Exposure of auto regressive models in doling out seismograms through GSN

The study of natural hazards like earthquakes, avalanche, tsunami, volcanic eruptions, etc. is a challenging domain in geo-spatial research. Seismology is the process of recording the seismograms on earth surface which are recorded through sensors called Seismometers. Seismology plays a vital role in handling disaster events. Seismogram provides the impact of natural disaster events by retrieving the bandwidth of seismic waves through seismographs. Seismograms are handled by Global Seismographic Network (GSN) which represents the spectral characteristics of disaster signals or elastic waves. Seismograms are acquired through various models by eliminating the frequency domain coefficients. Unless the auto regressive models, the acquisition of seismograms will be wide of the mark. Characteristic functions can be used to reduce noises that are outside the frequency range in seismograms. A characteristic function handles the signals in the spectrum and removes the noise through the regressive models. This paper provides the exposure of regressive models towards processing seismograms and GSN for acquiring the seismograms to handle the natural hazards using seismic imagery. The objective of this article is providing the knowledge on auto regressive models in terms of handling the seismograms.