Abstract. Manual approaches for analyzing fault scarps in the field or with
existing software can be tedious and time-consuming. Here, we
introduce an open-source, semiautomated, Python-based graphical user
interface (GUI) called the Monte Carlo Slip Statistics Toolkit (MCSST)
for estimating dip slip on individual or bulk fault datasets that (1)
makes the analysis of a large number of profiles much faster, (2)
allows users with little or no coding skills to implement the
necessary statistical techniques, (3) and provides geologists with a platform to incorporate their observations or expertise into the
process. Using this toolkit, profiles are defined across fault scarps
in high-resolution digital elevation models (DEMs), and then relevant
fault scarp components are interactively identified (e.g., footwall,
hanging wall, and scarp). Displacement statistics are calculated
automatically using Monte Carlo simulation and can be conveniently
visualized in geographic information systems (GISs) for spatial
analysis. Fault slip rates can also be calculated when ages of
footwall and hanging wall surfaces are known, allowing for temporal
analysis. This method allows for the analysis of tens to hundreds of
faults in rapid succession within GIS and a Python coding
environment. Application of this method may contribute to a wide range
of regional and local earthquake geology studies with adequate
high-resolution DEM coverage, enabling both regional fault source
characterization for seismic hazard and/or estimating geologic slip
and strain rates, including creating long-term deformation
maps. ArcGIS versions of these functions are available, as well as
ones that utilize free, open-source Quantum GIS (QGIS) and Jupyter
Notebook Python software.
Evidence From High‐Resolution Topography for Multiple Earthquakes on High Slip‐to‐Length Fault Scarps: The Bilila‐Mtakataka Fault, Malawi
