Petroleum Science >2022, lssue 3: - DOI: https://doi.org/10.1016/j.petsci.2021.12.005.
Complex spherical-wave elastic inversion using amplitude and phase reflection information Open Access
文章信息
作者:Guang-Sen Cheng, Xing-Yao Yin, Zhao-Yun Zong, Ya-Ming Yang
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引用方式:Guang-Sen Cheng, Xing-Yao Yin, Zhao-Yun Zong, Ya-Ming Yang, Complex spherical-wave elastic inversion using amplitude and phase reflection information, Petroleum Science, Volume 19, Issue 3, 2022, Pages 1065-1084, https://doi.org/10.1016/j.petsci.2021.12.005.
文章摘要
Abstract: Unlike the real-valued plane wave reflection coefficient (PRC) at the pre-critical incident angles, the frequency-and depth-dependent spherical-wave reflection coefficient (SRC) is more accurate and always a complex value, which contains more reflection amplitude and phase information. In near field, the imaginary part of complex SRC (phase) cannot be ignored, but it is rarely considered in seismic inversion. To promote the practical application of spherical-wave seismic inversion, a novel spherical-wave inversion strategy is implemented. The complex-valued spherical-wave synthetic seismograms can be obtained by using a simple harmonic superposition model. It is assumed that geophone can only record the real part of complex-valued seismogram. The imaginary part can be further obtained by the Hilbert transform operator. We also propose the concept of complex spherical-wave elastic impedance (EI) and the complex spherical-wave EI equation. Finally, a novel complex spherical-wave EI inversion approach is proposed, which can fully use the reflection information of amplitude, phase, and frequency. With the inverted complex spherical-wave EI, the velocities and density can be further extracted. Synthetic data and field data examples show that the elastic parameters can be reasonably estimated, which illustrate the potential of our spherical-wave inversion approach in practical applications.
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Keywords: Complex seismic traces; Spherical-wave theory; Reflection amplitude and phase; Elastic impedance; Bayesian inversion