An Automatic Method for Discontinuity Recognition in Coal-Measure Strata Borehole Images

Abstract

An automatic recognition of discontinuities in borehole images is a desirable way to overcome the inefficiency and inconsistency inherent in the conventional method of manual annotation. The rough borehole walls and prominent noise failed the application of existing recognition methods to borehole images taken in the coal-measure strata. This paper presents a novel approach to automatically convert coal-measure strata borehole images into identified discontinuity maps. The developed procedure formed an integrated feature representation for the borehole image through combining the color information of image and the textural features generated from multi-channel filtering. Image regions containing discontinuities are then separated from other regions by implementing fuzzy c-means clustering on the acquired feature representation. The identification of discontinuities is finally accomplished by searching for four predefined patterns (named topographic model) on the intensity transection of image regions. The proposed method is proven to be superior in the respects of noise suppression, discontinuity positioning, and recognition completeness.