Abstract
We employed multilayer perceptrons (MLP), self organizing feature maps (SOFM), and learning vector quantization (LVQ) to reveal and interpret statistically significant features of different categories of waveform parameter vectors extracted from three-component WEBNET velocigrams. In this contribution we present and discuss in a summarizing manner the results of (i) SOFM classification and MLP discrimination between microearthquakes and explosions on the basis of single-station spectral and amplitude parameter vectors, (ii) SOFM/LVQ recognition of initial onset polarities from PV'-waveforms, and (iii) a source mechanism study of the January 1997 microearthquake swarm based on SOFM classification of combined multi-station PV-onset polarity and SH/PVamplitude ratio (CPA) data.
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