ISSN 07479239, 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.
SEISAN Software Application for Developing an Automated Seismological Data Analysis Workstation A. V. Konovalov, A. A. Stepnov, and V. N. Patrikeev Institute of Marine Geology and Geophysics, Far East Branch, Russian Academy of Sciences, ul. Nauki 1b, YuzhnoSakhalinsk, 693022 Russia email:
[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 is considered in details. The experience in the seismological data interpretation taking recent velocity profiles of the region into account is demonstrated and the determination accuracy is justified. The information about the configuration of the local network of seismic stations and the velocity structure of the earth’s crust in the north of Sakhalin Island is presented. Keywords: velocity profile, seismic network, earthquake, SEISAN, Linux, automation, database. DOI: 10.3103/S0747923912030073
INTRODUCTION The methodology of seismological data processing is perfected, first of all, based on longterm experience in 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 most challenging methods of earthquake data processing to the existing and developing systems for instrumental observations performed on the territory of the Sakha lin oblast. One such team is a group of seismologists working at the Department of Solid Earth Physics, Bergen University (Norway), who developed the SEISAN 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 to pay attention to the functionality of the SEISAN earthquake data processing software package: its crossplatform (it can run in different operating sys tems), acceptable resource consumption, accessibility (the possibility of free use and updating), a possibility to 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 the equipment, waveform files, a database (DB), etc. A particular attraction of SEISAN is related to the possibility of using modern computational programs (for determining focal mechanisms, evaluation of the dispersion of group velocities of surface waves and medium quality, etc.) that do not require additional settings (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 separate fragments of the earthquake records and subsequent detection of the source parameters “all in one win dow” (in an automated mode including) can be imple mented very easily. Graphic applications, visualization of the statistical seismic data, etc., are implemented at a very high level. This paper describes the key stages of automation of the routine processing of the earth quake data using the SEISAN earthquake analysis software. The accuracy of determining the coordinates of hypocenters and representativity of the earthquake catalog based on the results of detailed seismological observations in the north of Sakhalin Island are sub stantiated. AUTOMATION OF ROUTINE PROCESSING OF EARTHQUAKE DATA The SEISAN software package for earthquake data processing has a set of profile files and its own DB for mat. The configuration files make a convenient tool that allows editing the system parameters. Within the DB, it is a set of files and directories organized in a cer tain manner and intended for storing waveforms and the results of their processing (earthquake bulletins and catalogs). The waveforms are subdivided into two arrays. 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”) wave functions tied to a specific event. Such an approach makes it possible to continuously look through the wave forms in a certain temporary window and saves us from the necessity of opening a new file (hour, minute, etc.)
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Fig. 1. A basic model of the workstation operator interaction with DBs.
each time during the visualization. One has only to specify 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 user as if the operator looks through one large seismogram. In addition to waveform files, the DB contains so called Sfiles, namely the DB elements containing the data on a recorded event. Addition of waveforms can also be an event; in this case, the Sfile will contain the name of the file with the seismogram, the name of the operator who made the addition, and the date and the time of the waveform beginning and end. Another ver sion of an event is an earthquake. In this case, the Sfile will describe both the input data (station channels, types of waves, etc.) and the calculated data formed in the result of processing (epicenter coordinates, source depth, magnitude, etc.). The Sfiles are written in the NORDIC format (Havskov and Ottemoller, 2000). The architecture of the interaction of the DB, profile files, and programs where a multiuser access to the sys tem is implemented should be structured so as, on the one hand, to grant shared access to the DB and the key configuration files (STATION0.HYP, SEISAN.DEF, MULPLT.DEF) and, on the other hand, to allow the specialists to change individual settings in their own workstations. Thus, implementation of multiuser access to the system necessitates placing the key confi guration files that are created at the stage of the system debugging and do not require any future changes on the DB file server. SEISMIC INSTRUMENTS
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When implementing the above model of multiuser access to the system, the specialists of the Institute of Marine Geology and Geophysics, Far East Branch, Russian Academy of Sciences (IMGG FEB RAS) used the server operating under the Linux operation system (OS), while the data was accessed following SMB pro tocols (Hertel, 2003) for the MS Windows clients, and FTP protocols (Postel et al., 1985) for Linux, Unix, and Solaris 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 in spite of the fact that the SEISAM is a crossplatform software tool (ST). For Linux, Unix, and Solaris, the network access is implemented at the level of the OS kernel: the network directories are mounted to the desired directory, the SEISAN addresses a directory, and the network subsystem of the OS kernel performs all 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 only editing of some profile files, but also modification of the source code in some SEISAN modules. The model of an automated workstation (AWS) of a seismologist developed at the IMGG FEB RAS uses SUN/ORA CLE VirtualBox virtualization technologies (Oracle .., 2011) and the DEBIAN OS (Krafft, 2005), which allows running the configured system under all popular OSs (MS Windows, GNU/Linux, SUN/ORACLE Solaris, MAC OS).
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Set of auxiliary programs (wadati, ttplot et al.)
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Fig. 2. Architecture of interaction of the input parameters, data, and system subprocedures.
Before starting, SEISAN adjusts and updates the configuration files, in particular, the tool panel of the visualization window (“hot” filters, window size, color, etc), namely MULPLT.DEF and COLOR.DEF files; specifies the physical residence of the source “raw” data (continuous waveforms) and the results of their processing (record fragments and Sfiles), namely the SEISAN.DEF file; the coordi nates of seismic stations, their names, and parame ters of one or several high speed columns (the STATION0.HYP file); calibration characteristics of the equipment (CAL directory); etc. Thus, the AWS of a seismologist is preliminarily prepared for the operation, and the system is preadapted to the conditions of the available network of seismic stations and regional pecu liarities (the parameters of the environment included). Anticipating the process of data preprocessing, it is necessary to organize a base of continuous data where an array of all waveforms is accumulated, which allows for simultaneous look up of all waveforms recorded by seismic stations or for routine identification of seismic events. It also makes it possible to set automated pro cessing of the seismic signal flux. The files are read in several generally accepted formats, namely SEISAN, GSE, SEED/MiniSEED, SAC binary, and SAC ASCII. Thus, the files of the waveforms recorded by digital stations are converted into the MiniSEED international format for the formats of the recorded data to be unified.
At the first stage of processing, the waveforms are looked through and the seismic events are identified; the records on them are immediately cut out and are automatically stored in the DB, the Sfiles in the event list being created. At the second stage, the records on earthquakes are preprocessed. This preprocessing starts with the wave form analysis and consists of the following sequence of events: 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 of the analyzed earthquake. These procedures are run in the MULPLT software that is responsible for the seis mogram visualization (Fig. 3) and interactive work of the operator with instrumental and processed data. INSTRUMENTAL NETWORK OF DETAILED SEISMOLOGICAL OBSERVATIONS IN NORTH SAKHALIN At present, a local network operating within North Sakhalin consists of five seismic stations with the aver age distance between the observation points of about 50 km (Fig. 4). The network of stationary seismic sta tions started working in September, 2006 with an aim to record induced seismicity in the northwest shelf zone of the island related to the industrial develop ment of oil and gas deposits. The hardware part of the SEISMIC INSTRUMENTS
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Fig. 3. An example of visualization of a fragment of the waveform of a weak shallowfocus 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 4pole Butterworth filter in the frequency band of 1–15 Hz was used.
instrumental network is represented by groundbased digital seismic stations, each being equipped with the KS2000/SP threecomponent shortperiod (1 Hz eigenfrequency of oscillations) seismometer, the SMART24R recorder of seismic signals, and the Trimble ACUTIME 2000 GPS receiver (see work specifications of the SMART24 recorder). This equipment was manufactured by Geotech Instru ments, LLC (USA). The stations operate in a contin SEISMIC INSTRUMENTS
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uous recording mode with the sampling increment of 100 counts per second. The imbedded clocks of the seismic station recorders are corrected every day based on the signals of the Global Positioning System that provides a nearly 10 ms accuracy of the timing refer ence. The data are collected in a delayed mode. Since December 2010, the LE3Dlite sensors (Lennartz Electronic, Germany) have been used as seismic detectors (see work specifications of the LE
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Fig. 4. Positioning of digital stations of the local observation network (1) and separate stations of the regional observation network of the Geophysical Service, Sakhalin Branch, RAS (2) and FEB RAS (3), and a map of the sedimentary cover of North Sakhalin and the adjacent water area: a – anticlinal zones overlapped by the Cainozoic sedimentary cover; b – anticlinal zones with the cropping basement c – synclinal zones with a very deep Cainozoic cover
3DLite MKII seismic detector). The eigenfrequency of the seismic sensor is 1 Hz, and its currentvoltage characteristic (CVC) at the frequencies higher than the eigenfrequency of oscillations is specified by a
constant coefficient, namely the sensor sensitivity. Homemade Delta 03 seismic stations are used as dig ital recorders (Gavrilov, Konovalov, and Nikiforov, 2011), which have given a good account of themselves SEISMIC INSTRUMENTS
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as reliable and relatively cheap devices. The sampling increment is 125 counts per second. Nowadays, the project on automation of the process of collection and transfer of the recorded data is being developed based on these devices. By the end of 2011, it is planned to mount and to launch an auxiliary seismic station on the 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 vskonAmur) (Khanchuk et al., 2011) and the Geo physical Service, Sakhalin Branch, RAS (Tymovskoe and Okha) are used for routine data processing. VELOCITY STRUCTURE OF THE EARTH’S CRUST The velocity of seismic waves and the depth of the earth’s crust layers of Northern Sakhalin and the water areas adjacent from the east and west were studied based on the materials of the methods of deep seismic sounding (DSS), refracted waves (RW), common depth point (CDP), and magnetotelluric sounding (MTS). Based on the seismic wave velocities and gra dients of their horizontal and vertical variations in the sections of Northern Sakhalin, three large geological complexes can be singled out, namely a sedimentary cover, a consolidated crust, and the upper mantle. The sedimentary cover is characterized by the most substantial variations in vertical velocities from 1.6 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 and increase in the regions with developed large folded SEISMIC INSTRUMENTS
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structures and fold faults. The depth of the cover within the analyzed region varies from 0 to 10 km. Based on the DSS materials (Glubinnoe …, 1971), the velocity characteristics of the upper mantle and the consolidated crust layers are almost constant over the entire analyzed territory but the crust depth varies within 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; Nikiforov et al., 1987) reveal largescale anticline and synclinal structures of a submeridional course, which consist of several local folds extended, as a rule, in accordance with a general course of the higher rank structure (Ale kseichik et al., 1963). Based on these data, the territory of Sakhalin Island and adjacent water areas was divided into the zones with similar conditions of recording of a seismic wave induced by earthquakes. This division was based on the depth of the sedimentary cover as a factor that to the utmost determines the discrepancy of the times of arrival of these waves. In the first approximation, the structure of the cover can be described in this case by an alternation of three submeridional zones with the 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 onland sections (Drobot and Telegin, 1978) performed based on the materials of seismic well logging, RW, CDP, and the correlation refraction method (CRM) revealed that interval (stratal) velocities are averaged to the depth of 3.0 km by the linear law: V = V0 (1 +βH), (1)
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Velocities of longitudinal seismic waves (V, km/s) and the depths of some crust layers (ΔH, km) as averaged velocity sections of the Earth’s crust for each of the zones shown in Fig. 4 Zone I
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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 depths over 3 km were mainly studied based on the CRM results (Argentov et al., 1997). The materials of these works are presented as a system of hodograph curves and consolidated seismic sections that illustrate the velocity structure of the sedimentary cover and base ment to the depths of 10 km. Four layers with stratal velocities 2.0, 2.5, 3.9, and 4.5 km/s (topdown) were distinguished within the cover. The boundary velocity in the basement is 6.0–6.4 km/s. A socalled basalt layer revealed by numerous investigations in the crust bottom 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 of some crust layers are generalized and are presented in the table as averaged velocity sections of the earth’s crust for each distinguished zone (see Fig. 4) with a submeridional orientation. Thus, to provide similar or close conditions for receiving vibrations from earth quakes, seismic stations should be positioned approx imately on the same parallel or within the limits of one of the distinguished zones. Zone IV is the most conve nient from the standpoint of accessibility since the Tymovsk–Okha motor road runs along it. The depth of the sedimentary cover within this zone varies from 6 km near its eastern and western outskirts to 4 km in the center, while in the north and in the south, i.e., in the Schmidt peninsula and in the branches of the Nabilsky ridge it decreases to several hundreds of meters, 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 the spread in times of the wave arrival will be from +0.18 to –0.27 s. When the stations are positioned within each of the different zones, the discrepancies can be even smaller since the variation in the sediment depth with respect to the average one is smaller and does not exceed 2 km. For the hypocenters of the earthquakes that occurred within zones I–III and VII to be localized, it is recommended to use combined models consisting of the velocity section of the upper part of the crust in the vicinity of the stations and the velocity section of the lower part of the crust in the earthquake epicenter. The combined 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 using the methods of inversion of the seismic wave travel time implemented as the HYPOCENTER computa tional software (Lienert et al., 1995). The essence of the method lies in the assumption that the difference between the real position of the source and the calcu lated one is small, and the residual difference can be specified by a linear functional dependence from the correction to the real hypocenter position. The calcu SEISMIC INSTRUMENTS
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lated time of arrival t ical (P or Swave) at the ith seis mic station can be written as (2) tical = t 0 + T (xi, yi, zi, x0, y0, z 0), where t0 is the time in the source and T is the travel time of the seismic wave as a function of the station coordinates (xi, yi, zi) and the hypocenter coordinates (x0, y0, z0). Due to nonlinear relationships between the travel times and the positions of the earthquakes, trun cated Taylor series will be used in the general case for linearization of Eq. (2). In this case, the difference between the measured and the calculated travel times linearly 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 corrections and leaving only the first terms in the expansion, we can find the residual difference ri (discrepancy): ri = (∂ T ∂ x i )Δ x + (∂ T ∂ y i )Δ y + (∂ T ∂ z i )Δ z + Δ t, (3) where
ri = tiobs − tical ,
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where are the measured travel times at the ith seis mic station. These equations are combined for all stations by joining 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 the matrix containing partial derivatives; X is the vector of unknown corrections that have to be determined; and W is a diagonal matrix with weighting corrections for each equation. The weights are used for taking the accuracy of determining travel times of seismic waves into account in case of manual processing of seismo grams. The coherence function for the considered waveforms is used for the cross correlation data. The set of linear equations (5) with four unknown quantities (three hypocenter parameters and the time in the source) is solved by minimizing the residual dif ference with the help of the least squares method using an interactive approach. First, the solution is specified in 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 for determining the corrections to the preassigned posi tion; then, the corrected solution becomes the input one, etc. As a rule, the iterative process rapidly con verges if the hypocenter position is close to the actual position of the source. If the data from not more than three stations are processed, the initial hypocenter position and the time in the source are determined using the method of B.B. Golitsin (the data on the azimuth to the station) with the help of local tables on the seismic wave travel times (hodograph curves). Then, the obtained values become the input data for the inversion method, and after that the hypocenter coordinates and the time in SEISMIC INSTRUMENTS
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the source are specified. The velocity model will be refined in the course of longterm observations, and the parameters of the earthquake hypocenters stored in the database will be automatically adjusted, the database will be completely updated, and the history of the introduced changes will be preserved. The accuracy of determining hypocenter coordi nates depends of the network geometry, available phases, the accuracy of measurements of arrival times, and the velocity model of the earth’s crust. When the earthquake source is azimuthally surrounded with a large number of stations, many modern algorithms give close results and demonstrate high stability of the solutions in the problem of determining hypocenter parameters 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), hypocentering becomes a real art (as was in the epoch of manual determination). To minimize the errors related to a subjective factor, we used in our study the procedure that optimizes the velocity model. For that, several velocity columns were prepared by varying the velocity model parameters (layer thickness and velocity); they were used for calculating the travel times of seismic waves. The source parameters of the velocity models were taken from the table. Then, the rootmean square discrepancy was determined based on the results of group processing of the data on several sta tions and events. The model which gave the least root meansquare discrepancy was taken as the optimal one. The results of the performed testing showed that the velocity model corresponding to zone IV in the table 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 a correlation dependence that was an equivalent of the empiric nomogram developed at the Seismology Lab oratory of the IMGG FEB RAS and recommended for analyzing earthquakes on Sakhalin Island (Safonov, 2008):
M = log A + 2.45log R − 5.39,
(6)
where A is the maximal amplitude of transverse waves in nm/s; and R is the epicentral distance in km. This expression is equivalent to the empirical nomogram of the T.G. Rautian class. However, the used magnitude scale (6) has several drawbacks. First, the source nomogram of the T.G. Rautian class was constructed based on the data of the regional observation network, while its asymptotic extension 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
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Calibration dependence (7) that was recom mended for estimating the magnitudes of earthquakes in the north of Sakhalin Island is used in this paper. As the digital databank is enlarged, the parameters of cal ibration dependence (7) can be specified.
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Fig. 6. Distribution of the rootmeansquare 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).
into account during routine processing, which can yield systematic underestimation of the magnitude. Thus, the calibration parameters and station cor rections for determining the local magnitude of crust earthquakes ML at the epicentral distances up to 300 km were obtained for the conditions of North Sakhalin for the first time (the results are planned to be published in a separate paper): ML = log A + 1.84 log R + 0.0011R − 2.97 + Sst , (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 each observation point. The local magnitude of earthquakes ML was deter mined using bandpass filtering of the source waveform in the frequency range from 1 to 25 Hz followed by the synthesis of the Wood–Anderson seismogram. The maximal amplitude was searched for on the vertical component during the first 10 s of the Swave record ing in accordance with the recommendations of the International Association of Seismology and Physics of the Earth’s Interior (IASPEI). The magnitudes of all recorded earthquakes were reestimated according to Eq. (7).
This paper analyzes the catalog of the earthquakes with magnitude ML ≥ 3.0 from September, 2006 to March, 2010. During this period, more than 1000 seis mic events with the magnitude ML ≥ 1.0 were recorded, and half of them were localized. Some methodological aspects the authors followed when compiling the earthquake catalog and the analysis of the results of determining the key source parameters are given below. When the hypocenter coordinates were determined based on the data from three or more stations, the traveltimes of P and Swaves recorded at the epicen tral distances over 150 km were eliminated from the processed data. This made it possible to substantially decrease the hypocenter spread and, thus, the root meansquare residual difference (4) did not exceed 0.3 s. The efficiency of the proposed approach is exemplified in Fig. 6 by the distribution of the root meansquare discrepancy (RMS) depending on the depth (H) obtained when determining the coordinates of the hypocenter of a weak shallowfocus earthquake (ML = 2.6) of August 18, 2007. The records of this earthquake are shown in Fig. 3. It follows from Fig. 6 that the source depth H = 18 km is characterized by a pronounced local minimum of the RMS function that takes on the value that slightly exceeds 0.2 s. When using travel times of seismic waves at the epicentral distances over 150–200 km, the RMS function does not reach a clear minimum. It “smears” over a broad depth range that is likely to be related to threedimen sional variations of the seismic wave velocities at large distances from the source. In addition, during each earthquake processing the measured parameters were tested with the help of the Wadati diagram that charac terizes the average relationship between the velocities of P and Swaves. The velocities of Swaves were cal culated based on the velocity of Pwaves from the ratio VP/VS = 1.8. Another example is the hodograph of body waves for a strong earthquake (MW = 5.8) that occurred on March 16, 2010 in the northwest of Sakhalin Island (see Fig. 7). The measured travel times of P and Swaves from the earthquake source to the observation stations are marked with crosses. The calculated travel times of P and Swaves plotted in accordance with the base velocity model (see table, zone IV) and the fixed depth source (H = 5 km) are shown with approximating solid lines: the lower line corresponds to the first arrivals of the Pphase; the upper one, to the first arrivals of the Sphase. It can be seen that the travel times of seismic SEISMIC INSTRUMENTS
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waves measured based on the data from several seismic stations (remote ones included) are of the same approximating lines of the hodographs, which con firms a high degree of the computational accuracy. The distribution of the rootmeansquare discrep ancy (Fig. 8a) for the analyzed earthquake catalog does 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 or more stations (Fig. 8b). The gap in the azimuthal cov erage with the observation network during source localization is 150°–240° (Fig. 8c), which is likely to be related to meridional positioning of the stations (along the island). Less than 30% of the total number of earthquakes corresponds to the gap in the range of 330°–360° These earthquakes occurred beyond the observation network, namely either to the north of the Okha station (OKHA) or to the south of Tymovskoe (TMSK, TYV). As a rule, it is possible to record and localize such events based on the data from not more than two stations (Fig. 8b). It should be noted that the meridional positioning of the observation network complicates the earthquake localization. The scattering parameters in the deter mined hypocenter coordinates shown in Fig. 9 point to this. The distribution of the error ellipses showed
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KONOVALOV et al. N/Ntotal, % 70 60 50 40 30 20 10 0 5 10
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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 noticeably decreases. In addition, the comparative analysis of the results of determination of parameters of strong earthquakes that occurred in 2006–2010 in North Sakhalin was N(≥M) 1000
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Fig. 10. The Guttenberg–Richter cumulative recurrence plot and the approximating loglinear relationship. The bar chart represents the recurrence density distribution.
performed in accordance with the catalogs of the National Earthquakes Information Center (NEIC), the Geophysical Service, Sakhalin Branch, RAS, and data of the local network. In spite of the expected dis crepancy with the data of international agencies, the error in determining the hypocenter coordinates with the 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 coordinates can be expected. This is related to the fact that such events 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 more than three stations as, e.g., is shown in Fig. 3. Thus, such events are characterized by similar determination errors as the earthquakes with the magnitude ML ≥ 3.0. Therefore, the authors pay special attention to nofail ure operation of the entire observation network for a standard earthquake catalog to be obtained. Based on the data of the catalog of the earthquakes that took part in the north of Sakhalin Island from September, 2006 to March, 2010 (ML ≥ 3.0), density and cumulative recurrence plots were constructed (Fig. 10). The analysis of the cumulative recurrence plot allows evaluating the level of the catalog represen tativity, i.e., the energy value of the earthquakes recorded without gaps. It follows from the figure that the range of representative magnitudes starts from 3.0, and the dependence of the frequency of events on the SEISMIC INSTRUMENTS
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magnitude is approximated within it by the loglinear Guttenberg–Richter relationship. CONCLUSIONS The accomplished work yielded the development of an automated seismological data analysis worksta tion operated using SEISAN, Linux, and VirtualBox software and based on x86 and x8664 hardware plat forms. The paper presents the experience in integra tion of the system components. The data storage model has been implemented. A release version of the earthquake catalog has been prepared with the help of the set of computa tional software entering the workstation and the accu mulated digital database. The accuracy of determining earthquake source parameters and representativity of the earthquake catalog has been substantiated. Earth quake hypocenters are localized based on the prepared velocity models. The developed tool, namely an automated seismo logical data analysis workstation, is the first step on the way to implementing modern techniques for instru mental data processing. The key directions of research include the system of automated recording of seismic events in real time, automated estimation of the earth quake source parameters, an increase in the coverage density of the local instrumental network of seismo logical observations, and the integration with interna tional seismological networks. The results obtained in the framework of this study served as a base for several projects that are underway at present. Moreover, the drawn up plans cannot be implemented without reli able observational devices and instruments, seismic stations included. The Russian product line of digital recording equipment meets all modern requirements concerning reliability and efficiency and, thus, the presented experience can be very useful for Russian manufacturers. REFERENCES Alekseichik, S.N., Gal’tsevBezyuk, S.D., Koval’ chuk, V.S., and Sychev, P.M., Tektonika, istoriya geolog icheskogo razvitiya i perspektivy neftegazonosnosti Sakhalina (Tectonics, Geological Evolution and Oil and Gas Perspec tives of Sakhalin), Moscow: Gostoptekhizdat, 1963. Argentov, V.V., Bikkenina, S.K., Zhigulev, V.V., et al., Experimental Studies of the Northwestern Shelf of Sakhalin by the Seismic Refraction Method, in Geodinamika tek tonosfery zony sochleneniya Tikhogo okeana s Evraziei, vol. 4: Struktura i veshchestvennyi sostav osadochnogo chek hla SeveroZapada Tikhogo okeana (Geodynamics of Tec tonosphere in the PacificEurasian Junction Zone, vol. 4: Structure and Material Composition of the Sedimentary Cover in the Northwest Pacific), YuzhnoSakhalinsk, 1997, pp. 90–118. SEISMIC INSTRUMENTS
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Drobot, V.D. and Telegin, A.N., Study of Velocity and Wave Characteristics of the Geologic Section of North Sakhalin by the Method of Vertical Seismic Profiling, in Geolog icheskaya interpretatsiya seismicheskikh nablyudenii v Dal’nevostochnom regione (Geological Interpretation of Seismic Observations in the Russian Far East), Yuzhno Sakhalinsk, 1978, pp. 79–84. Gavrilov, A.V., Konovalov, A.V., and Nikiforov, S.P., Results from Field and Stationary Tests of the Seismic Signal Recorder Delta 03, Seism. Instrum., 2011, vol. 47, no. 3, pp. 271–277. Glubinnoe seismicheskoe zondirovanie zemnoi kory SakhalinoKhokkaidoPrimorskoi zony (Deep Seismic Sounding of the Earth’s Crust in the SakhalinHokkaido Primorye Zone), Moscow: Nauka, 1971. Havskov, J. and Ottemoller, L., SEISAN Earthquake Anal ysis Software, Seismol. Res. Lett., 2000, vol. 70, pp. 532– 534. Hertel, C.R., Implementing CIFS: The Common Internet File System, Prentice Hall, 2003. Technical Specifications of a LE3DLite MKII Seismometer, LennartzElectronic. http://www.lennartzelectronic.de Technical Specifications of a SMART24 Recorder, Geotech Instruments. http://www.geoinstr.com/ dssmart24.pdf Khanchuk, A.I., Konovalov, A.V., Sorokin, A.A., Korolev, S.P., Gavrilov, A.V., Bormotov, V.A., and Serov, M.A., Instrumental and InformationalTechnological Support for Seismic Observations in the Russian Far East, Vestn. DVO RAN, 2011, no. 3, pp. 127–137. Krafft, M.F., The Debian System: Concepts and Techniques, Open Source Press, 2005. Krasikov, V.N., Kononov, V.E., and Pyatakov, Yu.V., A Method of 3D Simulation Based on Seismogravimetriñ Data for Assessment in Oil and Gas Perspective: North Sakhalin Case Study, in Stroenie zemnoi kory i perspektivy neftegazonosnosti v regionakh severozapadnoi okrainy Tikhogo okeana (Structure of the Earth’s Crust and Oil and Gas Perspectives of the Region of the Northwest Pacific Margin), YuzhnoSakhalinsk, 2000, vol. 1, pp. 167–201. Lienert, B.R.E. and Havskov, J., A Computer Program for Locating Earthquakes Both Locally and Globally, Seismol. Res. Lett., 1995, vol. 66, pp. 26–36. Nikiforov, V.M., Al’perovich, I.M., Gavrilov, A.I., et al., Structure of the Sedimentary Stratum of Northern Sakhalin from the Magnetotelluric sounding Data, Tikhookean. Geol., 1987, no. 3, pp. 52–60. Oracle VM VirtualBox User Manual, Oracle Corporation. 2011. http://download.virtualbox.org/virtualbox/UserManual.pdf Ottemoller, L., Voss, P., and Havskov, J., SEISAN Earthquake Analysis Software for Windows, Solaris, Linux and MacOSx, 2011. www.uib.no/rg/geodyn/artikler/2010/02/ software. Postel, J. and Reynolds, J., File Transfer Protocol (FTP), STD 9, RFC 959, USC/ISI, 1985. http://tools.ietf. org/pdf/rfc959. Safonov, D.A., Dinamika seismichnosti Yuzhnogo Sakha lina na osnove sovremennykh instrumental’nykh i mak roseismicheskikh dannykh, Extended Abstract of Cand. Sci. (Phys.Math.) Dissertation, YuzhnoSakhalinsk: IMGiG DVO RAN, 2008.
