Adaptive Segmentation for Discontinuity Detection on Karstified Carbonate Outcrop Images From UAV-SfM Acquisition and Detection Bias Analysis

The identification of fractures and discontinuities has great importance on the fluid flow estimation in hydrocarbon reservoirs since they influence the properties of porosity and permeability. Due to the inaccessibility and sparsity of reservoir data, the fracture characterization is generally assessed through the study of outcrop analogues using remote sensing or in situ observations by a specialist. Considering the remote sensing methods, the unmanned Aerial Vehicle (UAV) acquisition combined with Structure from Motion (SfM) photogrammetry is a low-cost way to generate products like orthorectified images, allowing manual and automated methods of fracture trace detection. Automatic approaches, commonly used to address this problem, present some known limitations and disadvantages due to the nature of the outcrops and weather conditions during UAV acquisitions. In this work, we focus on fracture detection over karstic regions that are highly fractured. For this, we evaluated a series of adaptive segmentation methods based on thresholding. The Sauvola local adaptive segmentation presented the best result when compared to a manually annotated ground truth. The segmentation results were further improved by the use of the binary denoising method Non-Local means. We also carried an evaluation of the influence of the sun position in the fracture detection, and to reduce this inherent bias we combined three UAV acquisitions done over the karstic carbonate outcrop, namely Rosário pavement in the Jandaíra formation northeast Brazil. With the proposed methodology we acquired more accurate fracture data over the study area, which follows the directional statistics of previous works carried out in the region.