M. Musil et A. Plesinger, DISCRIMINATION BETWEEN LOCAL MICROEARTHQUAKES AND QUARRY BLASTS BY MULTILAYER PERCEPTRONS AND KOHONEN MAPS, Bulletin of the Seismological Society of America, 86(4), 1996, pp. 1077-1090
The results of the application of artificial neural nets (ANNs) to dis
criminating microearthquakes from quarry and mining blasts in the West
Bohemia earthquake swarm region are presented and discussed. Input ve
ctors consisting of seven spectral and seven amplitude parameters, aut
omatically extracted from local three-component digital broadband (0.6
to 60-Hz) velocigrams, have been employed for training of different A
NN 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 estima
tors. The reason for comparative application of both techniques was to
increase the reliability of the discrimination: complementary informa
tion that a pattern has been recognized as a member of a conflict clus
ter allows detecting problematic patterns that an MLP may not be able
to classify correctly. The optimal MLP, trained by one randomly select
ed half of the complete set of 312 input vectors and tested by the oth
er half-set, and vice versa, correctly classified, on average, 99% of
all events. The optimal SOFM correctly classified as problematic patte
rns all events misinterpreted by the MLP, and about 20% of all events
were classified by them as ambiguous cases. The obtained results evide
nce that a relatively small number of spectral and amplitude parameter
s of observed ground velocity may suffice for a reliable discriminatio
n between local microearthquakes and quarry blasts by means of small n
eural nets. The MLP/SOFM combination discussed in this article has att
ained a discrimination reliability that allows it to be employed routi
nely in observatory practice.