High-resolution Seismic Spectral Decomposition And Its Application

The propagation of seismic waves beneath the surface is non-stable because thecomplexity of subsurface. Spectral decomposition, which can describe the frequencycharacteristics varying with time of seismic signal, is widely used in geophysics. Withthe increase of oil/gas exploration, the exploration targets are shifting into litologicreservoirs, making it more and more difficult because of the complexity of reservoirs.For this reason, conventional spectral decomposition technology is unable to meet theseismic data processing and interpretation needs, and the development of new spectralanalysis technique with high resolution is imminent.We first review the previous research on spectral decomposition, and thenintroduce two new techniques for high resolution spectral decomposition of seismicdata, including reassigned wavelet transform (RWT) and synchrosqueezing wavelettransform (SWT), which both are improved versions of original wavelet transform,making it helpful to seismic data processing and interpretation.One kind of the new seismic spectral decomposition methods is reassignedwavelet transform based on energy reassigment, which assigning its energy to a newposition (its local gravity center). The paper shows how the reassigned wavelettransform works and we compare its resolution with wavelet transform by syntheticdata. We conduct this method on post-stack seismic profile and reveal hydrocarbonreservoir using low-frequency abnormity. Although the readability of time-frequencydistribution can be improved after energy reassignment, it is not invertible and thereconstruction of signal is difficult, which make its application in high-resolutionseismic processing limited.Another high-resolution spectral decomposition method is synchrosqueezingwavelet transform, which is different from the reassigned wavelet transform, focusingthe time-frequency representation by squeezing its value along the frequency axis andoffers an exact reconstruction formula for signals. So it can be widely used in seismicdata processing and interpretation. The theory of synchrosqueezing wavelet transformis stated in this paper, and then we proposed two new denoising algorithms based on SWT, synthetic and field examples show these methods work well. And so on,considering the impact of seismic noise, we also propose an adaptive nonlocal meansseismic denoising method, which is based on the self-similarity of seismic datastructure.Since the absorption of underground layers and seismic wave attenuation, theresolution of seismic data can not meet the accuracy of geophysical and geologicalinterpretation. This paper proposed a seismic frequency width extension method basedon SWT without destorying the energy relationship between relative phase axis,making it conducive to subsequent geological interpretation and lithology inversionwork. Real seismic data show our method performs well.In addition to spherical spreading, filtering effect of earth, etc. It is known whenseismic signal penetrates through hydrocarbon reservoirs, its amplitude will showstrong attenuation and its high frequency components will be attenuated more thanlow-frequency components. This phenomenon is often accompanied by seismic wavedispersion, which has been confirmed by the majority of scientists. Combiningspectral decomposition with avo theory, a new dispersion attribute is proposed in thispaper, which can describe the presence of fluid in reservoirs partly like P-wavevelocity dispersion property, and can be used as a method for hydrocarbon detection.