Wavelet Based Seismogram Denoising: Application to Receiver Function Estimation
Abstract
Computation of Receiver Functions from observed seismograms involves the process of deconvolution which is highly sensitive to the presence of noise. Earthquake waveforms are non-stationary signals that contain colored Gaussian noise. The frequency content of the noise generally overlaps with the frequency content of the original signal and hence it is not possible to simply frequency filter the Gaussian noise. Techniques like the use of optimum filters are not suitable for seismic signals as seismic signals are non-stationary. Previous attempts to improve the signal-to-noise ratio in Receiver Functions involved stacking at the expense of path averaging of the crustal structure. Here we demonstrate that denoising of the seismic signal using the wavelet transform to sparsely represent the data, thresholding the amplitude and reconstructing the signal from remaining coefficients leads to a significant improvement in the signal to noise ratio. Hence it is useful in stabilizing the deconvolution process used to compute Receiver Functions. We have conducted tests on synthetic seismograms by adding different percentages of noise and trying to retrieve the original signal from the noisy traces. Cross-correlation coefficients between the denoised signal and the synthetic trace have been observed to improve over the cross-correlation coefficients between the noisy signal and the synthetic trace. It has been shown that this method of denoising seismograms does not introduce any noticeable distortions in the original signal [Galiana-Merino et al, 2003]. Receiver Function computed from the denoised seismograms showed a better correlation with the Receiver Function computed from synthetic seismograms than the Receiver Function computed from noisy seismograms. The signal-to-noise ratio was observed to have improved significantly. The improved stability of deconvolution due to denoising aided a more robust estimation of Receiver Functions from the retrieved signals. We show that this technique works well on real data without introducing any noticeable artifacts into the data and thus leads to a better Receiver Function estimation.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2008
- Bibcode:
- 2008AGUFM.S13B1797K
- Keywords:
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- 7290 Computational seismology