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共中心点(CMP)道集通常用把反射系数表示为对称反褶积子波的峰值振幅来提供振幅随炮检距(AVO)变化的信息,这就把反射系数R放在每个炮检距h上给出了一个函数R(h)。但是,我们怎么把入射角5φ与炮检距h相联系,从而得到反射系数函数R(φ)呢?这就需要进行振幅与入射角(AVA)关系的分析。本文的一个目的是通过不依赖于速度的倾角时差校正(DMO)、偏移径向剖面,以及叠前零炮检距偏移之后,求出振幅与入射角之间的关系式。过去进行的有关保持振幅DMO的研究,仅涉及到常炮检距DMO,但没有给出处理之后炮检距和入射角之间的联系。本文的研究结果说明,可以从已成像的数据体中提取相同的反射系数函数,不管这个数据体是使用径向道DMO加零炮检距偏移,还是使用常炮检距DMO加零炮检距偏移,或是直接使用叠前共炮检距偏移。这项研究的数据采集观测系统由平行的、规则间隔的多次覆盖测线组成,并且传播速度为常速。数据中的反射同相轴是由有任意反射函数的、任意方向的3-D平面产生的。DMO算法通过使用Stolt偏移法,对数据的每个径向平面剖面(即2h=V′t),用常速V′/2进行偏移,把每一条线的数据(m,h,t),即中点、半炮检距和时间转换成数据空间(m_1,k,t_1)。合并所有线的数据空间(m_1,k,t_1),将形成一个空间(x,y,k,t_1)。其中,x,y是震源检波点中点位置,k是新的半炮检距,t_1是时间。对每个k的子空间(x,y,t_1)进行动校正(NMO)以及3-D零炮检距偏移,就生成了一个真振幅成像数据体(X,Y,k,T)。数据体中的每个峰值振幅是和入射角有关的反射系数。
Common-center-point (CMP) gathers typically provide information about the amplitude as a function of offset (AVO) by representing the reflection coefficient as the peak amplitude of a symmetric deconvolution wavelet, which places the reflection coefficient R on each shot A function R (h) is given at h. However, how do we associate the angle of incidence 5φ with the offset h to obtain the reflection coefficient function R (φ)? This requires an analysis of the relationship between amplitude and angle of incidence (AVA). One of the purposes of this paper is to find the relationship between amplitude and angle of incidence using DMO independent of velocity, offset radial profile, and prestack zero offset offset. Previous research on maintaining amplitude DMO involved only constant offset DMO but did not give a link between offset and angle of incidence after processing. The results of this study demonstrate that the same reflection coefficient function can be extracted from an imaged volume, regardless of whether this volume is offset by a zero offset using a radial DMO or a DMO Offset, or direct use of prestack common offsets. The data acquisition observation system in this study consisted of multiple, regularly spaced coverage lines with a constant propagation velocity. The reflection events in the data are generated by a 3-D plane of arbitrary direction with an arbitrary reflection function. The DMO algorithm uses the Stolt migration method to shift each radial plane profile of the data (ie, 2h = V’t) with a constant velocity V ’/ 2 and the data for each line (m, h, t ), Ie midpoint, semi-offset and time are converted into data space (m_1, k, t_1). Merging the data space of all the lines (m_1, k, t_1) will create a space (x, y, k, t_1). Where x and y are the midpoint positions of the focal point of the source, k is the new semi-offset and t_1 is the time. A true amplitude imaging data volume (X, Y, k, T) is generated by performing a dynamic correction (NMO) and a 3-D zero offset offset for each k subspace (x, y, t_1). Each peak amplitude in the body of data is a reflection coefficient related to the angle of incidence.