波动方程的逆散射与迭代线性化反演

本文讨论的是非线性地球物理反演中目前最重要的方法．即逆散射与线性化这代算法相结合而形成的解析类反演方法。首先应用标量波动方程的逆散射理论推导出反射波场与波慢平方扰动之间满足的非线性积分方程，并阐明波慢平方扰动与反射体之间的关系。然后介绍迭代线性化反演算法的理论和具体计算公式，并专门对泛函求导的公式进行详细的推导。最后，讨论了这一类反演方法的计算技术及参数选择问题。

地震波反演成像方法与技术核心问题分析

常规的地震反演成像分为偏移速度分析与层析成像、叠前深度偏移(角度道集产生)和AVA分析/反演3个重要环节,其中的关键技术是当前勘探地震学中的核心技术。全波形反演(FWI)是理论意义下十分完善的地震波反演成像理论框架。原则上,FWI可以把上述3项常规的反演方法技术合为一体,给出比较理想的反演成像结果。但是,由于叠前地震数据的不完备、地震波正演模拟方法不能很好地模拟实测地震波场、初始模型不够精确、地震子波的未知和空变,使得严格意义下的FWI方法尚不能很好地解决实际问题。通过叠前地震数据和地下介质模型的特征表达,提出把经典的FWI分成透射波层析成像、最小二乘叠前深度偏移成像和反射波层析成像3个线性化反演方法的串联,构成FWI反演成像的实用化流程。针对我国陆上地震数据的特点,指出做好浅层速度模型建立、背景速度模型建立、成像道集层析速度模型建立、最小二乘叠前深度偏移成像、张角及界面倾角道集的产生以及小角度成像道集波阻抗反演,是当前推进FWI反演成像方法技术应用与发展的关键所在。

基于Zoeppritz方程的纵横波联合反演方法及应用

利用叠前道集数据进行叠前地震反演已经成为油藏储层预测及流体检测常用的技术手段。特别是对于致密油气、页岩油气等非常规油气藏的勘探开发,利用叠前地震反演进行地球物理“甜点”储层预测与描述是研究的热点及难点。常用的叠前地震反演方法是基于Zoeppritz方程的线性化近似式,且主要利用反射纵波数据。考虑到近似方程有着小角度入射的条件限制,对远偏移距数据不适用,而远偏移距数据有利于提高弹性参数反演精度,正演时近似方程与精确Zoeppritz方程存在误差,反演过程中累计误差会降低反演精度,因此使用精确Zoeppritz方程可以有效提高反演精度。为了提高叠前地震反演的适定性,本文提出了利用精确Zoeppritz方程解析表达式的反演方法,避免引入Zoeppritz方程近似式的限制条件,极大提高了叠前地震反演的精度,同时反演中联合使用了反射纵波及转换波数据。联合纵波与转换波数据进行反演,通过增加地震叠前反演中的输入信息,提高反演结果的准确度。而且,基于精确Zoeppritz方程的纵横波联合反演得到更准确的纵波速度、横波速度和密度三个参数,可以更好地对油气藏储层进行描述,确定油气藏的有利“甜点”区。同时,反演计算中采用IRLM方法,解决叠前多参数反演的强非线性和不适定问题,提高IRLM反演方法的抗噪能力。最后,通过合成数据及实际数据的应用,反演结果验证了反演方法的适用性,而且该方法抗噪性强,能够进行实际数据的应用。

小波多尺度井间地震波形层析成像方法

裴正林 ,余钦范 .小波多尺度井间地震波形层析成像方法 .石油地球物理勘探 ,2 0 0 1,36 (6 ) :6 80～ 6 89井间地震波形层析成像是一个很复杂的非线性反演问题。常规线性化反演方法的处理结果依赖于初始模型的选择 ,容易陷入局部极小 ;对于复杂模型和大扰动模型 ,用常规线性化反演方法无法得到满意的解。本文提出了小波多尺度井间地震波形层析成像方法。数值实验结果表明 ,小波变换多尺度反演方法极大地改善了现行的线性化反演方法的性能 ,成像效果显著 ;同时 ,该方法还能提供不同分辨率的反演图像 ,有利于图像解释。

最小二乘偏移研究现状及发展趋势

地震勘探的核心目标是尽可能定量地、精确地描述油气藏,地震波成像由定位反射(散射)点位置发展到当前的估计(角度)反射系数是地震勘探的核心需求。一般地,逆时偏移是复杂介质成像最精确的方法,最小二乘偏移成像是估计(角度)反射系数的理想选择。最小二乘偏移成像基于线性反演理论框架,理论上能够消除采集照明不佳的影响、均衡成像振幅以及提高成像分辨率。然而,该理论优势并没有被转化成预期的实用效果,最小二乘偏移技术的生产应用仍然处于试验探索阶段,不能大规模推广应用。在对国内外最小二乘偏移成像技术进行全面调研的基础上,介绍了该技术的方法原理,指明了该技术的理论优势,分析了数据域迭代反演算法和成像域非迭代反演算法两种最小二乘偏移成像技术的特点,认为最小二乘偏移成像技术至今尚未规模化应用于生产的原因在于:(1)背景速度的精度不能满足线性反演成像问题的假设条件;(2)Born近似正演算子不能很好地模拟实际观测数据中的一次反(散)射波;(3)噪声不满足高斯假设条件;(4)子波未知增加了模拟数据的误差;(5)计算量大等。最后指出,合理的数据匹配技巧、合适的正则化技术及近似计算Hessian逆矩阵是未来最小二乘偏移技术应用研究的方向,长期看应该将最小二乘偏移成像融入到全波形反演(FWI)中。

新疆和田小孔径台阵监测能力分析

利用2015年1月1日—6月30日期间和田小孔径台阵记录到的地震活动,用swa软件的线性化反演方法对拾取到的地震信息进行波形分析定位,得出完整的人工地震目录表,并对表格里的信息做数据综合分析。用MSDP分析软件的单纯型定位方法,计算分析新疆台网的地震活动,并和新疆台网在台阵周边200km内这一特定区域所记录的地震信息做对比分析,得出和田小孔径台阵的监测能力。和田台阵地震监测能力下限已达到-2.19级,250km范围内可记录零级以下地震,500km内可记录小于1级地震,800km内可记录小于2级地震。

Nonlinear inversion of electrical resistivity imaging using pruning Bayesian neural networks

Conventional artificial neural networks used to solve electrical resistivity imaging （ERI） inversion problem suffer from overfitting and local minima. To solve these problems, we propose to use a pruning Bayesian neural network （PBNN） nonlinear inversion method and a sample design method based on the K-medoids clustering algorithm. In the sample design method, the training samples of the neural network are designed according to the prior information provided by the K-medoids clustering results; thus, the training process of the neural network is well guided. The proposed PBNN, based on Bayesian regularization, is used to select the hidden layer structure by assessing the effect of each hidden neuron to the inversion results. Then, the hyperparameter αk, which is based on the generalized mean, is chosen to guide the pruning process according to the prior distribution of the training samples under the small-sample condition. The proposed algorithm is more efficient than other common adaptive regularization methods in geophysics. The inversion of synthetic data and field data suggests that the proposed method suppresses the noise in the neural network training stage and enhances the generalization. The inversion results with the proposed method are better than those of the BPNN, RBFNN, and RRBFNN inversion methods as well as the conventional least squares inversion.

Swarm intelligence optimization and its application in geophysical data inversion

The inversions of complex geophysical data always solve multi-parameter, nonlinear, and multimodal optimization problems. Searching for the optimal inversion solutions is similar to the social behavior observed in swarms such as birds and ants when searching for food. In this article, first the particle swarm optimization algorithm was described in detail, and ant colony algorithm improved. Then the methods were applied to three different kinds of geophysical inversion problems： （1） a linear problem which is sensitive to noise, （2） a synchronous inversion of linear and nonlinear problems, and （3） a nonlinear problem. The results validate their feasibility and efficiency. Compared with the conventional genetic algorithm and simulated annealing, they have the advantages of higher convergence speed and accuracy. Compared with the quasi-Newton method and Levenberg-Marquardt method, they work better with the ability to overcome the locally optimal solutions.

A two-stage CO-PSO minimum structure inversion using CUDA for extracting IP information from MT data

The study of induced polarization （IP） information extraction from magnetotelluric （MT） sounding data is of great and practical significance to the exploitation of deep mineral, oil and gas resources. The linear inversion method, which has been given priority in previous research on the IP information extraction method, has three main problems as follows： 1） dependency on the initial model, 2） easily falling into the local minimum, and 3） serious non-uniqueness of solutions. Taking the nonlinearity and nonconvexity of IP information extraction into consideration, a two-stage CO-PSO minimum structure inversion method using compute unified distributed architecture （CUDA） is proposed. On one hand, a novel Cauchy oscillation particle swarm optimization （CO-PSO） algorithm is applied to extract nonlinear IP information from MT sounding data, which is implemented as a parallel algorithm within CUDA computing architecture; on the other hand, the impact of the polarizability on the observation data is strengthened by introducing a second stage inversion process, and the regularization parameter is applied in the fitness function of PSO algorithm to solve the problem of multi-solution in inversion. The inversion simulation results of polarization layers in different strata of various geoelectric models show that the smooth models of resistivity and IP parameters can be obtained by the proposed algorithm, the results of which are relatively stable and accurate. The experiment results added with noise indicate that this method is robust to Gaussian white noise. Compared with the traditional PSO and GA algorithm, the proposed algorithm has more efficiency and better inversion results.

A Quadratic precision generalized nonlinear global optimization migration velocity inversion method

An important research topic for prospecting seismology is to provide a fast accurate velocity model from pre-stack depth migration. Aiming at such a problem, we propose a quadratic precision generalized nonlinear global optimization migration velocity inversion. First we discard the assumption that there is a linear relationship between residual depth and residual velocity and propose a velocity model correction equation with quadratic precision which enables the velocity model from each iteration to approach the real model as quickly as possible. Second, we use a generalized nonlinear inversion to get the global optimal velocity perturbation model to all traces. This method can expedite the convergence speed and also can decrease the probability of falling into a local minimum during inversion. The synthetic data and Mamlousi data examples show that our method has a higher precision and needs only a few iterations and consequently enhances the practicability and accuracy of migration velocity analysis （MVA） in complex areas.