Sub-Surface Soil Characterization Using Image Analysis: Material Recognition Using the Grey Level Co-Occurrence Matrix Applied to a Video-CPT-Cone

Abstract

The geotechnical characterization of the subsurface is a key requirement for most soil investigations, incl. those for reclaiming landfills and waste dumps associated with mining operations. New sensor technology, combined with intelligent analysis algorithms, allow for a faster and less expensive acquisition of the necessary information without loss of data quality. The use of advanced technologies to support and back up common site investigation techniques, such as cone penetration testing (CPT), can enhance the underground characterization process. This study aims to investigate the possibilities of image analysis for material recognition to advance the geotechnical characterization process. The grey level co-occurrence matrix (GLCM) image processing technique is used in a wide range of study fields to estimate textures, patterns and structure anomalies. This method was adjusted and applied to process the video recorded during a CPT sounding, in order to distinguish soil types by its changing surface characteristics. From the results of the video processing, it is evident that the GLCM technique can identify transitions in soil types that were captured in the video recording. This enables the prospect of image analysis not just for soil investigations, but also for monitoring of the conveyor belt in the mining field, to allow for efficient preliminary decision making, material documentation and quality control by providing information in a cost effective and efficient manner.