Fuzzy K-means clustering on infrasound sample

Infrasound is ideally suited to provide essential information of these earthquakes without any invasive measures. This necessitates automatic processing of the data as the captured phenomena need to be sorted before further analysis can be undertaken. Extracting the physical characters of different signals by signal processing such as Fourier Transform and Wavelets Transform are generally employed to carry out the necessary expansion. This article reviews pattern recognition as it applies to earthquake prediction and discusses the concept of fuzzy logic approach as a means of seismic infrasound classification. An example is presented in which this approach was used for classifying preprocessed infrasound signals to identify precursory strong earthquake.