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• W4309761239 abstract "Abstract Seafloor pressure sensor data is emerging as a promising approach to resolve vertical displacement of the seafloor in the offshore reaches of subduction zones, particularly in response to slow slip events (SSEs), although such signals are challenging to resolve due to sensor drift and oceanographic signals. Constraining offshore SSE slip distribution is of key importance to understanding earthquake and tsunami hazards posed by subduction zones. We processed seafloor pressure data from January to October 2019 acquired at the Hikurangi subduction zone, offshore New Zealand, to estimate vertical displacement associated with a large SSE that occurred beneath the seafloor array. The experiment included three self‐calibrating sensors designed to remove sensor drift, which, together with ocean general circulation models, were essential to the identification and correction of long‐period ocean variability remaining in the data after applying traditional processing techniques. We estimate that long‐period oceanographic signals that were not synchronous between pressure sensors and reference sites influenced our inferred displacements by 0.3–2.6 cm, suggesting that regionally deployed reference sites alone may not provide sufficient ocean noise correction. After incorporating long‐period ocean variability corrections into the processing, we calculate 1.0–3.3 cm of uplift during the SSE offshore Gisborne at northern Hikurangi, and 1.1–2.7 cm of uplift offshore the Hawke's Bay area at central Hikurangi. Some Hawke Bay displacements detected by pressure sensors near the trench were delayed by 6 weeks compared to the timing of slip onset detected by onshore Global Navigation Satellite System sites, suggesting updip migration of the SSE." @default.
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• W4309761239 date "2022-11-29" @default.
• W4309761239 modified "2023-10-14" @default.
• W4309761239 title "Using Seafloor Geodesy to Detect Vertical Deformation at the Hikurangi Subduction Zone: Insights From Self‐Calibrating Pressure Sensors and Ocean General Circulation Models" @default.
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• W4309761239 doi "https://doi.org/10.1029/2022jb023989" @default.
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