Real-Time Earthquake Intensity Estimation Using Streaming Data Analysis of Social and Physical Sensors
Earthquake intensity is one of the key components of the decision-making process for disaster response and emergency services. Accurate and rapid intensity calculations can help to reduce total loss and the number of casualties after an earthquake. Modern intensity assessment procedures handle a variety of information sources, which can be divided into two main categories. The first type of data is that derived from physical sensors, such as seismographs and accelerometers, while the second type consists of data obtained from social sensors, such as witness observations of the consequences of the earthquake itself. Estimation approaches using additional data sources or that combine sources from both data types tend to increase intensity uncertainty due to human factors and inadequate procedures for temporal and spatial estimation, resulting in precision errors in both time and space. Here we present a processing approach for the real-time analysis of streams of data from both source types. The physical sensor data is acquired from the U.S. Geological Survey (USGS) seismic network in California and the social sensor data is based on Twitter user observations. First, empirical relationships between tweet rate and observed Modified Mercalli Intensity (MMI) are developed using data from the M6.0 South Napa, CAF earthquake that occurred on August 24, 2014. Second, the streams of both data types are analyzed together in simulated real-time to produce one intensity map. The second implementation is based on IBM InfoSphere Streams, a cloud platform for real-time analytics of big data. To handle large processing workloads for data from various sources, it is deployed and run on a cloud-based cluster of virtual machines. We compare the quality and evolution of intensity maps from different data sources over 10-min time intervals immediately following the earthquake. Results from the joint analysis shows that it provides more complete coverage, with better accuracy and higher resolution over a larger area than either data source alone.
KeywordsStream computing high performance computing parallel computing physical sensors social sensors hazard estimators
- Amante, C., & Eakins, B. W. (2009). ETOPO1 1 arc-minute global relief model: procedures, data sources and analysis. NOAA Technical Memorandum NESDIS NGDC-24. National Geophysical Data Center, NOAA. doi:10.7289/V5C8276M. Accessed 10/10/2015.
- Burks, L., Miller, M., & Zadeh, R. (2014) Rapid estimate of ground shaking intensity by combining simple earthquake characteristics with tweets. s.l. Tenth U.S. national conference on earthquake engineering frontiers of earthquake engineering.Google Scholar
- Campagne, J., Dux, J., Guyot, P., & Julien, D. (2012) Twitter reaches half a billion accounts—More than 140 million in the U.S. http://semiocast.com/en/publications/2012_07_30_Twitter_reaches_half_a_billion_accounts_140m_in_the_US.
- Center for International Earth Science Information Network—CIESIN—Columbia University, and Centro Internacional de Agricultura Tropical—CIAT. (2005) Gridded Population of the World, Version 3 (GPWv3): Population Density Grid. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). doi:10.7927/H4XK8CG2. Accessed 10/10/2015.
- Delaunay, B. (1934). Sur la sphère vide. A la mémoire de Georges Voronoï. Bulletin de l’Académie des Sciences de l’URSS, Classe des sciences mathématiques et naturelles, 6, 793–800.Google Scholar
- Earle, P., Bowden, D., & Guy, M. (2011). Twitter earthquake detection: earthquake monitoring in a social world. Annals of Geophysics, 54(6), 708–715.Google Scholar
- IBM. (2014). IBM Knowledge Center. http://www-01.ibm.com/support/knowledgecenter/. Accessed 2015.
- Nepal Disaster Risk Reduction Portal (2015) Incident report of earthquake 2015. www.drrportal.gov.np. Accessed 12 Oct 2015.
- Richter, C. (1958). Elementary seismology. San Francisco: Freeman.Google Scholar
- Sakaki, T., Okazaki, M., & Matsuo, Y. (2010) Earthquake shakes Twitter users: real-time event detection by social sensors. World Wide Web Conference (WWW), Raleigh, NC.Google Scholar
- Severo, M., Giraud, T., & Pecout, H. (2015). Twitter data for urban policy making: an analysis on four European cities. In C. Levallois (Ed.), Handbook of Twitter for research. Écully: EMLYON.Google Scholar
- Terry, D., Goldberg, D., Nichols, D., & Andoki, B. (1992). Continuous queries over append-only databases. SIGMOD, pp. 321–330.Google Scholar
- The Center for Engineering Strong Motion Data. (2014) CESMD Internet data report. http://www.strongmotioncenter.org/cgi-bin/CESMD/archive.pl. Accessed 10 Sept 2014.
- Twitter. (2015). The Twitter platform documentation. https://dev.twitter.com/overview/documentation. Accessed 2015.
- United States Geological Survey (2010). National Strong Motion Project. [Online]. http://escweb.wr.usgs.gov/nsmp-data/smcfmt.html. Accessed 2015.
- United States Geological Survey (2014) M6.0—6 km NW of American Canyon, California. http://earthquake.usgs.gov/earthquakes/eventpage/nc72282711#general_summary. Accessed 10 Sept 2014.
- United States Geological Survey. (2015a). Earthquake facts and statistics. http://earthquake.usgs.gov/earthquakes/eqarchives/year/eqstats.php. Accessed 15 Oct 2015.
- United States Geological Survey. (2015b) The modified Mercalli intensity scale. http://earthquake.usgs.gov/learn/topics/mercalli.php. Accessed 15 Oct 2015.
- United States Geological Survey. (2015c) The San Andreas and other bay area faults. http://earthquake.usgs.gov/regional/nca/virtualtour/bayarea.php. Accessed 15 Oct 2015.
- Wald, D., Quitoriano, V., & Dewey, J. (2006a) USGS “Did you feel it?” community internet intensity maps: macroseismic data collection via the internet. Geneva, Switzerland, First European Conference on Earthquake Engineering and Seismology.Google Scholar
- Wald, D., Worden, B., Quitoriano, V., & Pankow, K. (2006b). ShakeMap manual: technical manual, users guide, and software guide. Boulder: United States Geological Survey.Google Scholar
- Wood, H., & Neumann, F. (1931). Modified Mercalli intensity scale of 1931. Seismological Society of America Bulletin, 21(4), 277–283.Google Scholar