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AMCS/STAT Graduate Seminar: Geostatistical modeling to capture seismic-shaking patterns from earthquake-induced landslides

Start Date: October 11, 2018
End Date: October 11, 2018

By Dr. Luigi Lombardo‚Äč (KAUST)

In this work, we investigate earthquake-induced landslides using a geostatistical model that includes a latent spatial effect (LSE). The LSE represents the spatially structured residuals in the data, which are complementary to the information carried by the covariates. To determine whether the LSE can capture the residual signal from a given trigger, we test whether the LSE is able to capture the pattern of seismic shaking caused by an earthquake from the distribution of seismically induced landslides, without prior knowledge of the earthquake being included in the statistical model. We assess the landslide intensity, i.e., the expected number of landslide activations per mapping unit, for the area in which landslides triggered by the Wenchuan (M 7.9, May 12, 2008) and Lushan (M 6.6, April 20, 2013) earthquakes overlap. We chose an area of overlapping landslides in order to test our method on landslide inventories located in the near and far fields of the earthquake. We generated three different models for each earthquake-induced landslide scenario: i) seismic parameters only (as a proxy for the trigger); ii) the LSE only; and iii) both seismic parameters and the LSE. The three configurations share the same set of morphometric covariates. This allowed us to study the pattern in the LSE and assess whether it adequately approximated the effects of seismic wave propagation. Moreover, it allowed us to check whether the LSE captured effects that are not explained by the shaking levels, such as topographic amplification. Our results show that the LSE reproduced the shaking patterns in space for both earthquakes with a level of spatial detail even greater than the seismic parameters. In addition, the models including the LSE perform better than conventional models featuring seismic parameters only.
Biography: Before joining KAUST in 2016 as a postdoc, I worked both in the industrial and academic sectors as a Geologist. During my studies, I have completed a BSc in Geological Sciences in 2008, followed by a MSc in Applied Geology in 2011 and a PhD in Landslide Hazards in 2015. My scientific interest lies in spatial predictive modeling, this being applied in natural hazards and soil science. My research primarily focuses on landslides susceptibility, hazard and their transferability between different geographic contexts. I have always used either frequentist or data mining approaches but recently I am trying to move to a Bayesian framework as it provides better spatial insights. At the same time, I am also investigating how effective spatial prediction can be when modeling other physical processes or properties (e.g. soil erosion, soil organic carbon, aquifer vulnerability or sea-bed pockmarks).

More Information:

For more info contact: Dr. Luigi Lombardo: email:
Date: Thursday 11th Oct 2018
Time:12:00 PM - 01:00 PM
Location: Building 9, Lecture Hall 1 Room 2322
Refreshments: Light Lunch will be served at 11:45 AM