Mapping of normal fault scarps in airborne laser swath mapping data using wavelet analysis

Abstract:

Wavelet analysis of Digital Elevation Models (DEMs) successfully identifies degraded fault scarps where earthquakes produce topographic steps and provides an estimate of their morphologic age. However, these methods may fail to detect relatively young, sloping scarps created by more gently-dipping normal faults, misidentifying them as mature, highly-degraded vertical scarps if they are detected at all. We present new wavelet templates incorporating initial scarp slope and above- and below-scarp surface angles to better describe the curvature of observed fault scarps. These templates are based on an analytic solution for scarp curvature, allowing for more accurate estimation of the relative age of the scarp.

Synthetic tests show that scarp-like landforms that went largely undetected by a vertical-scarp template are more clearly detected using profile geometries that reflect subtle changes in curvature due to scarp and far-field slope angles. Analysis of DEMs from sites in Surprise Valley in the northwestern Basin and Range and near Jenny Lake on the Teton rangefront illustrates the effects of along-strike variability in scarp morphology on best-fit template parameters. Where normal fault scarps have high slopes, they are identified by filters designed to detect topographic step functions. Scarps with finite initial slopes, as well as those that cut surfaces with different angles above and below the scarp, can be resolved with higher signal-to-noise ratios using more sophisticated template functions. Adaptive use of different wavelet templates could reduce the number of false negatives in wavelet analysis of data from complex faulting regimes, improving the robustness of these methods and enabling automated fault mapping of large areas.