Grobbelaar, MRG, Birch, D & Cichowicz, A 2017, 'Comparison of data from complementary seismograph networks in a mining district', in J Wesseloo (ed.), Proceedings of the Eighth International Conference on Deep and High Stress Mining
, Australian Centre for Geomechanics, Perth, pp. 117-124, https://doi.org/10.36487/ACG_rep/1704_06_Grobbelaar
South Africa has embarked on the installation of dense local surface networks within a number of mining regions in order to create a unique situation where data from an event can be recorded by three separate networks; at distances of: 500 km by the national seismograph network, 4 km by the local network and 100 m by the mining networks. The data can be used for hazard at the stope and hazard within the region and, thus, trends can be identified in order to assist in the investigation and mitigation of seismic related accidents. This is a first for the country.
The ML5.5 earthquake on 5 August 2014, provided an ideal opportunity for collaboration and comparison between the local surface and mining networks, especially since each network has its own unique challenges due to equipment specifications and capabilities. Thus, such a comparison would help quantify these differences.
The locations of the earthquakes, as well as other spectral parameters, obtained by each network were investigated. Many of the parameters were very similar, such as the estimated Mw magnitudes from both the networks. Similarly, the scalar seismic moments offered by both networks are comparable, but with a tendency to underestimate by the mining network in comparison to the local surface network. In addition, the radiated seismic energy estimated by both networks follow the same trend, however, significant scattering of the estimated energy is observed at small values.
The spatial patterns of seismicity associated with the aftershocks of the ML5.5 earthquake were compared and both networks reveal a very similar pattern, however, the mining network delineated the fault with a higher accuracy than the local surface network.
Keywords: comparison, spectral parameters, mining, seismograph networks
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