关于共反射面元叠加方法在实际应用中的一些思考

共反射面元（Common Reflection Surface=CRS）叠加是一种特殊的零偏移距成像方法,实践中它具有独立于宏观速度模型和完全数据驱动实现的鲜明特色,CRS叠加理论认为在得到高质量的零偏移距剖面的同时,还可以得到三个有用的波场属性参数剖面反演宏观速度模型,CRS叠加剖面之后的叠后深度偏移质量将超过叠前深度偏移.虽然CRS叠加倡导的成像方式和承诺的上述理想境界带来了全新的启示,但是实践中这些特色同样带来了令人困扰的问题,为此我们提出了倾角分解CRS叠加方法解决这些问题.本文即是作者通过上述实践之后对CRS叠加方法形成的一些思考和总结.     

基于共散射道集的叠前时间偏移/反偏移提高叠前数据质量

为了提高叠前数据质量,将叠前时间偏移/反偏移与共散射点道集相结合,提出了一种新的时间偏移/反偏移方法.利用改进的CRS参数建立精确的速度模型,提高偏移成像质量.将振幅映射到共偏移距顶点来生成共散射点道集,将偏移和中点上的多参数叠加,通过叠加数据,实现了叠前数据增强,道集的数量远高于传统叠前时间偏移的叠加数量.利用基于中点位移、半偏移距和偏移速度的算子进行反偏移处理,能量重新分配回时间域中的每个绕射同相轴,压制噪声,地震资料信噪比和成像精度均得到了提高.提高质量后的叠前数据可用于后续的速度分析、叠加、偏移等常规处理中,效果好于原始CMP道集.模型和实际数据的计算结果均验证了该方法的正确性和有效性,该方法在低信噪比资料的处理中将会有广阔的应用前景.     

倾角分解共反射面元叠加方法

共反射面元（Common Reflection Surface）叠加是一种独立于宏观速度模型的零偏移距剖面成像方法,传统的CRS叠加实现是以数据驱动的方式对属性参数进行自动搜索并对其进行优化合成相应的CRS叠加算子,通过该算子进行叠加能够得到信噪比和连续性更高的零偏移距剖面.但是数据驱动的实现方式带来了不可避免的“倾角歧视现象”,它造成了弱有效反射信号损失和运动学特征失真的问题.本文提出的倾角分解CRS叠加方法成功解决了上述问题,使CRS叠加方法更具实用价值.     

胜利探区基于最优孔径的共反射面元(CRS)叠加的成功应用(英文)

由于CRS叠加考虑了反射层的局部特征和第一菲涅耳带内的全部反射,从而更充分地利用了多次覆盖反射数据的信息。就目前的地震资料处理技术而言,它是最佳的零偏移距成像方式。本论文利用改进型的参数优化技术,得到高质量的CRS运动学参数剖面,并利用参数剖面计算出叠加孔径,实现了基于最优孔径的CRS叠加,使CRS参数的用途得到了充分利用。模型数据和实际资料的试算表明,基于最优孔径的CRS叠加的成像剖面与传统CRS叠加剖面相比,有着较高的信噪比和同相轴的连续性。     

输出道方式的共反射面元叠加方法Ⅱ--实践

CRS MZO方法是一种以输出道成像方式合成零偏移距剖面的共反射面元（Common Reflection Surface）叠加算法,它以完全不同的方式实现了CRS叠加.理论I已经对CRS MZO叠加方法的理论进行了详细介绍,本文进一步将CRS MZO方法用于对实际资料的处理.处理结果表明CRS MZO方法有效地改善了零偏移距剖面的成像质量,体现了CRS叠加理论的特点.在结合倾角分解策略消除了倾角歧视现象后,倾角分解CRS MZO方法完全能够用于处理实际数据,为得到高质量的零偏移距剖面提供了一个新的手段.     

复杂地表条件下共反射面元(CRS)叠加方法研究

在地表地形复杂的情况下,静校正不易做好,这是制约山地资料处理质量的一个很重要的因素.复杂地表共反射面元（CRS）叠加不需对叠前数据做静校正,而且在得到叠加剖面后可以利用叠加得到的波场参数剖面实现基准面重建.地震数据的试算表明,复杂地表CRS叠加得出的剖面与常规处理剖面相比有着较高的信噪比和同相轴连续性.与水平地表CRS叠加不同的是,在复杂地表CRS叠加的时距公式中,波场三参数耦合,难以通过简化CRS道集的方法将它们全部分离并逐个优化.引入模拟退火算法后,有效地解决了这一组合优化的难题.     

共反射面元叠加的应用实践

共反射面元（Common Reflection Surface）叠加是一种不依赖于宏观速度模型的零炮检距剖面成像方法,实现共反射面元叠加依赖于3个波场属性参数的确定,它们分别是零偏移距射线的出射角α、Normal波和Normal Incident Point波出射到地表的波前曲率半径RN和RNIP. 在CRS叠加的理论基础上,本文阐述如何在实际数据上实现CRS叠加. 首先,通过简洁的一维相关性分析在常规叠加剖面上找到对应该共反射面元的一组初始波场属性参数(α,RN,RNIP),然后在对应的叠前数据上应用最优化算法对这组参数进行优化处理,相比初始属性参数,优化后的属性参数能够更好地聚集来自地下反射层的能量,最后应用优化后的属性参数实现最优CRS叠加.     

输出道成像方式的共反射面元叠加方法I——理论

共反射面元（Common Reflection Surface）叠加是一种独立于宏观速度模型的零偏移距成像方法，该方法属于典型的克希霍夫型成像方法. 根据成像方式的不同，克希霍夫型成像方法可以分为两大类：输出道成像方式和输入道成像方式. 考察共反射面元叠加方法，它属于输入道成像方式. 本文基于理论模型数据，实现了输出道成缘方式的CRS叠加方法. 相比传统的输入道成像方式，它具有能够保证大偏移距反射信息的成像精度和计算效率较高的优点，而且更加容易推广到三维情形.     

部分共反射面元叠加技术在海上数据中的应用

本文对共反射面元(CRS)叠加方法做改进,利用得到的波场参数来提高叠前地震资料的质量.利用CRS波场参数做部分CRS叠加,对菲涅尔带内的多个相邻CMP道集做倾角、曲率等校正后合并为一个道集即CRS超道集,可以补齐缺失地震道,实现叠前数据规则化,并提高信噪比.从而使得叠前道集中的同相轴尤其是来自深层的反射有更好的连续性,有利于识别和追踪.提高质量后的叠前道集可用于后续的速度分析、叠加、偏移等常规处理中,效果好于原始CMP道集.模型和实际数据的计算结果验证了该方法的正确性和有效性.该方法在低信噪比资料的处理中将会有广阔的应用前景.     

波动方程法共成像点道集偏移速度建模

叠前深度偏移的成像效果对偏移速度场相当敏感，建立正确的偏移速度场是实现高质量叠前深度偏移成像的关键。首先应用成像精度高的波动方程法叠前深度偏移抽取共成像点道集；然后基于摄动法通过参数化速度函数和改进的剩余曲率分析建立偏移速度误差和成像深度误差的定量关系；最后采用单参数／多参数联合迭代反演实现偏移速度建模。对Marmousi模型的试算结果表明：该方法对复杂地质体具有较强的适应性和较好的建模和成像效果，一般只需分析和控制主要反射层，通过3－4次近代就可以满足精度要求。     

Migration velocity modeling based on common reflection surface gather

The common-reflection-surface (CRS) stacking is a new seismic imaging method, which only depends on seismic three parameters and near-surface velocity instead of macro-velocity model. According to optimized three parameters obtained by CRS stacking, we derived an analytical relationship between three parameters and migration velocity field, and put forward CRS gather migration velocity modeling method, which realize velocity estimation by optimizing three parameters in CRS gather. The test of a sag model proved that this method is more effective and adaptable for velocity modeling of a complex geological body, and the accuracy of velocity analysis depends on the precision of optimized three parameters.     

基于虚拟偏移距方法的各向异性转换波保幅叠前时间偏移

In this paper, we use the method of pseudo-offset migration （POM） to complete converted wave pre-stack time migration with amplitude-preservation in an anisotropic medium. The method maps the original traces into common conversion scatter point （CCSP） gathers directly by POM, which simplifies the conventional processing procedure for converted waves. The POM gather fold and SNR are high, which is favorable for velocity analysis and especially suitable for seismic data with low SNR. We used equivalent anisotropic theory to compute anisotropic parameters. Based on the scattering wave traveltime equation in a VTI medium, the POM pseudo-offset migration in anisotropic media was deduced. By amplitude-preserving POM gather mapping, velocity analysis, stack processing, and so on, the anisotropic migration results were acquired. The forward modeling computation and actual data processing demonstrate the validity of converted wave pre-stack time migration with amplitude-preservation using the anisotropic POM method.     

Velocity model updating using image gathers1

For successful prestack depth migration an accurate velocity model is needed. One method for model updating is based on image gather analysis. In an image gather all reflectors line up horizontally if the correct velocities are used for the depth migration. This is also true for dipping reflectors, as all traces of an image gather belong to the same surface coordinate. The images of the reflector in an image gather curve upwards if the velocity used for the migration is too low, or downwards if the velocity is too high. This deviation can be used for model updating. Curves which depend on depth, offset and a parameter which relates the estimated to the true model are fitted to the image. By calculating the coherence along the deviation curves, this parameter can be estimated and hence an update can be calculated. Formulae are derived for the deviation curves and the update of the velocity depth model for a multilayered model for both shot and common-offset migrated data, with and without gradients. The method is tested on synthetic data with satisfactory results.     

Multifocusing revisited ‐ inhomogeneous media and curved interfaces*

We review the multifocusing method for traveltime moveout approximation of multicoverage seismic data. Multifocusing constructs the moveout based on two notional spherical waves at each source and receiver point, respectively. These two waves are mutually related by a focusing quantity. We clarify the role of this focusing quantity and emphasize that it is a function of the source and receiver location, rather than a fixed parameter for a given multicoverage gather. The focusing function can be designed to make the traveltime moveout exact in certain generic cases that have practical importance in seismic processing and interpretation. The case of a plane dipping reflector (planar multifocusing) has been the subject of all publications so far. We show that the focusing function can be generalized to other surfaces, most importantly to the spherical reflector (spherical multifocusing). At the same time, the generalization implies a simplification of the multifocusing method. The exact traveltime moveout on spherical surfaces is a very versatile and robust formula, which is valid for a wide range of offsets and locations of source and receiver, even on rugged topography. In two‐dimensional surveys, it depends on the same three parameters that are commonly used in planar multifocusing and the common‐reflection surface (CRS) stack method: the radii of curvature of the normal and normal‐incidence‐point waves and the emergence angle. In three dimensions the exact traveltime moveout on spherical surfaces depends on only one additional parameter, the inclination of the plane containing the source, receiver and reflection point. Comparison of the planar and spherical multifocusing with the CRS moveout expression for a range of reflectors with increasing curvature shows that the planar multifocusing can be remarkably accurate but the CRS becomes increasingly inaccurate. This can be attributed to the fact that the CRS formula is based on a Taylor expansion, whereas the multifocusing formulae are double‐square root formulae. As a result, planar and spherical multifocusing are better suited to model the moveout of diffracted waves.     

Multiparametric Traveltime Inversion

In conventional seismic processing, the classical algorithm of Hubral and Krey is routinely applied to extract an initial macrovelocity model that consists of a stack of homogeneous layers bounded by curved interfaces. Input for the algorithm are identified primary reflections together with normal moveout (NMO) velocities, as derived from a previous velocity analysis conducted on common midpoint (CMP) data. This work presents a modified version of the Hubral and Krey algorithm that is designed to extend the original version in two ways, namely (a) it makes an advantageous use of previously obtained common-reflection-surface (CRS) attributes as its input and (b) it also allows for gradient layer velocities in depth. A new strategy to recover interfaces as optimized cubic splines is also proposed. Some synthetic examples are provided to illustrate and explain the implementation of the method.     

Non‐hyperbolic common reflection surface

The method of common reflection surface (CRS) extends conventional stacking of seismic traces over offset to multidimensional stacking over offset‐midpoint surfaces. We propose a new form of the stacking surface, derived from the analytical solution for reflection traveltime from a hyperbolic reflector. Both analytical comparisons and numerical tests show that the new approximation can be significantly more accurate than the conventional CRS approximation at large offsets or at large midpoint separations while using essentially the same parameters.