Statistical methods to enable practical on-site tomographic imaging of whole-core samples

Statistical methods enable the use of portable industrial scanners with sparse measurements, suitable for fast on-site whole-core X-ray computed tomography (CT), as opposed to conventional (medical) devices that use dense measurements. This approach accelerates an informed first-stage general assessment of core samples. To that end, this novel industrial tomographic measurement principle is feasible for rock-sample imaging, in conjunction with suitable forms of priors in Bayesian inversion algorithms. Gaussian, Cauchy, and total variation priors yield different inversion characteristics for similar material combinations. An evaluation of the inversion performance in rock samples considers, in a discrete form, conditional mean estimators, via Markov Chain Monte Carlo algorithms with noise-contaminated measurements. Additionally, further assessment indicates that this statistical approach better characterizes the attenuation contrast of rock materials, compared with simultaneous iterative reconstruction techniques. Benchmarking includes X-ray CT from numerical simulations of synthetic and measurement-based whole-core samples. To this end, we consider tomographic measurements of fine- to medium-grained sandstone core samples, with igneous-rich pebbles from the Miocene, off the Shimokita Peninsula in Japan, and fractured welded tuff from Big Bend National Park, Texas. Bayesian inversion results confirm that with only 16 radiograms, natural fractures with aperture of less than 2 mm wide are detectable. Additionally, reconstructed images found approximately spherical concretions of 6 mm diameter. To achieve similar results, filtered back projection techniques require hundreds of radiograms, only possible with conventional laboratory scanners.