The Application Of Nonlinear2D-TFPF For Seismic Random Noise Attenuation

With the global demand for oil, gas and mineral resources increasing, thedevelopment of the unknown oil/gas fields and mineral resources becomes important anddifficult in seismic exploration work. However, the subsurface structure and the uncertainresources burial conditions bring about many difficulties to the exploration work. Itrequires us to have an effective means for the seismic data processing to guarantee morevalid information can be found from the collected seismic data in the seismic explorationprocess so as to provide a favorable basis for the new resources. Due to the unknownburial conditions and complex exploration environment, the seismic data usually containsa lot of noise during the acquisition. It makes the signal-to-noise ratio (SNR) is so low thatthe effective information is difficult to identify. Seismic noise is usually divided into twocategories: random noise and correlated noise. In this paper, we mainly talk about therandom noise in land seismic exploration. Random noise is irregular, no laws, andunrelated to each other between the adjacent channels. It does not have a fixed frequencyand distributes in almost the entire frequency band. It has a serious impact on the seismicrecords in SNR. Therefore the random noise suppression is an important and difficult taskin seismic data processing. We analyze the shortcomings of the conventionaltime-frequency peak filtering (TFPF) algorithm in seismic signal processing and propose aquadratic-trace model of TFPF based upon the radial filtering trace ideas. Moreover, theperformance of the new model is verified on different synthetic records and applied in realdata processing.This paper analyzes the shortcomings of the conventional TFPF method in seismic dataprocessing. In the conventional TFPF, the input signal is a channel of seismic record thatcontains plurality of frequency components. According to the relationship between theoptimal window length (WL) and the dominant frequency of the effective signal, we knowthat different frequencies components have different optimal window lengths (WLs). Thus,the fixed window length (WL) used in the conventional TFPF can not be suitable for allfrequencies components. Some frequencies components will have serious damage due tothe unsuitable WL. According to the unbiased estimate property of time-frequency peakfiltering (TFPF) for the linear signal; we know that the higher of the linearity of theeffective signal in the input signal is, the smaller the deviation of TFPF brings about. Thus,improvement of the linearity of the effective signal through resampling the noisy recordsalong some filtering traces is the core idea of the principle of trace-based TFPF.Meanwhile, the matching degree of the filter trace (sample trajectory) and the reflectionevents determines the linearity enhancement of the effective signal. In2011, Wu et al. proposed a radial-trace time-frequency peak filtering (RT-TFPF) method using radialtraces. In this method, the noisy record is resampled along some radial traces to improvethe linearity of the effective signal. However, due to the fixed inclination direction oftraces, the reflection events are usually bent in shape, and the direction is not fixed.Sometimes, the reflection events and the trace can not achieve a good match. So, we focuson the case of bent events and develop the traces from parallel form to the quadratic form.In this paper, we propose a quadratic-trace time-frequency peak filtering includingparabolic-trace model and hyperbolic-trace model. For the case of bent events, thematching degree of the quadratic traces is clearly higher than the radial traces. Thus, thelinearity level of the effective signal is enhanced greater.In the selection of the optimal filtering traces, we have adopted edge detection methodto obtain the approximate edge of the events. Then, we get the envelope of the eventsthrough the curve fitting and compute the similarity of the envelope with quadratic curvesof different bending degree. The greatest similarity corresponds to the optimal filteringtrace. In the resampling process, we take the intersection point of time and the filteringtrace as the sample point to increase the number of samples in the extracted sequences. Inaddition, in order to ensure the smoothness of the extracted sample sequence, we alsomake use of interpolation techniques to reduce the spikes and glitches in the samplesequence. In the hyperbolic-trace model, the bending degree of the filtering trace isadjustable. Meanwhile, we deduce that along hyperbolic traces resampling process can beapproximated by the noised original seismic record x-t domain to the e-t domain. In thenew domain, the dominant frequency of the effective signal decreases and the linearity isimproved greatly. Different sample sequences have different eccentricity, which coincideswith the variable eccentricity mentioned originally. In the quadratic-trace model presentedin this paper, the noisy record is resampled along some quadratic traces. Then the noisyrecord is transformed into a new domain, where the linearity if the effective signal isgreatly improved and thereby the deviation of the instantaneous frequency estimation isreduced.Firstly, the basic principle of seismic exploration, the current research status and thenature of seismic wave and other aspects of seismic exploration related knowledge wereintroduced, so that we can get a deeper understanding of seismic exploration and seismicsignal processing. Moreover, the basic principles of TFPF and its existing improvedmethods are illustrated and the advantages and disadvantages are analyzed and discussed,respectively. The effects of the filtering trace and the window length (WL) on the noisereduction are discussed, respectively. Finally, we use different synthetic records to test theperformance of the quadratic-trace model, and compare it with several existing commonlyused de-noising methods. The experimental results verify that after the quadratic-tracetime-frequency peak filtering (TFPF), the random noise has been effectively suppressedand the effective events become more continuous. In addition, we further applied the novel model to the actual seismic data in random noise suppression. The entire shotrecords and some records denoising results are both given. Experimental results show thesuperiority of the novel model in the events recovery and random noise suppressionsufficiently under low SNR conditions. The reflection events become clearer, morecontinuous; meanwhile some originally broken events are linked to provide more validinformation.