简化光谱分解

S. Munadi, H. Purba
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引用次数: 1

摘要

光谱分解可以显著提高地震数据的分辨率,从而为绘制薄层(如水道砂和任何其他地层特征)提供了新的可能性。它也被用于储层表征。实现光谱分解的方法有三种,即短时傅里叶变换、连续小波变换和匹配追踪分解。其中,匹配追踪分解似乎是最复杂的一种。它是其中分辨率最高的。本文避免了复杂的数学公式,结合实际数据实例,给出了一种简单合理的方法来解释谱分解方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spectral Decomposition Made Simple
Spectral decomposition enables the resolution of seismic data to be improved significantly yielding a new possibility to map thin layers such channel sands and any other stratigraphic features. It has also been used in reservoir characterization. There are three methods for implementing spectral decomposition i.e., The Short Time Fourier Transform, the Continuous Wavelet Transform and the Matching Pursuit Decomposition. Among three of them, the Matching Pursuit Decomposition seems to be the most sophisticated one. It gives the best resolution among them. A simple and logical approach for explaining the spectral decomposition methods together with real data examples are presented in this paper by avoiding complex mathematical formulation.
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