Gulan Zhang

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.

Time–phase amplitude spectra based on a modified short-time Fourier transform

The short-time Fourier transform allows calculation of the amplitude and initial phase distribution of the real signal as functions of time and frequency, whereas the wavelet transform allows calculation of the amplitude and instantaneous phase distribution of the real signal as functions of time and scale. However, for a complete description of the non-stationary signal, we should obtain not only the amplitude, initial phase, and instantaneous phase distribution as functions of time and frequency simultaneously with high precision but also the amplitude distribution as a function of time and phase referred to as the time–phase amplitude spectrum. In this paper, the time–phase amplitude spectrum is presented based on the high-precision time–frequency amplitude spectrum and initial and instantaneous phase spectra that are generated simultaneously by the proposed modified short-time Fourier transform. To minimise the effect of noise on the high-precision time–frequency amplitude spectrum, initial and instantaneous phase spectra, and time–phase amplitude spectrum, the modified short-time Fourier transform is applied to the real signal reconstructed by the peak high-precision time–frequency amplitude spectrum and the high-precision time–frequency instantaneous phase spectrum at that location to obtain the stable high-precision time–frequency amplitude spectrum, initial and instantaneous phase spectra, and stable time–phase amplitude spectrum. Compared with the short-time Fourier transform and wavelet transform, the time–frequency amplitude spectrum and initial and instantaneous phase spectra obtained by the modified short-time Fourier transform have higher precision than those obtained by the short-time Fourier transform and wavelet transform. Analysis of synthetic data shows that the modified short-time Fourier transform can be used not only for the calculation of the high-precision time–frequency amplitude spectrum, initial and instantaneous phase spectra, and time–phase amplitude spectrum but also for signal reconstruction, stable high-precision time–frequency amplitude spectrum, initial and instantaneous phase spectra, and stable time–phase amplitude spectrum. Analysis of real seismic data applications demonstrates that the stable time–phase amplitude spectrum reveals seismic events with high sensitivity and is well-matched for seismic data processing and interpretation.

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.

An efficient and self-adaptive approach for Q value optimization

ABSTRACT The time‐invariant gain‐limit‐constrained inverse Q‐filter can control the numerical instability of the inverse Q‐filter, but it often suppresses the high frequencies at later times and reduces the seismic resolution. To improve the seismic resolution and obtain high‐quality seismic data, we propose a self‐adaptive approach to optimize the Q value for the inverse Q‐filter amplitude compensation. The optimized Q value is self‐adaptive to the cutoff frequency of the effective frequency band for the seismic data, the gain limit of the inverse Q‐filter amplitude compensation, the inverse Q‐filter amplitude compensation function, and the medium quality factor. In the processing of the inverse Q‐filter amplitude compensation, the optimized Q value, corresponding gain limit, and amplitude compensation function are used simultaneously; then, the energy in the effective frequency band for the seismic data can be recovered, and the seismic resolution can be enhanced at all times. Furthermore, the small gain limit or time‐variant bandpass filter after the inverse Q‐filter amplitude compensation is considered to control the signal‐to‐noise ratio, and the time‐variant bandpass filter is based on the cutoff frequency of the effective frequency band for the seismic data. Synthetic and real data examples demonstrate that the self‐adaptive approach for Q value optimization is efficient, and the inverse Q‐filter amplitude compensation with the optimized Q value produces high‐resolution and low‐noise seismic data.

Impact of Q value and gain-limit to the resolution of inverse Q filtering

The earth Q-filter, including the energy dissipation of high frequency wave components and the velocity dispersion, distorts seismic wavelets, reduces the seismic resolution, and causes difficulty to obtain high resolution seismic data. The process of inverse Q-filter attempts to remove the Q-effect to produce high-resolution seismic data, but the numerical instability of inverse Q-filter amplitude compensation reduces the signal-to-noise (S/N) ratio and limits its spatial resolution. In order to control the numerical instability, a large number of papers studying the gain-limit constrained inverse Q-filter amplitude compensation method. But, papers rarely discussing whether gain-limit constrained inverse Q-filter with the medium Q value can certainly improve the seismic data resolution or not, and what gain-limit and Q value should be used in inverse Q-filter in order to improve the resolution. In this paper, we focus on understanding the impact of Q value and gain-limit to seismic data resolution, and studying a novel method to optimize Q value within a certain gain-limit constrained inverse Q-filter amplitude compensation, by which we can achieve the optimum resolution seismic data.