Amy Williamson

Amy Williamson

Research Scientist at the Berkeley Seismo Lab

Publications

Performance of bEPIC Through the 2024 M 7.0 Mendocino Earthquake Sequence

Published in The Seismic Record, 2025

In this study, we compare bEPIC’s ability to detect and locate offshore earthquakes from the M 7.0 earthquake sequence against the EPIC solution. We focus on location accuracy, rates of earthquake detection, downstream parameter improvements to the magnitude, and first alert timing. We also highlight how utilizing bEPIC improves the potential alerting footprint. The performance during this sequence can act as an example of expected performance for future large earthquakes within this seismically active region.

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Improving out of network earthquake locations using prior seismicity for use in earthquake early warning

Published in BSSA, 2023

alt text In order to better constrain location solutions in this region, we propose to include information about contemporary past seismicity into EPIC’s grid search algorithm through a Bayesian framework. This prior information layer down-weights high error locations where EPIC’s proposed event location coincides with an area of low prior seismicity in preference for locations with a similar level of data fit that also have higher past seismicity. This addition to EPIC lowers the mean location error offshore northern California from 58 km to 14 km.

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A Source Clustering Approach for Efficient Inundation Modeling and Regional Scale PTHA

Published in Frontiers in Earth Science, 2020

In this study, we present an approach to estimating inundation depth probabilities (in the form of hazard curves at a set of coastal locations)that consists of two components. The first component uses a Karhunen-Loe`ve expansion to express the probability density function (PDF) for all possible events with PDF parameters that are geophysically reasonable for the Cascadia Subduction Zone (CSZ). It is then extremely easy and computationally cheap to generate a very large N number of samples from this PDF; doing so and performing a full tsunami inundation simulation for each provides a brute force approach to estimating probabilities of inundation. The estimate is simply the number of random realizations that reach specified inundation depth divided by N. However, to obtain reasonable results, particularly for extreme flooding due to rare events, N would have to be so large as to make the necessary tsunami simulations prohibitively expensive. The second component tackles this difficulty by using importance sampling techniques to ensure we adequately sample the tails of the distribution and properly re-weight the probability assigned to the resulting realizations, and by grouping the realizations into a small number of clusters that we believe will give similar inundation patterns in the region of interest.

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The 2018 Palu Tsunami: Coeval Landslideand Coseismic Sources

Published in Seismological Research Letters, 2020

In thisIn this study, we posit that subaerial, submarine landslides, and coseismic offsets all contributed to the 2018 Palu, Indonesia tsunami’s damaging impact. We explore many possible tsunami sources in addition to the earthquake’s rupture near the bay, solving for both coseismic offsets and potential landsliding events in a self-consistent model.

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Toward Near-Field Tsunami Forecasting Along the Cascadia Subduction Zone Using Rapid GNSS Source Models

Published in JGR Solid Earth, 2020

In this study, we take the first steps to systematically analyze thequality of nearfield tsunami forecasts that would rely on earthquake source products derived from high-rate and real time GNSS data for potential earthquakes originating on the Cascadia margin. Specifically, we focus on two aspects. First, we analyze the ability of regional GNSS sensors to rapidly estimate characteristics of an offshore earthquake such as magnitude, focal mechanism, and fault slip. Second, we show how well automated GNSS-driven slip models can reproduce observed tsunami amplitudes at local coastlines, proposing an assessment method based on user defined thresholds that we believe is beneficial for local tsunami warning.

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