摘要：Seismic spectral decomposition applications are often restricted to the magnitude component of time-frequency spectra because it has been very challenging to make a meaningful interpretation of the phase information. In seismically thin layers, nevertheless, phase decomposition can be useful for the enhanced delineation of subsurface lateral variations, as seismic phase singularities are believed to tightly relate to the geologic features and geofluid effects. In this letter, we introduce an improved phase decomposition approach based on a high-resolution complex-spectra decomposition technique for better thin reservoir characterizations. The proposed method is applied to decompose seismic responses into the various phase components that sum to reconstruct the original traces. Via assuming the 0 degrees phase seismic data, results from the synthetic and physical modeling examples imply that reflection anomalies associated with reservoir hydrocarbons can be magnified on the specific phase components. This magnification thus allows the reservoir geofluid variations to be better discriminated from certain lithologic influences that also substantially affect the total seismic reflection amplitudes, which are otherwise buried in the broadband responses.

关键字：Reservoirs; Wavelet transforms; Time-frequency analysis; Hydrocarbons; Data models; Approximation algorithms; Spectral analysis; Hydrocarbon detection; phase component; reservoir characterization; spectral decomposition; thin layer

ISSN号： 1545-598X

卷、期、页：卷: 19

发表日期：2022-05-01

影响因子：3.966000

期刊分区（SCI为中科院分区）：三区

收录情况：SCI（科学引文索引印刷版）,地学领域高质量科技期刊分级目录（国外T1）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：IEEE Geoscience and Remote Sensing Letters

参与作者：郭锐,Chen, Mo

第一作者：贺艳晓,王尚旭,袁三一,赵建国

论文类型：期刊论文

论文概要：贺艳晓,王尚旭,郭锐,袁三一,赵建国,Chen, Mo，An Efficient Phase Decomposition of Seismic Reflections From Thin-Layer Targets for Better Reservoir Characterization，IEEE Geoscience and Remote Sensing Letters，2022，卷: 19

论文题目：An Efficient Phase Decomposition of Seismic Reflections From Thin-Layer Targets for Better Reservoir Characterization