Time-Frequency Peak Filtering And Its Application On Noise Attenuation For Seismic Reconnoitered Data

With the rapid growth of the petroleum industrial, what is most wanted is the advanced theory of the gas prospecting and the technique that can solve difficult problems in production. Practice has been given in a couple of decades that the development of the theory and the improvement of the technique in gas prospecting play an very important role in some factors: going deep into the gas prospecting, increasing of gas reserves, improving benefit, etc. China has to do more research to develop three technique to adapt to the rapid growth of the gas prospecting, they are: 3-D prospecting, 3-high (high SNR, high resolution, high fidelity) processing, gas forecasting. The former two is considered as backbone technique of gas prospecting, he latter while, a perspective technique. As seismic data processing is concerned, 3-high is the goal of the geophysicists in the long run, and increasing the SNR is the basis of seismic data processing.IN practice, signals are often corrupted by noise, and this has the effect of hindering the recovery of important information encoded in the signal. All signal processing fields including radar, sonar, communications, seismology, and biomedicine suffer from this problem. The performance of signal enhancement algorithms generally depends on the signal-to-noise ratio (SNR). Many signal processing algorithms work well for high SNR situations, but most perform poorly when SNR decreases below a given threshold. In this case, if no adequate alternative algorithm is available, preprocessing or filtering is required to improve the SNR.Adaptive and fixed methods have been developed for signal enhancement of nonstationary signals in noise. Although both approaches have merits, adaptive techniques are generally superior in performance to fixed methods when the signal statistics are nonstationary and unknown. Some of the well-known adaptive filtering techniques are based on the least-mean-squares (LMS) algorithm, the recursive least-squares algorithm (LS), and the Kalman filter (KF).Adaptive filters could not perform very well in certain conditions, however. An example of this is filtering of a nonstationary signal whose spectral content changes quickly with time. The filter designed using LMS approach, for instance, may not adapt quickly enough to track the rapidly changing signal. This is due tothe delayed convergence of the algorithm, which is a function of the signal autocorrelation matrix Eigen values. Further, in many signal processing applications such as electroencephalogram (EEG) signal, the structure of the underlying signal is often unknown and too complicated to model accurately. An attempt to provide a model framework could probably lead to suboptimal results.Recently, efforts have been made to develop Time-frequency analysis, which describes the frequency varying with time. This method uses the joint time-frequency domain to analysis signal and maps the one-dimensional signal to two-dimensional time-frequency plane. It shows the information of the signal in both time domain and frequency domain. Time-frequency analysis can be used to exhibit significant energy concentration around the signal's IF and develop the time-frequency filtering.In 2004, Time-frequency peak filtering (TFPF) have recently been used by B.Boashash to enhance signals, which may be represented as a sum of band-limited nonstationary processes in additive white Gaussian noise(WGN). The noisy signal is encoded as the IF of a unit amplitude frequency modulated (FM) analytic signal. The instantaneous frequency (IF) of the analytic signal is then estimated using standard time-frequency peak detection methods based WVD to obtain an estimate of the underlying deterministic signal. TFPF which is a noncoherent method has already been applied to Newborn EEG data.The aim of this paper is to use TFPF to suppress random noise in a reflection seismic data. The experiments show that a very clean recovery of seismic reflection data can be accomplished. Therefore it indicates the efficiency of the algorithm as a noise-eliminated method for seismic data.Seismic data is the important information resource of geological prospect and exploration. Random noise in seismic reflection data can be introduced by various sources and is often a problem in geophysical data visualization because it obscures fine details and complicates identification of image features.In this paper, Time-frequency peak filtering is used to reconstruct signals from reflection seismic data corrupted by random noise. This method which is a one-dimensional signal enhancement algorithm doesn't need any assumptions to seismic data.Firstly, this paper discussed the basic principle and algorithm realization of TFPF. Secondly get the signal model according to the characteristic of seismic data. This signal model showed the signal composition in reason. Thirdly, simulation was conducted on synthetic seismic data with an event or multi-event and complicated seismic data. Simulations have demonstrated that TFPF could reduce random noise effectively. In comparison with other methods, TFPF used the discarding seismic recordings, so it could save economic resources. Finally, we analyzed the effect of each parameter on TFPF to assist the simulation. There are several factors: window length, sample frequency, iterative times, the noise, etc. When implementing the experiments, we should pay attention to these factors according to the seismic data.The different experiments conducted in this paper demonstrated that this method of signal enhancement could clearly show the position of axis in phase and the Ricker wavelets. We compared seismic data corrupted in noise with the filtering result from the wavelets, Wigner-Ville distribution and Fourier spectrum. It was concluded that reducing random noise of seismic data by TFPF is feasible and has research value.Finally, we use real data to testify the algorithm, the results are also obvious. In this paper, not only we show the figures after TFPF, but also we compare the different figures according to different parameters. We are glad to see that this algorithm is available in processing real seismic exploration data.