局部倾角约束最小二乘偏移方法研究

The research on local slope constrained least-squares migration

随着石油勘探难度的进一步加大,地震数据往往存在采样不规则、地震道缺失等现象,如果不对其进行处理,会对后续的地震成像产生影响,引入成像噪音.针对这一问题,一般是通过地震道插值或数据规则化对叠前数据进行处理,然后采用常规的偏移方法进行成像,本文则是将地震成像看作最小二乘反演问题,在共成像点道集引入平滑算子,在共偏移距/角度道集引入平面波构造算子(PWC)进行约束,通过预条件共轭梯度法使得反偏移后数据与输入数据之间的误差达到最小,最终得到信噪比更高、振幅属性更为可靠的成像结果.理论模型和实际资料处理表明,本文方法不仅可以有效压制数据不规则对成像产生的噪音,而且具有更高的成像精度.

As the difficulty of oil exploration increases, the phenomenon of irregular sampling and missing traces often exits in seismic data, which will introduce imaging noise without special data processing. In order to solve the problem, the conventional method is implementing seismic trace interpolation or data regularization to pre-staek data before imaging with conventional migration. In this paper, seismic imaging is considered as least-squares inverse problem, with constraints of smoothing operator in common image gathers and plane-wave constructor in common offset/angle gathers, to obtain an artifact-reduced seismic image by iteratively minimizing the difference between de-migrated data and input data with preconditioning conjugate gradient method. Experimental results on theoretical model and seismic field data show that the proposed method can suppress the imaging noise introduced by data irregularity thus providing a more accurate image.