prediction of the area affected by earthquake-induced landsliding based on seismological parameters
Clicks: 79
ID: 174848
2017
We present an analytical, seismologically consistent expression for the
surface area of the region within which most landslides triggered by an
earthquake are located (landslide distribution area). This expression is
based on scaling laws relating seismic moment, source depth, and focal
mechanism with ground shaking and fault rupture length and assumes a globally
constant threshold of acceleration for onset of systematic mass wasting. The
seismological assumptions are identical to those recently used to propose a
seismologically consistent expression for the total volume and area of
landslides triggered by an earthquake. To test the accuracy of the model we
gathered geophysical information and estimates of the landslide distribution
area for 83 earthquakes. To reduce uncertainties and inconsistencies in the
estimation of the landslide distribution area, we propose an objective
definition based on the shortest distance from the seismic wave emission line
containing 95 % of the total landslide area. Without any empirical
calibration the model explains 56 % of the variance in our dataset, and
predicts 35 to 49 out of 83 cases within a factor of 2, depending on how we
account for uncertainties on the seismic source depth. For most cases with
comprehensive landslide inventories we show that our prediction compares well
with the smallest region around the fault containing 95 % of the total
landslide area. Aspects ignored by the model that could explain the residuals
include local variations of the threshold of acceleration and processes
modulating the surface ground shaking, such as the distribution of seismic
energy release on the fault plane, the dynamic stress drop, and rupture
directivity. Nevertheless, its simplicity and first-order accuracy suggest
that the model can yield plausible and useful estimates of the landslide
distribution area in near-real time, with earthquake parameters issued by
standard detection routines.
Reference Key |
marc2017naturalprediction
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Authors | ;O. Marc;O. Marc;P. Meunier;N. Hovius;N. Hovius |
Journal | anziam journal |
Year | 2017 |
DOI | 10.5194/nhess-17-1159-2017 |
URL | |
Keywords |
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