This paper focuses on major issues related to the data reliability and network performance of 20 broadband (BB) stations of the Czech (CZ) MOBNET (MOBile NETwork) seismic pool within the AlpArray seismic experiments. Currently used high-resolution seismological applications require high-quality data recorded for a sufficiently long time interval at seismological observatories and during the entire time of operation of the temporary stations. In this paper we present new hardware and software tools we have been developing during the last two decades while analysing data from several international passive experiments. The new tools help to assure the high-quality standard of broadband seismic data and eliminate potential errors before supplying data to seismological centres. Special attention is paid to crucial issues like the detection of sensor misorientation, timing problems, interchange of record components and/or their polarity reversal, sensor mass centring, or anomalous channel amplitudes due to, for example, imperfect gain. Thorough data quality control should represent an integral constituent of seismic data recording, preprocessing, and archiving, especially for data from temporary stations in passive seismic experiments. Large international seismic experiments require enormous efforts from scientists from different countries and institutions to gather hundreds of stations to be deployed in the field during a limited time period. In this paper, we demonstrate the beneficial effects of the procedures we have developed for acquiring a reliable large set of high-quality data from each group participating in field experiments. The presented tools can be applied manually or automatically on data from any seismic network.
Broadband stations of the MOBNET pool involved in the AlpArray
project:
Schematic map of major tectonic units of the Bohemian Massif and seismic stations of the MOBNET pool involved in the network of the AlpArray-EASI complementary project (blue circles) and the AlpArray Seismic Network (AASN, green circles) of the AlpArray project. CRSN – Czech Regional Seismic Network (Institute of Geophysics, 1973); WEBNET – West Bohemia Seismic Network (Institute of Geophysics, 1991).
The long-term experience of some of the authors with passive-experiment data processing encourages the team to summarize the tools we have been using for testing and improving seismic data quality and which might be of interest to a broader community. The necessity of data quality control is evident nowadays and several procedures are applied automatically in data centres, e.g. MUSTANG software in IRIS, which identify data errors in the centre databases. Our endeavour is to identify data errors and correct them when possible, before supplying data to the centres. Data quality control before experimental data archiving is of great importance.
Data from passive seismic experiments of different lateral extent and a dense
station distribution became a crucial source of information for the modern
Earth interior researches. USArray (
Example of installation of one of the broadband MOBNET stations in the AlpArray-EASI complementary experiment. In addition to the photo-documentation of the installation of the AAE08 station (Manětín), we show its location (upper right), station coordinates and period of recordings (top panel), and we describe the installation of accessories (GPS antenna) and the bedrock (left panel in the middle). The central panel shows the probabilistic power spectral density of ambient noise in the vertical component during the first 5 months of the station registration.
Example of installation of one of the broadband MOBNET stations in the AlpArray Seismic Network (AASN). In addition to the photo-documentation of the installation of the A076A station (Maková Hora), we show its location (upper right), station coordinates, and start time of recordings (top panel), and we describe the installation including accessories (GPS antenna) and the bedrock (left panel in the middle). The central panel shows the probabilistic power spectral density of ambient noise during the first 4 months of the station registration.
Probabilistic power spectral density (PPSD) on the HHE horizontal component of the AAE01 station of the AlpArray-EASI experiment. Branching of the PPSD reflects both diurnal and seasonal variations of noise, and this is clearly visible in both the high-frequency and in the low-frequency bands, respectively. Noise level during the summer period is low and far below the required noise level (20 dB below the New High Noise Model, NHNM, upper grey curve; Peterson, 1993) even for periods larger than 10 s, whereas during winter time noise exceeds the limit of 10 dB below NHNM for periods larger than 30 s.
To achieve the objectives of the project, it is necessary to apply various
geological–geophysical imaging methods on data recorded by a homogeneous
network of broadband seismic stations in the greater Alpine area (Fig. 1).
Although the area is in some parts densely covered by permanent seismic
observatories, their distribution is far from homogeneous. Therefore, the
distribution of
Data completeness of the MOBNET stations in the AlpArray projects:
The AlpArray area, set as a region delimited by a 250 km distance from the
800 m altitude isoline surrounding the Alps, covers a large portion of the
Czech (CZ) part of the Bohemian Massif. Ten BB observatories of the Czech
Regional Seismological Network (CRSN), one permanent BB station of the West
Bohemia Seismic Network (WEBNET) along with 20 temporary BB stations from the
pool of seismic stations MOBNET (MOBile NETwork) of the Institute of
Geophysics of the Czech Academy of Sciences (IG CAS), cover the area with the
spacing required (Fig. 1). Apart of the AASN, which is the backbone of the
AlpArray project, the MOBNET stations were involved in the AlpArray Eastern
Alpine Seismic Investigation (EASI) complementary project (coded XT in the
European Integrated Data Archive, EIDA; AlpArray Seismic Network, 2014). The additional 10 stations have been
operational since June 2017 in another complementary project, AlpArray IVREA
(coded XK in the European Integrated Data Archive). The Czech team is
responsible for the deployment and maintenance of the MOBNET stations
included in the Czech part of the AASN (coded Z3 in the European Integrated
Data Archive system,
The main purpose of this paper is to describe technical parameters of the MOBNET stations, to present newly developed control units for setting sensor and data acquisition systems (DAS), and to document the significance of careful data quality control, which could help other groups in addition to those involved in the AlpArray project in preparing their seismic data for archiving. Special attention is paid to the detection of sensor misorientation, timing problems, interchange of components and/or their polarity reversal, mass centring problems, or anomalous channel amplitudes due to an imperfect gain. Maintaining the high quality of archived seismological data is crucial for the success of the AlpArray project, as well as of any passive seismic experiment.
List of Czech temporary stations from the MOBNET pool involved in the AlpArray-EASI complementary experiment (network code XT) and the AlpArray Seismic Network (AASN, network code Z3).
The first integration of the 20 MOBNET stations into the AlpArray project had
been realized during the Eastern Alpine Seismic Investigation project, which
was the first implemented AlpArray complementary experiment (Table 1; see
Fig. 1). The AlpArray-EASI transect was composed of 55 broadband seismic
stations. They operated from July 2014 to October 2015 and were configured in
a zigzag pattern on either side of the central longitude line of
13.35
The northernmost stations AAE01–AAE20 (Fig. 2) of the MOBNET pool involved in the AlpArray-EASI network were equipped mostly with the Streckeisen STS-2 seismometers, with two CMG-3T and three CMG-3ESP seismometers, and with the GAIA DAS. The stations were installed preferably in vaults of castles/chateaus, churches, or suitable abandoned buildings. Figure 3 shows an example of a station location, seismometer installation, the quality of the site, noise level, etc. Supplement Figs. S1–S19 give the same detailed information for the remaining 19 MOBNET stations employed in the AlpArray-EASI network. Following the notation of Molinari et al. (2016), we can characterize the locations as an urban free-field site and only exceptionally as a building site (Table 1). The stations ran at the autonomous regime and reported their state of health (SOH) daily via the SMS messages. Altogether, we recorded 280 GB of data stored in the miniSEED data format which contributed to the AlpArray-EASI studies.
After the end of the AlpArray-EASI field measurements in August 2015, 20
MOBNET stations were reinstalled in the Bohemian Massif as a part of the
newly designed AASN (see Fig. 2). With the exception of A090A, the stations
operate offline. The data from the offline stations are recorded on flash
cards with a capacity exceeding at least 4 times the space needed for data
sampled at a rate of 100 samples per second and collected at
3-month intervals to be checked and supplied to the ORFEUS Data Center (ODC)
EIDA node. Similarly to the AlpArray-EASI transect, most of the AASN CZ sites
are classified as urban free-field types (Table 1). Figure 4 shows the
installation of one of the MOBNET stations and Figs. S20–S38 give detailed
information for the remaining 19 MOBNET stations employed in the AASN of the
AlpArray project. Although the region of the BM is densely populated with
local industrial and agricultural sources of high-frequency noise, most of
the stations meet the requested noise limits (Peterson, 1993) as it is shown
in Fig. 4 for station A076A (see also Figs. S20–S38 and 8). Noise exceeds
the limit on vertical components at a long-period range
(
Data from the Czech temporary stations, with the access restricted according
to the AlpArray rules, are transferred to ODC
(
Our broadband temporary stations involved in the AlpArray project are
equipped mostly with broadband seismometers STS-2, several Guralp CMG sensors
(Table 1), and with GAIA data acquisition systems developed by the Vistec
company (
The Guralp host box developed in our laboratory (Fig. 7a) becomes an integral constituent of our CMG-3T, CMG-3ESP, and CMG-3ESPC seismometers. It connects the seismometer and the GAIA DAS and it is an analogy of the standard handheld unit produced by the Guralp company, or the host box of the STS-2 seismometer. The standard Guralp host box allows fundamental handlings of the seismometer, which are the lock–unlock function of pendulums, their centring, and a calibration with the use of an external signal. On top of the fundamental handlings, the Guralp host box developed in our laboratory enables the application of the built-in source of the calibration signals (Dirac and rectangular pulse functions). The busy LED light indicates the status of the seismometer. The host box is equipped with a connector for the Guralp control and calibration unit (see Sect. 3.2) or for a remote seismometer control (e.g. via GSM).
This device (Fig. 7b) enables the display of the positions of the pendulums and the calibration of the seismometer by the unit rectangular pulse signal or the Dirac delta pulse. It also has an input for an external calibrating signal of an arbitrary shape. The polarity of the calibrating signal can be changed and the signal size can be altered in two levels. There is a rotary switch between the calibration mode and the display mode of pendulum positions of the Z, NS, and EW components. A push button centres the pendulums. The Guralp control and calibration unit is plugged into the Guralp host box connector.
The Guralp centring unit (Fig. 7c) was developed for seismometer pendulums without electronic centring, e.g. CMG-40T. The unit displays pendulum positions of individual components and thus enables their manual centring. For the pendulum position checking, it is necessary to disconnect the seismometer from the DAS and to connect the Guralp centring unit. The deviation of the pendulum from the central position is proportional to the mass position voltage. The position of the pendulums of the Z, NS, and EW components is selected by a switch. Pendulum centring requires the mass position voltage close to zero. The unit has a built-in accumulator, which supplies energy to the seismometer during the control. The accumulator voltage is measured in the fourth position of the switch. In the case of insufficient accumulator capacity, the accumulator can be plugged in via an external charger. The Guralp centring unit, developed for seismometers with only a manual pendulum centring, can also be used for the pendulum position check of the seismometers with electronic control, but in such cases, the centring unit does not enable the correction of the pendulum position.
The STS-2 control and calibration unit (Fig. 7d) has been developed for centring pendulums and for calibrating the Streckeisen STS-2 seismometers. The device is connected to the host box provided by the seismometer producer. The host box forms the integral part of the system, through which the STS-2 seismometers are controlled and powered. The host box has two connectors. The first one is used to connect the digitizer; the second one (marked as “monitor”) serves to remotely control and monitor the seismometer via the STS-2 control and calibration unit. This unit displays the positions of the pendulums for the U, V, and W components, or it can be switched to show offsets of the standard Z, NS, and EW components of the output signal. The unit is equipped with a button to automatically centre the pendulum position (auto-zero push button) connected in parallel to a similar button in the host box. The 120 s/1 s switch of the control and calibration unit changes modes between the broadband and short-period regimes.
Each of the U, V, and W components can be calibrated separately with the unit rectangular signal or the Dirac delta pulse. There is also a switch for an external calibrating signal of an arbitrary shape, e.g. of a sinusoidal signal. If the components are calibrated together, the calibration currents and their polarities are chosen so that the output signals (components Z, NS, and EW) have the same amplitudes and polarities. This procedure guarantees the correct functioning of the seismometer.
Control and calibration units developed for the broadband seismometers and the GAIA DAS to guarantee the high quality of recorded data.
Medians of the probabilistic power spectral density (PPSD) of
seismic noise at the MOBNET stations involved
The waveform similarity method showing the interchange of the EW and
Z components
The GAIA gain and calibration unit (Fig. 7e) checks and calibrates inputs
into the GAIA DAS, but it can be used for the calibration of any type of
digitizer as well (Kinemetrics, Nanometrics, Ref Tek, Guralp, etc.) after
being equipped with the corresponding connector reductions. The unit enables
the calibration of analogue inputs to check the correct order of the
channels, to determine intensity of cross talks between the channels, and to
measure channel amplification and sensitivity (a voltage corresponding to the
LSB – least significant bit). The number of channels undergoing calibration and
channel polarity can be changed. The calibration is done by a defined voltage
jump. For the calibration of the analog inputs, we can use a differential or
a single-ended mode. In the differential mode, the voltage is connected
between inputs marked as
The high level of data quality has to be stable during a long time interval for seismological observatories and for the entire time of operation of the temporary stations within the passive experiments. Data quality control represents the necessary steps to achieve and maintain the high quality level of recorded data. We differentiate between (1) in situ controls with technical equipment, applied during station installation and servicing, and (2) subsequent software controls, applied to downloaded data.
The measure of seismic ambient noise level is nowadays a standard procedure when searching and selecting sites that are suitable for station installation. Therefore, we measured noise at each site before a station installation for a short time. Once a station is installed, the noise level has to be frequently checked to monitor potential changes in conditions of the recordings or to detect technical problems at the station. According to the AlpArray working group requirements, the average noise level should be 20 dB lower than the New High Noise Model (NHNM; Peterson, 1993) on all components within the 1–10 Hz frequency range. In the long-period range (30–200 s), the same noise level is required only for the vertical component. Because ambient noise is usually higher in the horizontal components, the average noise level is recommended to be only 10 dB less than the NHNM. To follow the ambient noise level, we use the seismic probabilistic power spectral density procedure (PPSD) by McNamara and Buland (2004) and Custodio et al. (2014), which is a part of the ObsPy module (Krischer et al., 2015).
Figure 8 shows the PPSD medians for all MOBNET stations deployed in the
AlpArray-EASI and AASN networks. While the noise level for periods below 1 s
fulfils the noise requirements for most of the stations and for the three
components, noise in the horizontal components for periods longer than 10 s
is often higher, especially in winter, but still acceptable for temporary
deployments. One has to bear in mind that a compromise between optimal site
conditions and the required array geometry has to be accomplished. Thus, at
the short-period range, we have to accept higher noise level at some sites,
where human activity is higher (e.g. AAE03 located in an
administrative building in a village). Microseisms dominate a period interval
of 1–10 s in central Europe and also increase in winter. The broadband
seismometers are sensitive to several external effects, especially in the
range of longer periods. The most significant of these are the temperature
changes, either diurnal or seasonal, and pressure fluctuations. An enhanced
insulation of seismometers might decrease the effects, particularly on the
horizontal components. Therefore, seismometer insulation plays an important
role in ensuring the high-quality data. The majority of our stations are
installed in vaults with only small temperature variations, which could
directly influence the seismometer pendulums. On the other hand, there are
also indirect effects of temperature, particularly an inclination of bedrocks
or buildings. Most of our stations are equipped with the STS2 seismometers
with three pendulums in 120
The Rayleigh-wave polarization-angle method showing the differences between
the Rayleigh-wave polarizations and theoretical back azimuths (BAZ) depending on the BAZ of earthquakes;
The exact orientation of seismometers in the geographic co-ordinate system is one
of the most important tasks during station installations. Misoriented sensors
negatively affect the results of the procedures based on modern three-component
seismological observations and can lead to false interpretations (Ekström
and Busby, 2008; Vecsey et al., 2014; Wang et al., 2016). The determination of
the northward direction has been routinely performed for years with the use of
standard compass, with the best accuracy being
Sensor orientation during the AlpArray-EASI complementary experiment measured by gyrocompass. The largest differences are in bold.
First measurements: checking original towards north orientation of seismometers determined during station installation with the use of a standard compass. Second measurements: towards the north reorientation of sensors with the gyrocompass. Third measurements: check of sensor orientation at the end of registration. Difference: difference between the third and second measurements.
To determine the correct sensor orientations, one can use the Rayleigh-wave
polarization-angle method (e.g. Stachnik et al., 2012), in which differences
between the Rayleigh-wave polarizations and their theoretical back azimuths
are plotted depending on event origin times. Of course, this method
cannot be as precise as measurements with the optical gyrocompass. Rueda and
Mezcua (2015) found only 1–5
When installing our stations for the AlpArray-EASI transect, we oriented the
seismometers carefully, but only with the use of a standard compass. Later we
checked the orientation of all sensors with an optical gyrocompass. We have
found deviations larger than 5
Seismometer gain check by the ambient noise gain method. Gain imperfection can be identified from monthly averages of normalized power spectral ratios. Both panels show that either gain of the EW component or gains of both the NS and Z components are not correct. To decide which amplitudes are correct and which are not, one has to check the gain of each component of the sensor–DAS pair with the calibration units, as described in Sect. 3.
The sensors in all our stations involved in the currently running AASN network
(A071-A090) have been installed with the use of our gyrocompass and their
orientation is regularly checked. During about a 1-year period of the array
operation, we recorded three unwanted changes in sensor
orientation due to human intervention. In addition to the necessary sensor
reorientation on the spot, previous inaccuracies in sensor orientations have been
corrected in the metadata. In the case where the deviation in the seismometer
orientation is larger than 5
Correct timing is crucial for studies based on exact arrival times of seismic waves. Incorrect time decreases the accuracy of picking arrival times of individual phases and causes a false phase identification or a complete loss of data. Here we address three important timing problems: the leap second recorded with a delay, switch between the UTC and GPS times, and malfunction of an oscillator tuning the station time or loss of time synchronization for a time period.
The first item – the leap second – is introduced into the Coordinated Universal Time (UTC) usually once or twice per year in order to keep the UTC time close to the mean solar time. The leap second is usually applied at midnight while clocks in data acquisition systems are being synchronized later, for example, with a 30–90 min delay. Moreover, the leap-second correction is applied at individual stations differently, because the times of their synchronizations differ. It is thus necessary to apply the leap second exactly at midnight (00:00) for all temporary stations before data archiving. Surprisingly, we have found a case when even a permanent observatory, out of Bohemian Massif, kept the uncorrected time for about 1 month.
The second item – the switch between the UTC and GPS times – can arise due to the wrong synchronization of the inner time (UTC) of a station and the GPS time. This can happen when the coordinated universal time in the “almanac”, transmitted by satellites, disappears from the memory of a station due to a number of reasons (e.g. low voltage of inner battery, incorrect satellite signal recorded). Existing time gaps and overlaps in miniSEED data can be calculated from the time of the first sample, number of samples, and sampling rate in each miniSEED block. Then the appropriate time shift is applied in miniSEED data for the identified time interval. Currently, the UTC and GPS times differ by 18 s. Such time shift can last for several hours or a full day and thus needs to be corrected.
Timing errors of 1 s or smaller are not clearly evident during routine seismological analyses but can be revealed from station “log” files, if provided by the registration system. Small time shifts can occur as a result of improper time synchronization due to the loss of the GPS signal or due to the failure of the oscillator tuning the station time. This third item is a more complicated issue and it allows only an approximate time reconstruction. A failure of the oscillator tuning can cause a jump or a linear increase in timing errors in data. However, such difficulties should occur only exceptionally. If they happen and we are able to identify such problems and reconstruct the real timing, it is necessary to correct times directly in the miniSEED data, which is a more complex task than applying corrections in the metadata. The same concerns a reconstruction of the correct time after a loss of the time synchronization. When checking our data, we have found an oscillator failure at station A087A, which resulted in a final time error of 0.18 s during 8 days in October 2015.
Keeping exact time in seismic data is a delicate issue. However, severe errors due to asynchronous application of the leap seconds or due to switches between the UTC and GPS times can be identified and corrected automatically in any data set, including the entire AlpArray data set. Small time changes must be solved individually.
Sometimes results from different studies dealing with waveforms raise a suspicion that the components of seismograms are interchanged and/or the polarities reversed. Although the case is rare, we found it several times in different data sets, including data from permanent observatories. Surprisingly, the component interchange can occur during station operation, e.g. twice in the AlpArray stations until now. The simplest way to verify the correct identification of the three components is a comparison of waveforms for a selected strong teleseismic event recorded on several nearby stations, which we call the waveform similarity method (Fig. 9a). Several other methods can be used as well, e.g. a visualization of daily means of signal amplitudes, sometimes called offsets (Fig. 9b), or a comparison of noise levels in the vertical and horizontal components in PPSD. In the case of correct component identification, the noise level in the vertical component should be lower than that in the horizontal components. Correction of interchanged components can be done either in the metadata or preferably directly in the miniSEED data.
Error in sensor mass centring. Flat curves in the probabilistic
power spectral density (PPSD;
Reversed polarity of components, arising from different technical reasons, is
not as rare as one would expect. We identified polarity reversals using the
manual waveform similarity method for nearby stations. We can also use a
single-station method that is based on a semi-automatic search of
Rayleigh-wave polarization (the Rayleigh-wave polarization-angle method)
originally developed for verification of sensor orientation. Differences
between the Rayleigh-wave polarization and the theoretical back azimuths are
plotted against the theoretical back azimuths (Fig. 10). If only one
horizontal component is reversed, the differences change linearly between
Anomalous signal amplitudes due to imperfectly set gains on one or more components are not very frequent in comparison with the sensor misorientations, but the danger of imperfect gains is similarly large for data analysis procedures. We can recognize anomalously large or low recorded amplitudes in two ways: first, by means of technical devices, such as control and calibration units (see Sect. 3), and, second, by means of software methods applied on recorded seismic signals.
One possible software inspection of the amplitude size can be based on ambient noise, which is evaluated regardless. Moreover, ambient noise is the only continuous signal in seismic data. We have implemented a new ambient noise gain method which compares ratios of normalized power spectra between the three components in a range of 4–8 s. In this range, the secondary microseisms are substantially larger than noise from local sources. The directionality of the microseisms due to different sources is eliminated by normalizing the spectrum of each trace via an average spectrum calculated over the traces of surrounding stations. The spectra are calculated within different time intervals, e.g. weeks, months, or a complete time range. The resulting ratios of the spectra provide a running record of individual channel sensitivity and allow us to follow potential changes in the amplitudes in a course of time. In combination with sporadic in situ gain controls by the Gain and calibration box (Sect. 3.5), we have reliable control of the potential anomalous size of recorded amplitudes and thus we can determine when a detected change in the gain occurred. We estimate the precision of the gain determination by the ambient noise gain method at 1–2 dB depending on the length of the time period analysed. Tiny variations of the curves in Fig. 11 are within this limit, but the differences between the curves are stable.
We document a successful use of hardware and software methods on data
from the two seismic experiments. During the data processing, we have found
that the power spectra of the EW components at stations AAE14 (AlpArray EASI)
and A087A (AASN) are lower by approximately 11 dB (Fig. 11a). The NS / Z
component ratio is close to zero, while the EW / Z and EW / NS ratios,
where the EW component is involved, are 10 dB lower. Station documentations
identified that stations AAE14 and A087A were equipped with an identical
sensor and data acquisition system. Therefore, afterwards we tested the gain
of each component of the sensor–DAS pair with the calibration boxes as
described in Sect. 3. The test confirmed the amplitudes recorded in the EW
component were 3.6 times smaller (20
Optimal workflow of temporary station control and data quality checks to assure the archiving of the high-quality data. The hardware and software procedures are shown with rectangular and rounded boxes, respectively.
One of artefacts seen in the PPSD reflects a failure of the automatic mass
recentring of the sensor (McNamara and Buland, 2004). If a seismometer is not
able to correct a drift of the mass position itself, the amplitudes of seismic
signals become saturated. The signal corresponding to such a time period has a
characteristic “flat” spectrum shape (Fig. 12a). The flat course in an
interval of
To summarize the application of different methods of seismometer–GAIA DAS pair operation and recorded data quality checking, either by software or hardware tools presented above, we plot an optimal workflow in Fig. 13. The scheme comes from our experience with data from several previous passive experiments. Some methods give indications about an error, which requires further verification. Some of the methods are repeated in time in attempts to detect changes which can occur during station operation and thus could not be revealed by the Huddle pre-installation test.
We have developed both the hardware and software tools to contribute reliable high-quality waveform data to passive seismic experiments. At present, 20 broadband stations of the Czech MOBNET pool of temporary stations are incorporated in the AlpArray Seismic Network. The stations were also deployed in the previous AlpArray-EASI complementary experiment. To assure a high-degree of reliability of the STS-2/CMG-seismometer–DAS pairs' performance, we have developed four special control devices for seismometers of different types and one for the GAIA DAS. The devices calibrate both the sensors and data acquisition systems in situ and allow us to check the gain and the polarity of all three components. We emphasise the importance of precise sensor orientation by using a gyrocompass both during station installations and during its regular checks during the field measurements. The information extracted from probabilistic power spectral density, spectra ratios, and averages of daily amplitudes and other parameters, followed by the designed procedures in routine data processing, allow us to identify several other problems, e.g. imperfectly set gains, interchange of components and polarity reversals, insufficient sensor mass centring, and, last but not the least, time issues. The hardware control in situ and the ex-post software data checking represent the double check of data quality. The former removes problems immediately in field, and the latter allows restoring data back in time, until the moment when a problem occurred. The fully automated software methods could be used for the entire AlpArray data set. We believe that the newly developed control and calibration units for setting sensor–DAS systems and the documentation of the significance of careful data quality control with the use of the software tools could be helpful for other groups participating in collaborative passive seismic experiments.
Data from the MOBNET pool as a part of the AlpArray project is stored in
EIDA (
The complete member list of the AlpArray working group can be found
at
The authors declare that they have no conflict of interest.
Cooperation with participants of the AlpArray projects is greatly appreciated as well as the suggestions and recommendations of both anonymous reviewers, which substantially improved the manuscript. Research of the Czech team was supported by grant no. M100121201 of the Czech Academy of Sciences and by the project CzechGeo/EPOS-Sci (CZ.02.1.01/0.0/0.0/16_013/0001800, OP RDE) financed from the Operational Programme Research, Development and Education. Data acquisition from permanent observatories and the development of the devices was supported by the large research infrastructure project CzechGeo/EPOS, grants nos. LM2010008 and LM2015079. The development of the software for data quality check was supported by grant no. COST LD15029 of the Ministry of Education, Youth and Sports. Several figures and calculations have been prepared with the use of the Generic Mapping Tools (Wessel and Smith, 1998) and ObsPy (Krischer et al., 2015). Edited by: Luis Vazquez Reviewed by: three anonymous referees