A parametric representation of Kamchatka seismicity over time

ISSN 0742�0463, Journal of Volcanology and Seismology, 2013, Vol. 7, No. 1, pp. 58–75. © Pleiades Publishing, Ltd., 2013.Original Russian Text © V.A. Saltykov, Yu.A. Kugaenko, N.M. Kravchenko, A.A. Konovalova, 2013, published in Vulkanologiya i Seismologiya, 2013, No. 1, pp. 65–84.

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INTRODUCTION

A regional network of seismograph stations was setup in Kamchatka and the Commander Islands in1961–1962; this was the beginning of detailed seismo�logical investigations in one of the most seismicallyactive regions in Russia. The network was rapidlyexpanded and is at present a system of seismologicalobservation that meets modern requirements. Itincludes 68 seismograph stations and strong�motionstations, as well as the necessary facilities for rapidtransmission, automatic and automated processing,and storage of seismic and geophysical data [Gordeevet al., 2006c; Chebrov et al., 2011]. This system pro�vides for effective monitoring of seismic and volcanicactivities, as well as for a tsunami warning service.

The 50 years of observation involved extensive,purposeful labors of Kamchatka seismologists todevelop one of the most complete data collections ofseismological information in the world, viz., the Kam�chatka Regional Catalog [Gordeev et al., 2008]. Theseismicity of Kamchatka and adjacent areas has beendescribed in many publications, e.g., [Fedotov et al.,1985, 1987; Gordeev et al., 2006a; Levina et al., 2011].Detailed information on the current seismic eventsthat occur in the zone of responsibility covered by theKamchatka regional network can be found in scien�tific annual publications Zemletryaseniya v SSSR(1964–1991), Zemletryaseniya Severnoi Evrazii(2003–2009), Zemleryaseniya Severnoi Evrazii(1992–2005). The high level of work on the acquisi�tion and processing of seismological information thatis reached at present allows us to not only determinethe spatial locations and energy parameters of

recorded earthquakes, but also to quickly monitor theparameters of the seismic process. This work has bothbasic�science interest as well as practical applications,since anomalies in the variation of seismicity may beprecursory and can be used in earthquake prediction[Mogi, 1985; Sobolev, 1993, 2011; Sobolev and Pono�marev, 2003; Sobolev, 2011; Wyss and Habermann,1988]. There can be no doubt that such research isurgently needed in Kamchatka, this being one of themost active seismic regions in the world.

A specialized scientific section was organized in theKamchatka Technique Testing Seismological Team in1996 (at present this is the Kamchatka Branch) of theGeophysical Service of the Russian Academy of Sci�ences (RAS); this section was to carry out a compre�hensive analysis of the seismic process; it was reorga�nized in 2001 to become the Laboratory of SeismicMonitoring. The chief tasks facing the Laboratoryincluded the development and introduction of tech�niques for keeping track of seismicity parameters, rou�tine assessment of the current seismic situation, andpreparation of expert conclusions as to the state andforecasts of Kamchatka seismic activity.

The precursory processes of large earthquakesinvolve, in particular, changes in the seismic regime inearth volumes adjacent to a future rupture zone [Sobo�lev, 2011]. The analysis of the current seismic situationin the region and monitoring the time–space distribu�tion of background seismicity enable us to detect suchanomalies with sufficient speed. This paper describesthe set of seismicity parameters that were estimated atthe Kamchatka Branch of the RAS Geophysical Ser�vice from the regional catalog in order to perform rou�

A Parametric Representation of Kamchatka Seismicity over TimeV. A. Saltykov, Yu. A. Kugaenko, N. M. Kravchenko, and A. A. Konovalova

Kamchatka Branch, Geophysical Service, Russian Academy of Sciences, Petropavlovsk�Kamchatskii, bul’var Piipa 9, 683006 Russia

e�mail: [email protected] June 18, 2012

Abstract—This paper presents a set of seismicity parameters that are estimated at the Kamchatka Branch ofthe Geophysical Service, Russian Academy of Sciences based on the regional catalog data with the purposeof routine monitoring of the current seismic situation in the region. The focus is on the identification ofchanges in the seismic regime (seismic quiescences and seismicity increases) in earth volumes adjacent to thematuring rupture zone of a large earthquake. The techniques we use include estimation of the seismicity levelfor the region using the SOUS'09 scale; calculation of the variations in the slope of the recurrence relation,identification of statistically significant anomalies in the slope using the Z test, and calculation of the seismicactivity A10; monitoring the RTL parameter and variations in the area of seismogenic ruptures; using the Z testto detect areas of statistically significant decreases in the rate of seismicity; and identification of earthquakeclusters. We furnish examples of such anomalies in these seismicity parameters prior to large earthquakes inKamchatka.

DOI: 10.1134/S0742046313010065

JOURNAL OF VOLCANOLOGY AND SEISMOLOGY Vol. 7 No. 1 2013

A PARAMETRIC REPRESENTATION OF KAMCHATKA SEISMICITY OVER TIME 59

tine monitoring of the seismic situation in the region.Examples are given for anomalous values of theseparameters prior to large earthquakes.

THE DATA SET

The information basis for the application of algo�rithms used in the analysis of background seismicity isthe regional catalog that has been collected during theperiod of detailed seismological observation, which iscontinually updated [Gordeev et al., 2008]. The char�acter of the earthquakes used for the purpose is con�trolled by the features of regional seismicity. On the

whole, subduction earthquakes dominate the Kam�chatka regional catalog.

The seismicity parameters are estimated for thearea limited by latitudes ϕ = 50.5° N and 56.5° N, lon�gitudes λ = 156.0° E and 167.0° E, with depthsbetween 0 and 300 km; this earth volume contains thepart of Kamchatka that shows the highest seismicactivity (Fig. 1). The seismological data that are to beused for this analysis should be homogeneous. For thisreason we defined the lowest energy level of earth�quakes as K = 8.5 according to S.A. Fedotov’s energyclassification [Fedotov, 1972]; this is in keeping with

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Longitude, deg EFig. 1. Area where the parameters of Kamchatka seismicity is monitored (the rectangle within dashed line) and the epicenters ofK ≥ 14.0 earthquakes that occurred at depths shallower than 300 km during the period of detailed observation (1962–2011).

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SALTYKOV et al.

the level of complete reporting for the entire Kam�chatka seismic zone [Chebrov et al., 2011].

The characteristics of background seismicity canbe significantly distorted by the aftershocks of a largeearthquake, as these are compact in space–time;aftershocks present the most frequent case of cluster�ing in interdependent seismic events. To cite an exam�ple, we studied the distortions that are caused by inter�dependent earthquakes in the estimate of the slope ofthe recurrence relation [Konovalova and Saltykov,2008] and found that the above supposition is con�firmed by these data.

The average number of clustered earthquakes forthe period of detailed observation (50 years) is nearlyone quarter of all earthquakes with energy classes Kequal to or greater than 8.5. For this reason, all esti�mates, except for some cases that are otherwise speci�fied, are based here on the catalog with distant after�shocks. Aftershock identification was carried out usingthe Molchan–Dmitrieva technique [Molchan andDmitrieva, 1991].

The moment magnitudes of earthquakes (MW) aregiven in this paper after the Global CMT Catalog(www.globalcmt.org).

ESTIMATION OF SEISMIC�ENERGY RELEASE

The state of seismicity in a region is estimated bythe empirical distribution function of seismic energythat has been released during a definite interval oftime, F(K) = P(logE ≤ K), where E is total seismicenergy in Joules for a specified time interval andP(logE ≤ K) the relative frequency of time intervalswith equivalent energy class logE not greater than agiven K.

The energy�distribution function based on the val�ues of seismic energy in a moving time window of adefinite length will yield a probability for any value ofE with a corresponding estimate of uncertainty. Onecan then provide a rapid estimate of the current seis�micity level in descriptive terms. This speed is due to

the fact that the energy of a specified space–timeblock is controlled by larger events, hence, firstly, it isa stable parameter that is relatively independent ofsmaller events and, secondly, one can dispense withthe necessity of using earthquakes in the entire rangethat was recorded [Saltykov, 2011].

Figure 2 shows the distribution function of annualseismic energy for the entire time interval of detailedobservation (1962–2011) with indication of the valuesfor 2009, 2010, and 2011. The total seismic energyrelease was 2.6 ⋅ 1013 J in 2009, 1.8 ⋅ 1014 J in 2010, and2.1 ⋅ 1014 J in 2011, with the mean (for those 50 years)annual value 5.8 ⋅ 1014 J and an annual median of 1.6 ⋅1014 J.

Saltykov [2011] proposed using a SOUS'09 scalefor seismicity levels; this scale gives the level of seis�micity based on the distribution function F. The scaleincludes five main grades (extremely low, F < 0.005;low, 0.005 <F < 0.025; background level, 0.025 < F <0.975; high, 0.975 < F < 0.995; and extremely high, F >0.995) and three extra grades (background lower,0.025 < F < 0.15; background medium, 0.15 < F < 0.85;and background higher, 0.85 < F < 0.975).

According to the distribution function of annualseismic energy release for the 1962–2011 time inter�val, in 2009 we had the lowest (for the entire observa�tion period) seismicity level. The 2009 distributionfunction of seismic energy is 0.028 ± 0.024. The seis�mic energy that was released during 2010 and 2011 isclose to the median value and the seismicity level wasfound to be background medium. The respective val�ues of the distribution function for 2010 and 2011 are0.53 ± 0.07 and 0.58 ± 0.07. It should be noted that theseismicity during the earlier half of 2010 for a timewindow of 1 year was at a level that was the lowest forthe entire time period of detailed seismological obser�vation in Kamchatka. The МW 6.3 earthquake of July30, 2010 modified the seismicity level in this time win�dow, making it background medium.

Figure 3 shows variations in estimated seismicitylevel for Kamchatka in 2009–2011 using differenttime windows in accordance with the SOUS'09 scale.We may note the interesting fact that a lower regionallevel of seismicity occurs in a short time windowbefore large earthquakes. For example, lower regionallevel values in July and November 2010 preceded theearthquake of July 30, 2010 (МW 6.3) and that ofNovember 16, 2010 (МW 5.9) in the southeasternAvacha Bay. The February 20, 2011 earthquake(МW 6.1, Kamchatskii Bay) was preceded by a decreasein regional seismicity level in time windows with lengthsbelow 90 days: the level of Kamchatka seismicity duringthe week before the earthquake was extremely low, it wasmerely low during the previous 15 days, and back�ground was lower for the previous 90 days.

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Fig. 2. Distribution function of annual seismic energyreleased by the 1962–2011 Kamchatka earthquakes in themonitoring area designated in Fig. 1. The values of the dis�tribution function are marked for 2009, 2010, and 2011.



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Fig. 3. Time�dependent behavior of estimates of seismicity rate in various time windows on the SOUS'09 scale in 2009–2011. Theearthquakes of July 30, 2010, November 16, 2010, and February 20, 2011 are marked with arrows.

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Table 1. A brief description of the techniques that are in use for monitoring Kamchatka seismicity parameters

Method. Brief descriptionPublications that reported results

from applications of the techniques for various regions of the world

Publications that reported results from applications

of the technique for Kamchatka

Identification of statistically significant variations in the slope of the recurrence relation γThe slope of the recurrence relation which connects the number of earthquakes that have occurred and released seismic energy is one of the major parameters of seismicity. A higher corresponds to a preponderance of smaller earthquakes and a lower value to a preponderance of larger earthquakes. Space–time blocks with statistically significant changes in γ are identified using the statis�tical Z test. The approach here proposed for use focuses on the statistical significance of the change in γ rather than on its abso�lute value.Example: Fig. 4.

[Zav'yalov, 2006; Mogi, 1985; Aki, 1965;Chan and Wu, 2012;Enescu and Ito, 2001; Imoto, 1991; Nuan�nin, 2006; Nuannin et al., 2005; Oncel and Wilson, 2007; Parsons, 2007; Wu and Chiao, 2006; Wu et al., 2008; Wyss and Wiemer, 2000; Zuniga and Wyss, 2001]

[Zav'yalov, 1984; Zemletr�yaseniya Rossii, 2009–2011; Konovalova and Saltykov, 2010; Saltykov and Kravchenko, 2009, 2010]

The RTL technique This technique was developed under the guidance of Sobolev at the Institute of Physics of the Earth, Russian Academy of Sci�ences [Sobolev et al., 1996; Sobolev and Tyupkin, 1996, 1998] for intermediate�term earthquake prediction. The predictive parameter is based on the idea that earthquakes are interrelated and affect one another. RTL is an integrated parameter that re�flects the influence of seismicity at a point being analyzed. The precursory zone of a large earthquake is a space–time re�gion having negative values of RTL. The anomaly is character�ized by the lowest value of RTL for the entire time it evolves and by duration which is defined as that interval of time during which RTL < –3.Example: Fig. 5.

[Sobolev, 2003, 2007; Sobolev and Ponomarev, 2003; Chen and Wu, 2006; Gentili and Bressan, 2007; Giovambattista and Tyup�kin, 1999, 2000, 2004; Huang, 2004, 2006; Huang et al., 2001, 2002; Huang and Nagao, 2002; Huang and Sobolev, 2002; Rong and Li, 2007; Sobolev et al., 2002; Wyss et al., 2004]

[Gordeev et al., 2006b; Zemletryaseniya Rossii, 2009–2011; Ivanov and Saltykov, 2004; Kopylova et al., 1998; Kravchenko, 2004; Kugaenko et al., 2009; Saltykov and Kravchenko, 2009; Sobo�lev, 2009; Sobolev and Tyupkin, 1996, 1998; Sobolev et al., 1996; Sobolev, 2001]

dS, variation of the area of seismogenic ruptures. The method fo�cuses on identifying foreshock activation and supplements the RTL technique. The difference dS is computed between the ac�cumulated area of seismogenic ruptures within a circular area of radius Rmax = 50 km for the last year (Tmax = 1 year) and the long�term value (Tmax is catalog length). Areas of higher values of dS are considered to be zones of foreshock activation.Example: Fig. 6.

[Zav'yalov, 2005; Sobolev and Ponomarev, 2003; Sobolev and Tyupkin, 1996; Sobolev et al., 1996]

[Zemletryaseniya Rossii, 2009–2011; Sobolev, 1999; Sobolev and Tyup�kin, 1998; Saltykov and Kravchenko, 2009, 2010]

Identification of earthquake clusters.A cluster is a sequence of earthquakes in which the space��time positions of the events are limited and the energy of each later event is not below that of a preceding. The technique aims at identifying foreshock activations and supplements the RTL: method: according to Sobolev and Tyupkin [1998], the occur�rence of clusters heralds the start of the third phase in the precur�sory process of a large earthquake [Sobolev, 1999, 2011].Example: Fig. 7.

[Sobolev and Ponomarev, 2003] [Zemletryaseniya Rossii, 2009–2011; Sobolev, 1999, 2008; Saltykov and Kravchenko, 2009, 2010, 2011]



Table 1. (Contd.)

Method. Brief description

Publications that reported re�sults from applications of the techniques for various regions

of the world

Publications that reported results from applications

of the technique for Kamchatka

Z function This technique aims at identifying seismically quiescent regions in space and time [Wyss and Habermann, 1988]. It consists in identifying signif�icant changes in the intensity of seismicity rate in a specified energy range. The analysis is based on the Z function test, which is a tool in sta�tistical hypothesis testing. When seismicity variations are investigated, the Z test is used for means with known variances to test the hypothesis of a difference between the means of two populations. The seismicity rates are considered in a limited volume for two consecutive time inter�vals to detect a significant difference between the two.In quantitative terms, a quiescence is characterized by values of Z (statis�tical significance), SRD (the decrease in seismicity rate), and quiescence duration dT. An anomaly (seismic quiescence) is identified when a sta�tistical significance Z > 3 has been reached, which corresponds to at least 99% of the anomaly reliability.Example: Fig. 8.

[Tikhonov, 2005; Katsuma�ta, 2011; Katsumata and Kasahara, 1999; Kisslinger, 1988; Kisslinger and Kindel, 1994; Murru et al., 1999; Po�lat et al., 2008; Toda and Matsumura, 2006; Tsukako�shi and Shimazaki, 2006; Wiemer and Wyss, 1994; Wu and Chiao, 2006; Wyss and Habermann, 1988; Wyss and Martyrosian, 1998;Wyss and Wiemer, 1999; Wyss et al., 1995, 1996, 1997, 1999, 2004]

[Gordeev et al., 2006b; Zemletryaseniya Rossii, 2009–2011; Kravchenko, 2005; Kugaenko et al., 2009; Saltykov et al., 1998; Saltykov and Kugaenko, 2000; Saltykov and Kravchenko, 2009, 2011; Sobolev and Tyupkin, 1996]

Activity A10 Activity A10 is found from the number of earthquakes N and the slope of the recurrence relation γ per area S = 103 km2 and per time T = 1 year

[Riznichenko, 1985] [Zemletryaseniya Rossii, 2009–2011; Saltykov and Kravchenko, 2009, 2010; Fedotov, 2005]

MONITORING OF BACKGROUND SEISMICITY CHANGES: THE METHOD

AND EXAMPLES

Numerous studies of the seismic process andresults from laboratory modeling of failure corrobo�rate that two phases alternate in the rupture zone of afuture earthquake, viz., quiescence and increasedbackground seismicity, see, e.g., [Sobolev, 1999; Sobo�lev and Ponomarev, 2003] (also consult the list of ref�erences in the monograph [Sobolev and Ponomarev,2003]). Quiescences are observed before nearly alllarge earthquakes; based on the model of avalanche–unstable fracturing, this shows that a phase of energybuildup must occur during the precursory process ofan earthquake. Foreshock activation may be detectedwith difficulty because the foreshocks release littleenergy.

Routine estimation of the seismic situation in orderto identify time–space anomalies of background seis�micity was conducted in Kamchatka using a set oftechniques, including:

Calculation of variations in the slope of the recur�rence relation;

Calculation of seismic activity A10;

Monitoring of the RTL parameter;

Identification of seismic quiescences using the Zfunction method;

Monitoring variations in the area of seismogenicfaults;

Identification of earthquake clustering.The techniques are briefly presented in Table 1.

More detailed descriptions of the algorithms andexamples of their use (both for a variety of regionsworldwide and in Kamchatka) can be found in thepublications given as references in Table 1, as well as intheir respective reference lists.

The software support for all these techniques wasdeveloped at the Kamchatka Branch of the RAS Geo�physical Service.

Visualization of monitoring results is done by plot�ting maps of the spatial distribution, the time behaviorof seismicity parameters, as well as using other graph�ical presentation methods (Figs. 4–9).

We now discuss examples of parametric representa�tion of seismicity as a function of time.

The mean long�term (1962–2008) values of theslope of the recurrence relation γ for the area of Kam�chatka with the highest level of seismicity are 0.500 ±0.003. The time�dependent variations in γ are exam�ined here based on the dimensionless quantity Zγ,which is a measure of statistical significance for devia�tions of current values of γ from its long�term (back�ground) values:

( )2 1

2 22 1

,Z γ

γ − γ=

σ + σ

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1 2 3 20; 50; 100 km3 2 1 0 –1 –2 –3 –4 –5

Fig. 4. Maps of parameter Z which characterize areas of statistically significant decreases in the slope of recurrence relation ascalculated in a moving time window of 6 years with a fixed number of events, N = 200. The background values of γ were calculatedfor the preceding 12 years. The scale of variable shading shows values of Z that characterize the statistical significance of the anomaly.(1) earthquake epicenters, (2) their aftershock areas, (3) areas whose earthquakes produced the maximum deviations of γ frombackground.

Fig. 5. RTL anomaly (seismic quiescence) prior to the MW 6.7 earthquake of June 21, 1996.(a) map of RTL at the time the anomaly reached its minimum. The shading gradation from light to dark corresponds to RTLdecreasing (increasing depth of the anomaly). The diamond marks the point of minimum RTL. The map also shows the epicenterof the June 21, 1996 earthquake and its larger (K 12–13) aftershocks; (b) RTL anomaly in 3D image; (c), (d) space–time repre�sentation of anomaly evolution along longitude and latitude lines that pass through the point of minimum RTL (January 1992 toJune 1996). Darker shades distinguish the columnar diagrams corresponding to the point of lowest RTL; (e) plot of time�depen�dent behavior of RTL at the point of lowest parameter value (January 1985 to June 1996). The June 21, 1996 earthquake is markedwith an arrow.

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where γ1 and γ2 are the slopes of the recurrence rela�tion for the intervals of time being compared; σ1 andσ2 are the rms deviations of γ1 and γ2 [Konovalova andSaltykov, 2010]. Figure 4 shows maps of Zγ for theperiod from 1983 to 2000, which reveal anomaliesprior to large Kamchatka earthquakes in the 1990s.Circles enclose those areas whose earthquakes havegone on to generate the maximum deviations of γ fromthe background values. The spatial scanning was car�ried out using cylindrical volumes with radius R ≤100 km for the area 51°–57° N and 156°–167° E. Thetime step was 1 year, the steps for scanning the seismicarea of Kamchatka were Δϕ = 0.125° N and Δλ =0.25° E. The depths of the earthquakes we used waslimited by 100 km. The radius R was varied, since thenumber of earthquakes N was fixed.

It should be noted that anomalies occupy most ofthese areas and the Zγ parameter reaches –5, whichprovides evidence of the intensity of this phenomenonin seismicity. The Kronotskii earthquake ofDecember 5, 1997 with MW = 7.8 was preceded by azone of abnormally low slope of the recurrence rela�tionship during more than 10 years. The anomaly wassituated within the future rupture zone of this earth�

quake and its size was comparable with that of the rup�ture zone. Figure 4 also shows the anomalies prior tothe earthquakes of August 17, 1983 (MW 7.0), June 21,1996 (MW 6.7), and March 8, 1999 (MW 6.9). No seis�micity anomalies were detected with this techniquebefore the MW 6.5 earthquake of June 1, 1998 and thelargest crustal earthquake in Kamchatka for the periodof detailed observation, viz., the Karymskii earth�quake of January 1, 1996 (MS 7.0). The technique hasconsiderable inertia, so that it is sufficient to do calcu�lations once a year when the monitoring of seismicactivity is in question. If the lower level of completereporting becomes lower, the technique may becomefaster.

Figure 5 shows an example, an anomaly in the RTLparameter before the earthquake of June 21, 1996(МW 6.7). In 1999 the authors of the RTL algorithmtransferred it to the Kamchatka Branch of the RASGeophysical Service for routine use. For the calcula�tion and visualization of results we developed a pro�gram to work with catalogs (the RTL Analyzer, seeIvanov and Saltykov [2004]) as adapted for the originaltechnique. The map of RTL and the plot of timebehavior of RTL shown in Figs. 5a and 5e at the pointof lowest parameter value demonstrate the abilities ofthe RTL Analyzer for detecting and visualizing ananomaly so as to make a detailed study of the seismicprocess easier. According to Fig. 5, the seismic quies�cence before the large earthquake of interest lastedover 1 year, with the minimum value reached in June1995. The earthquake of June 21, 1996 (MW 6.7)occurred at the edge of this anomaly 6 months after thequiescence was over. The occurrence time of theearthquake is marked by an arrow in the plot (seeFig. 5e).

The RTL Analyzer program also contains a func�tion that examines the seismogenic fault area; this sup�plements the RTL method and serves for detecting theforeshock activation, which may follow the seismicquiescence. Figure 6 shows a map of variations in theseismogenic fault area dS before the earthquake con�sidered above (June 21, 1996). This particular calcula�tion uses the complete (with aftershocks left unelimi�nated) catalog. The June 21, 1996 earthquake was pre�ceded by an activation (increased seismogenic faultarea compared with the long�term value) that wasdetected by the RTL parameter after the seismic quies�cence was over. The area of increased seismic activityas detected by the dS parameter is spatially contiguousto the RTL anomaly. The earthquake of June 21, 1996with МW 6.7 occurred at the edge of the dS anomaly 3months after the anomaly appeared.

A cluster [Sobolev and Ponomarev, 2003; Sobolevand Tyupkin, 1998] is considered to be the occurrenceof two or more earthquakes, provided the distancebetween their hypocenters is below a critical value,Rcr = 3li + ε where li is the length of the seismogenicrupture in km, ε is the correction for epicenter loca�

52

51

160158

dS > 5dS > 4dS > 3dS > 2June 21, 1996MW = 6.7

K = 12–13

N

E

Fig. 6. A map showing variations in the area of seismogenicruptures before the MW 6.7 earthquake of June 21, 1996(the date as predicted was March 23, 1996). The shadinggradation from light to dark corresponds to increasing areaof seismogenic ruptures. The map also shows the epicenterof the June 21, 1996 earthquake and its larger (K 12–13)aftershocks.



tion uncertainty (adopted here to be equal to 10 km)and the time between these events is less than Tcr =

0.01 × (years). The length is found fromthe log li = 0.244 × Ki – 2.266 relationship[Riznichenko, 1985]. Clusters were classified as suchonly if a preceding earthquake was not larger than thesubsequent one. Figure 7 shows the positions of mainevents in the clusters that were identified in Kam�chatka during 1 year before the Kronotskii earthquakeof December 5, 1997 (MW 7.8). Remarkably enough,the positions of these clusters are in agreement withthe dS anomalies prior to the Kronotskii earthquake asdescribed [Sobolev and Ponomarev, 2003].

There is another well�known technique that strivesto detect a seismic quiescence; it is used in the moni�toring of seismicity parameters in Kamchatka. It wasproposed by Wyss and Habermann [1988] and is basedon the statistical Z test. This approach quickly becamepopular and is now widely used for a variety of regionsworldwide (see the references in Table 1). The initialname of Z test was later variously modified to becomeZ mapping, Z value, and Z function. In Kamchatkathis technique began to be employed in 1996. We callit the Z function or simply Z. When sites with statisti�cally significant decreases in the rate of seismicity weredetermined, we also calculate the decrease in the rateof seismicity at these sites: SRD(t) = 1 – R2/R1 (R1, R2

100.4 Ki 8.5–( )

and R2 are mean seismicity rates in the time intervalsthat are being compared). We find the greatest value ofSRD = max(SRD(t)) and the time t corresponding to it.The value SRD = max would correspond to an absoluteseismic quiescence, SRD = 0.875 to a decrease by afactor of 8, SRD = 0.75 to a factor of 4, and SRD = 0.5to a factor of 2. Calculated sites with identical values ofSRD are combined when adjacent in time and spaceand give an area of seismic quiescence. The identifica�tion of a seismic quiescence using the Z function]method is illustrated in Fig. 8, taking the situationbefore the MW 6.8 earthquake of March 2, 1993 as anexample. The dashed line encloses the seismically qui�escent area with a decrease by a factor of 8 observedthere during 2 years, from November 1989 to October1991. The March 2, 1993 earthquake occurred at theedge of the anomalous zone.

An earthquake of magnitude MW = 6.1 (K = 13.8)occurred at a depth of 47 km in the Kamchatskii Bay(55.7° N, 162.5° E) on February 20, 2011. The earth�quake was preceded by several anomalies of seismicitythat were precursory in character [Saltykov andKravchenko, 2011]. In particular, seismic quiescenceswere identified close to the earthquake epicenter usingtwo independent techniques (Z and RTL, see Fig. 9),in addition to a cluster. It was also noticed that theearthquake was preceded by a lower level of regional

56

55

54

53

52

51

166164162160158156

N

E

Dec. 5, 1997MW = 7.8

Fig. 7. Epicenters of main events in earthquake clusters with energy classes K ≥ 9.5 during 1 year before the MW 7.8 Kronotskiiearthquake of December 5, 1997. The map also shows the instrumental epicenter and the rupture zone of the Kronotskii earth�quake (dashed line).

68


SALTYKOV et al.

Table 2. Seismicity anomalies that preceded large Kamchatka earthquakes of 2004–2011 and that were identified in real time

Earthquake Method Area of expected earthquake. Forecast formulation Advance time Comment

Apr. 14, 2004 MW = 6.2 ϕ = 55.16° N, λ = 162.97° E h = 38 km

RTLKamchatskii Bay, coordinates: 54.0�56.5° N, 160–163.5° E

*18 months**6 months

Minimum of RTL (RTL = –18) was re�corded in February 2003, the anomaly lasted 18 months. The earthquake oc�curred within the anomalous area 6 months after the RTL parameter re�turned to background level (October 2003). The precursory anomaly was de�tected in real time1.

Mar. 10, 2007MW = 5.8 ϕ = 55.1° N, λ = 162.33° E h = 40 km

RTL

Kamchatskii Bay, eastern off�shore waters of Kronotskii Penin�sula and Kronotskii Bay, coordinates:52.5–56° N, 161.5 –165.5° E

*10 months

Minimum RTL (RTL = –19) was re�corded in April 2006, the anomaly lasted 20 months. The earthquake occurred at the edge of the anomalous area 2 months before RTL returned to normal (May 2007)The precursory anomaly was detected in real time2 .

May 30, 2007 MW = 6.4 ϕ = 51.89° N, λ = 157.90° Eh = 128 km

Z

Forecast at KB REC given on January 19, 2007: an M ≥ 6.0 earthquake can occur during 2007 in the latitude strip 51.0°–54.5° N [Chebrov et al., 2011]

***2 months

The anomaly is characterized by a lower seismicity rate during 21 months (July 2005 to March 2007) by a factor of 8 compared with the background value.The precursor was detected in real time. A successful forecast as stated by the KB REC [Chebrov et al., 2011]

July 24, 2007MW = 6.2 ϕ = 50.8° N, λ = 158.3° Eh = 36 km

RTL

Southern Kamchatka, from Avacha Bay to Cape Lopatka, coordinates: 50.5–52.5° N, 157°–161° E

*12 months

Minimum RTL (RTL = –15) was re�corded in July 2007, the anomaly lasted about 1 year. The earthquake occurred at the edge of anomalous area as RTL was returning to background level.

Z

Forecast at KB REC on Jan. 25, 2008 and May 29, 2008 that an M ≥ 6.0 ± 0.5 earthquake could occur during 2008 in the latitude strip 51.0°–55.0° N [Chebrov et al., 2011]

***14 months

Seismicity rate decreased by a factor of 15 (SRD = 0.94) during 13 months. The earthquake occurred at a distance of 20 km from the anomaly. The precursor was detected in real time. The forecast was successful as stated by KB REC [Chebrov et al., 2011]

July 30, 2010 MW = 6.3 ϕ = 52.23° N, λ = 160.46° Eh = 38 km

RTL

southern Kamchatka, from Avacha Bay to Cape Lopatka, coordinates: 50.5–52.5°N, 157 –161°E.


Minimum RTL (RTL = –15) was re�corded in July 2007, the anomaly lasted about 1 year. The earthquake occurred at the edge of anomalous area as RTL was returning to background level.

Z

Forecast at KB REC on Jan. 25, 2008 and May 29, 2008 that an M ≥ 6.0 ± 0.5 earthquake could occur during 2008 in the latitude strip 51.0°–55.0° N [Chebrov et al., 2011]

Epicenter of July 30, 2007 was in area of anomalous SRD, but the anomaly was not statistically significant. As stated by KB REC, the precursor was detected in real time. The forecast was successful as to time of occurrence and magnitude, but was in error as to location3 .



Table 2. (Contd.)

Earthquake Method Area of expected earthquake. Forecast formulation Advance time Comment

Feb. 20, 2011MW = 6.1 ϕ = 55.73° N, λ = 162.48° Eh = 47 km

RTL

Forecast at KB REC on December 3, 2010 for an M ≥ 6 earthquake in the area with coordinates 53.5–57.5 N, 159–164.5 E.Kamchatskii Bay, Kronotskii Pen�insula, and northern Kronotskii Bay


Minimum RTL (RTL = –21) was re�corded in November 2009, the anomaly lasted 1.5 years. The earthquake occurred at the edge of anomalous area 10 months after RTL returned to normal (April 2010) [Saltykov and Kravchenko, 2011]. As stated by KB REC, the precursor was de�tected in real time. The forecast was suc�cessful as to time of occurrence and loca�tion, but was in error as to energy4 .

Z

Forecast at KB REC on December 3, 2010 for an M ≥ 6 earthquake in the area with coordinates 53.5–57.5 N, 159–164.5 E.Kamchatskii Bay, Kronotskii Pen�insula, and northern Kronotskii Bay

***2 months

Earthquake occurred at the edge of the area where seismicity rate was recorded to have decreased by a factor of 8 from September 2009 to December 2010. The precursor was detected in real time. The forecast was successful as stated by the KB REC4.

May 18, 2011 MW = 5.5 ϕ = 55.39° N, λ = 164.03° Eh = 69 km

SOUS’09

Forecast at KB REC on May 12, 2011: “Since seismic energy tends to be released in lower amounts in the region as a whole, there is the possibility of an earthquake with magnitude greater than 6.0 in Kam�chatka (area: 50.5–56.5° N, 156–167° E)”.4

As stated by the KB REC, the precursor was detected in real time. The forecast was successful as to time of occurrence and location, but was in error as to energy4.

Note: * Marks the time interval measured from the moment RTL began to rise from its minimum until the earthquake occurrence time;** marks the interval of time measured from the moment RTL came back to normal until the earthquake occurrence time; *** marksthe interval of time between the termination of a quiescence as determined by the Z technique and the subsequent earthquake.

1 Chebrov, V.N., Abubakirov, I.R., Bakhtiarov, V.F., et al., Multidisciplinary Seismological and Geophysical Studies of Kamchatka and theKuril Islands: January 1 to December 31, 2003, Report, Petropavlovsk�Kamchatskii, Kamchatka Technique Testing Seismological Team,Geophysical Service, Russian Academy of Sciences, 2004.)

2 Research report Multidisciplinary Seismic and Geophysical Monitoring of Geodynamic Processes in the Junction Zone of the Kuril–Kam�chatka and the Aleutian Island Arc. Directed by V.N. Chebrov. Archives of the Center of Information Technologies and Systems of ExecutiveAuthorities (CIT&S). No. 02200950632, 2009.)

3 Research report Multidisciplinary Monitoring of Geodynamic Processes in Kamchatka Krai, directed by V.N. Chebrov. Archives of the Cen�ter of Information Technologies and Systems of Executive Authorities (CITS), no. 02201257401, 2012.)

4 Chebrov, V.N., Abubakirov, I.R., Voropaeva, N.P., et al., Technical Report on the State of the Seismic Monitoring System Operated by theKamchatka Branch of the RAS Geophysical Service, 2011, Petropavlovsk�Kamchatskii, Geophysical Service of Russian Acad. Sci., 2012.)

seismicity on the SOUS'09 scale [Saltykov, 2009]. TheRTL anomaly appeared in 2008, with this parameterreaching the minimum values in the autumn of 2009.The quiescence lasted 1.5–2 years. The February 20,2011 earthquake occurred within the RTL anomaly 10months after the RTLparameter returned to the back�ground level. A decrease in seismicity rate by a factorof 8 (a Z anomaly) was observed during 15 months,from October 2009 to December 2010.The earthquakeof February 20, 2011 occurred at the edge of the Zanomaly 2 months since the anomaly ceased to exist.The anomalies in seismicity parameters were detectedin real time (i.e., before the earthquake).

Quantitative Assessment of Efficiency and Reliability for Seismic Quiescence Identification

The results of retrospective identification of quies�cences by the RTL and Z function methods were usedto assess the efficiency and reliability achieved in thisidentification. The period of observation from 1980 to2011 was considered.

The efficiency of a precursor is the ratio between thenumber of seismic quiescences that precede largeearthquakes according to the model and the number ofidentified anomalies.

Twenty�five anomalies of the quiescence type wereidentified by the Z function method in the monitoringzone (see Fig. 1), the most active in Kamchatka, from

70


SALTYKOV et al.

54

53

52

51

162160158156

1.000

0.7500.875

0.6250.500

1.0

162

13

8

6

Jan

. 25,

198

6

July

24,

198

6

Jan

. 20,

198

7

July

19,

198

7

Jan

. 15,

198

8

July

13,

198

8

Jan

. 9,

1989

July

8,

1989

Jan

. 4,

1990

July

3,

1990

Dec

. 30,

199

0

Jun

e 28

, 19

91D

ec. 2

5, 1

991

Jun

e 22

, 19

92

Dec

. 19,

199

2

Jun

e 17

, 19

93

Dec

. 14,

199

3

Jun

e 12

, 19

94

Dec

. 9,

1994

Jun

e 7,

199

5

Dec

. 4,

1995

Jan

. 25,

198

6

July

24,

198

6

Jan

. 20,

198

7

July

19,

198

7

Jan

. 15,

198

8

July

13,

198

8

Jan

. 9,

1989

July

8,

1989

Jan

. 4,

1990

July

3,

1990

Dec

. 30,

199

0

Jun

e 28

, 19

91

Dec

. 25,

199

1

Jun

e 22

, 19

92

Dec

. 19,

199

2

Jun

e 17

, 19

93

Dec

. 14,

199

3

Jun

e 12

, 19

94

Dec

. 9,

1994

Jun

e 7,

199

5

Dec

. 4,

1995

SRD= 1> 0.875> 0.75> 0.5

Mar. 2, 1992MW = 6.8

Mar. 2, 1992MW = 6.8

Mar. 2, 1992MW = 6.8

52

0.7

10

16252

11.5

0.9

0.8

0.7

0.6

0.5

0.4

164

160

8

6

4

2

164

160

51

53

10.5

9.5

8.5

7.5

6.5

5.5

4.5

3.5

2.5

1.5

0.8

0.9

1.0

0.6

0.5

0.4

0.3

51

53

E

N

SRD

Longitude Latitu

de

а b

c d

Z

Latitu

deLongitude

SR

DZ

Fig. 8. Seismic quiescence before the MW 6.8 earthquake of March 2, 1992 as identified using the Z function technique:(a) map of maximum values of the decrease in seismicity rate SRD (the date as predicted was November 30, 1990). The dashedline encloses the area of low seismicity rate (by a factor of 8). (b) plots of time�dependent behavior of SRD and Z for the area wherethe seismicity rate decreased by a factor of 8. The March 2, 1993 earthquake is marked with an arrow; (c), (d) 3D image of Z andSRD anomalies (the date as predicted was November 30, 1990).

1980 to 2011. Earthquakes of M ≥ 6.0 were set in cor�respondence to fifteen anomalies, with five of thesebeing associated with M ≥ 6.8 earthquakes. The pre�cursor efficiency by the Z function method was 0.6 (15of 25 cases) for M ≥ 6.0 earthquakes and 0.2 (5 of25 cases) for М ≥ 6.8 earthquakes.

For the same interval of time we identified19 anomalies of the quiescence type using the RTLtechnique. Earthquakes of M ≥ 6.0 were set in corre�spondence to twelve of these, with five of these anom�alies being associated with M ≥ 6.8 earthquakes. The

efficiency of the predictive parameter RTL was 0.63(12 of 19 cases) for M ≥ 6.0 earthquakes and 0.26 (5 of19 cases) for M ≥ 6.8 earthquakes.

The reliability of a precursor is the ratio between thenumber of earthquakes for which precursory seismicquiescences were identified and the number of allearthquakes with magnitudes equal to or greater thanthe threshold value.

Thirty�two earthquakes with magnitudes M ≥ 6occurred in the monitoring zone at depths of 100 kmor less between 1980 and 2011, with 10 of these earth�



quakes having magnitudes M ≥ 6.8. Seismic quies�cences using the Z function technique were identifiedbefore 17 M ≥ 6.0 earthquakes, with 6 of these havingmagnitudes M ≥ 6.8. The earthquakes occurred in thearea of decreased seismicity rate by a factor of 8 orgreater (SRD ≥ 0.875) in time intervals within 3 yearsafter the quiescent phase. The precursor reliabilitywhen detected by the Z function technique was 0.53(17 of 32 cases) for M ≥ 6.0 earthquakes and 0.6 (6 of10 cases) for M ≥ 6.8 earthquakes.

Seismic quiescences using the RTL technique wereidentified during the same interval of time before 17M ≥ 6.0 earthquakes, with 8 of these events havingmagnitudes M ≥ 6.8. Earthquakes generally occurredat the edge of the anomalies during 2 years after theRTL parameter returned to the background level. Thereliability of the predictive parameter RTL was 0.53(17 of 32 cases) for M ≥ 6.0 earthquakes and 0.8 (8 of10 cases) for M ≥ 6.8 earthquakes.

Real�Time Identification of Precursory Seismicity Anomalies

Conclusions as to the current seismic situationbased on analyses of seismicity dynamics are trans�ferred to the Kamchatka Branch of the Russian ExpertCouncil on Earthquake Prediction, Assessment ofVolcanic Hazard and Risk (KB REC) [Gordeev et al.,2006b; Chebrov et al., 2011]. The level of current seis�

mic activity on the SOUS'09 scale [Saltykov, 2011] isassessed on a weekly basis in different time windows(from 7 days to 1 year). Calculations of the other seis�micity parameters considered here are published asofficial quarterly conclusions. There are cases of suc�cessful forecasts of earthquakes.

Table 2 summarizes examples of timely identifica�tion of precursors before large Kamchatka earthquakes,mostly using the RTL and Z function techniques. Theresults of this work are given in KB REC proceedings,publications and reports. Table 2 contains forecaststatements, gives advance times, and provides refer�ences to document timely detection of precursors orpronouncement of a prediction statement.

We would like to conclude this section with remarkson the current behavior of Kamchatka seismicityparameters. The following results were obtained usingthe regional catalogs of Kamchatka earthquakes (as ofearly 2011):

An RTL anomaly and a Z anomaly are now over innorthern Kamchatka (in the Kamchatskii Bay), thereare increased values of activity A10 and a set of clusters;

A Z anomaly is terminating in the area of the Kro�notskii Peninsula and the Kronotskii Bay, there areincreased values of activity A10;

An absolute seismic quiescence (Z anomaly) is atpresent observed in the Avacha Bay area, there are

56

54

52

166164162160158156

–3–12

–21

1.0000.875

0.500

9

6

3

0.7500.625

Dec

. 5,

1997

Jan

. 31,

200

2

Sep

t. 2

8, 2

002

May

26,

200

3

Jan

. 21,

200

4

Sep

t. 1

7, 2

004

May

15,

200

5

Jan

. 10,

200

6

Sep

t. 7

, 20

06

May

5,

2007

Dec

. 31,

200

7

Au

g. 2

7, 2

008

Ap

r. 2

4, 2

009

Dec

. 20,

200

9

Au

g. 1

7, 2

010

Ap

r. 1

4, 2

011

Jun

e 5,

200

1

Jan

. 31,

200

2

Sep

t. 2

8, 2

002

May

26,

200

3

Jan

. 21,

200

4

Sep

t. 1

7, 2

004

May

15,

200

5

Jan

. 10,

200

6

Sep

t. 7

, 20

06

May

5,

2007

Dec

. 31,

200

7

Au

g. 2

7, 2

008

Ap

r. 2

4, 2

009

Dec

. 20,

200

9

Au

g. 1

7, 2

010

Ap

r. 1

4, 2

011

Jun

e 5,

200

1

Jan

. 31,

200

2

Sep

t. 2

8, 2

002

May

26,

200

3

Jan

. 21,

200

4

Sep

t. 1

7, 2

004

May

15,

200

5

Jan

. 10,

200

6

Sep

t. 7

, 20

06

May

5,

2007

Dec

. 31,

200

7

Au

g. 2

7, 2

008

Ap

r. 2

4, 2

009

Dec

. 20,

200

9

Au

g. 1

7, 2

010

Ap

r. 1

4, 2

011

20.02.2011MW = 6.1

RTL < –12RTL < –9RTL < –6RTL < –3

N

E

RT

LZ

SR

D

а b

Fig. 9. Seismic quiescence that was detected in real time before the MW 6.1 earthquake of February 20, 2011 using the RTL andZ function techniques:(a) map of RTL values at the time the anomaly reached its minimum. The area of decrease in seismicity rate by a factor of 8(Z anomaly) is enclosed in a dashed line. The map also shows the epicenter of the February 20, 2011 earthquake (circle) and thepoint of lowest RTL (diamond); (b) plots of time�dependent behavior of Z, SRD, and RTL for 2000–2011. The February 20, 2011earthquake is marked with an arrow.

72


SALTYKOV et al.

increased values of activity A10 and a set of clusters withlargest main events;

There are increased values of activity A10 in south�ern Kamchatka and increased values of the slope of therecurrence relation γ. Remarkably enough, there was aprevious statistically significant decrease in γ in thearea.

The most spectacular anomalies in the Kamchatkaseismicity for 2009–2011 should be considered theabsolute minimum of total seismic energy released byearthquakes in 2009 during the period of detailedobservations (1962–2011), a regional change(increase) in the slope of the recurrence relationship,and a change of sign in the γ anomaly in southernKamchatka.

CONCLUSIONS

We in Kamchatka deal with a wide range of prob�lems that are related to seismicity studies, from theorganization of observations to real�time monitoringof seismicity parameters.

The seismic situation is assessed on a regular basisusing a set of techniques and the regional catalog dueto the Kamchatka Branch of the RAS GeophysicalService. The techniques are as follows:

Assessing the seismicity level in the region on theSOUS'09 scale;

Calculating variations in the slope of the recur�rence relation and identification of statistically signif�icant anomalies using the Z test;

Calculating the seismic activity parameter A10;Monitoring the RTL parameter;Identification of seismic quiescences using the Z

function technique;detecting areas of statistically significant decreases

in seismicity rate with the help of the Z test;Monitoring the parameter dS, which consists in

variations in the area of seismogenic ruptures;Identification of earthquake clusters.Conclusions about the seismic situation are trans�

ferred to the Kamchatka Branch of the Russian ExpertCouncil on Earthquake Prediction following a set pro�cedure, which implies weekly assessments of seismic�ity level and quarterly assessments by all the othertechniques.

Retrospective identification of seismic quiescences(from 1980 to 2011) using the RTL and Z functiontechniques gave us estimates of precursor reliability(the ratio of the number of earthquakes for which pre�ceding seismic quiescences have been identified to thenumber of all earthquakes with magnitudes equal to orgreater than a specified value). When estimated inquantitative terms, the reliability of a precursor is con�sidered in the assessment and furnishes the basis fordeveloping scenarios of how to respond to forecasts.The reliability of seismic quiescences as identified bythe Z function technique in 1980–2011 was 0.53 (17 of

32 cases) for M ≥ 6.0 earthquakes and 0.6 (6 of10 cases) for M ≥ 6.8 earthquakes. The reliability ofthe RTL predictive parameter for the same interval oftime was 0.53 (17 of 32 cases) for M ≥ 6.0 earthquakesand 0.8 (8 of 10 cases) for M ≥ 6.8 earthquakes.

Since 2006, summaries from the analysis of Kam�chatka seismicity parameters are reported in theannual issues of the Zemletryaseniya v Rossii publica�tions, thus enhancing the presentation of seismic evi�dence to an essentially new level, that of quantitativeassessment.

The technique as developed for Kamchatka to cal�culate the distribution function and the SOUS'09 scalefor seismicity level has received approval and is cur�rently used to assess the level of seismic activity forregions throughout the area of Russia. The results arealso reported in the annual Zemletryaseniya v Rossiipublications.

In the near future it is planned to introduce assess�ments of seismicity level on the SOUS'09 scale forthose Kamchatka volcanoes for which seismic moni�toring is in progress that will provide detailed informa�tion on small local seismic events that accompany vol�canic activity. The first objects of analysis to be tackledare the Northern and the Avacha volcanic cluster.

The various phases in the development and intro�duction of techniques for the analysis of seismicitywere part of the Federal target program Reduction ofRisk and Impact from Natural and Main�InducedEmergencies in the Russian Federation until 2010(Measure 23 Modernization and Development of theSeismological Observation System, the Analysis of theSeismic Situation, and Earthquake Prediction andMeasure 25 Conducting Urgent R&D Orientedtoward the Development of an Essentially NovelInstrumentation, Methodological, and Software Sup�port for the Functioning of the Seismological Obser�vation System and Earthquake Prediction);

Program no. 16 of the RAS Presidium Environ�ment and Climate Changes: Natural Disasters, theproject Real�Time Study of the Fine Structure of Dif�ferent�Scale Seismic Processes in Russia in Order toCreate a Parametric Basis for Developing and Improv�ing the Methods of Earthquake Prediction.

ACKNOWLEDGMENTS

The authors are grateful to Corresponding Memberof the RAS G.A. Sobolev for aid in introducing theseismicity monitoring techniques in the KamchatkaBranch of the RAS Geophysical Service and to Cand.Sci. (Phys.–Math.) V.B. Smirnov for lending us a pro�gram for aftershock identification. Special thanks goto our colleagues O.G. Volovich and V.V. Ivanov forthe program that was used in the present study.



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Documents

A parametric representation of Kamchatka seismicity over time