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We suggest a new clustering approach to classify focal mechanisms from large moment tensor catalogues, with the purpose of automatically identify families of earthquakes with similar source geometry, recognize the orientation of most active faults, and detect temporal variations of the rupture processes. The approach differs in comparison to waveform similarity methods since clusters are detected even if they occur in large spatial distances. This approach is particularly helpful to analyse large moment tensor catalogues, as in microseismicity applications, where a manual analysis and classification is not feasible. A flexible algorithm is here proposed: it can handle different metrics, norms, and focal mechanism representations. In particular, the method can handle full moment tensor or constrained source model catalogues, for which different metrics are suggested. The method can account for variable uncertainties of different moment tensor components. We verify the method with synthetic catalogues. An application to real data from mining induced seismicity illustrates possible applications of the method and demonstrate the cluster detection and event classification performance with different moment tensor catalogues. Results proof that main earthquake source types occur on spatially separated faults, and that temporal changes in the number and characterization of focal mechanism clusters are detected. We suggest that moment tensor clustering can help assessing time dependent hazard in mines.
Crustal earthquake swarms are an expression of intensive cracking and rock damaging over periods of days, weeks or month in a small source region in the crust. They are caused by longer lasting stress changes in the source region. Often, the localized stressing of the crust is associated with fluid or gas migration, possibly in combination with pre-existing zones of weaknesses. However, verifying and quantifying localized fluid movement at depth remains difficult since the area affected is small and geophysical prospecting methods often cannot reach the required resolution.
We apply a simple and robust method to estimate the velocity ratio between compressional (P) and shear (S) waves (upsilon(P)/upsilon(S)-ratio) in the source region of an earthquake swarm. The upsilon(P)/upsilon(S)-ratio may be unusual small if the swarm is related to gas in a porous or fractured rock. The method uses arrival time difference between P and S waves observed at surface seismic stations, and the associated double differences between pairs of earthquakes. An advantage is that earthquake locations are not required and the method seems lesser dependent on unknown velocity variations in the crust outside the source region. It is, thus, suited for monitoring purposes.
Applications comprise three natural, mid-crustal (8-10 km) earthquake swarms between 1997 and 2008 from the NW-Bohemia swarm region. We resolve a strong temporal decrease of upsilon(P)/upsilon(S) before and during the main activity of the swarm, and a recovery of upsilon(P)/upsilon(S) to background levels at the end of the swarms. The anomalies are interpreted in terms of the Biot-Gassman equations, assuming the presence of oversaturated fluids degassing during the beginning phase of the swarm activity.
This article presents comparisons among the five ground-motion models described in other articles within this special issue, in terms of data selection criteria, characteristics of the models and predicted peak ground and response spectral accelerations. Comparisons are also made with predictions from the Next Generation Attenuation (NGA) models to which the models presented here have similarities (e.g. a common master database has been used) but also differences (e.g. some models in this issue are nonparametric). As a result of the differing data selection criteria and derivation techniques the predicted median ground motions show considerable differences (up to a factor of two for certain scenarios), particularly for magnitudes and distances close to or beyond the range of the available observations. The predicted influence of style-of-faulting shows much variation among models whereas site amplification factors are more similar, with peak amplification at around 1s. These differences are greater than those among predictions from the NGA models. The models for aleatory variability (sigma), however, are similar and suggest that ground-motion variability from this region is slightly higher than that predicted by the NGA models, based primarily on data from California and Taiwan.
Marine seismology usually relies on temporary deployments of stand alone seismic ocean bottom stations (OBS), which are initialized and synchronized on ship before deployment and re-synchronized and stopped on ship after recovery several months later. In between, the recorder clocks may drift and float at unknown rates. If the clock drifts are large or not linear and cannot be corrected for, seismological applications will be limited to methods not requiring precise common timing. Therefore, for example, array seismological methods, which need very accurate timing between individual stations, would not be applicable for such deployments.
We use an OBS test-array of 12 stations and 75 km aperture, deployed for 10 months in the deep sea (4.5-5.5 km) of the mid-eastern Atlantic. The experiment was designed to analyse the potential of broad-band array seismology at the seafloor. After recovery, we identified some stations which either show unusual large clock drifts and/or static time offsets by having a large difference between the internal clock and the GPS-signal (skew).
We test the approach of ambient noise cross-correlation to synchronize clocks of a deep water OBS array with km-scale interstation distances. We show that small drift rates and static time offsets can be resolved on vertical components with a standard technique. Larger clock drifts (several seconds per day) can only be accurately recovered if time windows of one input trace are shifted according to the expected drift between a station pair before the cross-correlation. We validate that the drifts extracted from the seismometer data are linear to first order. The same is valid for most of the hydrophones. Moreover, we were able to determine the clock drift at a station where no skew could be measured. Furthermore, we find that instable apparent drift rates at some hydrophones, which are uncorrelated to the seismometer drift recorded at the same digitizer, indicate a malfunction of the hydrophone.