复杂地震波场的自适应流预测插值方法

发布时间：2018-12-09 14:12

【摘要】：地震数据本质上是非平稳的,如何解决复杂非平稳地震波场的数据缺失问题是地震勘探数据处理的重要环节之一。预测滤波器在地震数据处理和分析中具有重要的作用,该技术可以有效地解决地震数据缺失问题,但传统的平稳预测滤波方法无法很好地适应地震数据的非平稳特征;因此,开发高效的复杂地震波场自适应预测插值方法具有重要的工业价值。本文将预测滤波器加入"流处理"的概念,滤波器系数随着地震数据的变化同时更新,此计算过程仅需矢量点积运算,能够提高计算效率并降低内存空间;并以此为基础开发基于流预测滤波的地震数据插值方法。利用多次波的动力学信息,通过互相关技术构建虚拟一次波,有效地解决了缺失数据位置滤波系数估计不准的问题,为插值过程提供了更为合理的滤波器估计,更好地解决了非平稳地震数据的重建问题。对Sigsbee 2B模型和实际数据的测试结果表明,该方法可以合理地针对复杂地震信息完成缺失数据的重建。
[Abstract]:Seismic data is essentially non-stationary. How to solve the problem of missing data of complex non-stationary seismic wave field is one of the important links of seismic exploration data processing. Prediction filter plays an important role in seismic data processing and analysis. This technique can effectively solve the problem of seismic data missing, but the traditional stationary prediction filtering method can not adapt to the non-stationary characteristics of seismic data. Therefore, it is of great industrial value to develop an efficient interpolation method for adaptive prediction of complex seismic wave fields. In this paper, the predictive filter is added into the concept of "stream processing". The filter coefficient is updated with the change of seismic data at the same time. The calculation process only needs vector dot product operation, which can improve the calculation efficiency and reduce the memory space. Based on this, the interpolation method of seismic data based on flow prediction filter is developed. Based on the dynamic information of multiple waves, virtual primary wave is constructed by cross-correlation technique, which effectively solves the problem of inaccurate estimation of the position filtering coefficient of missing data, and provides a more reasonable filter estimation for the interpolation process. A better solution to the problem of non-stationary seismic data reconstruction. The test results of Sigsbee 2B model and actual data show that the method can be used to reconstruct the missing data reasonably for complex seismic information.
【作者单位】：吉林大学地球探测科学与技术学院
【基金】：国家自然科学基金项目(41522404,41430322)~~
【分类号】：P631.4

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