Application of Dense Offshore Tsunami Observations from Ocean Bottom Pressure Gauges (OBPGs) for Tsunami Research and Early Warnings View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2019

AUTHORS

Mohammad Heidarzadeh , Aditya R. Gusman

ABSTRACT

We introduce a new data source of dense deep-ocean tsunami records from Ocean Bottom Pressure Gauges (OBPGs) which are attached to Ocean Bottom Seismometers (OBS) and apply them for far-field and near-field tsunami warnings. Tsunami observations from OBPGs are new sources of deep-ocean tsunami observations which, for the first time, provide dense tsunami data with spacing intervals in the range of 10–50 km. Such dense data are of importance for tsunami research and warnings and are capable of providing new insights into tsunami characteristics. Here, we present a standard procedure for the processing of the OBPG data and extraction of tsunami signals out of these high-frequency data. Then, the procedure is applied to two tsunamis of 15 July 2009 Mw 7.8 Dusky Sound (offshore New Zealand) and 28 October 2012 Mw 7.8 Haida Gwaii (offshore Canada). We successfully extracted 30 and 57 OBPG data for the two aforesaid tsunamis, respectively. Numerical modeling of tsunami was performed for both tsunamis in order to compare the modeling results with observation and to use the modeling results for the calibration of some of the OBPG data. We successfully employed the OBPG data of the 2012 Haida Gwaii tsunami for tsunami forecast by applying a data assimilation technique. Our results, including two case studies, demonstrate the high potential of OBPG data for contribution to tsunami research and warnings. The procedure developed in this study can be readily applied for the extraction of tsunami signals from OBPG data. More... »

PAGES

7-22

Book

TITLE

Geological Disaster Monitoring Based on Sensor Networks

ISBN

978-981-13-0991-5
978-981-13-0992-2

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-981-13-0992-2_2

DOI

http://dx.doi.org/10.1007/978-981-13-0992-2_2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1106084158


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