Processing ambient noise using wavelet analysis tools: the Iraqi Seismological broadband Network Data at Iraqi Meteorological Organization and seismology (IMOS)

Abstract

The purpose of this study is to reduce the noise of passive seismic signals recorded across the Iraqi Seismological Network Data at Iraqi Meteorological Organization and seismology (IMOS). The seismic records from six broadband stations of IMOS are largely affected by human activities in the populated cities of Iraq. We utilized several well-developed amplitude power spectrum and wavelet analysis techniques to improve signal-to-noise ratio. The results obtained from this comparison show that continuous wavelet transform can largely improve signal with minimal changes in the waveform shape of interest, even in presence of high noise levels. In addition, has overwhelmingly strong vitality and gains substantial development in the field of signal processing. Several mother wavelets were tested and Morlet and Coiflet selected as the optimum waves based on the signal characteristics and the noise reduction objective (maximizing SNR or reducing errors). Hence, the quality of the final seismic signal is crucial for the moment tensor analysis. However, traditional filters including discrete wavelet packet transform and 1-D wavelet analysis methods can be affective to reduce distortion in the signal.