Improvements on Mapping Soil Liquefaction at a Regional Scale
Zhu, Jing.
2017-04-18T11:39:53.787Z
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Abstract: Earthquake induced soil liquefaction is an important secondary hazard during earthquakes and can lead to significant damage to infrastructure. Mapping liquefaction hazard is important in both planning for earthquake events and guiding relief efforts by positioning resources once the events have occurred. This dissertation addresses two aspects of liquefaction hazard mapping at a regional... read morescale including 1) predictive liquefaction hazard mapping and 2) post-liquefaction cataloging. First, current predictive hazard liquefaction mapping relies on detailed geologic maps and geotechnical data, which are not always available in at-risk regions. This dissertation improves the predictive liquefaction hazard mapping by the development and validation of geospatial liquefaction models (Chapter 2 and 3) that predict liquefaction extent and are appropriate for global application. The geospatial liquefaction models are developed using logistic regression from a liquefaction database consisting of the data from 27 earthquake events from six countries. The model that performs best over the entire dataset includes peak ground velocity (PGV), VS30, distance to river, distance to coast, and precipitation. The model that performs best over the noncoastal dataset includes PGV, VS30, water table depth, distance to water body, and precipitation. Second, post-earthquake liquefaction cataloging historically relies on field investigation that is often limited by time and expense, and therefore results in limited and incomplete liquefaction inventories. This dissertation improves the post-earthquake cataloging by the development and validation of a remote sensing-based method that can be quickly applied over a broad region after an earthquake and provide a detailed map of liquefaction surface effects (Chapter 4). Our method uses the optical satellite images before and after an earthquake event from the WorldView-2 satellite with 2 m spatial resolution and eight spectral bands. Our method uses the changes of spectral variables that are sensitive to surface moisture and soil characteristics paired with a supervised classification.
Thesis (Ph.D.)--Tufts University, 2017.
Submitted to the Dept. of Civil Engineering.
Advisor: Laurie Baise.
Committee: Elena Naumova, Magaly Koch, Richard Vogel, and Eric Thompson.
Keywords: Civil engineering, and Remote sensing.read less - ID:
- 76537d01j
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