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Imaging the next Cascadia earthquake: Optimal design for a seafloor GNSS- A network

September 21, 2021

The Cascadia subduction zone in the Pacific Northwest of the United States of America capable of producing magnitude ∼9 earthquakes, likely often accompanied by tsunamis. An outstanding question in this region is the degree and spatial extent of interseismic strain accumulation on the subduction megathrust. Seafloor geodetic methods combining GNSS and underwater acoustic ranging (GNSS-A) are capable of imaging this strain accumulation on the offshore portion of the subduction zone and therefore anticipating the potential size and rupture pattern of a future earthquake. However, the high cost of seafloor geodesy means that only a limited number of stations may be deployed and monitored. To facilitate expansion of current geodetic networks offshore, we develop a quantitative recommendation of optimal locations for future seafloor geodetic observations, based on the amount of new information provided by that observation. The optimal network depends on the problem that one is trying to solve with those observations (mapping subduction locking rates, coupling rates, constraining total moment rate, etc.), and on a number of modelling and data uncertainty assumptions. In particular, data uncertainty assumptions will change over time, as more position observations reduce velocity uncertainties. We find that near-trench observations on the megathrust hangingwall, distributed along-strike, consistently provide significant reduction in differential entropy over a large suite of assumptions, and that a well-placed seafloor observation can provide up to ∼30 times the information gain of the most optimal onshore observation.

Publication Year 2022
Title Imaging the next Cascadia earthquake: Optimal design for a seafloor GNSS- A network
DOI 10.1093/gji/ggab360
Authors Eileen L. Evans, Sarah E. Minson, David Chadwell
Publication Type Article
Publication Subtype Journal Article
Series Title Geophysical Journal International
Index ID 70237101
Record Source USGS Publications Warehouse
USGS Organization Earthquake Science Center