地震优化非线性反演方法及应用研究

地震优化非线性反演方法是一种集遗传算法和人工神经网络技术的优势于一体的新技术,它采用混合智能优化学习方法,这种优化学习方法是将BP算法作为一个算子嵌入到自适应遗传算法中,以概率Pbp的方式进行搜索运算,从而,快速而精确地找到全局最优解。在嘉陵江组的实际反演研究中,由井点出发构造测井资料与井旁地震道的非线性映射关系,并自适应地更新这种非线性映射关系和自适应外推,以获得嘉陵江组的高分辨率反演剖面,这种高分辨率剖面清晰而详细地反映出嘉陵江组在纵横方向上的变化特征,有效识别这种薄储层,获得了很好的储层预测效果,并为钻井所证实,在嘉陵江组储层中获得高产工业气流。因此,地震优化非线性反演方法具有广阔的应用前景。

非线性最优化反演在河西务南部石炭—二叠系滚动勘探中的应用

非线性最优化反演技术是基于地震信息以及构造模型和测井信息形成的约束，采用最优化方法进行反演来最大限度地推断储层横向变化。应用非线性最优化反演技术预测河西务南部石炭-二叠系的含油储层分布取得了良好的效果，为本区滚动勘探的突破提供了有力的决策依据。

地震道非线性最优化反演

在非线性最优化理论的基础上提出了地震道反演的非线性最优化方法,推导出地震道非线性最优化反演所需的梯度向量、Hasse矩阵,并对测井约束反演、反演稳定性等问题进行了探讨.理论模型和实际资料的反演结果表明,这一方法是地震道反演的一种有效手段.

动水射流多参数多目标非线性遗传优化耦合反演方法研究

本文将遗传算法和变密度各项异性湍浮力射流模型有机结合,提出了满足最佳稀释效果和最低能源消耗的射流比与射流喷角多参数多目标非线性遗传优化耦合反演的新方法:将射流模型和运行能源消耗公式嵌入到遗传算法模型中,以最佳污水稀释效果和最低能源消耗为目标,进行射流模型多参数的优化耦合反演。研究结果表明基于非线性遗传优化的多参数多目标耦合反演方法可以获得最优的射流参数,有利于提高射流水体与环境水体间的掺混效果,降低污水输送能源消耗。

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.

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.

Inversion of 3D density interface with PSO-BP method

BP( Back Propagation) neural network and PSO( Particle Swarm Optimization) are two main heuristic optimization methods,and are usually used as nonlinear inversion methods in geophysics. The authors applied BP neural network and BP neural network optimized with PSO into the inversion of 3D density interface respectively,and a comparison was drawn to demonstrate the inversion results. To start with,a synthetic density interface model was created and we used the proceeding inversion methods to test their effectiveness. And then two methods were applied into the inversion of the depth of Moho interface. According to the results,it is clear to find that the application effect of PSO-BP is better than that of BP network. The BP network structures used in both synthetic and field data are consistent in order to obtain preferable inversion results. The applications in synthetic and field tests demonstrate that PSO-BP is a fast and effective method in the inversion of 3D density interface and the optimization effect is evident compared with BP neural network merely,and thus,this method has practical value.

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.