Comparision of Haar and SYM Wavelet Tranforms in the Detection of Magnitude for Earthquake Using Seismic Signals
Kishor Kumar Reddy C, Anisha P R, Narasimha Prasad L V, Vijaya Kumar B
Progress in Signals and Telecommunication Engineering, Vol. 5, No. 1, 2016.
DOI: 
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Abstract:
Natural disaster is a foremost undesirable event resultant from natural processes of the Earth and causes
loss of life or property damage. Earth quakes are among the most damaging event caused by the earth itself. As urbanization
progresses universally, earthquakes pose severe risk to lives and properties for urban areas and all the subduction
zones. Short term earthquake prediction, months in advance, is an elusive goal of earth sciences, of great importance for
fundamental science and for disaster preparedness. Detection of earthquake was done earlier based on W-MLP and MLP,
Wavelet-Aggregated Signal and Synchronous Peaked Fluctuations model, detection using the P waves of the earthquake,
prediction based on radon emissions, EEW algorithm, M8 algorithm, prediction using extraction of instantaneous frequency
from underground water, but neither of them could provide an effective and efficient result. In the present research,
seismic signals are analyzed by using Haar wavelet transform and SYM wavelet transform in order to evaluate
the energy, frequency, magnitude of the signal. The minor quakes are neglected and the surface wave magnitude of the
quakes that show impact on earthâ€™s surface is calculated and found as 3.0. The obtained results from Haar and SYM
wavelet transforms are taken up as datasets and are tested using classification algorithms such as J48, Random Forest,
REP tree, LMT, NaÃ¯ve Bayes and Back propagation model of neural networks to evaluate the accuracy, precision and
recall performance measures.