Continuous Hidden Markov Models: Application to automatic earthquake detection and classification at Las Canمdas caldera, Tenerife

A possible interaction of (volcano-) tectonic earthquakes with the continuous seismic noise recorded in the volcanic island of Tenerife was recently suggested. Also recently the zone close to Las Canadas caldera shows unusual high number of near (< 25 km), possibly volcano-tectonic, earthquakes indicating signs of reawakening of the volcano putting high pressure on the risk analyst. Certainly for both tasks consistent earthquake catalogues provide valuable information and thus there is a strong demand for automatic detection and classification methodologies generating such catalogues. Therefore we adopt methodologies of speech recognition where statistical models, called Hidden Markov Models (HMMs), are widely used for spotting words in continuous audio data. In this study HMMs are used to detect and classify volcano-tectonic and/or tectonic earthquakes in continuous seismic data. Further the HMM detection and classification is evaluated and discussed for a one month period of continuous seismic data at a single seismic station. Being a stochastic process, HMMs provide the possibility to add a confidence measure to each classification made, basically evaluating how “sure” the algorithm is when classifying a certain earthquake. Moreover, this provides helpful information for the seismological analyst when cataloguing earthquakes. Combined with the confidence measure the HMM detection and classification can provide precise enough earthquake statistics, both for further evidence on the interaction between seismic noise and (volcano-) tectonic earthquakes as well as for incorporation in an automatic early warning system.