The Study Of Seismic Noise Attenuation And Thin-layer Prediction Technique Based On Hilbetr-huang Transform

The improving of signal-to-noise ratio is always an important task accompaniedby the entire seismic data processing and interpretation. In the high-resolutionseismic data processing, the improving of signal-to-noise ratio is the fundamentaland premise. A general review of various maturity or development of seismic datadenoising method, we can find that, most of the seismic denoising methods andtechniques are built on a basis of common digital technology, these digitaltechnologies often used in similar in other areas, such as enhancement orcompression algorithm of image and graphics, the strengthen or analysis of voiceand mechanical vibration signal, etc., and each digital technology often have a widerange of applications.Advanced digital signal processing technology is conducive tothe imaging and analysis of seismic reflection data with complex geological targets.At present a method to handle non-linear non-stationary signals is the empiricalmode decomposition (EMD) and Hilbert-Huang transform (HHT). First of all, thesignal is decomposed into a series of sub-signal by the empirical modedecomposition, these sub-signals would fall on the same frequency region afterhilbert transform.HHT provides an instantaneous attributes of time and frequencydomain analysis method for random signal or pseudo-random signal. In this paper,the main application of EMD and HHT on the seismic data is:1) evaluate the abilityto identify meaningful geological information of the EMD and HHT in a timelymanner in the time domain to frequency domain,2) Use of EMD decomposition tosuppress random noise in the the f-x doman of the seismic data,3) design a betterfilter to improve the seismic data signal-to-noise ratio by the instantaneous attributesof HHT. In filter design and the actual filtering process, the experience characteristicof the HHT demonstrating, can improve the signal-to-noise ratio better than anyother filte.In this paper we use the simulated data and real seismic data to prove thatthe HHT and EMD can be successfully used in the processing of seismic reflectiondata. In an experiment, a strong industrial AC noise can be eliminated by HHT notchfilter. In another experiment, using the HHT method, surface wave is eliminated,compared with other methods, its better denoising is shown. The main research work of this paper is how to use the EMD and HHT technology applications better inseismic reflection data, try to use the new features of EMD and the HHT in otherareas in the seismic exploration to improve the quality of seismic reflection data.These studies not intended to replace the traditional seismic data processing methods,but rather to supplement the EMD and HHT technology to the processing methods.EMD is a very efficient time-domain filter, the HHT is a good way to analysissignals in terms of time and frequency. Meanwhile, the biggest advantage of HHT isthat it can provide you the instantaneous amplitude the instantaneous frequency andinstantaneous phase information of the signal without any priori information. Prioriinformation usually refers to the sampling rate, the local distribution of time seriessuch as the noise peak and step functions.In this paper, we first introduced the basic concepts and implementation processof the EMD. A signal which is mixed by some harmonic with different frequency istaken for example to illustrate the EMD practices, related concepts, the result ofdecomposition and the original signal reconstruction method. Then we discussed theproblems in EMD decomposition and problems should be noted when the improperEMD decomposition will produce. In order to discuss the f-x domain seismic datadenoising method described f-x deconvolution of the basic principles anddevelopment process, and discusses its limitations, which leads to the view that weshould use the EMD filtering method in the f-x domain to eliminate seismic noiseUsing simulated data with and without noise adding, by comparing thecross-frequency information in the f-x domain to illustrate the feasibility of EMDfilter to suppress noise.F-x domain A EMD and wavelet thresholding joint denoisingmethod was put forward. Using simulated data of simple models and complexmodels we verified the effectiveness of this method and by comparison with othermethods to remove random noise we explained the advantages and disadvantages.Subsequent a denoising test is taken by the actual prestack and poststack seismicdata with random noise, then detailed analysised the denoising results.In this paper, the joint noise attenuation method of the f-x domain EMD andwavelet thresholding is improved from the Battista, BM's EMD denoising method inf-x domain,by the analysis of EMD process we can get a conclusion that directlydeleting IMF1component denoising method is not strictly, this proposed the f-xdomain EMD and wavelet thresholding joint noise attenuation greater degree ofeffective noise attenuation at the same time to retain effective signal components, the overall effect is better than f-x deconvolution and other denoising methods.The HHT filter technology was first introduced into the process in the regularnoise attenuation of seismic data in this article. Including: HHT-based powerfrequency noise attenuation, based on HHT surface wave attenuation methods andHHT-based marine seismic data swell noise attenuation. First analysised thecharacteristics of these types of noise, they are easily separated in the process ofEMD and Hilbert-Huang Transform, and draw conclusions from which that it issuitable to attenuation these kinds of regular seismic data noise for HHT filtermethod. Results show that the HHT-based approach compared to the FFT-basedapproach can better suppress noise and to retain effective signal noise tests onsimulated and real seismic data using the design method.This article briefly introduced surface wave interference with conventionaldenoising methods, and then discusses the advantages and disadvantages of thesemethods.Subsequently, a brief introduction to the basic process of the Hilbert-Huangtransform, and then gived the concept and implementation process of thetime-frequency domain filtering which is based on HHT. Based on the outstandingability of separation and time-frequency analysis of EMD and HHT,we design anotch filter to eliminate the power frequency noise in seismic data based on HHTtechnology. The results of EMD and HHT calculation of seismic data includingpower frequency signal show that in the process of EMD decomposition powerfrequency signal has a more complete separation and a well corresponds to thefrequency signal frequency display on the HHT spectrum. The experimental resultsof single channel containing the power frequency noise of seismic signals to HHTnotch filter denoising show that this method can effectively suppress noise and retaineffective signal. Compared with the FFT-based filtering method, the denoising effectof this method is the best. Then a experiment by the actual data noise was done,frequency filtering methods were compared with the ordinary trap. In this article aresearch of suppression of wave interference on HHT time-frequency filteringopposite was carried out. Synthesis and actual data of surface wave were andanalysis HHT spectrum obtained when the surface waves corresponding to thetime-frequency information, the actual data using the the HHT filter method and FKfiltering are two ways to suppress noise, comparative analysis of the results of thismethod due to the FK surface wave suppression methods. The section3.5is aboutthe study of marine seismic data the HHT filtering method to suppress swell noise. Through the analysis of actual data that directly using the EMD method to weed outthe swell noise is not appropriate, the characteristics of surge noise of the actual data,this paper designs a of HHT low cut filter method, the effective filtering in additionto the at-sea data the noise of the surge, were analyzed with FFT low-cut filterdenoising results show that this method effectively suppress noise while a greaterdegree of retained effectively reflected signal.Two-dimensional de-noising technology and seismic attributes based on BEMDextraction technology is studied. Firstly briefly describes the basic process of BEMDdecomposition, including how to get the local extreme point of the two-dimensionaldata and how to calculate the structural envelope surface through the interpolationcalculation. Then introduced some existing problems in the currently BEMDdecomposition technique and the improving direction. BEMD is used in the filteringof two-dimensional surface data, comparing with other filtting method such an themedian filtering, mean filtering, wiener filtering and wavelet threshold filtering,noise cancellation of the BEMD filtering of the data is the better. BEMD technologyis introduced into the extraction of seismic attribute and the fault information in thecoherent data body. BEMD's separation characteristics of the low and highfrequency in two-dimensional signal can be used to identify seismic structure such asinsidious trap on the time slices. BEMD also has the ability of edge detection andtexture recognition, slices of coherent data BEMD treatment to suppress thenon-structural noise signal to be used to improve the ability to identify faults inseismic coherence data slice, with good results. Fault signal more clearly continuous,fault identification capability in the coherent data.The application of logarithm spectrum in the conventional seismic dataprocessing is deconvolution and wavelet extraction, in this paper, logarithmspectrum is used to estimate the thickness of thin layer In other areas of digital signalprocessing, such as the audio signal processing, the logarithm spectrum (cepstrum) isdefined signal spectrum of the natural logarithm of the logarithm spectrum and thenseek its spectrum. This paper analyzes the spectrum characteristics of the logarithmspectrum, first of all how to use the logarithmic spectral to test the distortion of thesine wave signal generation by a seismograph. Logarithm spectrum used in thedetection of the seismograph signal distortion of which can judgment sine wavedistortion of the signal more quickly and intuitively. Logarithmic spectrum can beused in a thin layer thickness predicting when the resolution is higher than that obtained on the thin layer thickness directly in the time domain and frequencydomain, the thickness of the ability to identify, we use the wedge model test to verifythe identification effect of the thin layer using the logarithmic spectrum, when thedominant frequency of the signal is30Hz, the recognition ability of the thin layer canbe less than10ms. In this paper, a low-frequency signal detection method usinglogarithmic spectra is put forward, comparison with other spectral decompositionmethods. Finally the results show that the capability of logarithmic spectrum in thelow-frequency signal detection process is better comparing with other spectraldecomposition method, in the time and frequency resolution.