Abstract

Abstract. Longwall mining activity in the Ruhr coal mining district leads to mining-induced seismicity. For detailed studies the seismicity of a single longwall panel beneath the town of Hamm-Herringen in the eastern Ruhr area was monitored between June 2006 and July 2007 with a dense temporary network of 15 seismic stations. More than 7000 seismic events with magnitudes between –1.7 ≤ ML ≤ 2.0 were detected and localized in this period. Most of the events occurred in the vicinity of the moving longwall face. In order to find possible differences in the brittle failure types of these events an association of the events to distinct clusters is performed based on their waveform characteristics. This task is carried out using a new clustering algorithm utilizing a network similarity matrix which is created by combining all available 3-component single station similarity matrices. The resultant network matrix is then sorted with respect to the similarity of its rows leading to a sorted matrix immediately indicating the clustering of the event catalogue. Finally, clusters of similar events are extracted by visual inspection. This approach results in the identification of several large clusters which are distinct with respect to their spatial and temporal characteristics as well as their frequency magnitude distributions. Comparable clusters are also found with a conventional single linkage approach, however, the new routine seems to be able to associate more events to specific clusters without merging the clusters. The nine largest observed clusters can be tentatively divided into three different groups that indicate different types of brittle failure. The first group consists of the two largest clusters which constitute more than half of all recorded events. Results of a relative relocation using cross-correlation data suggest that these events are confined to the extent of the mined out longwall and cluster close to the edges of the active longwall at the depth of active mining. These events occur in lockstep with the longwall advance and exhibit a high b value of the Gutenberg–Richter relation (GR) of about 1.5 to 2.5 and consist of small magnitude events. Thus, these events represent the immediate energy release adjacent to the mined out area. The second group consists of clusters located either slightly above or below the depth of active mining and occurring at the current position of the longwall face within the confines of the longwall. They consist of generally stronger events and do not follow GR. This activity might be linked to the failure of more competent layers above and below the mined out seam resulting in larger magnitude events. Finally, one cluster represents seismic activity with a rather low b value below 1 and events located partly towards the north of the longwall which are delayed with respect to the advance of the longwall face. These events are interpreted as brittle failure on pre-existing tectonic structures reactivated by the mining activity.

Highlights

  • In order to find possible differences in the brittle failure types of these events an association of the events to distinct clusters is performed based on their waveform characteristics

  • The resultant network matrix is sorted with respect to the similarity of its rows leading to a sorted matrix immediately indicating the clustering of the event catalogue

  • The first group consists of the two largest clusters which constitute more than half of all recorded events

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Summary

Establishing similarity matrices

Clustering in general means accumulation of individual data to groups with members each sharing one or more properties with all the others. L denotes the size of the catalogue These contain the maximum cross-correlation coefficient of Cab(τ ) and the corresponding time lags τ for each event pair, respectively. This was done because high-frequency content in the waveforms tends to artificially lower the cross-correlation coefficients due to scattering This effect has been revealed by inspection of amplitude spectra of the records of two stations that showed increasing similarity with increasing event to station distance and vice versa (see Fig. 3). The network matrix is element-wise divided by the temporary count matrix This approach is able to find similar events even in time intervals during which a single station might exhibit unusually bad noise conditions or might even be out of operation. A cluster analysis can be performed for the entire data set with one single matrix

Sorting of similarity matrices
Visual cluster extraction from sorted network similarity matrices
Clusters found with the single-linkage method
Relative event relocation
Frequency magnitude distributions
Cluster association
Findings
Conclusions
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