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Enhancing Signal Decomposition Using Adaptive Frequency Distributions

Abstract

We present an improved method of signal decomposition to better image stratigraphic features, such as channels, in seismic through the use of adaptive filtering techniques. The seismic attribute relies on having an adaptive algorithm that adapts to the nature of the seismic data input, creating higher resolved distributions of the frequencies. Hence, minimizing over-and-under sampling and resulting in higher resolution imaging. This method proves to be significant in highlighting both minor and major channel features. Our results show that even the smallest and finest of features can be delineated using this technique. According to Marfurt and Chopra (Seismic Attributes for Prospect Identification and Reservoir Characterization, 2007) there are several methods to decompose a seismic signal. We have chosen to adopt a variation of the short windowed Fourier transform, in which we introduce a novel technique of adaptive operator adjustments to the seismic input. The criterion on which it adapts relies on the seismic sampling interval. What distinguishes our method is that we not only decompose the signal, but do so while preserving an even distribution of the sum of frequency occurrences. This in return enables better distinction of seismic features, and as it is adaptive it preserves both minor and major elements. To validate our method, we conducted a comparison study using a standard frequency decomposition attribute. Data from the Gulf of Mexico was used to perform the study. This dataset contains a significant amount of channels, both small and larger channel structures at the same time-depth. Our results show a better delineation of the channel features, an increase in imaging resolution and contrast, and a reduction of geological noise. Overall, our method is giving considerably better decomposition of the seismic signal.