Jun-Xing Cao

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.

Interval Q inversion based on zero-offset VSP data and applications

In order to obtain stable interval Q factor, by analyzing the spectrum of monitoring wavelet and down-going wavelet of zero-offset VSP data and referring the spectrum expression of Ricker wavelet, we propose a new expression of source wavelet spectrum. Basing on the new expression, we present improved amplitude spectral fitting and spectral ratio methods for interval Q inversion based on zero-offset VSP data, and the sequence for processing the zero-offset VSP data. Subsequently, we apply the proposed methods to real zero-offset VSP data, and carry out prestack inverse Q filtering to zero-offset VSP data and surface seismic data for amplitude compensation with the estimated Q value.

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

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

Application of synchrosqueezed wavelet transforms to estimate the reservoir fluid mobility: Synchrosqueezed wavelet transforms

This paper presents a new methodology for estimating reservoir fluid mobility using synchrosqueezed wavelet transforms. Synchrosqueezed wavelet transforms, which adopts a reassignment method, can improve the temporal and spatial resolutions of conventional time-frequency transforms. The synchrosqueezed wavelet transformsbased fluid mobility estimation requires the favourable selection of sensitive lowfrequency segment and more accurate estimation of the change rate of the low frequency segment in the spectrum. The least-squares fitting method is employed in the synchrosqueezed wavelet transforms-based fluid mobility estimation for improving the precision of the estimation of change rate of the low-frequency segment in the spectrum. We validate our approach with a model test. Two field examples are used to illustrate that the fluid mobility estimation using the synchrosqueezed wavelet transforms-based method gives a better reflection of fluid storage space and monitors hydrocarbon-saturated reservoirs well.

AVO forwarding modeling in two-phase media: multiconstrained matrix mineral modulus inversion

AVO forward modeling is based on two-phase medium theory and is considered an effective method for describing reservoir rocks and fluids. However, the method depends on the input matrix mineral bulk modulus and the rationality of the two-phase medium model. We used the matrix mineral bulk modulus inversion method and multiple constraints to obtain a two-phase medium model with physical meaning. The proposed method guarantees the reliability of the obtained AVO characteristicsin two-phase media. By the comparative analysis of different lithology of the core sample, the advantages and accuracy of the inversion method can be illustrated. Also, the inversion method can be applied in LH area, and the AVO characteristics can be obtained when the porosity, fluid saturation, and other important lithology parameters are changed. In particular, the reflection coefficient amplitude difference between the fast P wave and S wave as a function of porosity at the same incidence angle, and the difference in the incidence angle threshold can be used to decipher porosity.

3D P-wave traveltime computation in transversely isotropic media using layer-by-layer wavefront marching: Traveltime computation using layer-by-layer wavefront marching

Subsurface rocks (e.g. shale) may induce seismic anisotropy, such as transverse isotropy. Traveltime computation is an essential component of depth imaging and tomography in transversely isotropic media. It is natural to compute the traveltime using the wavefront marching method. However, tracking the 3D wavefront is expensive, especially in anisotropic media. Besides, the wavefront marching method usually computes the traveltime using the eikonal equation. However, the anisotropic eikonal equation is highly non-linear and it is challenging to solve. To address these issues, we present a layer-by-layer wavefront marching method to compute the P-wave traveltime in 3D transversely isotropic media. To simplify the wavefront tracking, it uses the traveltime of the previous depth as the boundary condition to compute that of the next depth based on the wavefront marching. A strategy of traveltime computation is designed to guarantee the causality of wave propagation. To avoid solving the non-linear eikonal equation, it updates traveltime along the expanding wavefront by Fermat’s principle. To compute the traveltime using Fermat’s principle, an approximate group velocity with high accuracy in transversely isotropic media is adopted to describe the ray propagation. Numerical examples on 3D vertical transverse isotropy and tilted transverse isotropy models show that the proposed method computes the traveltime with high accuracy. It can find applications in modelling and depth migration.

Mixed Cadzow filtering method in fractional Fourier domain

Conventional frequency domain singular value decomposition (SVD) filtering method used in random noise attenuation processing causes bending event damage. To mitigate this problem, we present a mixed Cadzow filtering method based on fractional Fourier transform to suppress random noise in 3D seismic data. First, the seismic data is transformed to the time-frequency plane via the fractional Fourier transform. Second, based on the Eigenimage filtering method and Cadzow filtering method, the mixed high-dimensional Hankel matrix is built; then, SVD is performed. Finally, random noise is eliminated effectively by reducing the rank of the matrix. The theoretical model and real applications of the mixed filtering method in a region of Sichuan show that our method can not only suppress noise effectively but also preserve the frequency and phase of effective signals quite well and significantly improve the signal-to-noise ratio of 3D post-stack seismic 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.