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based on the analysis of microseisms are now being ... Abstract—A description of the Data Analysis Kit (DAK) software package that was developed specially for.

ISSN 07479239, Seismic Instruments, 2014, Vol. 50, No. 1, pp. 75–83. © Allerton Press, Inc., 2014. Original Russian Text © D.V. Popov, K.B. Danilov, R.A. Zhostkov, Z.I. Dudarov, E.V. Ivanova, 2013, published in Seismicheskie Pribory, 2013, Vol. 49, No. 2, pp. 44–57.

Processing the Digital Microseism Recordings Using the Data Analysis Kit (DAK) Software Package D. V. Popova, K. B. Danilovb, R. A. Zhostkovc, Z. I. Dudarovd, and E. V. Ivanovab, e a

JSC Nordavia  Regional Airlines, Arkhangelsk Airport 4/1, Arkhangelsk, 163000 Russia email: [email protected] b Institute of Ecological Problems of the North, Ural Branch, Russian Academy of Sciences, nab. Severnoi Dviny 23, Arkhangelsk, 163000 Russia c Schmidt Institute of Physics of the Earth, Russian Academy of Sciences, ul. Bol’shaya Gruzinskaya 10/1, Moscow, 123995 Russia d KabardinoBalkarian State University, ul. Chernyshevskogo 173, Nalchik, KabardinoBalkar Republic, 360004 Russia e Sector of Seismic Monitoring of the North of the Russian Plate, Geophysical Service, Russian Academy of Sciences, nab. Severnoi Dviny 23, Arkhangelsk, 163000 Russia Abstract—A description of the Data Analysis Kit (DAK) software package that was developed specially for processing large arrays of digital recordings of microseismic vibrations is presented. Much attention is paid to the possibility of using the described software package to study the upper layers of the earth’s crust via the microseismic sounding method. An alternative algorithm that obviates the need to crosscheck the station recordings is proposed. Such additional procedures as averaging and enveloping the spectra are analyzed. The results of applying the described procedures are presented. Keywords: processing program, microseisms, microseismic sounding DOI: 10.3103/S074792391401006X

to carry out calculations in the analysis of microseisms. The use of a set of mismatched programs caused considerable problems and necessitated the introduction of a great number of routine intermediate operations in the data processing algorithm. This prompted the development of a proprietary and more sophisticated program: the Data Analysis Kit (DAK) software package. It implements a set of computa tional procedures and algorithms for specialized pro cessing of seismic recordings (Popov et al., 2011). One may perform both the standard (e.g., viewing the waveforms or calculating their spectra) and specific (e.g., calculating the signal intensity variations in a certain frequency range, plotting the generalized spec tra, performing the spectral–temporal analysis (STA), superposing the STA diagrams, searching for noises of several classes in large (for example, yearlong) arrays of seismic recordings, etc.) tasks using this software package. The DAK software package proved to be very efficient in analyzing the anthropogenic quasihar monic interference from the sawing equipment in the recordings made by stationary stations of the Arkhan gelsk Network: their source was determined tentatively based on their onset time and duration (Frantsuzova and Ivanova, 2008), and the response of the underly ing environment to such influences was studied in a first approximation (Ivanova and Frantsuzova, 2009). In the present work we center our attention on the pos

INTRODUCTION Microseismic vibrations in the seismic station recordings are nowadays regarded as a useful signal and used for various purposes (Nikolaev, 1997; Yuda khin and Kapustyan, 2004). Specifically, various methods aimed at studying the earth’s crust structure based on the analysis of microseisms are now being actively developed (Nikolaev, 1997; Shakhova and Antonovskaya, 2004; Gorbatikov et al., 2008). Such studies are also carried out by the Institute of Ecolog ical Problems of the North in cooperation with the Sector of Seismic Monitoring of the North of the Rus sian Plate. The seasonality of changes in the microseismic background at the sites of the Arkhan gelsk Network stations (Popov et al., 2007), the anthropogenic impact and the environmental response to it (Frantsuzova and Ivanova, 2008; Danilov, 2009; Ivanova and Frantsuzova, 2009), and several other factors are studied. The microseismic sounding method is being adapted to the north of the Russian Plate over the last 3 years within the frame work of studies of the geological environment struc ture (Frantsuzova et al., 2009; Danilov, 2011). The processing of digital recordings of microseis mic vibrations involves converting large data arrays and performing specific operations, and the need to automate this process is obvious. The programs capa ble of performing individual operations were first used 75


POPOV et al.

Data acquisition

Data processing Data visualization

Analysis of the results

Fig. 1. Block diagram of the operation of seismic data pro cessing programs.

sibilities of processing the microseismic sounding data using the DAK software package. BASIC STRUCTURE OF THE DAK SOFTWARE PACKAGE Figure 1 shows a block diagram of the DAK soft ware package. The data acquisition process (the first part of the diagram) often involves converting the data from their initial format. Each equipment type usually uses an original data format, as the programs are tied to their own databases. The system being developed solves this problem by allocating an abstract data level that does not depend on the used database. This makes it possi ble to use several different data sets simultaneously without additional conversions. Based on the infrastructure of the data processing center of the Arkhangelsk Network and the specifics of data processing, it was decided to arrange interaction of the DAK package with the database of the Windows Seismic Grapher (WSG) software package for seismic data processing developed by the Geophysical Service, Russian Academy of Sciences and individual text files and SEISAN format files. The WSG software package allows one to promptly perform various operations over the seismic station recordings. This turns out to be useful at the first data processing stage where the gen eral operations (e.g., the recorded data quality check, selection of data segments for processing, etc.) are performed. Besides, the use of a common database precludes data duplication and provides prompt access to these data. The recordings are read out only from the WSG database when the data obtained via the microseismic sounding method are processed. This restriction is imposed due to the need for prompt and reliable anal ysis of large amounts of data.

The second part of the diagram consists of two mutually dependent components (data processing and visualization). Their interdependence stems from the fact that the data processing result may turn out to be a number, text string, certain data array, graphic pic ture, etc. The user also should analyze the data processing results. Depending on the output data, this analysis may be performed either visually or using other pro grams (e.g., Microsoft Excel). Thus, the entire seismic data processing system is split into two parts: the basic one and the auxiliary one. The basic part controls the data acquisition and pro cessing functions, ensures their joint use, lets the user access them, saves the current state, and loads it from a text file. The auxiliary part implements the acquisi tion of specific data and their processing. USING THE DAK SOFTWARE PACKAGE TO PROCESS THE DATA OBTAINED VIA THE MICROSEISMIC SOUNDING METHOD General Description of the Method The microseismic sounding method is based on the fact that the spectral amplitudes of Rayleigh waves at certain frequencies increase when passing over the lowvelocity heterogeneities and decrease when pass ing over the highvelocity ones (Gorbatikov, 2006). Two assumptions are taken into account when imple menting this method. First, the vertical displacement component in the microseismic background is deter mined by the vertical component of a Rayleigh wave. Second, the microseism sources are evenly distributed in space. The heterogeneity itself may be located at a certain depth, and the corresponding intensity varia tions may be sensed at the surface. The heterogeneity occurrence depth may be estimated based on the fact that the wave responds to a heterogeneity most strik ingly when it is located at a depth close to a halfwave length (Gorbatikov et al., 2008). The main complications arising when this method is applied are attributable to the random spectral com position of the microseismic field and its temporal variations. These complications are overcome on the basis that the microseisms consist predominantly of surface waves, as they are less attenuated than spheri cal waves. A system incorporating base and mobile sta tions is used to obviate the influence of temporal vari ations of microseisms. The base station recordings may be used as a reference in the process of screening the mobile station data. Seas and oceans are the sources of surface waves in the lowfrequency range, and their influence is felt at a distance of several hundreds of kilometers (Mona khov, 1977). This fact makes the proposed method particularly appealing, as the needed sounding signal SEISMIC INSTRUMENTS

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is omnipresent. When implementing the discussed method, one should take into account that it is aimed at isolating vertical and subvertical inhomogeneities. At the first processing stage, the data are screened based on the waveforms and the spectra of microseism recordings. The generalized spectra are then calcu lated, and the relative microseism intensity (Ii) that responds to the velocity inhomogeneities is calculated for each subband at all measurement points: A I i = 20 log ip , Aio where Aip and Aio are the spectral amplitudes for recordings at the ith point of the mobile and base sta tions at the considered frequency. The depth (h) is calculated at the next stage: h = VR /(2ν), where VR is the Rayleigh wave velocity and ν is the fre quency. The velocity values for the corresponding fre quencies are set separately. Thus, the data processing results are presented in the form of diagrams of I i distribution with depth and distance along the profile. The areas with lower I i val ues on these diagrams correspond to highvelocity regions and vice versa (Gorbatikov et al., 2008). Microseism I i Calculation Algorithm The data processing is based on calculating the ratios of spectral amplitudes of the mobile and base sta tions from all points of the outlined profile in a certain frequency range. The spatial and temporal variations of the microseismic field are regarded as its distinctive fea tures. The differences in physical properties of the stud ied environment are revealed by the microseism varia tions attributable to the spatial factor. The base station is used so as to exclude the time factor influence. The microseism I i is calculated for each frequency in order to determine the spatial factor influence using the microseism recordings. Two algorithms for calcu lating the relative microseism intensity are imple mented in the DAK software package. The first I i calculation algorithm is based on the method detailed in (Gorbatikov et al.., 2008) and nor malizes the mobile station data to the base station data (i.e., the value of I i relative to the base station record ings is calculated). At the first stage, the mobile and base station recordings are crosschecked in order to normalize the data recorded by various equipment: A κ = so , Asp where κ is the crosscheck coefficient at the considered frequency and Aso, Asp are the spectral amplitudes of the base and mobile stations according to the crosscheck ing data for their recordings. SEISMIC INSTRUMENTS

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The corrections for the difference in the device amplitude–frequency characteristics (AFCs) are introduced for the mobile station based upon these coefficients: Aip' = Aip κ.

Here Aip' , Aip are the spectral amplitudes in the ith point for the mobile station recording with and without regard for the recordings crosscheck. These values are used to calculate

Aip' , Aio where Ii is the relative microseism intensity and Aio is the spectral amplitude in the ith point for the base sta tion recording. The second algorithm calculates the I i value rela tive to one of the microseism recording points of the mobile station (the first point is used below as an example). The data processing here takes into account all the aspects of the microseismic sounding method, but excludes the station recordings crosscheck from the process. After the power spectrum calculation, the base station recordings are used only to monitor the time variations. In order to do that, the time variation coefficients for each profile point are calculated rela tive to the first point using the base station recordings: A ki = io , A1o where ki is the time variation coefficient for the ith point at the considered frequency and Aio, Alo are the spectral amplitudes in the ith and first points for the base station recording. The corrections for time variations are introduced for the mobile station based upon these coefficients: A Aip'' = ip , ki I i = 20 log

where Aip'', Aip are the spectral amplitudes in the ith point for the mobile station recording with and with out regard for time variations. As a result, the mobile station data depend only on spatial variations. The I i values are calculated in deci bels relative to the data of the first microseism record ing point at the set profile:

I i' = 20 log

Aip'' , A1 p

where I i' is the microseism intensity relative to the pro file point. Any other point may be used in the same way, but the first point is always present. A mathematical comparison between the microseism I i values obtained using different algo rithms is given below:


POPOV et al.

Aip'' A A A κA A A' A = ip = ip 1o = ip 1o = ip 1o ; A1 p ki A1p Aio A1 p Aio κA1p Aio A1' p I ′i = 20 log = 20 log

⎛ A' A ⎞ Aip' = 20 log ⎜ ip 1 o ⎟ ⎜ Aio A1′ p ⎟ A1 p ⎝ ⎠

Aip' A' A + 20 log 1 o = 20 log ip + C = I i + C. Aio Aio A1' p

Thus, the use of different algorithms allows one to obtain the values differing by a constant that is differ ent for each frequency and equals a negative I i value in the first point calculated relative to the base station. In view of the fact that the aim of the proposed method is to obtain a diagram of the relative microseism inten sity distribution, the second algorithm is fully fit for use. Since the processing method involves subtracting the average at each frequency, exactly the same results are obtained in both cases. The second proposed algorithm may turn out to be useful if the station recordings crosscheck is not com pleted due to a fault (e.g., the accumulators discharge or strong anthropogenic interference occurs). It may also be rather difficult to perform a crosscheck (e.g., when a stationary station with limited access is used as a base station). Additional Data Processing Procedures It is required to obtain the data that are statistically stable in time when processing the recordings within the framework of the microseismic sounding method (Gorbatikov et al., 2008) in order to be sure that the Rayleigh waves prevail over the waves of other types. The distorting influence associated with the random nature of the microseismic field may be minimized by accumulating the signal and screening the recordings fit for processing based on the spectral characteristics and waveforms. As a result, the recordings represented mainly by the Rayleigh waves are used for analysis if the field work and data screening are organized correctly. However, the influence of distorting factors (the microseismic vibration recordings being contaminated with nonRayleigh waves) is not completely excluded, and additional techniques of isolating the useful data may prove to be very useful. The procedures of enveloping and averaging the spectra facilitate the data interpretation when the amplitudes of neighboring frequencies differ signifi cantly and are applied to the spectra obtained after summation. The enveloping procedure comes down to estimating the peak spectral amplitude values in a cer tain frequency window and connecting these peaks, while the spectra averaging procedure involves con secutive calculations of the average value based on a certain number of neighboring values with a subse quent shift by one half of the considered window (Popov et al., 2011). Therefore, one should expect that

the enveloping procedure will isolate narrowband peaks from the general value set. The averaging proce dure makes it possible to isolate the most probable value from a set of neighboring spectral amplitudes (Danilov, 2011; Popov et al., 2011). The recorded data may get distorted under the influence of technical factors. For example, the zero level is often shifted in the recordings made in the field. This is usually caused by the measurement sys tem stabilization in novel operating conditions. The Earth’s surface vibrations themselves are recorded reliably, but a smoothly varying zero vibration level makes the recording spectra noisy, especially in the lowfrequency domain. In order to exclude such an external influence, one should wait for some time before starting to record the microseisms and protect the sensors from the environmental effects (tempera ture variations, winds, etc.). It is not always possible to create the conditions required for normal operation of the equipment. Besides, the time period needed to stabilize the mea surement system varies from case to case, and it is rather difficult to estimate its duration. Under labora tory conditions, the zero level shift may be eliminated by applying the waveform adjustment procedure described below. As a result, the spectra closer to the genuine ones would be used in the microseism pro cessing. Algorithm for plotting the spectra envelope. We shall understand the spectra envelope as a broken line the vertices of which are the peak values in the recording spectrum. Thus, the enveloping procedure substitutes the obtained spectrum with a broken line passing through the maximum values (i.e., this proce dure excludes nonpeak values). The algorithm depends on parameter p (0 < p < 1) that characterizes the size of a sliding window in which the values for the envelope would be searched for. The p parameter is numerically equal to the ratio of the analyzed window width to the entire range being considered. Let si be the set amplitude spectrum (0 ≤ i ≤ N − 1) and e j be the desired envelope (0 ≤ j ≤ N − 1). Let us assume that e0 = 0 . In order to find e j among all the spectrum points s k ( Npj + 1 ≤ k ≤ Np( j + 1) + 1) , we choose such a point that would maximize the angle between a straight line passing through it and the pre vious chosen point and the horizontal axis. If this point is s α , e j = s α . Thus, the procedure analyzes a window of Np points of the initial spectrum. The desired broken line is assigned a zero value at a frequency of 0 Hz. The maximum value is then selected in the analyzed win dow. The selected value becomes the desired vertex. At the next step, the analyzed window is shifted in such a way that the frequency of the chosen value becomes the lowest in the window, and the described procedure is then repeated. SEISMIC INSTRUMENTS

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Обработка микросейcм по методу микросейcмического зондирования Файл с временами измерений в формате: gDates.txt Количество Точек Обзор Дата1 Врем1 Названия станций и каналов в файле gData.txt {сек} Длительность1 РасстояниеОтНачала1 {= 0} Канал SHZ Опорная станция PDG ... ДатаN ВремяN Передвижная станция Канал SHZ KBS ДлительностьN РасстояниеОтНачалаN Длина блока (мин) 5 ТочкаНачалаСбивки1 ТочкаКонцаСбивки1 p:\data\ Месторасположение БД WSG ДатаСбивки1 ВремяСбивки1 {сек} ДлительностьСбивки1 ... Файл со скоростями волн в формате gFreqs.txt Обзор КоличествоТочек Сбивка станций Кэширование данных Вычитание среднего Частота1 Скорость1 ... Выходной файл ЧастотаN СкоростьN Обзор


Преобразование спектра Нет


Огибающая (k = 0.05)

Выравнивать данные ст. мн–а



Усреднение (N = 7)

Fig. 2. Parameter setting window of the DAK software package.

We then proceed in a similar way until the spectrum points are exhausted. At the edge of the spectrum, when the number of remaining points that were not analyzed drops below Np, only the remaining points are analyzed until the broken line reaches the last spectrum point. It is worth noting that the algorithm is finite and ends no later than after N – 1 steps (i.e., the envelope would have no more than N – 1 segments). Only 2 p steps on the average are required in practical applications. Spectra averaging algorithm. We shall under stand averaging as a procedure that substitutes the ini tial recording spectra with a line passing along the cen tral part of the band of a real spectrum. The degree of averaging depends on the width of the chosen sliding window the size of which is determined by an integer valued parameter ω (3 ≤ ω ≤ N ) that may take on only odd values. The algorithm substitutes each point s i with an average value of the neighboring ω points according to the following formula: i + ω−1

2 si = 1 s α. ω j =i −ω−1

si =


N −1

s α. N − i + ω − 1 j =i −ω−1 2 2 Thus, only the initial spectrum values are used in the process of its averaging. Waveform adjustment. The zero level is usually altered as a result of the measurement equipment sta bilization, and this distorts the spectra in the lowfre quency domain. The adjustment procedure consists in subtracting the zero level determined as an approxi mating polynomial calculated according to the least squares method (Bakhvalov et al., 2004) from the recorded waveforms. The degree of the polynomial is in the general case dependent on the degree of the ini tial data distortion and the length of the considered recording segment and is set individually in each spe cific case. Thus, the waveform adjustment in the overwhelm ing majority of cases allows one to obtain the data reflecting actual vibrations with respect to the zero level in each measurement instant and thus avoid dis torting the lowfrequency spectrum (Popov et al., 2011).


If i ∈ ⎡0; ω − 1⎤ , a part of the sliding window enters ⎣⎢ 2 ⎦⎥ the negative frequency domain when the previous for mula is used. Such points are substituted with si =


i + ω−1 2

i +1+ ω −1 2

∑s. α

j =0

In much the same way, if i ∈ ⎡N − 1 − ω − 1 , N − 1⎤ , ⎥⎦ ⎣⎢ 2 such points are substituted with SEISMIC INSTRUMENTS

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Data Processing Sequence The user is invited to specify a set of the required processing parameters prior to calculations. A special interface window (see Fig. 2) enables the user to spec ify the input files with the defined recording segments that represent the studied profile, the output files that would store the calculation results, and the files with the frequency distributions of velocities, select stations and channels, and specify several other parameters described below. The data on the stations and channels may be entered both via the input files and separately


POPOV et al. Точка №0: UGR54. 07. 07. 08 18:05:00

0.01 – 20




Fig. 3. Data processing window of the DAK software package.

via the interface window. The inclusion of data on the stations and channels into the input files makes their structure significantly more complicated, but also simplifies the process of repeated data processing and lowers the probability of the handler committing an error. Besides, the latter input data arrangement option is better suited for processing the measure ments made by a set of stations, as it is required to take into account the individual equipment characteristics for almost every point. The software package provides the possibility of processing the initial data for the purpose of improving the quality of the result. The corresponding processing procedures (e.g., the spectra processing procedures or the use of an alternative processing algorithm) are selected in the same window. The “data caching” function saves the recording segments selected for processing for the purpose of using them again in subsequent processing. This func tion proves useful when it is needed to process the same recordings under different settings. It allows one to do a prompt comparison between different process ing variants excluding the human factor (the probabil ity of including noisy segments into the processing procedure). The “test” function checks for the availability of the set recording segments. This function implements all the data processing stages, but automatically selects only the first recording segments at each point as the useful ones. As a result, the successful generation of the output file may be used to verify the correctness of the specified parameters. The data processing itself starts when all the required parameters are set. The processing window

(see Fig. 3) displays the spectra of subsegments and the waveform of the entire recording segment for each profile point. If needed, the user may alter the consid ered frequency range and the maximum spectra ampli tude. It is also possible to present the spectra on a loga rithmic scale. The user denotes the least noisy record ing segments for each measurement point. The selected segments are stored in random access mem ory, and the calculation of I i , conversion of frequen cies into the depth values, and the output file genera tion are performed after the last recording segment is processed. Thus, the DAK software package algorithm for processing the data obtained via the microseismic sounding method is a flexible chain of operations that is configured by the handler. The data processing structure is shown in Fig. 4. Alternative or optional algorithm steps are shown with thin arrows. RESULTS OF USING THE DAK SOFTWARE PACKAGE FOR PROCESSING THE DATA OBTAINED VIA THE MICROSEISMIC SOUNDING METHOD The results of processing the recordings made along the profiles crossing the volcanic pipe of the Nenoksa field in the Arkhangelsk diamondiferous province and the Beshtau laccolith in the Pyatigorsk volcanic center are presented as an example to illus trate the usage of the DAK software package for pro cessing the seismic recordings obtained via the microseismic sounding method. SEISMIC INSTRUMENTS

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PROCESSING THE DIGITAL MICROSEISM RECORDINGS Converting the field microseism recordings into the database


Waveform adjustment

Calculating the spectra based on individual recording segments

Station recordings banding

Data screening

Processing the spectra summation results

Data caching

Spectra summation Enveloping


Calculating the relative intensity relative to a reference point

a profile point

Calculating the relative intensity distribution

Subtracting the frequency average

Plotting the profiles in geoinformation systems

Fig. 4. Data processing structure of the DAK software package.

A number of areas with differing velocity properties (Fig. 5) are distinguished based on the results of anal ysis of the data obtained along the profiles crossing the volcanic pipe of the Nenoksa field. A highvelocity area extending up to 500 m at a depth ranging from 70 to 1800 m coincides in plan with the volcanic pipe studied based on the results of geophysical works and drilling (Danilov and Frantsuzova, 2011). We used the data caching function to calculate I i relative to the mobile station (Fig. 5b) and thus excluded the human factor. The complete equivalence of two diagrams proves that the algorithm of Ii calcula tion relative to the mobile station is applicable without any restrictions. The sole condition consists in the use of mobile stations with identical amplitude–fre quency characteristics. Is this condition is not satis fied, it is required to do a crosscheck between the mobile station recordings, and this feature is not implemented yet. The results of studying the eruptive objects of the Nenoksa field via the microseismic sounding method suggest the possibility of obtaining additional and inde pendent qualitative data on the location, structure, and other parameters of the pipes. This proves the possibility of using the DAK software package efficiently in geo physical research when exploring kimberlite bodies. The largest laccolith in the Pyatigorsk volcanic cen ter (the Beshtau mountain) was studied in 2011 (Zhost kov et al., 2012). These geophysical studies revealed the structure specifics of the fluid–magmatic system of the Pyatigorsk volcanic center and served as a supplement for the study conducted by (Masurenkov and Sobisevich, 2010) that revealed the connection between SEISMIC INSTRUMENTS

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H, m dB –500

11 10 9 8 7 6 5 4 3 2 1 0 –1 –2 –3 –4 –5 –6 –7

–1000 –1500 –2000 –2500 –3000 –3500 –4000 –4500 0

500 1000 0 500 1000 Distance along the profile, m

Fig. 5. Reults of processing the microseism recordings made along the profile crossing the volcanic pipe of the Nenoksa field with an algorithm relative to (a) the base sta tion and (b) the mobile station point.


POPOV et al. H, km 1.4 1.0

Profile of the Beshtau mountain


–1 –2

I, dB


–3 (a) –4 –5 –1 10 –15

–2 Altitude, km

Altitude, km



–3 –4

–20 –1 –10 –25



–3 –4

–30 0 5 10 Distance along the profile, km


5 Distance along the profile, km


Fig. 6. Velocity inhomogeneities distribution along the profile crossing the Beshtau laccolith constructed by processing the data (a, b) without the use of additional procedures and with the use of (c) averaging and (d) enveloping.

the hydrochemical properties of the Caucasian spas and the petrogeochemical specifics of the Pyatigorsk volca nic center. The fact that the Caucasian spas belonged to a hydrothermal element of this system was thus proved. The microseismic sounding method was used to determine the exact position of decompacted and soft rocks (Gorbatikov et al., 2008). A vertical geophysical profile with a length of about 10 km that goes from the southwest to the northeast through the Beshtau moun tain peak is constructed based on the results of processing the experimental recordings made by the SM3OS seis mometers at 34 points located every 300 m along the studied profile using the DAK software package (Popov et al., 2011). The algorithm for calculating Ii relative to the first profile point (located at 0 km) was used for processing. A profile stretching down to the depth of 30 km was con structed based on the processing results without using any additional data processing procedures (Fig. 6a). It can be seen that two large vertical areas with lowered Rayleigh wave propagation velocities that are indicative of the presence of decompacted rocks (most likely partially melted fluidsaturated rocks) are present in the studied profile. The existence of these areas agrees with the observed specifics of the distribution of equal tempera

ture gradient lines in the studied region (Masurenkov and Sobisevich, 2010) and explains their curved nature. The profile crossing the Beshtau mountain was processed both using the averaging (Fig. 6c) and enveloping (Fig. 6d) procedures and without the use of these procedures (Fig. 6b) in order to check the effi ciency and choose the optimal method of increasing the spectra informativeness (Danilov, 2011). The degree of influence of different procedures was esti mated at a depth of up to 5 km. The waveform adjust ment procedure was used to eliminate the zero level shift of the seismic receivers (Popov et al., 2011). All three methods single out an anomalous area at the 7th kilometer of the profile, but the use of averag ing helps filter out the noise and smooth the picture by removing small inhomogeneities and thus make large objects clearer (e.g., a cold highvelocity area at the 9th kilometer of the profile that is not seen in Fig. 6b). The enveloping procedure blurred the picture and resulted in the appearance of artifacts. Although it is not entirely certain that these results are erroneous (the areas at the 5th and 6th kilometer stood out clearly), other geophysical (e.g., geothermal) studies of this region prove that there is no lowvelocity area below the 4th kilometer of the profile. Therefore, it is not SEISMIC INSTRUMENTS

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always recommended to apply the spectra enveloping procedure when processing the data, while the averaging procedure helps single out the large anomalies. The obtained results reveal the presence of highly heated rocks at a depth of only about 1 km. This means that it will be economically advantageous to install thermal pumps in the studied region and organize clean and inexpensive energy production. Therefore, it is fair to say that the use of the DAK soft ware package makes it possible to determine (with a suf ficient degree of accuracy) the qualitative character of the deep structure of real geophysical objects with vertical inhomogeneities via the microseismic sounding method. CONCLUSIONS The DAK software package is a standalone multi functional instrument that allows one to process the data obtained via the microseismic sounding method. A laconic program interface and the minimization of the number of routine procedures help simplify the processing operations significantly. The availability of an alternative I i calculation algorithm and the spectra processing and waveform adjustment possibilities help improve the quality of the material being interpreted, and the DAK software package may also be easily expanded and modified due to its multilevel structure. REFERENCES Bakhvalov, N.S., Zhidkov, N.P., and Kobel’kov, G.M., Chislen nye metody (Numerical Methods), Moscow: Nauka, 2004. Danilov, K.B., Response of the microseismic background to industrial explosions in the Pokrovskoe mine, Desyataya Ural’skaya molodezhnaya nauchnaya shkola po geofizike (Tenth Ural Youth Scientific School on Geophysics), Perm, 2009, pp. 76–80. Danilov, K.B., Using the microseismic sounding method for studying the Lomonosov volcanic pipe (the Arkhangelsk diamondiferous province), Vestn. KRAUNTS, Nauki Zemle, 2011, no. 17, pp. 231–237. Danilov, K.B. and Frantsuzova, V.I., Isolating the volcanic pipe of the Nenoksa field in the Arkhangelsk diamondifer ous province with the use of background microseisms, Glu binnoe stroenie, geodinamika, teplovoe pole Zemli, interpre tatsiya geofizicheskikh polei: Shestye nauchnye chteniya pam yati Yu. P. Bulashevicha (Deep Structure, Geodynamics, Thermal Field of the Earth, and Interpretation of the Geo physical Fields: Sixth Scientific Readings in Memory of Yu. P. Bulashevich), Yekaterinburg, 2011, pp. 115–118. Frantsuzova, V.I. and Ivanova, E.V., On the source of the periodic modulation of the upper part of the Earth crust at the detection stations of the Arkhangelsk network, in Sovre mennye metody obrabotki i interpretatsii seismologicheskikh dannykh: Materialy Tret’ei mezhdunarodnoi seismolog icheskoi shkoly (Modern Methods for Processing and Inter preting the Seismological Data: Materials of the Third International Seismological Workshop), Obninsk, 2008, pp. 202–206. Frantsuzova, V.I., Gorbatikov, A.V., and Danilov, K.B., Structure of the upper part of the sedimentary cover on the profile of Arkhangelsk, Geodinamika. Glubinnoe stroenie. Teplovoe pole Zemli. Interpretatsiya geofizicheskikh polei: SEISMIC INSTRUMENTS

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Pyatye nauchnye chteniya pamyati Yu. P. Bulashevicha (Geodynamics. Deep Structure. Thermal Field of the Earth. Interpretation of the Geophysical Fields: Fifth Sci entific Readings in Memory of Yu. P. Bulashevich), Yekat erinburg, 2009, pp. 502–506. Gorbatikov, A.V., RF Patent 2271554, Byull. Izobret., 2006, no. 7. Gorbatikov, A.V., Stepanova, M.Yu., and Korablev, G.E., Microseismic field affected by local geological heterogene ities and microseismic sounding of the medium, Izv., Phys. Solid Earth, 2008, vol. 44, no. 7, pp. 577–592. Ivanova, E.V. and Frantsuzova, V.I., Studying the response of the upper part of the Earth crust to the influence of a peri odic modulation source at the detection stations of the Arkhangelsk seismic network, in Sovremennaya tektonofiz ika. Metody i rezul’taty: Materialy pervoi molodezhnoi shkolyseminara (Modern Tectonophysics. Methods and Results: Materials of the First Youth Workshop), Moscow: Inst. Phiz. Zemli, 2009, pp. 61–68. Masurenkov, Yu.P. and Sobisevich, A.L., The modern fluid magmatic system of the Pyatogorsk volcanic center, Dokl. Earth Sci., 2010, vol. 435, no. 2, pp. 1692–1697. Monakhov, F.I., Nizkochastotnyi seismicheskii shum Zemli (LowFrequency Seismic Background of the Earth), Mos cow: Nauka, 1977. Nikolaev, A.V., Geotomography issues, in Problemy geoto mografii (Geotomography Issues), Moscow: Nauka, 1997, pp. 4–38. Popov, D.V., Danilov, K.B., and Frantsuzova, V.I., The influence of natural factors on the formation of the microseismic oscillation field, in Ekologiya 2007: Materialy mezhdunarodnoi molodezhnoi konferentsii (Ecology 2007: Materials of the International Youth Conference), Arkhan gelsk: Inst. Ekolog. Problem Severa, 2007, pp. 76–77. Popov, D.V., Danilov, K.B., and Ivanova, E.V., Using an original DAK software package for processing digital microseism recordings, in Sovremennye metody obrabotki i interpretatsii seismologicheskikh dannykh: Materialy Shestoi mezhdunarodnoi seismologicheskoi shkoly (Modern Methods for Processing and Interpreting the Seismological Data: Materials of the Sixth International Seismological Work shop), Obninsk: Geophys. Service Ross. Akad. Nauk, 2011, pp. 263–266. Shakhova, E.V. and Antonovskaya, G.N., Results of a field express study of the seismic activity of disjunctive disloca tions in the Arkhangelsk oblast, Sovremennye problemy geofiziki: Pyataya Ural’skaya molodezhnaya nauchnaya shkola po geofizike (Modern Geophysics Problems: Fifth Ural Youth Scientific School on Geophysics), Yekaterin burg, 2004, pp. 175–178. Yudakhin, F.N. and Kapustyan, N.K., Mikroseismicheskie nablyudeniya (Microseismic Observations), Arkhangelsk: Inst. Ekolog. Problem Severa, 2004. Zhostkov, R.A., Masurenkov, Yu.P., Dudarov, Z.I., Shevchenko, A.V., Dolov, S.M., and Danilov, K.B., Study ing the deep structure of the Pyatigorsk volcanic center with the use of the microseismic sounding method, in Sessiya Nauchnogo soveta RAN po akustike i XXV sessiya Rossiiskogo akusticheskogo obshchestva: Sbornik trudov Nauchnoi kon ferentsii (Session of the Russian Academy of Sciences Research Council on Acoustics and XXV Session of the Russian Acoustic Society in Taganrog, 2012: Proceedings of the Scientific Conference), Moscow: GEOS, 2012, vol. 1, pp. 325–329. Translated by D. Safin