| When the technique of collection develops that it is multi-track and multi-variable, data of protecting are increasing fast, and these give us matter for memory and transmission, so it is in urgent need of data compression, and it is going to be the important content of research in the geophysical domain. At present the weak research is in data compression of seismic prospecting, and we need to develop and perfect this technique in order to make it systematic, all-around, practical. I choose "the correlative technical research and application of data compression in seismic protecting" as my content of study, and emphasize two techniques: the discrete cosine transform coding and the orthogonal wavelet transformation coding. In the discrete cosine transform coding, the keystone is that discrete cosine transform coding can wipe off the correlation and recompose data. In the orthogonal wavelet transformation coding, it is to make use of wavelet frequency splitting that may deal with data, and then get the better result of compression. The applied software of the two parts has been executed.The discrete cosine transform coding consists of discrete cosine transform and run length coding mainly. Although discrete cosine transform has been applied to signal and image processing generally, it is seldom applied to data processing of protecting, for there are much difference between them, which seismic prospecting data are of big dynamic range and wealth of low-intermediate frequency information. In the paper, discrete cosine transform is first applied to seismic model. After we make a summary, it is applied to real data of seismic protecting by us. By the way of experiment we find this way has several characters:Discrete cosine transform can utilize correlativity of traces, and concentrate energy of data, so it may distribute bit number reasonably.When transformation coefficient is quantized by run length coding, it is not sensitive to high frequency, therefore this way is restricted within narrow limitsDiscrete cosine transform is orthogonal transform, but there are some small changes between real data and transpositional data. For these changes are difficult to identify on the seismic section, it may not affect geologic interpretation.After compression disposing, along with the different ratio of compression there is different to aberrance in the resumptive data with the appearance of false high frequency. This way has one shortcoming, which can appear the domino offect of "diamonds", so restricted the application of the method. It is to reduce the compression ratio that is the best resoluble measure, and the domino offect of "diamonds" can not be identified by eyes.It is difficult to show quantificationally that data of protecting is compressed by discrete cosine transform, as its effect is relative with the incompact degree and Signal-to-Noise. Hence we give one appropriate Signal-to-Noise on basis of practical situation.Signal-to-Noise is small utilizing discrete cosine transform, and its effect is not perfect, so we need increase signal-to noise with wavelet transformation. Wavelet transformation is called "mathematical microscope", and it is a local transform in the time-frequency domain, then it may analyze function and signal on multi-resolution by flex and translation. We process data using wavelet transform on above character, and can divide data into four parts: low frequency and low wave number, low frequency and high wave number, high frequency and low wave number, high frequency and high wave number. And then we process data to code on basis of character. We must understand lower several questions: firstly, we ought to analyze the reason of bedding in order to know data's character of frequency and wave number. Secondly, different wavelet basis has different effect. The research adopts double quadrature wavelet, which can make the transform reversible and avoid the phasic distortion. Here is daubechies wavelet. Thirdly, we must know the data's resolution, before we c...
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