The results of the application of artificial neural nets (ANNs) to discriminating microearthquakes from quarry and mining blasts in the West Bohemia earthquake swarm region are presented and discussed. Input vectors consisting of seven spectral and seven amplitude parameters, automatically extracted from local three-component digital broadband (0.6 to 60 Hz) velocigrams, have been employed for training of different ANN configurations. Multi-layer perceptrons (MLP) trained in supervised mode by different subsets of a representative set of 312 events have been used as discriminators, and unsupervised Kohonen self-organizing feature maps (SOFM) have been used as complementary reliability estimators. The reason for comparative application of both techniques was to increase the reliability of the discrimination: complementary information that a pattern has been recognized as a member of a conflict cluster allows detecting problematic patterns that an MLP may not be able to classify correctly.

The optimal MLP, trained by one randomly selected half of the complete set of 312 input vectors and tested by the other half-set, and vice versa, correctly classified, on average, 99% of all events. The optimal SOFM correctly classified as problematic patterns all events misinterpreted by the MLP, and about 20% of all events were classified by them as ambiguous cases.

The obtained results evidence that a relatively small number of spectral and amplitude parameters of observed ground velocity may suffice for a reliable discrimination between local microearthquakes and quarry blasts by means of small neural nets. The MLP/SOFM combination discussed in this article has attained a discrimination reliability that allows it to be employed routinely in observatory practice.

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