1887

Abstract

Summary

Digital seismic processing is one of the important steps in exploration seismology. In seismic processing, deconvolution is a key step in order to improve seismic resolution. The vital part of deconvolution is estimating a reliable wavelet. Non-minimum phase wavelet estimation based on cumulant matching is known as a statistical wavelet estimation approach. We used fourth-order cumulant matching to estimate a mixed-phase wavelet. Comparing the fourth-order cumulant of a trace with the fourth-order moment of an all pass operator, and convolving the appropriate all pass with a minimum phase wavelet leads to a mixed-phase wavelet. The comparison should be optimized by a non-linear approach such as simulated annealing. In this paper, we applied the mentioned method to synthetic and real data sets. Moreover, we investigated the effect of signal-to-noise ratio, the number of data and iteration to the specified algorithm. The correlation between the estimated wavelet and real wavelet is more than 95%. Besides, we analyzed this algorithm in the Fourier domain and found that the maximum correlation can be found at high frequencies.

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/content/papers/10.3997/2214-4609.201601251
2016-05-30
2024-04-24
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