Hierarchical cluster analysis and multiple event relocation of seismic event clusters in Hungary between 2000 and 2016
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The objective of our paper is to develop a workflow that allows us to calculate more accurate hypocenter locations in seismic event clusters of aftershock sequences or artificial events. Due to the increased sensitivity of the seismological instruments and density of the network, we are able to record small natural and artificial events. The discrimination of these events is necessary to investigate the recent tectonic movements in the Pannonian Basin. As a first step, we performed a hierarchical cluster analysis on the events in the Hungarian National Seismological Bulletin using the spatial distances between the events to obtain event clusters. We selected 5 different test clusters from the list of clusters where two clusters consist of quarry blasts, another two consist of earthquakes, and the last one is a mixture of earthquakes and anthropogenic events. In the second step, to prepare for the double-difference multiple event location analysis, we manually revised the arrival time picks in the Hungarian National Seismological Bulletin in order to increase the consistency and accuracy of the arrival times. We obtained improved single-event locations with the iLoc algorithm using the RSTT 3D global velocity model to provide initial locations for the double-difference relocation. We applied waveform cross-correlation at every station to obtain the differential times and correlation matrices. In order to discriminate the events in the mixed event cluster, we repeated the hierarchical cluster analysis, but this time, we used the correlation matrix as a distance metric. Examining the shape of the resulting dendrogram, it is clear that certain subclusters are well separated. In these subclusters, the coordinates of the events are close to the mines, where explosive quarrying takes place. With this technique, we are able to identify explosions that were listed as earthquakes in the catalogue.
KeywordsHypoDD Double-difference Hierarchical cluster analysis Multiple event relocation Pannonian Basin
The seismicity of the Pannonian Basin can be described as moderate. The recent seismic activity is caused by the Adriatic microplate’s movement, which rotates counterclockwise relative to Europe. Based on geophysical studies, the current stress field is typically characterized by compression (Bada et al. 1999; Gerner et al. 1999). The main active tectonic structures are flower structures (Fodor et al. 2005) linked to reactivated faults, shear zones. Additional geological structural studies require the most accurate earthquake catalogue. Nearly 40% of the Hungarian earthquakes are anthropogenic: quarry explosions and mine blasts. Hence, the earthquake catalogue may contain unidentified explosions that makes geological interpretation more difficult. In order to make the Hungarian National Seismological Bulletin (HNSB, Gráczer et al. 2016) more accurate, it is necessary to identify these anthropogenic events.
In the routine observatory practice, a single-event location algorithm is used to calculate the hypocenter parameters, which often suffers from high uncertainty and location bias. The location errors can be significantly reduced by multiple event relocation methods combined with high-quality data. In this work, we use one of the most commonly used algorithms, the double-difference algorithm (Waldhauser and Ellsworth 2000).
2 Bulletin and data sources
The Kövesligethy Radó Seismological Observatory has been reporting events and phase data to the international data centers (e.g., ISC) since the beginning of the twentieth century. The annual Hungarian Earthquake Bulletin (HEB) publishing began in 1995 after the deployment of the Paks Microseismic Monitoring Network (PMMN; Tóth et al. 1996) operated by Georisk Earthquake Engineering. From 2002 to 2010, the HEB was published by the KRSO and the Georisk Ltd. collectively. Since 2011, data of all earthquakes detected by the Seismological Observatory have been collected and yearly published with detailed information in the Hungarian National Seismological Bulletin (HNSB, Gráczer et al. 2016). Since 2014, magnitudes and event locations are determined with the iLoc location algorithm (Bondár and Storchak 2011) using the 3D global RSTT velocity model (Myers et al. 2010).
3 Hierarchical cluster analysis and selected clusters
4 Waveform analysis
A significant part of the errors is caused by errors in the arrival time measurements (picking errors) as well as the inaccuracies in the applied velocity model. These errors constitute the data covariance matrix. The measurement errors are usually characterized as a Gaussian, zero-mean process (Billings et al. 1994; Pavlis 1986). However, the accuracy of the measurement depends on the signal to noise ratio, i.e., the accuracy of the arrival time measurements also depends on the magnitude (Kværna 1996) as arrival times picked consistently late with decreasing magnitude results in a heavy-tailed distribution of time residuals. It has been demonstrated that repicking the phases on the original waveforms could provide improvements over the bulletin picks in the Hungarian National Seismological Bulletin (Czecze et al. 2018). In order to improve the quality of the arrival times, we revised all of the relocated events, on all available stations and waveforms. We used Seisgram2K (Lomax 1991) to pick arrival times. We filtered the waveforms with a band-pass filter where the frequency depended on the epicentral distance.
After repicking the phase arrivals, we performed waveform cross-correlation at each station. The correlation coefficient can quantify the similarity between the waveforms in each cluster, which later served as a basis for a second hierarchical cluster analysis. We performed correlations on the filtered vertical, radial, and transversal components of the seismograms and obtained P- and S- differential times on every station between all event pairs. We have determined the time window of the correlation using predicted arrival times from a local velocity model (Gráczer and Wéber 2012). We set the correlation threshold to 0.6 and discarded differential times below-threshold correlation coefficients. We also performed manual quality control to remove noise correlations from the database. We created SV, SH, and P correlation matrices at each station, because during the next hierarchical cluster analysis, the correlation coefficients serve as distance metrics. With the waveform cross-correlation, a significant amount of good-quality differential time data has been obtained and we noticed that man-made events had considerably more acceptable correlations than the natural events.
5 Initial locations
The double-difference algorithm requires the coordinates of the absolute initial locations. The Earth’s velocity structure is typically approximated by a 1D velocity model to calculate predicted travel times. In the case of complex tectonic structures, this can cause systematic travel-time prediction errors over certain raypaths, which may result in location bias. We relocated the events in the test clusters by the iLoc single-event location algorithm that is based on the ISC locator (Bondár and Storchak 2011). This algorithm accounts for the correlated travel-time prediction errors due to unmodeled velocity heterogeneities by using a priori estimation of the full data covariance matrix (Bondár and McLaughlin 2009b). Ignoring the correlated errors in travel-time estimates will lead to underestimation of the errors in determining the locations (error-ellipse) and could result in systematic location bias. The area of this study is geologically diverse, and it was previously shown that the RSTT 3D global velocity model (Myers et al. 2010) outperforms the 1D velocity models on all counts, and it is able to capture the major 3D heterogeneities in the area (Bondár et al. 2018).
6 Relocation with hypoDD
The double-difference algorithm (Waldhauser and Ellsworth 2000) is a relative event location method in which both absolute travel-time measurements and the P- and S-wave differential travel times from the waveform cross-correlation can be used. It combines the differential times from waveform cross-correlation and the differences between the arrival times of each phase in the bulletin data by minimizing double difference for each pair of events, specifying the vector difference between hypocenters. Therefore, there is no need for the use of station corrections. It determines the distance between correlating events within the cluster with the accuracy of differential times (phase correlation of P- and/or S-waves), and the distance between non-correlating events with the accuracy of ordinary travel-time measurements.
Relocation with hypoDD (Waldhauser 2001) is a two-step process. The first step involves analyzing the phase data, creating the travel-time differences between the earthquake pairs. The most important control parameters in hypoDD are the maximum distance between the event pairs and the stations and the maximum distance between the event pairs and the minimum number of measurements. Too strictly defined limitations might lead to the deletion of some events, and too-weak constraints might worsen the result of the relocation. Therefore, we ran a number of tests to fine-tune the parameters. Some percentage of the total number of pairs were considered outliers (with delay times larger than the expected delay time) and were removed from the data set.
The second step is to define the constraints used during the iterations. In the relocation process, we always used the P and S arrivals and differential times together. HypoDD can only use a 1D velocity model so we used a local velocity model (Gráczer and Wéber 2012). The program solves the double-difference equations iteratively by adjusting the model vector (hypocenter parameters) after each iteration. The equations are solved by the method of conjugate gradients (Paige and Saunders 1982) that solves the damped least squares problem, and requires a damping factor. This factor attenuating the model adjustment vector if it becomes unstable or the change in the hypocenter parameters is too big. This factor strongly depends on the condition number of the system. During the iterations, we applied different weightings, depending on the reliability of the data, and we gradually introduced the residual threshold and hypocentral separation limit. Until the solution became stable, an initial weighting was applied, and each iteration was performed with the re-weighted data and the weights were recalculated based on the distance between the events and the magnitude of the residuals. The iterations continued until the RMS residual dropped below the noise level of the data or the differences between the solutions were sufficiently small. In all cases, we assigned to S-phase data a lower initial weight (0.5) than to P-phase data (1.0), due to the higher uncertainty of measuring secondary phases.
Figure 9 also shows the depth distribution of the hypoDD solutions in the case of the C12 earthquake cluster. We assume that the depth of the transition zone between the brittle and ductile deformation is shallow (upper crust) in the Pannonian Basin due to the high geothermal gradient (Lenkey et al. 2002), and according to the iLoc solutions with the RSTT velocity model, the events are in the shallow brittle crust. However, relocalized hypocenters are deeper; thus, we will not provide depth solutions in this study.
7 C2 cluster
We performed a second hierarchical cluster analysis now using the correlation coefficients as distance metric. Note that during the waveform cross-correlation, the P, SV, and SH correlation matrices were created at each station.
8 Conclusions and discussion
We have performed single-linkage hierarchical cluster analysis on the entire seismicity of the Pannonian Basin and successfully applied the Dynamic Tree Cut algorithm (Langfelder et al. 2008) to identify event clusters. We selected five test clusters to demonstrate the feasibility of our methodology to relocate event clusters and possibly discriminate between earthquakes and explosions. In order to provide the best-quality data for the multiple event locations, we repicked all the phases for the test clusters in the Hungarian National Seismological Bulletin. We relocated the events with the state-of-the-art single-event location algorithm, iLoc, using the global 3D RSTT velocity model. For each cluster presented, the distribution of the depths of the events varies over a relatively large interval, but none of the selected clusters had a sufficiently close station for reliable depth determination. Note that for the known quarry blast, we fixed the depth to 1 km as hypoDD would not allow fixing the depth to zero. To obtain differential times, we performed waveform correlation; this step also allowed us to form correlation matrices at each station. The hypoDD relocations concentrate the initial locations into smaller clusters, and provide improved solutions for events determined even with unfavorable station geometry. Combining the differential times from waveform cross-correlation with absolute travel time significantly contributes to the accuracy of the final solutions. Despite the poor station geometry, the final solution of the C3 cluster is considerably more accurate than the original locations as the original NW-SE bias is almost completely eliminated. Even though the hypoDD analysis did not bring dramatic improvements for cluster C2, the cluster analysis using the correlation matrices as distance metrics allowed us to identify and associate events with active quarries and correctly reidentify them as explosions. Our methodology opens a way for a systematic analysis of event clusters in the Hungarian National Seismic Bulletin and helps in the discrimination between earthquakes and explosions and thus allows for a more reliable determination of the natural seismicity of the Pannonian Basin.
We are grateful to the Kövesligethy Radó Seismological Observatory of the Hungarian Academy of Sciences and Georisk Earthquake Engineering for providing all of the data, support, and software for the study. The reported investigation was financially supported by the National Research, Development and Innovation Fund (K128152, K124241, and 2018-1.2.1-NKP-2018-00007). The figures were prepared with QGIS Geographic Information System and Generic Mapping Tool (GMT, Wessel and Smith 1991).
Open Access funding provided by Eötvös Loránd University (ELTE).
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