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Evaluation of the ability to extract crustal deformation signal from ocean bottom pressure time series using principal component analysis

Urheber*innen

Otsuka,  Hideto
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Yusaku,  Ohta
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Ryota,  Hino
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Tatsuya,  Kubota
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Daisuke,  Inazu
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Tomohiro,  Inoue
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Narumi,  Takahashi
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

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Zitation

Otsuka, H., Yusaku, O., Ryota, H., Tatsuya, K., Daisuke, I., Tomohiro, I., Narumi, T. (2023): Evaluation of the ability to extract crustal deformation signal from ocean bottom pressure time series using principal component analysis, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-1390


Zitierlink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5017211
Zusammenfassung
OBP (Ocean bottom pressure-gauge) is an essential sensor in crustal deformation observation in the marine region. Still, non-tidal oceanographic fluctuations in OBP data are a significant source of noise in seafloor transient crustal deformation observations, including SSEs (slow slip events), so it is essential to evaluate them properly. To extract and assess the significant characteristics of the oceanographic fluctuations, we applied PCA (principal component analysis) to the DONET (Dense Oceanfloor Network System for Earthquakes and Tsunamis, southwest margin of Japan) OBP time series of 40 stations for three years (2016–2019). PCA could separate several oceanographic signals based on the characteristics of their spatial distributions. The higher-order modes of the PC were distributed in a varying manner with sea depth, and we interpreted that they were caused by the strength and meandering of the ocean geostrophic currents based on similar spatial distribution with the global ocean models. Since PCA cannot detect the significant tectonic signal in observed data, we applied a synthetic ramp simulating SSE to the observation to evaluate the performance of PCA in tectonic signal detection. The assumed synthetic ramp could be separated into an independent principal component according to its amplitude (i.e., the scale of the events). We proposed a detection method of a transient event using the spatial distribution change of the principal component with and without the synthetic ramp using their normalized inner product. Our method was able to detect the tectonic signal of events of MW5.9 or larger from the OBP time series.