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Conclusions

We have presented a novel approach to nonstationary identification of apparent (locally observed) phase. Our approach is based on a new attribute, local skewness. In synthetic and field-data examples, local skewness exhibits a tendency to pick focused signals and a higher dynamical range than the previously used local kurtosis. Its computation involves a local similarity between the input signal and its square. Practical applications of using local skewness for zero-phase correction of seismic signals should be combined with well-log analysis in order to better separate the locally-observed phase from the propagating-wavelet phase.




2014-02-15