保重心脉冲压缩滤波及其在速度自动拾取中的应用

To pick velocity automatically is not only helpful to improve the efficiency of seismic data process, but also to provide quickly the initial velocity for prestack depth migration. In this thesis, we use the Viterbi algorithm to do automatic picking, but the velocity picked usually is immoderate. By thorough study and analysis, we think that the Viterbi algorithm has the function to do quickly and effectually automatic picking, but the data provided for picking maybe not continuous on derivative of its curved surface, viz., the curved face on velocity spectrum is not slick. Therefore, the velocity picked may include irrational velocity information. To solve the problem above, we develop a new method to filter signal by performing nonlinear transformation of coordinate and filter of function. Here, we call it as Gravity Center Preserved Pulse Compressed Filter (GCPPCF). The main idea to perform the GCPPCF as follows: separating a curve, such as a pulse, to several subsection, calculating the gravity center (coordinate displacement), and then assign the value (density) on the subsection to gravity center. When gravity center departure away from center of its subsection, the value assigned to gravity center is smaller than the actual one, but non other than gravity center anastomoses fully with its subsection center, the assigned value equal to the actual one. By doing so, the curve shape under new coordinate breadthwise narrows down compare to its original one. It is a process of nonlinear transformation of coordinate, due to gravity center changing with the shape of subsection. Furthermore, the gravity function is filter one, because it is a cause of filtering that the value assigned from subsection center to gravity center is obtained by calculating its weight mean of subsetion function. In addition, the filter has the properties of the adaptive time delay changed filter, owing to the weight coefficient used for weight mean also changes with the shape of subsection. In this thesis, the Viterbi algorithm inducted, being applied to auto pick the stack velocity, makes the rule to integral the max velocity spectrum ("energy group") forward and to get the optimal solution in recursion backward. It is a convenient tool to pick automatically velocity. The GCPPCF above not only can be used to preserve the position of peak value and compress the velocity spectrum, but also can be used as adaptive time delay changed filter to smooth object curved line or curved face. We apply it to smooth variable of sequence observed to get a favourable source data ta provide for achieving the final exact resolution. If there is no the adaptive time delay-changed filter to perform optimization, we can't get a finer source data and also can't valid velocity information, moreover, if there is no the Viterbi algorithm to do shortcut searching, we can't pick velocity automatically. Accordingly, combination of both of algorithm is to make an effective method to do automatic picking. We apply the method of automatic picking velocity to do velocity analysis of the wavefield extrapolated. The results calculated show that the imaging effect of deep layer with the wavefield extrapolated was improved dominantly. The GCPPCF above has achieved a good effect in application. It not only can be used to optimize and smooth velocity spectrum, but also can be used to perform a correlated process for other type of signal. The method of automatic picking velocity developed in this thesis has obtained favorable result by applying it to calculate single model, complicated model (Marmousi model) and also the practical data. The results show that it not only has feasibility, but also practicability.