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Optimal Seismic Network Density for Earthquake Early Warning : A Case Study from California
| Content Provider | Semantic Scholar |
|---|---|
| Author | Kuyuk, H. Serdar Allen, Richard M. |
| Copyright Year | 2013 |
| Abstract | Earthquake Early Warning Systems (EEWS) rapidly detect the initiation of earthquakes and issue warning alerts of possible forthcoming ground shaking. Currently, public warning systems exist in Japan and Mexico, and the development of other EEWS are ongoing in many other regions of the world including the U.S. West Coast (Allen et al., 2009). Probing the way business and the public actively use early warning information is a crucial factor in early warning system design (Aktas et al., 2010; Kuyuk, 2010; Kuyuk et al., 2008; Nakamura, 1988). During the 2011 M 9 Tohoku-oki, Japan, earthquake, an earthquake warning was successfully issued although the magnitude was underestimated (Hoshiba et al., 2011). To determine the usefulness of the alerts, the Japan Meteorological Agency (JMA) conducted a public survey (JMA, 2012). Results from the ∼2000 people who answered the survey indicated that most people want two main pieces of information from an EEWmessage: the time when strong shaking is expected to begin at their location and the estimated shaking intensity. The survey also showed that although the JMA warnings provide additional information about the earthquake location, magnitude, and depth, people are less interested in this information and more interested in the potential impending dangers the large earthquake might cause at their location. Many factors contribute to the time between the issued earthquake warning and the subsequent ground shaking at a given location. In this paper, we refer to this period as the warning time. The warning-time duration is dictated by many factors, of which the most important are the proximity of stations to the earthquake epicenter, data telemetry speed, data processing time, and the time needed to disseminate the warning. Once an alert is generated, the amount of warning time is a function of the distance of the user from the epicenter, in which more distant locations receive longer warning time. One of the challenges with EEWS is minimizing the blind zone, that is, the region around the epicenter where no warning is possible because the strong shaking has already occurred by the time the alert is generated. Some factors that influence the radius of the blind-zone area are simply out of our control. For example, we cannot dictate exactly where earthquakes occur and how deep an earthquake hypocenter is. However, there are many things we can do to reduce the size of the blind zone. For example, (a) using the most advanced telecommunication technologies that can potentially decrease the current telemetry delay; (b) decreasing data packet size to less than 0.5 s; (c) improving event detection and alert-filtering algorithms; and (d) well-developed seismic networks with improved station density deployed across seismogenic zones. The degree to which these improvements can be made depend on how close the seismic stations are to the earthquake epicenter, the distance between the warning site and the earthquake epicenter, the depth of earthquake, the density of the seismic network, the telemetry delay, and the time needed for decision making in regard to the type of warning that should be issued. The blind zone as defined in this paper is the radius from the epicenter to the distance traveled by the seismic S wave at the time the alert is issued. It is a minimum value, as for any practical use, the blind zone will be larger depending on the time required for a specific action to be taken once the alert is received. One of the most advanced EEWS was developed by the National Research Institute for Earth Science and Disaster Prevention (NIED) and the JMA in Japan. This advanced system includes 1089 stations from two separate networks: Hi-net and JMAwith an average of 18.7 km station spacing. In California, the California Integrated Seismic Network (CISN), which consists of multiple, complementary seismic networks (∼2900 stations, http://www.cisn.org/; last accessed May 2013) operate seismometers and accelerometers; 587 of them, located at 377 sites (Fig. 1), provide real-time waveforms to CISN/ShakeAlert EEWS (Boese et al., 2013). Interstation distances between stations in the California network vary significantly from region to region ranging from 2 to 100 km. For example, the interstation distance is less than 5 km in densely populated regions such as the San Francisco Bay and Los Angeles regions; however, in northeastern California the interstation distances tend to be much larger at a spacing of ∼70 km. This nonuniformity in seismic-station spacing throughout California differs drastically from the station spacing in the Japan network, which has made significant strides in this area by deploying a dense array of seismic stations, leaving few areas without service. The incentive to upgrade and densify seismic networks is often driven by devastating large earthquakes, which get the attention of not only the public, but also policy makers. For example, the USGS/Caltech Southern California Seismic Network (CI) in southern California were upgraded after the |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://seismo.berkeley.edu/annual_report/2013_contributions/kuyuk13_2.pdf |
| Alternate Webpage(s) | http://seismo.berkeley.edu/~rallen/pub/2013kuyuk/KuyukAllen-EEWNetworkDesign-SRL-2013.pdf |
| Alternate Webpage(s) | http://rallen.berkeley.edu/pub/2013kuyuk/KuyukAllen-EEWNetworkDesign-SRL-2013.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Accident caused by earthquake Alert:Type:Point in time:^Patient:Nominal Cautionary Warning Departure - action Distance Earthquakes Emoticon Encephalitis Virus, California Ephrin Type-B Receptor 1, human How True Feel Alert Right Now Large Metals, Rare Earth Meteorological Factors Network packet Open Knowledge Initiative Population Real-time clock Spacing Systems design Telemetry Tremor accelerometers algorithm hemoglobin D Punjab hemoglobin Matsue-Oki |
| Content Type | Text |
| Resource Type | Article |
