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  • GFZ Data Services  (8)
  • Springer  (4)
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  • 1
    Publication Date: 2016-04-21
    Description: We analyze the ground motion time histories due to the local seismicity near the Itoiz reservoir to estimate the near-source, surface 3D displacement gradients and dynamic deformations. The seismic data were obtained by a semipermanent broadband and accelerometric network located on surface and at underground sites. The dynamic deformation field was calculated by two different methodologies: first, by the seismo-geodetic method using the data from a three-station microarray located close to the dam, and second, by single station estimates of the displacement gradients. The dynamic deformations obtained from both methods were compared and analyzed in the context of the local free-field effects. The shallow 1D velocity structure was estimated from the seismic data by modeling the body wave travel times. Time histories obtained from both methods result quite similar in the time window of body wave arrivals. The strain misfits between methods vary from 1.4 to 35.0 % and rotational misfits vary from 2.5 to 36.0 %. Amplitudes of displacement gradients vary in the range of 10−8 to 10−7 strains. From these results, a new scaling analysis by numerical modeling is proposed in order to estimate the peak dynamic deformations for different magnitudes, up to the expected maximum Mw in the region (M5.5). Peak dynamic deformations due to local Mw5.5 earthquakes would reach amplitudes of 10−5 strain and 10−3 radians at the Itoiz dam. The single station method shows to be an adequate option for the analysis of local seismicity, where few three-component stations are available. The results obtained here could help to extend the applicability of these methodologies to other sites of engineering interest. © 2016, Springer Science+Business Media Dordrecht.
    Print ISSN: 1383-4649
    Electronic ISSN: 1573-157X
    Topics: Geosciences , Physics
    Published by Springer
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  • 2
    Publication Date: 2018-11-21
    Print ISSN: 0033-4553
    Electronic ISSN: 1420-9136
    Topics: Geosciences , Physics
    Published by Springer
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  • 3
    Publication Date: 2018-09-03
    Print ISSN: 0033-4553
    Electronic ISSN: 1420-9136
    Topics: Geosciences , Physics
    Published by Springer
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  • 4
    Publication Date: 2018-11-27
    Print ISSN: 0033-4553
    Electronic ISSN: 1420-9136
    Topics: Geosciences , Physics
    Published by Springer
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  • 5
    Publication Date: 2022-05-02
    Description: Abstract
    Description: The dataset presented here is an earthquake catalog for the central Sea of Marmara (Turkey) obtained by applying a traditional STA/LTA technique to the continuous waveforms. The magnitude of completeness of this catalog is MW = 1.4. The full description of the data processing and creation of the catalog is provided in the paper “Near - fault monitoring reveals combined seismic and slow activation of a fault branch within the Istanbul-Marmara seismic gap in NW Turkey” published by Martínez-Garzón et al., in Seismological Research Letters. The data are provided as the following two ASCII tables: The file 2021-004_Martinez-Garcon-et-al_Initial_seismicity_catalog contains the seismic events for which we could successfully calculate an earthquake location. The ASCII table has the following columns: columns: id, year, month, day, hour, minute, second, serial time, latitude, longitude, depth [km], magnitude, horizontal error [km], vertical error [km], RMS, maximum azimuthal gap [degree]. The table 2021-004_Martinez-Garcon-et-al_Relocated_seismicity_catalog contains the seismic events for which we could refine the initial location and obtain a double-difference refined location. The ASCII table has the following columns: id, latitude, longitude, depth [km], horizontal error [km], vertical error [km].
    Keywords: Earthquake catalog ; Marmara region ; near-fault monitoring ; SMARTnet ; GONAF ; EARTH SCIENCE 〉 SOLID EARTH 〉 GEOMORPHIC LANDFORMS/PROCESSES 〉 TECTONIC LANDFORMS 〉 FAULTS ; EARTH SCIENCE 〉 SOLID EARTH 〉 TECTONICS 〉 EARTHQUAKES ; EARTH SCIENCE 〉 SOLID EARTH 〉 TECTONICS 〉 EARTHQUAKES 〉 EARTHQUAKE MAGNITUDE/INTENSITY
    Type: Dataset , Dataset
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  • 6
    Publication Date: 2022-08-22
    Description: Abstract
    Description: This data publication contains seismic catalog developed by the analysis of seismicity recorded during hydraulic stimulation campaign performed in May 2020 in the 5.8-km deep OTN-2 well near Helsinki, Finland as part of the St1 Deep Heat project (Kwiatek et al., 2022). The original seismic data to develop the seismic catalog were acquired with the high-resolution seismic network composed of 22 geophones surrounding the project site. The centerpiece of the network was a 10-level borehole array of Geospace OMNI-2400 geophones (3C/15 Hz) sampled at 2 kHz placed in the OTN-3 well adjacent to the OTN-2 injection well, and located at 1.93 - 2.55 km depth, approx. 3km from injection intervals. Additional 12 stations at distances 〈10 km from project site formed the satellite network that was equipped with short-period 3C 4.5 Hz Sunfull PSH geophones, completing the seismic network. Near-real-time processing of induced seismicity data started on Jan 26, 2020, i.e. about 3 months prior to the onset of the injection, covering entire period of the stimulation campaign in May 2020. The monitoring stopped end of June 2020, about one month after the stimulation finished. The monitoring campaign resulted in initial industrial seismicity catalog containing 6,243 events that was refined and further extended (cf. Kwiatek et al., 2022). The final catalog associated with this data publication contains 6,318 earthquakes, including 197, 5427 and 694 events recorded before, during, and after stimulation campaign. The core catalog data contains origin time, local magnitude, (re)location and focal mechanism data.
    Description: Methods
    Description: The initial induced seismicity catalog provided by the industrial operator contained 6,243 detections including earthquakes, electronic noises and signals originating from or near the surface. A coincidence trigger was first run on the database of P-wave arrivals not associated with any earthquake (see details of the procedure in Kwiatek et al., 2019; Leonhardt et al., 2021a,b). This enhanced the initial catalog by 3,720 newly detected events resulting in a total number of 9,963 detected events. Automated inspection of observed hodographs was then performed, comparing the observed patterns of P- and S-wave arrivals on sensors forming the OTN-3 array with those predicted for earthquakes originating from injection site. This allowed to confirm that 6,318 detections out of the 9,963 are seismic events originating from the stimulated crustal volume, and these events constitute the catalog included in the data publication. The remaining 3,645 events, manually identified as electronic noises of various types or surface blasts were excluded from the catalog. The vast majority of the 6,318 confirmed induced seismic events were visible only on seismograms from sensors forming the OTN-3 array, but not on the satellite sensors located within Helsinki municipal area. This means that the accurate hypocentral locations could not be achieved for most of events without additional P- and S- phase arrivals from sensors forming the satellite network. In fact, only the 72 largest events could be reliably located using additional P- and S-wave onsets available from the satellite network. These 72 earthquakes were further relocated using the Double-Difference method (Waldhauser and Ellsworth, 2000). For the location and relocation, the 1D P-wave velocity model based on a vertical seismic profiling campaign (Leonhardt et al., 2021a,b) assuming a V_P⁄V_S ratio of 1.71 for inverting S-wave arrival times was used. The relocation limited the final hypocentral location catalog to 45 events. The achieved relative location precision (95% confidence interval) for these events in horizontal and vertical direction was not exceeding ±85 m and ±42 m, respectively. Although limited number of hypocenter locations could be estimated, the local “Helsinki” magnitude was calculated for all 6,318 events. This was possible because S-P times could be easily extracted from downhole recordings of OTN-3 array. To calculate the local magnitude, we followed the procedure presented by Uski and Tuppurainen (1996) and Uski et al. (2015), which was applied in our previous studies as well (Kwiatek et al., 2019; Leonhardt et al., 2021a,b). Finally, 8 double-couple constrained focal mechanism were calculated using the hybridMT moment tensor inversion package (Kwiatek et al., 2016) and time integrals of the first P-wave ground displacement pulses including sign information (e.g. Amemoutou et al., 2021).
    Keywords: induced seismicity ; hydraulic fracturing ; triggering ; earthquake interaction ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 ECONOMIC RESOURCES 〉 ENERGY PRODUCTION/USE 〉 GEOTHERMAL ENERGY PRODUCTION/USE ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 HUMAN SETTLEMENTS 〉 URBAN AREAS ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 EARTHQUAKES ; EARTH SCIENCE SERVICES 〉 DATA ANALYSIS AND VISUALIZATION 〉 STATISTICAL APPLICATIONS ; EARTH SCIENCE SERVICES 〉 HAZARDS MANAGEMENT 〉 HAZARDS MITIGATION
    Type: Dataset , Dataset
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  • 7
    Publication Date: 2023-01-10
    Description: Abstract
    Description: The three datasets presented here are high-resolution catalogs containing origin time of seismic events for the same region and time range that have derived using AI-based techniques and a matched filter search. The corresponding standard catalogs from the agencies AFAD and KOERI are available under https://tdvms.afad.gov.tr/ (last accessed 28/07/2022) and http://www.koeri.boun.edu.tr/sismo/2/earthquake-catalog/ (last accessed 28/07/2022), respectively, when searching in the bulletin for longitude 28.80-29.10, latitude 40.4-40.625, and from November 1st 2018 to January 31th, 2019. Specifications for the three catalogs are. (i) Catalog derived utilizing AI-based techniques. We applied the PhaseNet deep learning method (Zhu & Beroza, 2019) to detect and pick the P-and S- waves of seismic events embedded in continuous seismic recordings from 16 stations surrounding the region of interest resampled at 100 Hz. The method was trained on a dataset from Northern California, but has been shown to generalize well to other tectonic settings. The picks were associated into seismic events using the GaMMA association method (Zhu et al., 2022). Manual check of the waveforms from all detections led to 516 seismic events with clear waveforms retained for further processing. (ii) Template matching catalog A. We applied the matched filter algorithm EQcorrscan (Chamberlain et al., 2017) to the two nearby seismic stations with the largest data recovery during the period of interest, ARMT and MDNY. We utilized 14 manually picked template events with M 〉 2 that occurred in the region of interest during the analyzed time period, which were recorded in both stations. As a first criteria to remove false detections, we retained only detections exhibiting a Median Absolute Deviation (MAD) larger than eight. We required detections from different templates to be at least 1.5 seconds apart. To remove duplicate detections (e.g., detections of the same event by different templates), we retained the detections with the highest average correlation if multiple detections occurred within 2.5 seconds. As a second criteria, we calculated cross-correlation derived phase-picks. A pick was declared if the maximum normalized correlation between the signal of the template event and of the detection exceeds 0.7. We correlated the signals in a short window of ±0.3 seconds around the assumed pick time based on a time-shifted version of the template phase-pick. We retained the S-pick exhibiting the higher cross-correlation value with respect to the template. Following this step, we considered only detections with ≥ 2 picks. In case of events with only two picks we ensured that that were from the same station to have control on the ts-tp and therefore the distance of the event from the detecting station. This catalog contains 2,462 seismic events (all manually reviewed) with magnitudes MW in the range [-2.4, 4.5]. Since we were not able to locate the events from this catalog, we considered as “origin time” the time of the first arrival. (iii) Template matching catalog B. We derived a second template matching catalog utilizing twelve of the closest seismic stations displaying high seismic data recovery during the analyzed time period. An initial list of detections was generated following the same steps as for the Template Matching Catalog A, with the additional requirement that all detections must contain at least one picks from one of the two closest stations, ARMT and MDNY. All detections from this catalog were also manually reviewed. The full description of the data processing and creation of the catalog is provided in the article “Stress changes can trigger earthquake sequences in a hydrothermal region south of Istanbul” by Martínez-Garzón et al., currently under review in Geophysical Research Letters.
    Keywords: sea level changes ; seismicity ; enhanced seismicity catalog ; Sea of Marmara region ; EARTH SCIENCE 〉 OCEANS 〉 OCEAN WINDS ; EARTH SCIENCE 〉 OCEANS 〉 TIDES ; EARTH SCIENCE 〉 SOLID EARTH 〉 TECTONICS 〉 EARTHQUAKES ; EARTH SCIENCE 〉 SOLID EARTH 〉 TECTONICS 〉 EARTHQUAKES 〉 EARTHQUAKE OCCURRENCES
    Type: Dataset , Dataset
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  • 8
    Publication Date: 2023-03-06
    Description: Abstract
    Description: The dataset is an extended and updated version of the homogenized regional earthquake catalogue of the Marmara region, north-western Turkey, presented in Bohnhof et al. (2017) and Wollin et al. (2018). It is built on the regional Turkish seismicity catalogues provided by AFAD (Disaster and Emergency Management Presidency of Turkey) and KOERI (Kandilli Observatory and Earthquake Research Institute) and spans the time interval 2006-2020. All events available in these two catalogues in the wider Marmara region were combined and dublicate events removed. A total of 13812 events having at least 6 P- and/or S-picks were located using the NLLoc software (Lomax et al., 2000, 2009) in Octtree mode utilizing automatic picks (see Wollin et al., 2018 for details) for all available waveforms. The magnitude range is between M0.3 and M5.7 with time-variable magnitude of completeness and covers the area 39.70S-41.50S and 26.0E-30.65E. The full description of the data and methods is provided in the data description file.
    Keywords: seismicity ; enhanced seismicity catalog ; Sea of Marmara region ; AFAD ; KOERI ; EARTH SCIENCE 〉 SOLID EARTH 〉 TECTONICS 〉 EARTHQUAKES ; EARTH SCIENCE 〉 SOLID EARTH 〉 TECTONICS 〉 EARTHQUAKES 〉 EARTHQUAKE OCCURRENCES ; EARTH SCIENCE 〉 SOLID EARTH 〉 TECTONICS 〉 NEOTECTONICS
    Type: Dataset , Dataset
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  • 9
    Publication Date: 2023-06-12
    Description: Abstract
    Description: This data set is supplementary to the BSSA research article of Blanke et al. (2019), in which the local S-wave coda quality factor at The Geysers geothermal field, California, is investigated. Over 700 induced microseismic events recorded between June 2009 and March 2015 at 31 short-period stations of the Berkeley-Geysers Seismic Network were used to estimate the frequency-dependent coda quality factor (Q_C) using the method of Phillips (1985). A sensitivity analysis was performed to different input parameters (magnitude range, lapse time, moving window width, total coda length and seismic sensor component) to gain a better overview on how these parameters influence Q_C estimates. Tested parameters mainly show a low impact on the outcome whereas applied quality criteria like signal-to-noise ratio and allowed uncertainties of Q_C estimates were found to be the most sensitive factors.Frequency-dependent mean-Q_C curves were calculated from seismograms of induced earthquakes for each station located at The Geysers using the tested favored input parameters. The final results were tested in the context of spatio-temporal behavior of Q_C in the reservoir considering distance-, azimuth and geothermal production rate variations. A distance and azimuthal dependence was found which is related to the reservoir anisotropy, lithological-, and structural features. By contrast, variations in geothermal production rates do not influence the estimates. In addition, the final results were compared with previous estimated frequency-independent intrinsic direct S-wave quality factors (Q_D) of Kwiatek et al. (2015). A match of Q_D was observed with Q_C estimates obtained at 7 Hz center-frequency, suggesting that Q_D might not be of an intrinsic but of scattering origin at The Geysers. Additionally, Q_C estimates feature lower spreading of values and thus a higher stability.The Geysers geothermal field is located approximately 110 km northwest of San Francisco, California in the Mayacamas Mountains. It is the largest steam-dominated geothermal reservoir operating since the 1960s. The local seismicity is clearly related to the water injections and steam production with magnitudes up to ~5 occurring down to 5 km depth, reaching the high temperature zone (up to 360°C). The whole study area is underlain by a felsite (granitic intrusion) that shows an elevation towards the southeast and subsides towards northwest. A fracture network induces anisotropy into the otherwise isotropic rocks featuring different orientations. Moreover, shear-wave splitting and high attenuating seismic signals are observed and motivate to analyze the frequency-dependent coda quality factor.Two data sets were analyzed: one distinct cluster located in the northwest (NW) close to injection wells Prati-9 and Prati-29, and the other one southeast (SE) of The Geysers, California, USA, close to station TCH (38° 50′ 08.2″ N, 122° 49′ 33.7″ W and 38° 46′ 59.5″ N, 122° 44′ 13.2″ W, respectively).The frequency-dependent coda quality factor is estimated from the seismic S-wave coda by applying the moving window method and regression analysis of Phillips (1985). Different input parameters including moving widow width, lapse time and total coda length are used to obtain Q_C estimates and associated uncertainties. Within a sensitivity analysis we investigated the influence of these parameters and also of magnitude ranges and seismic sensor components on Q_C estimates. The coda analysis was performed for each event at each sensor component of each station. The seismograms were filtered in predefined octave-width frequency bands with center-frequencies ranging from 1-69 Hz. The moving window method was applied starting in the early coda (after the S-onset) for each frequency band measuring the decay of Power Spectral Density spectra. The decay of coda amplitudes was fitted with a regression line and Q_C estimates were calculated from its decay slope for each frequency band. In a final step a mean-Q_C curve was calculated for each available station within the study area resulting in different curves dependent on event location sites in the northwest and southeast.Data DescriptionThe data contain final mean-Q_C estimates of the NW and SE Geysers, coda Q estimates at 7 Hz center-frequency calculated by using the NW cluster, and initial direct Q estimates of Kwiatek et al. (2015) using the same data of the NW cluster. Table S1 shows final mean coda quality factor estimates obtained from the NW cluster at injection wells Prati-9 and Prati-29. The column headers show stations (station), center-frequencies of octave-width frequency bands in Hertz (f[Hz]), mean coda Q estimates (meanQc) and related standard deviations (std), all obtained by coda analysis. Table S2 shows the final mean coda quality factor estimates obtained from additional selected 100 events in the SE Geysers. Column headers correspond to those in Table S1. Table S3 shows coda Q estimates related to 7 Hz center-frequency. The column headers show stations (station), center-frequency of octave-width frequency bands in Hertz (f[Hz]), coda Q estimates at 7 Hz center-frequency (Q_C) and related standard deviations (std2sigma; 95% confidence level), all obtained by coda analysis. Table S4 shows selected direct S-wave quality factors of Kwiatek et al. (2015) obtained by spectral fitting. The column headers show stations (station) and direct S-wave Q estimates (Q_D). The four tables are provided in tab separated txt format.Tables S3 and S4 are used for a comparative study and displayed in Figure 12 of the BSSA article mentioned above.
    Keywords: coda ; coda analysis ; coda quality factor ; Q ; local S-wave quality factor ; S-wave scattering ; scattering ; attenuation ; scattering attenuation ; frequency-dependent ; microseismicity ; isotropic single scattering model ; moving window method ; widow width ; coda length ; regression analysis ; sensitivity analysis ; parameters sensitivity ; lapse time ; source parameters ; stability of coda Q ; spatio-temporal ; The Geysers ; NW Geysers ; SE Geysers ; California ; Berkeley-Geysers Seismic Network ; geothermal field ; analysis 〉 sensitivity analysis ; analysis 〉 data analysis ; EARTH SCIENCE 〉 SOLID EARTH 〉 TECTONICS 〉 EARTHQUAKES 〉 SEISMIC PROFILE 〉 SEISMIC BODY WAVES
    Type: Dataset
    Format: 2 Files
    Format: application/octet-stream
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  • 10
    Publication Date: 2024-02-22
    Description: Abstract
    Description: This field campaign aimed at densifying the station coverage on the Armutlu Peninsula in the eastern Sea of Marmara. The Armutlu peninsula is directly crossed by the Armutlu fault, located roughly ~50 km away from the Istanbul metropolitan region. The main objective of this experiment is to characterize the seismic and aseismic deformation of this region. Waveform data are available from the GEOFON data centre, under network code 9P.
    Keywords: Broadband seismic waveforms ; Seismic monitoring ; temporary local seismic network ; Monitoring system ; EARTH SCIENCE 〉 SOLID EARTH ; In Situ/Laboratory Instruments 〉 Magnetic/Motion Sensors 〉 Seismometers ; In Situ Land-based Platforms 〉 GEOPHYSICAL STATIONS/NETWORKS ; In Situ Land-based Platforms 〉 GEOPHYSICAL STATIONS/NETWORKS 〉 SEISMOLOGICAL STATIONS
    Type: Dataset , Seismic Network
    Format: ~600G
    Format: .mseed
    Format: XML
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