基于匹配追踪方法的地震信号时频分析研究

Seismic signal is a typical non-stationary signal, whose frequency is continuously changing with time and is determined by the bandwidth of seismic source and the absorption characteristic of the media underground. The most interesting target of seismic signal’s processing and explaining is to know about the local frequency’s abrupt changing with the time, since this kind of abrupt changing is indicating the changing of the physical attributes of the media underground. As to the seismic signal’s instantaneous attributes taken from time-frequency domain, the key target is to search a effective, non-negative and fast algorithm time-frequency distribution, and transform the seismic signal into this time-frequency domain to get its instantaneous power spectrum density, and then use the process of weighted adding and average etc. to get the instantaneous attributes of seismic signal. Time-frequency analysis as a powerful tool to deal with time variant non-stationary signal is becoming a hot researching spot of modern signal processing, and also is an important method to make seismic signal’s attributes analysis. This kind of method provides joint distribution message about time domain and frequency domain, and it clearly plots the correlation of signal’s frequency changing with the time. The spectrum decomposition technique makes seismic signal’s resolving rate reach its theoretical level, and by the method of all frequency scanning and imaging the three dimensional seismic data in frequency domain, it improves and promotes the resolving abilities of seismic signal vs. geological abnormal objects. Matching pursuits method is an important way to realize signal’s self-adaptive decomposition. Its main thought is that any signal can be expressed by a series of time-frequency atoms’ linear composition. By decomposition the signal within an over completed library, the time-frequency atoms which stand for the signal itself are selected neatly and self-adaptively according to the signal’s characteristics. This method has excellent sparse decomposition characteristics, and is widely used in signal de-noising, signal coding and pattern recognizing processing and is also adaptive to seismic signal’s decomposition and attributes analysis. This paper takes matching pursuits method as the key research object. As introducing the principle and implementation techniques of matching pursuits method systematically, it researches deeply the pivotal problems of atom type’s selection, the atom dictionary’s discrete, and the most matching atom’s searching algorithm, and at the same time, applying this matching pursuits method into seismic signal’s processing by picking-up correlative instantaneous messages from time-frequency analysis and spectrum decomposition to the seismic signal. Based on the research of the theory and its correlative model examination of the adaptively signal decomposition with matching pursuit method, this paper proposes a fast optimal matching time-frequency atom’s searching algorithm aimed at seismic signal’s decomposition by frequency-dominated pursuit method and this makes the MP method pertinence to seismic signal’s processing. Upon the research of optimal Gabor atom’s fast searching and matching algorithm, this paper proposes global optimal searching method using Simulated Annealing Algorithm, Genetic Algorithm and composed Simulated Annealing and Genetic Algorithm, so as to provide another way to implement fast matching pursuit method. At the same time, aimed at the characteristics of seismic signal, this paper proposes a fast matching atom’s searching algorithm by means of designating the max energy points of complex seismic signal, searching for the most optimal atom in the neighbor area of these points according to its instantaneous frequency and instantaneous phase, and this promotes the calculating efficiency of seismic signal’s matching pursuit algorithm. According to these methods proposed above, this paper implements them by programmed calculation, compares them with some open algorithm and proves this paper’s conclusions. It also testifies the active results of various methods by the processing of actual signals. The problems need to be solved further and the aftertime researching targets are as follows: continuously seeking for more efficient fast matching pursuit algorithm and expanding its application range, and also study the actual usage of matching pursuit method.

f-x-y域去除随机噪音方法研究

The seismic survey is the most effective prospecting geophysical method during exploration and development of oil/gas. The structure and the lithology of the geological body become increasingly complex now. So it must assure that the seismic section own upper resolution if we need accurately describe the targets. High signal/noise ratio is the precondition of high-resolution. For the sake of improving signal/noise ratio, we put forward four methods for eliminating random noise on the basis of detailed analysis of the technique for noise elimination using prediction filtering in f-x-y domain. The four methods are put forward for settling different problems, which are in the technique for noise elimination using prediction filtering in f-x-y domain. For weak noise and large filters, the response of the noise to the filter is little. For strong noise and short filters, the response of the noise to the filter is important. For the response of the noise, the predicting operators are inaccurate. The inaccurate operators result in incorrect results. So we put forward the method using prediction filtering by inversion in f-x-y domain. The method makes the assumption that the seismic signal comprises predictable proportion and unpredictable proportion. The transcendental information about predicting operator is introduced in the function. The method eliminates the response of the noise to filtering operator, and assures that the filtering operators are accurate. The filtering results are effectively improved by the method. When the dip of the stratum is very complex, we generally divide the data into rectangular patches in order to obtain the predicting operators using prediction filtering in f-x-y domain. These patches usually need to have significant overlap in order to get a good result. The overlap causes that the data is repeatedly used. It effectively increases the size of the data. The computational cost increases with the size of the data. The computational efficiency is depressed. The predicting operators, which are obtained by general prediction filtering in f-x-y domain, can not describe the change of the dip when the dip of the stratum is very complex. It causes that the filtering results are aliased. And each patch is an independent problem. In order to settle these problems, we put forward the method for eliminating noise using space varying prediction filtering in f-x-y domain. The predicting operators accordingly change with space varying in this method. Therefore it eliminates the false event in the result. The transcendental information about predicting operator is introduced into the function. To obtain the predicting operators of each patch is no longer independent problem, but related problem. Thus it avoids that the data is repeatedly used, and improves computational efficiency. The random noise that is eliminated by prediction filtering in f-x-y domain is Gaussian noise. The general method can't effectively eliminate non-Gaussian noise. The prediction filtering method using lp norm (especially p=l) can effectively eliminate non-Gaussian noise in f-x-y domain. The method is described in this paper. Considering the dip of stratum can be accurately obtained, we put forward the method for eliminating noise using prediction filtering under the restriction of the dip in f-x-y domain. The method can effectively increase computational efficiency and improve the result. Through calculating in the theoretic model and applying it to the field data, it is proved that the four methods in this paper can effectively solve these different problems in the general method. Their practicability is very better. And the effect is very obvious.