Ya-juan Xue

Application of the Variational-Mode Decomposition for Seismic Time–frequency Analysis

Seismic time-frequency analysis methods play an important role in seismic interpretation for its superiority in significantly revealing the frequency content of a seismic signal changes with time variation. Variational-mode decomposition (VMD) is a newly developed methodology for decomposition on adaptive and quasi-orthogonal signal and can decompose a seismic signal into a number of band-limited quasi-orthogonal intrinsic mode functions (IMFs). Each mode is an AM-FM signal with the narrow-band property and nonnegative smoothly varying instantaneous frequencies. Analysis on synthetic and real data shows that this method is more robust to noise and has stronger local decomposition ability than the empirical mode decomposition (EMD)-based methods. Comparing with the short-time Fourier transform (STFT) or wavelet transform (WT), instantaneous spectrum after VMD promises higher spectral and spatial resolution. Application of the VMD on field data demonstrates that instantaneous spectrum after VMD targets the thickness variation in the coal seam more sensitively than the conventional tools and highlights the fine details that might escape unnoticed. The technique is more promising for seismic signal processing and interpretation.

Wavelet-based cepstrum decomposition of seismic data and its application in hydrocarbon detection

How to use cepstrum analysis for reservoir characterization and hydrocarbon detection nis an initial question of great interest to exploration seismologists. In this npaper, wavelet-based cepstrum decomposition is proposed as a valid technology for nenhancing geophysical responses in specific frequency bands, in the same way as ntraditional spectrum decomposition methods do. The calculation of wavelet-based ncepstrum decomposition, which decomposes the original seismic volume into a series nof common quefrency volumes, employs a sliding window to move over each seismic ntrace sample by sample. The key factor in wavelet-based cepstrum decomposition nis the selection of the sliding-window length as it limits the frequency ranges of the ncommon quefrency section. Comparison of the wavelet-based cepstrum decomposition nwith traditional spectrum decomposition methods, such as short-time Fourier ntransform and wavelet transform, is conducted to demonstrate the effectiveness of nthe wavelet-based cepstrum decomposition and the relation between these two technologies. nIn hydrocarbon detection, seismic amplitude anomalies are detected using nwavelet-based cepstrum decomposition by utilizing the first and second common nquefrency sections. This reduces the burden of needing dozens of seismic volumes nto represent the response to different mono-frequency sections in the interpretation nof spectrum decomposition in conventional spectrum decomposition methods. The nmodel test and the application of real data acquired from the Sulige gas field in nthe Ordos Basin, China, confirm the effectiveness of the seismic amplitude anomaly nsection using wavelet-based cepstrum decomposition for discerning the strong amplitude nanomalies at a particular quefrency buried in the broadband seismic response. nWavelet-based cepstrum decomposition provides a new method for measuring the ninstantaneous cepstrum properties of a reservoir and offers a new field of processing nand interpretation of seismic reflection data.

Application of Local Wave Decomposition in Seismic Signal Processing

Local wave decomposition (LWD) method plays an important role in seismic signal processing for its superiority in significantly revealing the frequency content of a seismic signal changes with time variation. The LWD method is an effective way to decompose a seismic signal into several individual components. Each component represents a harmonic signal localized in time, with slowly varying amplitudes and frequencies, potentially highlighting different geologic and stratigraphic information. Empirical mode decomposition (EMD), the synchrosqueezing transform (SST), and variational mode decomposition (VMD) are three typical LWD methods. We mainly study the application of the LWD method especially EMD, SST, and VMD in seismic signal processing including seismic signal de‐noising, edge detection of seismic images, and recovery of the target reflection near coal seams.