Development of a Web Application System for Seismic Waveform Data Observed in Real Time With the Seafloor Seismic Network DONET

Takaesu, Morifumi; Horikawa, Hiroki; Sueki, Kentaro; Takahashi, Narumi; Sonoda, Akira; Tsuboi, Seiji

doi:10.4031/mtsj.50.3.14

Cited by 4 publications

(1 citation statement)

References 3 publications

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“…The DONET data acquisition and distribution are carried out by JAMSTEC [9]. This study was supported by the Japan Society for the Promotion of Science (JSPS) and the Russian Foundation for Basic Research (RFBR) under the Japan Russia Research Cooperative Program.…”

Section: Acknowledgmentmentioning

confidence: 99%

Ocean-bottom pressure and seismic signals at tsunamigenic earthquake

Matsumoto

Носов

Kaneda

et al. 2015

2015 IEEE Underwater Technology (UT)

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Offshore observations make it possible to detect tsunamis in advance prior to their arrivals at the shoreline. For this purpose, ocean bottom pressure gauges are widely used. However, in near-and intermediate fields, ocean bottom pressure records usually exhibit a complicated interference of signals related not only to gravitational wave (Le., tsunami), but also to hydroacoustic and seismic waves. Network of offshore observatories recently deployed in Japan is capable of providing high sampling rate of records of ocean bottom pressure and seismic (acceleration and velocity) signals. In the present study, by taking advantage of simultaneous measurements of pressure and seismic signals that were recorded during the recent tsunamigenic earthquakes, i.e., the 2011 Tohoku earthquake and the related 2013 October earthquake, we reveal particular features of these signals. The bottom pressure follows the bottom acceleration in the inter-mediate frequency band.

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Section: Acknowledgmentmentioning

confidence: 99%

Ocean-bottom pressure and seismic signals at tsunamigenic earthquake

Matsumoto

Носов

Kaneda

et al. 2015

2015 IEEE Underwater Technology (UT)

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Research on Data Management of Marine Disaster Mitigation Based on Big Data

Chen¹,

Liu²,

Zhang³

et al. 2020

J. Phys.: Conf. Ser.

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With the explosive growth of ocean data, ocean big data has received more and more attention. It mainly analyzes and summarizes the current research status and key technologies of ocean big data, and focuses on the examples of machine learning model prediction research in ocean big data. With the advancement of 3S technology, China’s marine monitoring network has achieved all-round development, including air, ground, and sea monitoring platforms, which has promoted the rapid growth of marine big data from the original GB and TB levels to PB levels. Taking the typhoon disaster search and situation display implementation as an example, the thesis has preliminarily completed the realization and application of the intelligent ocean search engine. Combined with the design of typhoon disaster search and the overall structure and function design of the situation display, module development was carried out, and the development was initially completed to realize the business data management of marine typhoon disaster mitigation based on big data.

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Research on Disaster Prevention and Mitigation System Construction Based on Marine Business Work

Sun¹,

Chen²,

Jun³

2020

IOP Conf. Ser.: Earth Environ. Sci.

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As global climate change intensifies, the po Marine business work, disaster reduction, disaster prevention, spatial analysis, risk synthesisability of marine disasters becomes greater and greater. In order to reduce the damage caused by marine disasters and to be able to shoulder the responsibility of major powers in facing marine disaster risks, China needs to establish China’s domestic marine disaster prevention and relief system and international cooperation mechanism for disaster prevention. On this basis, a preliminary idea for establishing a disaster risk assessment system was proposed. After the system is completed, the spatial analysis function based on GIS, combined with the disaster risk assessment database, can quickly, practically and accurately provide special disaster risk results for disaster prediction and prediction of relevant departments and select effective and scientific disaster prevention, mitigation and relief Measures provide the basis.

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Development of a Web Application System for Seismic Waveform Data Observed in Real Time With the Seafloor Seismic Network DONET

Cited by 4 publications

References 3 publications

Ocean-bottom pressure and seismic signals at tsunamigenic earthquake

Ocean-bottom pressure and seismic signals at tsunamigenic earthquake

Research on Data Management of Marine Disaster Mitigation Based on Big Data

Research on Disaster Prevention and Mitigation System Construction Based on Marine Business Work

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