A region-growing approach for automatic outcrop fracture extraction from a three-dimensional point cloud

TL;DR

This paper introduces a novel region-growing method for automatically extracting and estimating the orientation of fractures from 3D point clouds of outcrops, significantly improving efficiency and accuracy over manual surveys.

Contribution

The study presents a new automated fracture extraction technique using region-growing based on local surface normals and curvature, capable of capturing full fracture extents from 3D point clouds.

Findings

High accuracy in fracture identification and extraction

Better quality data compared to manual surveys

Effective in complex outcrop environments

Abstract

Conventional manual surveys of rock mass fractures usually require large amounts of time and labor; yet, they provide a relatively small set of data that cannot be considered representative of the study region. Terrestrial laser scanners are increasingly used for fracture surveys because they can efficiently acquire large area, high-resolution, three-dimensional (3D) point clouds from outcrops. However, extracting fractures and other planar surfaces from 3D outcrop point clouds is still a challenging task. No method has been reported that can be used to automatically extract the full extent of every individual fracture from a 3D outcrop point cloud. In this study, we propose a method using a region-growing approach to address this problem; the method also estimates the orientation of each fracture. In this method, criteria based on the local surface normal and curvature of the point cloud are used to initiate and control the growth of the fracture region. In tests using outcrop point cloud data, the proposed method identified and extracted the full extent of individual fractures with high accuracy. Compared with manually acquired field survey data, our method obtained better-quality fracture data, thereby demonstrating the high potential utility of the proposed method.