Ocean-Bottom Strong-Motion Observations in the Nankai Trough by the DONET Real-Time Monitoring System

2018 ◽  
Vol 52 (3) ◽  
pp. 100-108 ◽  
Author(s):  
Takeshi Nakamura ◽  
Narumi Takahashi ◽  
Kensuke Suzuki

AbstractThe deployment of real-time permanent ocean-bottom seismic and tsunami observatories is significant for disaster mitigation and prevention during the occurrence of large subduction earthquakes near trough areas. On April 1, 2016, a moderate-sized suboceanic earthquake occurred beneath Dense Oceanfloor Network System for Earthquakes and Tsunamis (DONET) stations that were recently deployed in deep ocean-bottom areas near the Nankai Trough in southwest Japan. P-waves arrived at the ocean-bottom station within 4 s of the origin time, which was 6 and 13 s earlier than the arrival of P- and S-waves at a land station in the coastal area, respectively; this implies earlier detection of strong motion than at land stations. However, the waveforms are amplified by sediment layers and even contaminated with acceleration offsets at some stations, which would lead to overestimations during source investigations. Such amplification and offset did not occur at a borehole station connected to DONET. The amplifications caused by the sediment layers and the offset were found to have a considerable spatial variation, not only between the DONET stations and land and borehole stations but also among the DONET stations, implying that the amplitude evaluation could be unstable. Therefore, procedures for correcting or suppressing the amplification and offset problem are required for conducting waveform analyses, such as magnitude estimations and source modeling, during large subduction earthquakes.

Author(s):  
Pablo Koch ◽  
Francisco Bravo ◽  
Sebastian Riquelme ◽  
Jorge G. F. Crempien

ABSTRACT Recent efforts have been made to model the rupture process of large earthquakes in near‐real time (NRT) in Chile. In this study, we propose an automated procedure using strong‐motion data in an integrated system, which can characterize large earthquakes with a finite‐fault model (FFM) in NRT. We developed several heuristic rules using the preliminary W‐phase solutions to automatically set up the search ranges of the finite‐fault inversions. The results show using strong‐motion data and a W‐phase magnitude, it is possible to obtain a rapid kinematic FFM in just a few minutes after the earthquake origin time.


2007 ◽  
Vol 7 (2) ◽  
pp. 2-16 ◽  
Author(s):  
Hiroyuki FUJIWARA ◽  
Takashi KUNUGI ◽  
Shigeki ADACHI ◽  
Shin AOI ◽  
Nobuyuki MORIKAWA

2017 ◽  
Vol 209 (3) ◽  
pp. 1408-1417 ◽  
Author(s):  
Rui Tu ◽  
Jinhai Liu ◽  
Cuixian Lu ◽  
Rui Zhang ◽  
Pengfei Zhang ◽  
...  

2011 ◽  
Vol 101 (6) ◽  
pp. 2904-2925 ◽  
Author(s):  
Y. Bock ◽  
D. Melgar ◽  
B. W. Crowell
Keyword(s):  

Author(s):  
Jui-Chun Freya Chen ◽  
Wu-Cheng Chi ◽  
Chu-Fang Yang

Abstract Developing new ways to observe tsunami contributes to tsunami research. Tidal and deep-ocean gauges are typically used for coastal and offshore observations. Recently, tsunami-induced ground tilts offer a new possibility. The ground tilt signal accompanied by 2010 Mw 8.8 Chilean earthquake were observed at a tiltmeter network in Japan. However, tiltmeter stations are usually not as widely installed as broadband seismometers in other countries. Here, we studied broadband seismic records from Japan’s F-net and found ground tilt signals consistent with previously published tiltmeter dataset for this particular tsunamic event. Similar waveforms can also be found in broadband seismic networks in other countries, such as Taiwan, as well as an ocean-bottom seismometer. We documented a consistent time sequence of evolving back-azimuth directions of the tsunami waves at different stages of tsunami propagation through beamforming-frequency–wavenumber analysis and particle-motion analysis; the outcomes are consistent with the tsunami propagation model provided by the Pacific Tsunami Warning Center. These results shown that dense broadband seismic networks can provide a useful complementary dataset, in addition to tiltmeter arrays and other networks, to study or even monitor tsunami propagation using arrayed methods.


2010 ◽  
Vol 10 (8) ◽  
pp. 1759-1780
Author(s):  
O. Boebel ◽  
M. Busack ◽  
E. R. Flueh ◽  
V. Gouretski ◽  
H. Rohr ◽  
...  

Abstract. The German-Indonesian Tsunami Early Warning System (GITEWS) aims at reducing the risks posed by events such as the 26 December 2004 Indian Ocean tsunami. To minimize the lead time for tsunami alerts, to avoid false alarms, and to accurately predict tsunami wave heights, real-time observations of ocean bottom pressure from the deep ocean are required. As part of the GITEWS infrastructure, the parallel development of two ocean bottom sensor packages, PACT (Pressure based Acoustically Coupled Tsunameter) and OBU (Ocean Bottom Unit), was initiated. The sensor package requirements included bidirectional acoustic links between the bottom sensor packages and the hosting surface buoys, which are moored nearby. Furthermore, compatibility between these sensor systems and the overall GITEWS data-flow structure and command hierarchy was mandatory. While PACT aims at providing highly reliable, long term bottom pressure data only, OBU is based on ocean bottom seismometers to concurrently record sea-floor motion, necessitating highest data rates. This paper presents the technical design of PACT, OBU and the HydroAcoustic Modem (HAM.node) which is used by both systems, along with first results from instrument deployments off Indonesia.


2010 ◽  
Vol 10 (7) ◽  
pp. 1617-1627 ◽  
Author(s):  
A. Y. Babeyko ◽  
A. Hoechner ◽  
S. V. Sobolev

Abstract. We present the GITEWS approach to source modeling for the tsunami early warning in Indonesia. Near-field tsunami implies special requirements to both warning time and details of source characterization. To meet these requirements, we employ geophysical and geological information to predefine a maximum number of rupture parameters. We discretize the tsunamigenic Sunda plate interface into an ordered grid of patches (150×25) and employ the concept of Green's functions for forward and inverse rupture modeling. Rupture Generator, a forward modeling tool, additionally employs different scaling laws and slip shape functions to construct physically reasonable source models using basic seismic information only (magnitude and epicenter location). GITEWS runs a library of semi- and fully-synthetic scenarios to be extensively employed by system testing as well as by warning center personnel teaching and training. Near real-time GPS observations are a very valuable complement to the local tsunami warning system. Their inversion provides quick (within a few minutes on an event) estimation of the earthquake magnitude, rupture position and, in case of sufficient station coverage, details of slip distribution.


2011 ◽  
Vol 54 (1) ◽  
Author(s):  
Paolo Augliera ◽  
Marco Massa ◽  
Ezio D'Alema ◽  
Simone Marzorati

2016 ◽  
Vol 59 ◽  
Author(s):  
Marco Massa ◽  
Ezio D'Alema ◽  
Chiara Mascandola ◽  
Sara Lovati ◽  
Davide Scafidi ◽  
...  

<p><em>ISMD is the real time INGV Strong Motion database. During the recent August-September 2016 Amatrice, Mw 6.0, seismic sequence, ISMD represented the main tool for the INGV real time strong motion data sharing.  Starting from August 24<sup>th</sup>,  the main task of the web portal was to archive, process and distribute the strong-motion waveforms recorded  by the permanent and temporary INGV accelerometric stations, in the case of earthquakes with magnitude </em><em>≥</em><em> 3.0, occurring  in the Amatrice area and surroundings.  At present (i.e. September 30<sup>th</sup>, 2016), ISMD provides more than 21.000 strong motion waveforms freely available to all users. In particular, about 2.200 strong motion waveforms were recorded by the temporary network installed for emergency in the epicentral area by SISMIKO and EMERSITO working groups. Moreover, for each permanent and temporary recording site, the web portal provide a complete description of the necessary information to properly use the strong motion data.</em></p>


Sign in / Sign up

Export Citation Format

Share Document