Automatic detection and picking of seismic signals is crucial for seismic networks, which continuously monitor and work with huge volumes of data. In this situation, manual picking is tedious work in which some small events can go unnoticed and others can produce false alarms.
Accordingly, automatic picking algorithms are in constant development. New methodologies based on energy analysis, artificial neural networks, maximum likelihood methods, fuzzy logic theory, polarization analysis, hidden Markov models, autoregressive techniques, higher order statistics, wavelet transform, or template matching, among others, are continuously being investigated.
Accurate and reliable identification and detection of seismic phases is essential for subsequent real-time analysis. The information contained in the different seismic phases allows the expected magnitude, the epicentral location of an event, and other parameters that might be used by earthquake early-warning systems to be estimated.
The aim of this Special Issue is to present the most recent advances in the automatic detection and phase picking of seismic signals. Topics related to this Special Issue of Sensors include, but are not limited to:
Automatic seismic event detection;
Accurate seismic phase picking;
Real-time processing of seismic signals;
New methodologies for the automatic estimation of earthquake parameters;
Monitoring and early-warning systems.