An early warning system for coastal earthquakes

Advances in Space Research 37 (2006) 636 642 www.elsevier.com/locate/asr An early warning system for coastal earthquakes Guido Cervone a, *, Menas Kafatos a, Domenico Napoletani b, Ramesh P. Singh a,c a Center for Earth Observing and Space Research, School of Computational Sciences, George Mason University, 4400 University Drive, MSN 5C3, Fairfax, VA 22030, USA b School of Computational Sciences, George Mason University, Fairfax, VA 22030, USA c Department of Civil Engineering, Indian Institute of Technology, Kanpur 208 016, India Received 27 July 2004; received in revised form 23 February 2005; accepted 8 March 2005 Abstract Earthquakes are very common natural hazards, and every year a few damaging events occur throughout the globe. Satellite remote sensing data are found to be useful in providing information about changes in land, ocean, atmosphere and ionosphere. Recent studies have shown that some of the parameters observed from the remote sensing data are associated with impending coastal earthquakes. An automatic system CQuake has been developed to carry out spatial and temporal data mining analysis in real time. CQuake performs retrospective analysis of earthquakes, and performs forecast for predefined regions of the world based on the analysis of Surface latent heat flux (SLHF) or any other geophysical parameters. Details of CQuake, and an example of the Colima earthquake of January 22, 2003, are discussed. Ó 2005 COSPAR. Published by Elsevier Ltd. All rights reserved. Keywords: Earthquake early warning; Data mining; Surface latent heat flux; Wavelet maxima curves; Colima earthquake 1. Introduction An earthquake is a complex process that occur from few to several hundred kilometers below the surface, usually in known areas, such as along plate boundaries, or in seismically active regions. Although it is possible to predict areas where earthquakes are most likely to occur, it is very difficult to determine when an event will strike in any specific location. Prior to an earthquake, due to the build up of stress in the focal and surrounding regions, changes in various land, ocean and atmosphere parameters are observed (Singh et al., 2001a,b; Tramutoli et al., 2001; Tronin et al., 2002; Dey and Singh, 2003; Bello et al., 2004; Cervone et al., 2004; Maekawa and Hayakawa, 2004; Ouzounov and Freund, 2004; Pulinets and Boyarchuk, 2004). * Corresponding author. Tel.: +1 703 993 1799. E-mail address: gcervone@gmu.edu (G. Cervone). It is believed that the routine monitoring of such changes can provide early warning information about impending earthquakes. Earthquake precursors have been studied for centuries using ground based measurements, but due to the lack of observations, it is not possible to study the complementary behavior of different geophysical parameters. Using remote sensing, it has become easier to monitor various parameters of land ocean and atmosphere, day and night, and to study their complementary behavior. The present paper introduces CQuake (CEOSR Earthquake), an earthquake monitoring and forecasting system based on satellite derived data, based on the methodology proposed by Cervone et al. (2004). This system is used for the retrospective analysis of past earthquakes and to monitor predefined regions of the world for signals which might give early warning information about an impending earthquake. The goal of CQuake is to build knowledge about the type and shape 0273-1177/$30 Ó 2005 COSPAR. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.asr.2005.03.071

G. Cervone et al. / Advances in Space Research 37 (2006) 636 642 637 of precursory signals associated with earthquakes, and to apply this knowledge to identify early warning signals of impending earthquakes. The CQuake program is discussed using data of the Colima earthquake of January 22, 2003. 2. Data CQuake uses remote sensing, ground and model data and knowledge about the geophysical and seismological information of the region (plate boundary, tectonic and fault lines). CQuake can work with any relevant data from the IRI/LDEO Climate Data Library, 1 and early experiments are performed using the NCEP/NCAR reanalysis data. The IRI/LDEO Climate Data Library contains over 300 datasets from a variety of Earth science disciplines and climate-related topics. All the datasets are gridded and distributed using a common netcdf format. They can be downloaded freely using a HTML based interface, making it the ideal source for the development of a fully automated operational system. The NCEP/NCAR Reanalysis Project is a joint project between the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR). The goal of this project is to produce long time series for climate studies using new atmospheric analysis of historical data and current atmospheric data (Climate Data Assimilation System, CDAS). The reanalysis data presently available is of high quality due to the use of state-of-the-art data assimilation based on both satellite and ground observations, quality control and solid modeling algorithms. The disadvantage is in the coarse resolution, which varies from 1.8 to 2.5 depending on the parameter. Table 1 summarizes the major characteristics for the most important parameters of the NCEP/NCAR reanalysis data that can be used by CQuake. NCEP data is usually available for download with a delay of 4 days. Although this imposes a restriction on the methodology and parameters that can be used, it is not a major problem because early experiments have shown that precursory signals usually appear 10/12 days prior to the earthquake. Therefore, the 4-day delay in the data, does not preclude the possibility of studying the precursory signals ahead of time, and consequently it is possible to use this data for an early warning operational system. An additional constraints is introduced by the use of wavelet transformations, which cannot determine the singularity of the last data point. Table 1 NCEP/NCAR reanalysis data that can be used by CQuake Parameter ID Source Spatial resolution (km) Surface latent heat flux SLHF NCEP 190 Water vapor PWT NCEP 250 Low cloud cover convective LCCC NCEP 190 Surface temperature ST NCEP 190 Air temperature AT NCEP 190 Precipitation rate PR NCEP 190 Ground heat flux GHF NCEP 190 3. CQuake CQuake is a fully automated, real time operational system, developed to study precursory signals associated with earthquakes. It is based on the methodology described by Cervone et al. (2004). It can be used to study past earthquakes or to obtain information about impending earthquakes. CQuake has been developed in GNU/Linux using C, Java and R programming languages paired with several open source packages for data analysis and visualization. For example, the wavelet analysis is performed using the Wavelab library developed at Stanford University and Octave developed at the University of Wisconsin. The relevant maps are generated using the Generic Mapping Tool (GMT) developed at the University of Hawaii. The GTOPO30 data are used for bathymetry and topography. These data are derived from a global digital elevation model (DEM) with a horizontal grid spacing of 30 00 (approximately 1 km), and are currently being maintained by USGS. The data describing the plate boundaries and the fault lines are a part of the PLATES project developed at the University of Texas. 2 The output of CQuake is in HTML format and javascript, and can be accessed using current internet browsers, independently of the operating system used. 3 Numerous earthquakes are analyzed using CQuake to understand the atmospheric/meteorological varying parameters associated with the earthquakes. The data and all the required parameters used in the experiments are provided to the user, in order to easily reproduce the results. 3.1. Algorithm The CQuake algorithm consists of the following steps: Data acquisition. Identify anomalous peaks. 1 http://iridl.ldeo.columbia.edu/. 2 ftp://ftp.ig.utexas.edu. 3 http://cervone.gmu.edu/cquake/.

638 G. Cervone et al. / Advances in Space Research 37 (2006) 636 642 Determine the time and space continuity of the identified anomalies. Generate time and space graphs to show the continuity. Match identified precursory signals of different parameters. Generate the output. 3.2. Data acquisition CQuake needs to acquire seismic and atmospheric data to perform experiments. The seismic data are used to illustrate periods of increased seismic activity or period of relative quiescence, as well as particular spatial distributions of the epicenters of previous earthquakes. CQuake works in three different modes, namely analysis, interactive and forecast. Analysis and interactive modes are used to study past earthquakes for which the epicenter and magnitude are known. These modes are important in order to understand the shape and the extent of the precursory signals of different regions. It is believed that characteristics of precursory signals differ among regions, due to the relevant geological settings, ocean depth, currents and local atmospheric phenomena. For each experiment several years of data are downloaded, usually five, in order to compute the statistical significance of the detected anomalies. 3.2.1. Analysis of past earthquakes In analysis mode, CQuake downloads information on the location, time, magnitude and depth of new earthquakes from the USGS website 4 at an interval of 20 min, while in interactive mode, the information on the location, time, magnitude and depth are provided by the user. For each new earthquake, CQuake downloads data from the NCEP website for various parameters, and performs the temporal and spatial data mining analysis to determine the existence of prominent anomalies. At the end of each experiment, CQuake sends a notification email to the address cquake-notify@cervone.gmu.edu, containing the time, location, depth, magnitude of the earthquake analyzed, as well as a web link where it is possible to view the results. 3.2.2. Forecasting impending earthquakes The forecasting mode is used to analyze predefined regions of the planet to study signals which might be associated with impending earthquakes. CQuake downloads at regular intervals data for predetermined regions, and performs the temporal and spatial analysis to determine the existence of prominent anomalies. If 4 http://neic.usgs.gov/neis/finger/quake.asc. a signal with a characteristic geometrical shape and extent is found, an email is sent to interested users. 3.3. Identify anomalous peaks This step consists of identifying in the time series of each grid location the anomalous peaks which are most likely to be associated with an earthquake. The methodology proposed by Cervone et al. (2004) employs 1D wavelet transformations performed on remote sensing data retrieved for several locations (grids) adjacent to the epicentral region to identify singularities, or anomalies, in the data, defined as abrupt changes in the first derivative of the time-series, while eliminating low frequency seasonal component and high frequency noise. In this methodology, the use of wavelets differs from the most frequent applications to image compression or signal denoising. First, the maxima of the wavelet coefficients are computed for every scale, and later are interpolated across all scales of the time/scale plane. Such interpolation yields to the generation of wavelet maxima curves, identifying precise points in time where singularities are likely. Only the maxima curves whose length is larger than a predefined fraction of the total number of scales are considered to be significant. Since the maxima curves do not have to propagate all the way to coarser scales, low frequency components associated with seasonal variations are disregarded. Only the singularities with statistical significance are kept, namely those whose magnitude is above the noise level for the region, computed using five years of prior data. This wavelet transformations is carried out not only for the grid comprising the epicenter of the earthquake, but also for several adjacent regions. 3.4. Determine the time and space continuity of the identified anomalies This step consists in discriminating among the detected anomalies those associated with earthquakes from those due to other atmospheric or oceanic phenomena. Such task is carried out using the concept of spatial and time continuity. Time continuity means that the detected anomalies occur at the same time or with a short delay of each other, while space continuity means that the detected anomalies are distributed in space according to a precise geometry conforming to the geological settings of the region. Anomalies associated with earthquakes are caused by a large scale event, and thus their extent is not confined only to the epicentral area. Such anomalies show characteristic geometrical shape, which can be used to discriminate from other anomalies. The identifications of anomalies associated with earthquakes are carried out by selecting anomalies with a characteristic geometrical shape and occurring all at the same time or within a short time period (usually 1

G. Cervone et al. / Advances in Space Research 37 (2006) 636 642 639 or 2 days). The geometrical shape of the anomalies are likely to be related to the geological characteristics of the region, such as continental boundaries or fault lines. A collection of grids that satisfy these geometrical constraints is called a grid path. 3.5. Complementary nature of precursory signals of different parameters Experiments are performed using different parameters, such as SLHF, water vapor (PWT), surface temperature (ST), etc. Each of these parameters may contain Table 2 Regions used in forecast mode Region Longitude Latitude Location 1 39N 18E Italy Greece 3 41N 143E Tokachi, Japan 4 35N 120W Central California, USA 5 23N 120E Taiwan 6 40N 31E Turkey 7 18N 103W Mexico 8 13N 93E Andaman, India Each region is 1000 km 2, centered at the latitude and longitude. different precursory signals, which are likely to be related with each other, and thus must show complementary nature. A statistical analysis of the temporal and spatial extent of different parameters is performed to determine their correlation, and can help discriminate anomalies due to atmospheric perturbations, from those due to earthquakes. 3.6. Visualization of the results The results of each experiment, in either analysis or forecast mode, are shown through an HTML page that includes information on the time, location, magnitude and depth of the earthquake, the parameters used in the experiment, several maps and animations of the data used in the experiments, the results of the wavelet transformations, and a summary of all the anomalies. Each experiment requires about 50 MB of storage space, which includes 5 years of data to determine the statistical significance of the results, several images, videos, and the all parameters required to reproduce the results. A topographic map is generated to show the location of the epicenter, continental boundaries, fault lines, and the location of the epicenters of previous earthquakes. Fig. 1. Grid path used in the experiments to study the extent of the SLHF anomalies. The grids were chosen using an automatic localized search algorithm. The epicenter is shown with a star.

640 G. Cervone et al. / Advances in Space Research 37 (2006) 636 642 Maps are also generated to show the grid path used in the experiments, and its proximity to important tectonic features, such as coastlines, continental boundaries, fault lines, etc. Maps of the original and normalized data are generated to show the spatial changes in the parameter, in order to determine if a signal is the result of a perturbation that slowly moves in an orderly fashion across the region, or a signal that originates in the epicentral region, and it is likely related to geological activity. Animations of both the original and the normalized data are included in the results. A map showing the spatial distribution of the detected anomalies is generated, which illustrates the extent of the anomalies, and their geometrical shape with respect to local tectonic settings. The results of the wavelet transformations and maxima curves are shown for each grid. Such graphs are crucial to determine the magnitude of the anomalies, and to establish their statistical significance. Spatial and time continuity of the anomalies graphs are generated for a one year period. Fig. 2. Spatial and temporal analysis of the SLHF anomalies associated with the Colima earthquake of January 22, 2003. (a) Wavelet transformation of SLHF data relative to grid 37. The graph shows the SLHF values and the 5 years average, the loci of anomalous peaks, and the wavelet coefficients with the wavelet maxima curves. (b) Time and space analysis of SLHF data. Two prominent signals are found within a one year period, and one of them occurs between 3 and 5 days prior to the earthquake event. (c) Magnitude of the SLHF anomalies associated with the earthquake event. The strongest anomaly is seen in grid 36, and is more than three times above the standard deviation.

G. Cervone et al. / Advances in Space Research 37 (2006) 636 642 641 4. Experiments The retrospective analysis of past earthquakes are performed using the CQuake program to validate the hypothesis that atmospheric parameters carry early warning information. Experiments are performed for different regions of the world, analyzing earthquakes of different magnitudes and focal depths. Efforts are made to compare SLHF and other anomalies with precursory signals observed by others to establish a relationship to better understand the earthquake initiation processes. CQuake is operational since January 1, 2004, and it automatically analyzes all earthquakes with magnitude greater or equal to 5.0, regardless of their location or focal depth. Additionally, some large destructive earthquakes occurred in the period from January 1, 1997, to December 31, 2003, are studied using CQuake interactive mode. About 400 earthquakes have been analyzed for different regions of the world (http://cervone.gmu.edu/ cquake). The results include not only coastal earthquakes, but also intraplate earthquakes. In the case of earthquake occurring inside the mainland and away from the ocean, no noticeable signals are observed due to the weak coupling between the lithosphere, hydrosphere and atmosphere. SLHF for 20N 105W Grid 36 Test for 20N 105W Grid 36 W_M^2 20 40 60 80 100 Quantile 2 1 0 1 2 3 SLHF for 20N 103.12W Grid 37 Test for 20N 103.12W Grid 37 W_M^2 20 40 60 80 100 120 Quantile 1 0 1 2 3 SLHF for 18.09N 103.12W Grid 29 Test for 18.09N 103.12W Grid 29 W_M^2 50 100 150 200 Quantile 1 0 1 2 3 4 Fig. 3. Statistical analysis of the SLHF anomalies associated with the Colima earthquake of January 22, 2003.

642 G. Cervone et al. / Advances in Space Research 37 (2006) 636 642 CQuake can be used daily in forecast mode to monitor several regions of the world to provide early warning information about an impending earthquake. Each region covers an area of 1000 km 2, and it is centered at the longitude and latitudes (Table 2). 4.1. The Colima earthquake of January 22, 2003 On January 22, 2003 a strong earthquake (Mw = 7.6) occurred in the coastal region of the state of Colima, Mexico, at location 18.84N and 103.82W, with a focal depth of 24.0 km. This earthquake claimed about 30 lives, injured 3000 and left about 10,000 people homeless. This earthquake occurred at shallow depth in a seismically active coastal zone of central Mexico. Fig. 2(a) shows the wavelet transformation and the resulting maxima curves using SLHF data, where prominent anomalies higher than three sigma are seen 5 days prior to the earthquake event. Prominent anomalies above two sigma are also seen about one week after the earthquake event, which are likely to be associated with the aftershock events. Fig. 1 shows the spatial and temporal extent of the detected anomalies. The grid path is defined automatically by CQuake using a localized search of the most prominent anomalies in the epicentral area. The selected grids lie over the continental boundary, and anomalies are consistently seen in all grids between 3 and 5 days prior to the main earthquake event. The strongest anomaly is detected near the epicenter. The space and time continuity associated with the selected grid path, shows that two significant signals are found within one year, one of them occurring between 3 and 5 days prior to the main earthquake event (Fig. 2(b)). The magnitude of the SLHF anomalies for the signal associated with the earthquake event is shown in Fig. 2(c). The strongest, anomaly is found to be larger than three standard deviations, and likely to be associated with the impending earthquake. Fig. 3 shows the result of the statistical test performed using data from December 1, 2002 to February 28, 2003, to determine the significance of the anomalies detected using SLHF data. The anomalies detected prior to the earthquake in grids 36, 37 and 29 are found to be statistically significant. There are also several statistically significant anomalies after the earthquake, which are likely to be associated with aftershocks events. 5. Conclusions The present paper introduces CQuake, a real time system developed to analyze remote sensing data associated with earthquake events. CQuake is based on the assumption that there exist an interaction between the lithosphere, atmosphere and hydrosphere, and that the build up of stress prior to earthquakes causes prominent changes in atmospheric parameters which provide early warning information about an impending earthquake. CQuake can be useful not only to forecast earthquakes, but also to understand the processes that govern their occurrence in order to build physical models that quantitatively explain the observed anomalies. Acknowledgments This research was partially supported by NASAÕs office of Earth Science Applications under grants NAG12-01009, NAG13-02054 and NAG13-03019, VAccess/ MAGIC projects. The authors thank the three anonymous reviewers and Dr. Peggy Shea for their comments and suggestions which have helped to improve the earlier version of the paper. References Bello, G.D., Filizzola, C., Lacava, T., Marchese, F., Pergola, N., Pietrapertosa, C., Piscitelli, S., Scaffidi, I., Tramutoli, V. 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