from each other. For example, the highest computed permeability value may differ from the lowest one by a factor of 1.5. Nevertheless, all these result s fall within the ranges consistent with the relevant laboratory data. Our analysis provides the DRP community with a range of possible outcomes which can be expected depending on the solver and its setup.