Abstract :
Careful observation has shown that mining-induced seismicity follows a multimodal
distribution, which we assume to arise from many distinct physical processes. The two major modes
however, arise from those seismic events that are associated in some way with geological features on the
one hand, and those that are associated, among other things, with fracturing in the volume of extreme
stress concentrations ahead of the stope faces, on the other. We call the former ‘‘genuine’’ events and the
latter ‘‘spurious’’ events.
Untangling these modes has been a major problem for those researchers wishing to work with
unimodal seismic catalogs. Partial separation of the genuine events from a catalog can be obtained by
a careful selection from a scatter diagram of log (radiated seismic energy) against log (scalar seismic
moment) or equivalently by selecting a threshold value of magnitude say, from an inspection of the
Gutenberg-Richter diagram. This threshold is usually considerably greater than the threshold of
completeness that can be achieved by modern seismic networks on mines.
The main objective of this paper will be the demonstration that a simple neural network can improve
this separation. In this study, for example, simple elimination below the threshold of log (scalar seismic
moment) 9.5 resulted in 206 genuine events remaining in the catalog. After running the eliminated
events through a trained neural network, an additional 72 genuine events were found, representing an
increase of nearly 35%.
This has important consequences for statistical hazard analysis and for the identification of active
geological structures in mines.
