Locci-Lopez, Daniel (School of Geosciences, University of Louisiana at Lafayette) | Zhang, Rui (School of Geosciences, University of Louisiana at Lafayette) | Oyem, Arnold (Department of Earth and Atmospheric Sciences, University of Houston) | Castagna, John (Department of Earth and Atmospheric Sciences, University of Houston)
Summary Multi-resolution spectral decomposition methods such as the S-transform and the Continuous Wavelet Transform, are known to distort spectral attributes such as peak frequency. We introduce a spectral decomposition approach via a multi-scale Fourier Transform that utilizes a frequency-dependent temporal window to achieve any desired combination of temporal and frequency resolution. We investigate a specific frequency-dependent window that focusses the analysis on the full-width at half-maximum of a frequency-dependent Gaussian function. The resulting time-frequency analysis has significantly improved timeresolution relative to the S-transform. This is demonstrated on real seismic data in the Permian Basin.
We propose a new technique to capture the acoustic impedance (AI) from the poststack attenuated seismic data. To eliminate the discontinuity of inverted AI profile with single-trace processing strategy, a dimensional reduction operation and a modified alternating direction method of multipliers (ADMM) are introduced to search the global optimal AI solution of inversion function. On the ADMM framework the L-BGFS algorithm and generalized iterated shrinkage algorithm are utilized to solve the sub-optimization problems separately. New technique could recover the absolute AI from attenuated seismicdata directly. Even this inversion problem is strong ill-posed and nonlinear. In accordance with the assumption of sparse reflectivity generated by the AI, we append the
Presentation Date: Monday, October 15, 2018
Start Time: 1:50:00 PM
Location: Poster Station 11
Presentation Type: Poster