Study On High Resolution Processing Of CBM Seismic Data And Prediction Of CBM Content

Coalbed methane is a kind of clean energy.The exploration and development of coalbed methane has been gradually concerned all aroud the world.Coalbed methane(CBM)research has become one of the frontiers of seismology,and it has great theoretical significance and realistic significance to deeply understanding geologic structure,explorating complex reservoir and predicting geologic hazard.The resolution of seismic data is to further improve the seismic inversion,seismic attribute analysis,coal reservoir hydrocarbon prediction precision of the important research content.In fact due to the formation of the absorption and the influence of acquisition the conventional seismic exploration seismic record of narrow frequency band,low resolution.To compensate the amplitude and phase of seismic data at the same time,improve the effective bandwidth of seismic data,make the premise in keeping the signal-to-noise ratio of seismic data to improve resolution.This paper discusses the inverse Q filtering method to improve the resolution of seismic data.Time-frequency decomposition method is capable of decomposing seismic trace into a variety of different wavelet with different frequency,width,position and time.The time-frequency analysis as a powerful tool for analyzing time-varying non-stationary signals,become a hotspot in the research of the modern signal processing.Seismic signal belongs to a kind of nonstationary signals,this method provides the time domain and frequency domain analysis of the joint distribution of information,clearly for us describe the relationship between seismic signal frequency changes over time ? This paper mainly carries on time-frequency analysis of seismic data through the matching pursuit and spectra of video.Thin Coalbed caused High lateral variation of CBM in HeShun Area,during the research on high-resolution processing of CBM seismic data,both amplitude and phase can be compensated,the effective bandwidth was expanded,the seismic resolution was improved while keeping S/N ratio.Time-frequency decomposition by matching pursuit was used for analysis on thin layers and prediction of CBM content,the spatial distribution of coal layers and its CBM content can be described by clustering neural network algorithm according to the variation of seismic data in frequency domain.