Detection of Organic-Rich Oil Shales of the Green River Formation, Utah, with Ground-Based Imaging Spectroscopy

Oil shales contain abundant immature organic matter and are a potential unconventional petroleum resource. Prior studies have used visible/shortwave infrared imaging spectroscopy to map surface exposures of deposits from satellite and airborne platforms and image cores in the laboratory. Here, we work at an intermediate, outcrop-scale, testing the ability of field-based imaging spectroscopy to identify oil shale strata and characterize the depositional environments that led to enrichment of organic matter in sedimentary rocks within the Green River Formation, Utah, USA. The oil shale layers as well as carbonates, phyllosilicates, gypsum, hydrated silica, and ferric oxides are identified in discrete lithologic units and successfully mapped in the images, showing a transition from siliciclastic to carbonate- and organic-rich rocks consistent with previous stratigraphic studies conducted with geological fieldwork

Current research opportunities of image processing and computer vision

Image processing and computer vision is an important and essential area in today’s scenario. Several problems can be solved through computer vision techniques. There are a large number of challenges and opportunities which require skills in the field of computer vision to address them. Computer vision applications cover each band of the electromagnetic spectrum and there are numerous applications in every band. This article is targeted to the research students, scholars and researchers who are interested to solve the problems in the field of image processing and computer vision. It addresses the opportunities and current trends of computer vision applications in all emerging domains. The research needs are identified through available literature survey and classified in the corresponding domains. The possible exemplary images are collected from the different repositories available for research and shown in this paper. The opportunities mentioned in this paper are explained through the images so that a naive researcher can understand it well before proceeding to solve the corresponding problems. The databases mentioned in this article could be useful for researchers who are interested in further solving the problem. The motivation of the article is to expose the current opportunities in the field of image processing and computer vision along with corresponding repositories. Interested researchers who are working in the field can choose a problem through this article and can get the experimental images through the cited references for working further.