SEISAN Software Application for Developing an Automated...

ISSN 0747�9239, Seismic Instruments, 2012, Vol. 48, No. 3, pp. 270–281. © Allerton Press, Inc., 2012.Original Russian Text © A.V. Konovalov, A.A. Stepnov, V.N. Patrikeev, 2011, published in Seismicheskie Pribory, 2011, vol. 47, no. 4, pp. 34–49.

270

INTRODUCTION

The methodology of seismological data processingis perfected, first of all, based on long�term experiencein using digital recording equipment, databank accu�mulation, and intense development of computer tech�nologies. The experience gained by the leading seis�mological research teams allows for adjusting the mostchallenging methods of earthquake data processing tothe existing and developing systems for instrumentalobservations performed on the territory of the Sakha�lin oblast. One such team is a group of seismologistsworking at the Department of Solid Earth Physics,Bergen University (Norway), who developed theSEISAN earthquake data processing software (Otte�moller et al., 2011), granted an public access to it(source code is available), and continue its developing.

Within the framework of this study, it is necessary topay attention to the functionality of the SEISANearthquake data processing software package: itscross�platform (it can run in different operating sys�tems), acceptable resource consumption, accessibility(the possibility of free use and updating), a possibilityto modify the program source code, a modular struc�ture (easy adding of new or modified system compo�nents), network support (group work), a unified for�mat of presentation of calibration parameters of theequipment, waveform files, a database (DB), etc.

A particular attraction of SEISAN is related to thepossibility of using modern computational programs(for determining focal mechanisms, evaluation of thedispersion of group velocities of surface waves andmedium quality, etc.) that do not require additionalsettings (except control computational parameters),and the input data preparation made in the program

environment. The process of simultaneous multichan�nel processing of both continuous data and separatefragments of the earthquake records and subsequentdetection of the source parameters “all in one win�dow” (in an automated mode including) can be imple�mented very easily. Graphic applications, visualizationof the statistical seismic data, etc., are implemented ata very high level. This paper describes the key stages ofautomation of the routine processing of the earth�quake data using the SEISAN earthquake analysissoftware. The accuracy of determining the coordinatesof hypocenters and representativity of the earthquakecatalog based on the results of detailed seismologicalobservations in the north of Sakhalin Island are sub�stantiated.

AUTOMATION OF ROUTINE PROCESSINGOF EARTHQUAKE DATA

The SEISAN software package for earthquake dataprocessing has a set of profile files and its own DB for�mat. The configuration files make a convenient toolthat allows editing the system parameters. Within theDB, it is a set of files and directories organized in a cer�tain manner and intended for storing waveforms andthe results of their processing (earthquake bulletins andcatalogs). The waveforms are subdivided into twoarrays. The first array stores contiguous files that serve,as a rule, for searching and sampling of seismic events.The second one contains singled out (“cut out”) wavefunctions tied to a specific event. Such an approachmakes it possible to continuously look through the waveforms in a certain temporary window and saves us fromthe necessity of opening a new file (hour, minute, etc.)

SEISAN Software Application for Developingan Automated Seismological Data Analysis Workstation

A. V. Konovalov, A. A. Stepnov, and V. N. PatrikeevInstitute of Marine Geology and Geophysics, Far East Branch, Russian Academy of Sciences,

ul. Nauki 1b, Yuzhno�Sakhalinsk, 693022 Russiae�mail: [email protected]

Abstract—The key stages of automation of routine earthquake data processing are described. The software,technologies, and equipment are specified. The method for determining earthquake source parameters isconsidered in details. The experience in the seismological data interpretation taking recent velocity profilesof the region into account is demonstrated and the determination accuracy is justified. The information aboutthe configuration of the local network of seismic stations and the velocity structure of the earth’s crust in thenorth of Sakhalin Island is presented.

Keywords: velocity profile, seismic network, earthquake, SEISAN, Linux, automation, database.

DOI: 10.3103/S0747923912030073

SEISMIC INSTRUMENTS Vol. 48 No. 3 2012

SEISAN SOFTWARE APPLICATION FOR DEVELOPING 271

each time during the visualization. One has only tospecify the initial date, time, and the window width,and the necessary files with the waveforms will be auto�matically opened in the visualization window. More�over, a transition from file to file is as clear for the useras if the operator looks through one large seismogram.In addition to waveform files, the DB contains so�called S�files, namely the DB elements containing thedata on a recorded event. Addition of waveforms canalso be an event; in this case, the S�file will contain thename of the file with the seismogram, the name of theoperator who made the addition, and the date and thetime of the waveform beginning and end. Another ver�sion of an event is an earthquake. In this case, the S�filewill describe both the input data (station channels,types of waves, etc.) and the calculated data formed inthe result of processing (epicenter coordinates, sourcedepth, magnitude, etc.). The S�files are written in theNORDIC format (Havskov and Ottemoller, 2000).

The architecture of the interaction of the DB, profilefiles, and programs where a multiuser access to the sys�tem is implemented should be structured so as, on theone hand, to grant shared access to the DB and the keyconfiguration files (STATION0.HYP, SEISAN.DEF,MULPLT.DEF) and, on the other hand, to allow thespecialists to change individual settings in their ownworkstations. Thus, implementation of multiuseraccess to the system necessitates placing the key confi�guration files that are created at the stage of the systemdebugging and do not require any future changes on theDB file server.

When implementing the above model of multiuseraccess to the system, the specialists of the Institute ofMarine Geology and Geophysics, Far East Branch,Russian Academy of Sciences (IMGG FEB RAS) usedthe server operating under the Linux operation system(OS), while the data was accessed following SMB pro�tocols (Hertel, 2003) for the MS Windows clients, andFTP protocols (Postel et al., 1985) for Linux, Unix, andSolaris clients (Fig. 1). To avoid data losses and unau�thorized actions, the file server was equipped with a sys�tem for authorization and authentication. A connec�tion to the file server is different for different OSs inspite of the fact that the SEISAM is a cross�platformsoftware tool (ST). For Linux, Unix, and Solaris, thenetwork access is implemented at the level of the OSkernel: the network directories are mounted to thedesired directory, the SEISAN addresses a directory,and the network subsystem of the OS kernel performsall other operations: gets authorization on the server,reads and records the data, etc. The setting for MS Win�dows is more complicated since it can require not onlyediting of some profile files, but also modification of thesource code in some SEISAN modules. The model ofan automated workstation (AWS) of a seismologistdeveloped at the IMGG FEB RAS uses SUN/ORA�CLE VirtualBox virtualization technologies (Oracle ..,2011) and the DEBIAN OS (Krafft, 2005), whichallows running the configured system under all popularOSs (MS Windows, GNU/Linux, SUN/ORACLESolaris, MAC OS).

SMB/FTP SMB/FTP

SMB/FTP

Portable computerof expeditionary group

Workstation of theseismologist operator



Portable HDDs with sourceand processed data)

Uni�fied database(protected file server under Linux)

Fig. 1. A basic model of the workstation operator interaction with DBs.

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Before starting, SEISAN adjusts and updates theconfiguration files, in particular, the tool panel of thevisualization window (“hot” filters, window size,color, etc), namely MULPLT.DEF andCOLOR.DEF files; specifies the physical residenceof the source “raw” data (continuous waveforms) andthe results of their processing (record fragments andS�files), namely the SEISAN.DEF file; the coordi�nates of seismic stations, their names, and parame�ters of one or several high speed columns (theSTATION0.HYP file); calibration characteristics of theequipment (CAL directory); etc. Thus, the AWS of aseismologist is preliminarily prepared for the operation,and the system is preadapted to the conditions of theavailable network of seismic stations and regional pecu�liarities (the parameters of the environment included).

Anticipating the process of data preprocessing, it isnecessary to organize a base of continuous data wherean array of all waveforms is accumulated, which allowsfor simultaneous look up of all waveforms recorded byseismic stations or for routine identification of seismicevents. It also makes it possible to set automated pro�cessing of the seismic signal flux. The files are read inseveral generally accepted formats, namely SEISAN,GSE, SEED/MiniSEED, SAC binary, and SACASCII. Thus, the files of the waveforms recorded bydigital stations are converted into the MiniSEEDinternational format for the formats of the recordeddata to be unified.

At the first stage of processing, the waveforms arelooked through and the seismic events are identified;the records on them are immediately cut out and areautomatically stored in the DB, the S�files in the eventlist being created.

At the second stage, the records on earthquakes arepreprocessed. This preprocessing starts with the wave�form analysis and consists of the following sequence ofevents: a qualitative analysis of the seismogram, identifi�cation of the events, their signs and accuracy, measure�ment of the amplitudes and periods of seismic waves,and determination of the key parameters of the source ofthe analyzed earthquake. These procedures are run inthe MULPLT software that is responsible for the seis�mogram visualization (Fig. 3) and interactive work of theoperator with instrumental and processed data.

INSTRUMENTAL NETWORK OF DETAILED SEISMOLOGICAL OBSERVATIONS

IN NORTH SAKHALIN

At present, a local network operating within NorthSakhalin consists of five seismic stations with the aver�age distance between the observation points of about50 km (Fig. 4). The network of stationary seismic sta�tions started working in September, 2006 with an aimto record induced seismicity in the north�west shelfzone of the island related to the industrial develop�ment of oil and gas deposits. The hardware part of the

Visualization

Statistics

Network of digitalseismic stations

Data unification “Raw” dataEarthquake sampling “Cut”

waveforms

Set of auxiliary programs

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Fig. 2. Architecture of interaction of the input parameters, data, and system subprocedures.



instrumental network is represented by ground�baseddigital seismic stations, each being equipped with theKS�2000/SP three�component short�period (1 Hzeigenfrequency of oscillations) seismometer, theSMART�24R recorder of seismic signals, and theTrimble ACUTIME 2000 GPS receiver (see workspecifications of the SMART�24 recorder). Thisequipment was manufactured by Geotech Instru�ments, LLC (USA). The stations operate in a contin�

uous recording mode with the sampling increment of100 counts per second. The imbedded clocks of theseismic station recorders are corrected every day basedon the signals of the Global Positioning System thatprovides a nearly 10 ms accuracy of the timing refer�ence. The data are collected in a delayed mode.

Since December 2010, the LE�3Dlite sensors(Lennartz Electronic, Germany) have been used asseismic detectors (see work specifications of the LE�

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Fig. 3. An example of visualization of a fragment of the waveform of a weak shallow�focus earthquake (August 18, 2007, ML =2.6) reliably recorded by several seismic stations. The times and the amplitudes of arrivals of the first seismic waves are shown.The beginning of the record corresponds to 17:58 GMT. A 4�pole Butterworth filter in the frequency band of 1–15 Hz was used.

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3DLite MKII seismic detector). The eigenfrequencyof the seismic sensor is 1 Hz, and its current�voltagecharacteristic (CVC) at the frequencies higher thanthe eigenfrequency of oscillations is specified by a

constant coefficient, namely the sensor sensitivity.Home�made Delta 03 seismic stations are used as dig�ital recorders (Gavrilov, Konovalov, and Nikiforov,2011), which have given a good account of themselves

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Fig. 4. Positioning of digital stations of the local observation network (1) and separate stations of the regional observation networkof the Geophysical Service, Sakhalin Branch, RAS (2) and FEB RAS (3), and a map of the sedimentary cover of North Sakhalinand the adjacent water area: a – anticlinal zones overlapped by the Cainozoic sedimentary cover; b – anticlinal zones with thecropping basement c – synclinal zones with a very deep Cainozoic cover



as reliable and relatively cheap devices. The samplingincrement is 125 counts per second. Nowadays, theproject on automation of the process of collection andtransfer of the recorded data is being developed basedon these devices. By the end of 2011, it is planned tomount and to launch an auxiliary seismic station onthe territory of the “Lunskoe” joined coastal techno�logical complex (LNSK).

In addition, the records of the digital seismic sta�tions of the regional network of FEB RAS (Nikolae�vsk�on�Amur) (Khanchuk et al., 2011) and the Geo�physical Service, Sakhalin Branch, RAS (Tymovskoeand Okha) are used for routine data processing.

VELOCITY STRUCTUREOF THE EARTH’S CRUST

The velocity of seismic waves and the depth of theearth’s crust layers of Northern Sakhalin and the waterareas adjacent from the east and west were studiedbased on the materials of the methods of deep seismicsounding (DSS), refracted waves (RW), commondepth point (CDP), and magnetotelluric sounding(MTS). Based on the seismic wave velocities and gra�dients of their horizontal and vertical variations in thesections of Northern Sakhalin, three large geologicalcomplexes can be singled out, namely a sedimentarycover, a consolidated crust, and the upper mantle.

The sedimentary cover is characterized by the mostsubstantial variations in vertical velocities from1.6 km/s near the surface to 5.4 km/s near its bottom.Horizontal velocity variations are also observed here,but they are minimal in case of still bedding andincrease in the regions with developed large folded

structures and fold faults. The depth of the coverwithin the analyzed region varies from 0 to 10 km.Based on the DSS materials (Glubinnoe …, 1971), thevelocity characteristics of the upper mantle and theconsolidated crust layers are almost constant over theentire analyzed territory but the crust depth varieswithin a broad range, namely from 25 km in the Der�yugina depression to 35 km on Sakhalin Island.

The maps of the sedimentary cover depth(Krasikov, Kononov, and Pyatakov, 2000; Nikiforovet al., 1987) reveal large�scale anticline and synclinalstructures of a submeridional course, which consist ofseveral local folds extended, as a rule, in accordancewith a general course of the higher rank structure (Ale�kseichik et al., 1963).

Based on these data, the territory of SakhalinIsland and adjacent water areas was divided into thezones with similar conditions of recording of a seismicwave induced by earthquakes. This division was basedon the depth of the sedimentary cover as a factor thatto the utmost determines the discrepancy of the timesof arrival of these waves. In the first approximation,the structure of the cover can be described in this caseby an alternation of three submeridional zones withthe sediment depths from 0 to 2 km, from 2 to 6 km,and from 6 to 10 km (Fig. 5).

The analysis of velocity peculiarities of on�landsections (Drobot and Telegin, 1978) performed basedon the materials of seismic well logging, RW, CDP,and the correlation refraction method (CRM)revealed that interval (stratal) velocities are averaged tothe depth of 3.0 km by the linear law:

V = V0 (1 +βH), (1)

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100 200 300 400 km

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where V0 = 1.6–2.0 km/s; the gradient is β = 0.4–0.5 1/s;and H is the depth in km.

The velocity peculiarities of sections at the depthsover 3 km were mainly studied based on the CRMresults (Argentov et al., 1997). The materials of theseworks are presented as a system of hodograph curvesand consolidated seismic sections that illustrate thevelocity structure of the sedimentary cover and base�ment to the depths of 10 km. Four layers with stratalvelocities 2.0, 2.5, 3.9, and 4.5 km/s (top�down) weredistinguished within the cover. The boundary velocityin the basement is 6.0–6.4 km/s. A so�called basaltlayer revealed by numerous investigations in the crustbottom is occasionally traced at the depths of 15–20 km. It is characterized by the seismic wave veloci�ties of 6.5–6.7 km/s.

The data on seismic wave velocities and depths ofsome crust layers are generalized and are presented inthe table as averaged velocity sections of the earth’scrust for each distinguished zone (see Fig. 4) with asubmeridional orientation. Thus, to provide similar orclose conditions for receiving vibrations from earth�quakes, seismic stations should be positioned approx�imately on the same parallel or within the limits of oneof the distinguished zones. Zone IV is the most conve�nient from the standpoint of accessibility since theTymovsk–Okha motor road runs along it. The depthof the sedimentary cover within this zone varies from6 km near its eastern and western outskirts to 4 km inthe center, while in the north and in the south, i.e., inthe Schmidt peninsula and in the branches of theNabilsky ridge it decreases to several hundreds ofmeters, respectively. Such depth variations yield

changes in the times of arrival of waves from earth�quakes at different positions of stations in this zone. If,for each of the stations we use the model of the envi�ronment averaged for this zone according to the veloc�ity column IV (see table), the a priori error or thespread in times of the wave arrival will be from +0.18to –0.27 s. When the stations are positioned withineach of the different zones, the discrepancies can beeven smaller since the variation in the sediment depthwith respect to the average one is smaller and does notexceed 2 km.

For the hypocenters of the earthquakes thatoccurred within zones I–III and VII to be localized, itis recommended to use combined models consisting ofthe velocity section of the upper part of the crust in thevicinity of the stations and the velocity section of thelower part of the crust in the earthquake epicenter. Thecombined velocity columns (IV–I, IV–II, IV–III,and IV–VII) are listed in the table.

TECHNIQUE FOR DETERMINING EARTHQUAKE SOURCE PARAMETERS

The hypocenter parameters were determined usingthe methods of inversion of the seismic wave traveltime implemented as the HYPOCENTER computa�tional software (Lienert et al., 1995). The essence ofthe method lies in the assumption that the differencebetween the real position of the source and the calcu�lated one is small, and the residual difference can bespecified by a linear functional dependence from thecorrection to the real hypocenter position. The calcu�

Velocities of longitudinal seismic waves (V, km/s) and the depths of some crust layers (ΔH, km) as averaged velocity sectionsof the Earth’s crust for each of the zones shown in Fig. 4

Zone I Zone II Zone III Zone IV Zone V Zone VI

ΔH V ΔH V ΔH V ΔH V ΔH V ΔH V

0–1.25 2.5 0–1.25 2.5 0–0.25 2.5 0–1.25 2.5 0–1.25 2.5 0–2.5 2.5

1.25–3.25 3.8 1.2–2 3.8 2.5–4.5 3.9 1.25–2 3.9 1.25–2.5 3.9 2.5–4.5 3.9

3.25–8 4.5 2–4 4.5 4.5–8 4.5 2–4 4.5 2.5–5 4.5 4.5–8 4.5

8–15 6.2 4–12.5 6.2 8–14.75 6.2 4–15 6.0 5–15 6.0 8–15 6.0

15–25 6.6 12.5–29.5 6.6 14.75–31 6.6 15–35.5 6.6 15–36 6.6 15–35 6.6

25 8.0 29.5 8.0 31 8.0 35.5 8.0 36 8.0 35 8.0

Zone VII Zone VIII IV–III IV–II IV–I IV–VII

ΔH V ΔH V ΔH V ΔH V ΔH V ΔH V

0–1.25 2.5 0–0.75 2.5 0–1.25 2.5 0–1.25 2.5 0–1.25 2.5 0–1.25 2.5

1.25–2 3.9 0.75–1.75 3.9 1.25–2.0 3.9 1.25–2.0 3.9 1.25–2.0 3.9 1.25–2.0 3.9

2–4 4.5 1.75–4 4.5 2.0–4.0 4.5 2.0–4.0 4.5 2.0–4.0 4.5 2.0–4.0 4.5

4–15 6.0 4–17.5 6.0 4.0–15 6.0 4.0–15 6.0 4.0–15 6.0 4.0–15 6.0

15–26.25 6.6 17.5–35 6.6 15–32 6.6 15–30 6.6 15–26 6.6 15–27 6.6

26.25 8.0 35 8.0 32 8.0 30 8.0 26 8.0 27 8.0



lated time of arrival (P� or S�wave) at the ith seis�mic station can be written as

(2)where t0 is the time in the source and T is the traveltime of the seismic wave as a function of the stationcoordinates (xi, yi, zi) and the hypocenter coordinates(x0, y0, z0). Due to nonlinear relationships between thetravel times and the positions of the earthquakes, trun�cated Taylor series will be used in the general case forlinearization of Eq. (2). In this case, the differencebetween the measured and the calculated travel timeslinearly belong to corrections, namely three hypocen�tral parameters and the time in the source (Δx, Δy, Δz,Δt). Expanding the travel time function from Eq. (2)into the Taylor series over the degrees of correctionsand leaving only the first terms in the expansion, wecan find the residual difference ri (discrepancy):

(3)where

(4)

where are the measured travel times at the ith seis�mic station.

These equations are combined for all stations byjoining Eqs. (3) and (4) so as to get a set of linear equa�tions in the form

Wr = WGX, (5)where r is the vector of residual differences; G is thematrix containing partial derivatives; X is the vector ofunknown corrections that have to be determined; andW is a diagonal matrix with weighting corrections foreach equation. The weights are used for taking theaccuracy of determining travel times of seismic wavesinto account in case of manual processing of seismo�grams. The coherence function for the consideredwaveforms is used for the cross correlation data.

The set of linear equations (5) with four unknownquantities (three hypocenter parameters and the timein the source) is solved by minimizing the residual dif�ference with the help of the least squares method usingan interactive approach. First, the solution is specifiedin the form of the calculated travel times for the ana�lyzed phases (in some region where the source is hypo�thetically localized). Then, this solution is checked fordetermining the corrections to the pre�assigned posi�tion; then, the corrected solution becomes the inputone, etc. As a rule, the iterative process rapidly con�verges if the hypocenter position is close to the actualposition of the source.

If the data from not more than three stations areprocessed, the initial hypocenter position and the timein the source are determined using the method of B.B.Golitsin (the data on the azimuth to the station) withthe help of local tables on the seismic wave travel times(hodograph curves). Then, the obtained valuesbecome the input data for the inversion method, andafter that the hypocenter coordinates and the time in

calit

0 0 0 0( , , , , , ),cali i i it t T x y z x y z= +

( ) ( ) ( ) ,i i i ir T x x T y y T z z t= ∂ ∂ Δ + ∂ ∂ Δ + ∂ ∂ Δ + Δ

,obs cali i ir t t= −

obsit

the source are specified. The velocity model will berefined in the course of long�term observations, andthe parameters of the earthquake hypocenters storedin the database will be automatically adjusted, thedatabase will be completely updated, and the history ofthe introduced changes will be preserved.

The accuracy of determining hypocenter coordi�nates depends of the network geometry, availablephases, the accuracy of measurements of arrival times,and the velocity model of the earth’s crust. When theearthquake source is azimuthally surrounded with alarge number of stations, many modern algorithmsgive close results and demonstrate high stability of thesolutions in the problem of determining hypocenterparameters that weakly depend on the initial approxi�mation and the velocity model of the earth’s crust.However, when the surrounding of the source with sta�tions becomes far from ideal and the number of obser�vation points is few (as is in our case), hypocenteringbecomes a real art (as was in the epoch of manualdetermination). To minimize the errors related to asubjective factor, we used in our study the procedurethat optimizes the velocity model. For that, severalvelocity columns were prepared by varying the velocitymodel parameters (layer thickness and velocity); theywere used for calculating the travel times of seismicwaves. The source parameters of the velocity modelswere taken from the table. Then, the root�mean�square discrepancy was determined based on theresults of group processing of the data on several sta�tions and events. The model which gave the least root�mean�square discrepancy was taken as the optimalone. The results of the performed testing showed thatthe velocity model corresponding to zone IV in thetable and Fig. 4 gives the minimal discrepancy and,thus, can be recommended for routine processing.Based on this, the profile files of the processing soft�ware were adjusted.

The energy magnitude (M magnitude) of local seis�mic events has been evaluated for a long time using acorrelation dependence that was an equivalent of theempiric nomogram developed at the Seismology Lab�oratory of the IMGG FEB RAS and recommendedfor analyzing earthquakes on Sakhalin Island(Safonov, 2008):

(6)

where A is the maximal amplitude of transverse wavesin nm/s; and R is the epicentral distance in km. Thisexpression is equivalent to the empirical nomogram ofthe T.G. Rautian class.

However, the used magnitude scale (6) has severaldrawbacks. First, the source nomogram of theT.G. Rautian class was constructed based on the data ofthe regional observation network, while its asymptoticextension to local distances does not take local charac�teristics of the seismic wave damping into account. Sec�ond, station corrections to the magnitude are not taken

log 2.45log 5.39,M A R= + −

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into account during routine processing, which can yieldsystematic underestimation of the magnitude.

Thus, the calibration parameters and station cor�rections for determining the local magnitude of crustearthquakes ML at the epicentral distances up to300 km were obtained for the conditions of NorthSakhalin for the first time (the results are planned to bepublished in a separate paper):

(7)

where A is the maximal amplitude of shifts in the S�wave (nm); R is the hypocentral distance (km); and Sst

is the station correction to the magnitude for eachobservation point.

The local magnitude of earthquakes ML was deter�mined using bandpass filtering of the source waveformin the frequency range from 1 to 25 Hz followed by thesynthesis of the Wood–Anderson seismogram. Themaximal amplitude was searched for on the verticalcomponent during the first 10 s of the S�wave record�ing in accordance with the recommendations of theInternational Association of Seismology and Physicsof the Earth’s Interior (IASPEI). The magnitudes ofall recorded earthquakes were re�estimated accordingto Eq. (7).

L log 1.84 log 0.0011 2.97 ,stM A R R S= + + − +

Calibration dependence (7) that was recom�mended for estimating the magnitudes of earthquakesin the north of Sakhalin Island is used in this paper. Asthe digital databank is enlarged, the parameters of cal�ibration dependence (7) can be specified.

ANALYSIS OF DETERMINATION ERRORS

This paper analyzes the catalog of the earthquakeswith magnitude ML ≥ 3.0 from September, 2006 toMarch, 2010. During this period, more than 1000 seis�mic events with the magnitude ML ≥ 1.0 wererecorded, and half of them were localized. Somemethodological aspects the authors followed whencompiling the earthquake catalog and the analysis ofthe results of determining the key source parametersare given below.

When the hypocenter coordinates were determinedbased on the data from three or more stations, thetraveltimes of P� and S�waves recorded at the epicen�tral distances over 150 km were eliminated from theprocessed data. This made it possible to substantiallydecrease the hypocenter spread and, thus, the root�mean�square residual difference (4) did not exceed0.3 s. The efficiency of the proposed approach isexemplified in Fig. 6 by the distribution of the root�mean�square discrepancy (RMS) depending on thedepth (H) obtained when determining the coordinatesof the hypocenter of a weak shallow�focus earthquake(ML = 2.6) of August 18, 2007. The records of thisearthquake are shown in Fig. 3. It follows from Fig. 6that the source depth H = 18 km is characterized by apronounced local minimum of the RMS function thattakes on the value that slightly exceeds 0.2 s. Whenusing travel times of seismic waves at the epicentraldistances over 150–200 km, the RMS function doesnot reach a clear minimum. It “smears” over a broaddepth range that is likely to be related to three�dimen�sional variations of the seismic wave velocities at largedistances from the source. In addition, during eachearthquake processing the measured parameters weretested with the help of the Wadati diagram that charac�terizes the average relationship between the velocitiesof P� and S�waves. The velocities of S�waves were cal�culated based on the velocity of P�waves from the ratioVP/VS = 1.8.

Another example is the hodograph of body wavesfor a strong earthquake (MW = 5.8) that occurred onMarch 16, 2010 in the northwest of Sakhalin Island (seeFig. 7). The measured travel times of P� and S�wavesfrom the earthquake source to the observation stationsare marked with crosses. The calculated travel times ofP� and S�waves plotted in accordance with the basevelocity model (see table, zone IV) and the fixed depthsource (H = 5 km) are shown with approximating solidlines: the lower line corresponds to the first arrivals ofthe P�phase; the upper one, to the first arrivals of theS�phase. It can be seen that the travel times of seismic

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0.3

503010 200 40H, km

RMS, s

Fig. 6. Distribution of the root�mean�square discrepancy(RMS) as a function of the source depth (H) during deter�mination of hypocenter coordinates of a weak shallow�focus earthquake (August 18, 2007, ML = 2.6).



waves measured based on the data from several seismicstations (remote ones included) are of the sameapproximating lines of the hodographs, which con�firms a high degree of the computational accuracy.

The distribution of the root�mean�square discrep�ancy (Fig. 8a) for the analyzed earthquake catalogdoes not exceed 0.4 s (and even 0.3 s for 95% of cases),which is in agreement with the a priori estimates pre�sented in one of the previous chapters. More than 70%of the earthquake sources are localized by three ormore stations (Fig. 8b). The gap in the azimuthal cov�erage with the observation network during sourcelocalization is 150°–240° (Fig. 8c), which is likely tobe related to meridional positioning of the stations(along the island). Less than 30% of the total numberof earthquakes corresponds to the gap in the range of330°–360° These earthquakes occurred beyond theobservation network, namely either to the north of theOkha station (OKHA) or to the south of Tymovskoe(TMSK, TYV). As a rule, it is possible to record andlocalize such events based on the data from not morethan two stations (Fig. 8b).

It should be noted that the meridional positioningof the observation network complicates the earthquakelocalization. The scattering parameters in the deter�mined hypocenter coordinates shown in Fig. 9 pointto this. The distribution of the error ellipses showed

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T, sS

P

Δ, kmC

HM

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YS

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OS

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IS

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Fig. 7. A hodograph curve of body waves obtained duringlocalization of the source of a strong earthquake(March 16, 2010, MW = 5.8).

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(c)

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Fig. 8. Bar charts of the calculated parameters: (a) – root�mean�square discrepancy (RMS); (b) – the number of used stations (n);and (c) – the gap in the azimuthal covering with the observation network during source localization (GAP).

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that in addition to depth the most vulnerable parame�ter is longitude. However, in case of sublatitude mov�ing from the network the error spread noticeablydecreases.

In addition, the comparative analysis of the resultsof determination of parameters of strong earthquakesthat occurred in 2006–2010 in North Sakhalin was

performed in accordance with the catalogs of theNational Earthquakes Information Center (NEIC),the Geophysical Service, Sakhalin Branch, RAS, anddata of the local network. In spite of the expected dis�crepancy with the data of international agencies, theerror in determining the hypocenter coordinates withthe local network does not exceed 10 km and some�times is even smaller. For weak events (with M ∼ 2.0),a higher spread in determined hypocenter coordinatescan be expected. This is related to the fact that suchevents can be recorded by only several stations. How�ever, when we speak about stable operation of all sta�tions, even weak events are reliably recorded by morethan three stations as, e.g., is shown in Fig. 3. Thus,such events are characterized by similar determinationerrors as the earthquakes with the magnitude ML ≥ 3.0.Therefore, the authors pay special attention to no�fail�ure operation of the entire observation network for astandard earthquake catalog to be obtained.

Based on the data of the catalog of the earthquakesthat took part in the north of Sakhalin Island fromSeptember, 2006 to March, 2010 (ML ≥ 3.0), densityand cumulative recurrence plots were constructed(Fig. 10). The analysis of the cumulative recurrenceplot allows evaluating the level of the catalog represen�tativity, i.e., the energy value of the earthquakesrecorded without gaps. It follows from the figure thatthe range of representative magnitudes starts from 3.0,and the dependence of the frequency of events on the

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Fig. 9. Distribution of determination errors: (a) – latitudes (Δϕ), (b) – longitudes (Δλ), and (c) – depths (ΔH).

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M42 31 5

N(≥M)

Fig. 10. The Guttenberg–Richter cumulative recurrenceplot and the approximating log�linear relationship. Thebar chart represents the recurrence density distribution.



magnitude is approximated within it by the log�linearGuttenberg–Richter relationship.

CONCLUSIONS

The accomplished work yielded the developmentof an automated seismological data analysis worksta�tion operated using SEISAN, Linux, and VirtualBoxsoftware and based on x86 and x86�64 hardware plat�forms. The paper presents the experience in integra�tion of the system components. The data storagemodel has been implemented.

A release version of the earthquake catalog hasbeen prepared with the help of the set of computa�tional software entering the workstation and the accu�mulated digital database. The accuracy of determiningearthquake source parameters and representativity ofthe earthquake catalog has been substantiated. Earth�quake hypocenters are localized based on the preparedvelocity models.

The developed tool, namely an automated seismo�logical data analysis workstation, is the first step on theway to implementing modern techniques for instru�mental data processing. The key directions of researchinclude the system of automated recording of seismicevents in real time, automated estimation of the earth�quake source parameters, an increase in the coveragedensity of the local instrumental network of seismo�logical observations, and the integration with interna�tional seismological networks. The results obtained inthe framework of this study served as a base for severalprojects that are underway at present. Moreover, thedrawn up plans cannot be implemented without reli�able observational devices and instruments, seismicstations included. The Russian product line of digitalrecording equipment meets all modern requirementsconcerning reliability and efficiency and, thus, thepresented experience can be very useful for Russianmanufacturers.

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