【摘要】 初至拾取是近地表反演的基础数据,采集的原始数据呈指数增长,尤其对于复杂山地采集的低信噪比单炮地震记录,越来越需要具有较高精度的自动初至拾取技术来完成巨大的工作量。这里采用了空变炮内和炮间的初至拾取时窗,实现了基于分维和相关性相结合的初至自动拾取技术。首先根据分形维的变化特征来判别地震波的初至走时;然后再根据拾取的是波峰、波谷,或过零点进行细微的调整,而对于山地采集的低信噪比单炮资料,还不能调整为最优;最后,在此基础上,将相关性技术应用于局部的初至最优化。实际资料的初至拾取结果表明:该方法能有效地改善初至拾取的精度。使用拾取的初至时间进行层析反演,并计算层析静校正量,可用于后续的处理。更多还原

【Abstract】 The first-break of seismic wave is the basic data of near surface inversion. The size of original data has exponential growth, and the ratio of signal and noise (S/N) is low in the common shot point (CSP) gather data of the complex mountainous region. Therefore, the high precision automatic first-break picking technique is more needed to complete a great deal of the work. We combined fractal dimension with correlation method to realize the automatic first-break picking by using the space-variance time window in CSP gather and cross CSP gathers. Firstly, the first-break time of seismic data is distinguished on the basis of variation of fractal dimension, then, the subtle adjustment is operated according to peak, through, or crossing zero point, but for low S/N CSP data, the adjustment is not enough, so the correlation method is applied to local optimization of first-break time. The picked results of real data demonstrate that the technique can effectively improve precision of first-break picking. Tomography inversion is completed on first-break time, and static correct value is calculated for later processing.更多还原