Improvements on Mapping Soil Liquefaction at a Regional Scale
Zhu, Jing.
2017
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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 regi... read moreonal scale 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
- Component ID:
- tufts:20685
- To Cite:
- DCA Citation Guide EndNote
