Auto-detection and integration of tectonically significant lineaments from SRTM DEM and remotely-sensed geophysical data

Abstract

A set of techniques was developed for automatically detecting tectonic lineaments from multi-source remotely-sensed data at various scales. The techniques include adaptive shading of grid data to enhance linear features, a segment-tracing algorithm to extract line segments from the shaded grid data, grouping of the segments by concatenating short segments, and connecting them by proximity and co-linearity criteria to form a lineament that represents significant tectonic structure. B-spline smoothing was adopted for lineament representation. Finally, a technique for assessing the orientations and styles of faulting (normal, reverse, and strike-slip types) was developed for use in characterizing the extrapolated fracture planes. The applicability of the developed techniques was examined using 30 arc-second topography/bathymetry grids, 1-min gravity anomaly grids, and 2-min total field magnetic intensity grids covering Egypt and its surroundings. Lineaments derived from data types so diverse in composition and from various depths corresponded well with the referenced tectonic features over much of the region. Prominent trends and faulting styles of lineaments provided important clues as to the timing of their development as well as strong support for a structural inheritance model. Results demonstrated the effectiveness of the developed techniques combined with integration of remotely-sensed data in detecting regional fracture systems accurately and in characterizing geodynamics over a long timeframe.