A Study of Time-varying Seismic Wavelet and Reflection Coefficient Separation Method Based on EMD and Smooth Filtering

Abstract

The accuracy of seismic wavelet extraction influences the accuracy of analysis and dealing for seismic data directly. In fact, the wavelet in seismic data has the characteristics of time-varying. There are many methods for wavelet extraction, but the results using existing methods are not satisfying. This paper studies a new method for the separation of seismic wavelet and reflection coefficient from seismic data using Empirical Mode Decomposition (EMD) which have the superiorities of adaptive decomposition and multi-scale analysis. Firstly, we cut the seismic data into different segmentations and regard each segmentation as stationary signals while combining the characteristics of wavelet and reflection coefficient based on the hypothesis of stationarity. Then, we do preprocessing which is an important step. After preprocessing, Mirror extension inhibit the endpoint effect. Finally, using EMD decomposes the logarithmic amplitude spectrum of each segmentation and selecting different Intrinsic Mode Functions (IMF) which are smooth and continuous restructures the wavelet. The simulation results show that this method can implement the separation of seismic wavelet and reflection coefficient precisely. This paper lay a foundation for later high-precision extraction of seismic wavelet.