Detection and identification of seismic signals recorded at Krakatau volcano (Indonesia) using artificial neural networks

The Anak Krakatau volcano (Indonesia) has been monitored by a multi-parametric system since 2005. A variety of signal types can be observed in the records of the seismic stations installed on the island volcano. These include volcano-induced signals such as LP, VT, and tremor-type events as well as signals not originating from the volcano such as regional tectonic earthquakes and transient noise signals. The work presented here aims at the realization of a system that automatically detects and identifies the signals in order to estimate and monitor current activity states of the volcano. An artificial neural network approach was chosen for the identification task. A set of parameters was defined, describing waveform and spectrogram properties of events detected by an amplitude-ratio-based (STA/LTA) algorithm. The parameters are fed into a neural network which is, after a training phase, able to generalize input data and identify corresponding event types. The success of the identification depends on the network architecture and training strategy. Several tests have been performed in order to determine appropriate network layout and training for the given problem. The performance of the final system is found to be well suited to get an overview of the seismic activity recorded at the volcano. The reliability of the network classifier, as well as general drawbacks of the methods used, are discussed.