Nonlinear amplitude inversion using a hybrid quantum genetic algorithm and the exact zoeppritz equation

The amplitude versus offset/angle(AVO/AVA)inversion which recovers elastic properties of subsurface media is an essential tool in oil and gas exploration.In general,the exact Zoeppritz equation has a relatively high accuracy in modelling the reflection coefficients.However,amplitude inversion based on it is highly nonlinear,thus,requires nonlinear inversion techniques like the genetic algorithm(GA)which has been widely applied in seismology.The quantum genetic algorithm(QGA)is a variant of the GA that enjoys the advantages of quantum computing,such as qubits and superposition of states.It,however,suffers from limitations in the areas of convergence rate and escaping local minima.To address these shortcomings,in this study,we propose a hybrid quantum genetic algorithm(HQGA)that combines a self-adaptive rotating strategy,and operations of quantum mutation and catastrophe.While the selfadaptive rotating strategy improves the flexibility and efficiency of a quantum rotating gate,the operations of quantum mutation and catastrophe enhance the local and global search abilities,respectively.Using the exact Zoeppritz equation,the HQGA was applied to both synthetic and field seismic data inversion and the results were compared to those of the GA and QGA.A number of the synthetic tests show that the HQGA requires fewer searches to converge to the global solution and the inversion results have generally higher accuracy.The application to field data reveals a good agreement between the inverted parameters and real logs.

Nonlinear Inversion for Complex Resistivity Method Based on QPSO-BP Algorithm

The significant advantage of the complex resistivity method is to reflect the abnormal body through multi-parameters, but its inversion parameters are more than the resistivity tomography method. Therefore, how to effectively invert these spectral parameters has become the focused area of the complex resistivity inversion. An optimized BP neural network (BPNN) approach based on Quantum Particle Swarm Optimization (QPSO) algorithm was presented, which was able to improve global search ability for complex resistivity multi-parameter nonlinear inversion. In the proposed method, the nonlinear weight adjustment strategy and mutation operator were used to enhance the optimization ability of QPSO algorithm. Implementation of proposed QPSO-BPNN was given, the network had 56 hidden neurons in two hidden layers (the first hidden layer has 46 neurons and the second hidden layer has 10 neurons) and it was trained on 48 datasets and tested on another 5 synthetic datasets. The training and test results show that BP neural network optimized by the QPSO algorithm performs better than the BP neural network without initial optimization on the inversion training and test models, and the mean square error distribution is better. At the same time, a double polarized anomalous bodies model was also used to verify the feasibility and effectiveness of the proposed method, the inversion results show that the QPSO-BP algorithm inversion clearly characterizes the anomalous boundaries and is closer to the values of the parameters.

GBO algorithm for seismic source parameters inversion

The use of geodetic observation data for seismic fault parameters inversion is the research hotspot of geodetic inversion, and it is also the focus of studying the mechanism of earthquake occurrence. Seismic fault parameters inversion has nonlinear characteristics, and the gradient-based optimizer(GBO) has the characteristics of fast convergence speed and falling into local optimum hardly. This paper applies GBO algorithm to simulated earthquakes and real LuShan earthquakes in the nonlinear inversion of the Okada model to obtain the source parameters. The simulated earthquake experiment results show that the algorithm is stable, and the seismic source parameters obtained by GBO are slightly closer to the true value than the multi peak particle swarm optimization(MPSO). In the 2013 LuShan earthquake experiment, the root mean square error between the deformation after forwarding of fault parameters obtained by the introduced GBO algorithm and the surface observation deformation was 3.703 mm, slightly better than 3.708 mm calculated by the MPSO. Moreover, the inversion result of GBO algorithm is better than MPSO algorithm in stability. The above results show that the introduced GBO algorithm has a certain practical application value in seismic fault source parameters inversion.

Seismic fluid identification using a nonlinear elastic impedance inversion method based on a fast Markov chain Monte Carlo method

Elastic impedance inversion with high efficiency and high stability has become one of the main directions of seismic pre-stack inversion. The nonlinear elastic impedance inversion method based on a fast Markov chain Monte Carlo （MCMC） method is proposed in this paper, combining conventional MCMC method based on global optimization with a preconditioned conjugate gradient （PCG） algorithm based on local optimization, so this method does not depend strongly on the initial model. It converges to the global optimum quickly and efficiently on the condition that effi- ciency and stability of inversion are both taken into consid- eration at the same time. The test data verify the feasibility and robustness of the method, and based on this method, we extract the effective pore-fluid bulk modulus, which is applied to reservoir fluid identification and detection, and consequently, a better result has been achieved.

Application of artificial bee colony algorithm in Rayleigh wave inversion

In order to solve the problems of multi-parameter,multi-extreme and multi-solution in the nonlinear iterative optimization process of Rayleigh wave inversion,the artificial bee colony(ABC)algorithm is selected for global nonlinear inversion.The global nonlinear inversion method does not rely on a strict initial model and does not need to calculate the derivative of the objective function.The ABC algorithm uses the local optimization behavior of each individual artificial bee to finally highlight the global optimal value in the colony,and the convergence speed is faster.While searching for the global optimal solution,an effective local search can also be performed to ensure the reliability of the inversion results.This paper uses the ABC algorithm to perform Rayleigh wave dispersion inversion on the actual seismic data to obtain a clear undergrounding of shear wave velocity profile and accurately identify the location of the high-velocity interlayer.It is verified that the ABC algorithm used in the inversion of the Rayleigh wave dispersion curve is stable and converges quickly.

Genetic Algorithm-Based Estimation of Nonlinear Transducer

This paper describes an innovative, genetic algorithm based inverse model of nonlinear transducer. In the inverse modeling, using a genetic algorithm, the unknown coefficients of the model are estimated accurately. The simulation results indicate that this technique provides greater flexibility and suitability than the existing methods. It is very easy to modify the nonlinear transducer on line. Thus the method improves the transducer's accuracy. With the help of genetic algorithm (GA), the model coefficients' training are less likely to be trapped in local minima than traditional gradient based search algorithms.

Zoeppritz-based AVO inversion using an improved Markov chain Monte Carlo method

The conventional Markov chain Monte Carlo （MCMC） method is limited to the selected shape and size of proposal distribution and is not easy to start when the initial proposal distribution is far away from the target distribution. To overcome these drawbacks of the conventional MCMC method, two useful improvements in MCMC method, adaptive Metropolis （AM） algorithm and delayed rejection （DR） algorithm, are attempted to be combined. The AM algorithm aims at adapting the proposal distribution by using the generated estimators, and the DR algorithm aims at enhancing the efficiency of the improved MCMC method. Based on the improved MCMC method, a Bayesian amplitude versus offset （AVO） inversion method on the basis of the exact Zoeppritz equation has been developed, with which the P- and S-wave velocities and the density can be obtained directly, and the uncertainty of AVO inversion results has been estimated as well. The study based on the logging data and the seismic data demonstrates the feasibility and robustness of the method and shows that all three parameters are well retrieved. So the exact Zoeppritz-based nonlinear inversion method by using the improved MCMC is not only suitable for reservoirs with strong-contrast interfaces and longoffset ranges but also it is more stable, accurate and antinoise.

基于改进蝴蝶优化算法的瑞利波频散曲线反演方法

瑞利波频散曲线反演问题的多解性和反演目标函数的多极值特点,使得常规非线性优化算法可能会产生收敛不稳定、易陷入局部最优等现象。在基本蝴蝶优化算法的基础上采用动态开关概率,引入非线性自适应权重因子,既增加了算法前期的全局探索能力,也保证了后期的局部开发能力。同时,在迭代过程中对最优解进行逐维柯西变异,利用贪婪算法更新最优位置,引导种群向全局最优靠近,通过对4种常用的Benchmark函数的性能测试,表明改进的蝴蝶优化算法无论是在单峰函数还是多峰函数上的全局寻优能力明显优于遗传算法和粒子群算法。采用不同算法针对3种理论地质模型的频散曲线进行反演,发现改进蝴蝶优化算法在频散曲线含10%随机噪声的情况下仍然能够得到与理论模型更加接近的反演结果。最后将改进蝴蝶优化算法应用于实际瑞利波数据,反演结果与实际钻孔揭露的地层分布情况高度吻合。且与遗传算法和粒子群算法相比,改进蝴蝶优化算法在收敛速度、求解精度和稳定性方面都有显著的提升,具有一定的实用价值和应用前景。

融合非线性收敛因子与变异准反射学习的哈里斯鹰优化算法

针对哈里斯鹰优化算法(Harris Hawks optimization,HHO)易早熟收敛、寻优精度低、收敛速度慢等问题,提出一种融合非线性收敛因子与变异准反射学习的哈里斯鹰优化算法(improved Harris Hawks optimization,IHHO)。首先,初始化阶段引入Circle混沌映射,提高初始化种群多样性和种群位置质量;其次,引入Sigmoid非线性收敛因子,平衡全局探索和局部开发能力;最后,针对HHO算法易陷入局部最优问题,提出变异准反射学习(quasi-reflection-based learning,QRBL)策略,提高种群活力,进一步提高算法局部收敛能力。仿真实验采用13个标准测试函数和1个经典工程问题对改进算法进行测试,结果表明改进算法收敛精度、收敛速度均有较大提高,适用于解决实际问题。

由简正波群延时及幅度反演海底参数

由简正波过滤技术得到1号与2号简正波群延时及幅度。通过匹配简正波群延时差来反演海底的声速、密度等参数,衰减系数由简正波的幅度反演得到。遗传算法用来在参数域内搜索待反演的参数。在 200～500 Hz频率范围内海底衰减系数随频率的变化关系为α=0.29f1.9dB/m（f的单位为 kHz）。最后给出由反演出的参数计算得到的传播损失与实验的声传播损失数据的比较,结果符合的较好。

电阻率层析成像非线性反演

本文通过概率成像法、α中心法、遗传算法的有机结合,实现了2-D 电阻率层析成像非线性反演.首先,根据概率成像结果指示出的2-D 地质异常体的相对电性和大致分布范围,合理地确定出α中心的位置与强度系数的变化范围.其次,以α中心法作为2-D地电结构的正演手段,采用遗传算法反演出各α中心的强度系数.在此基础上,应用α中心法重新构建出地下介质电阻率的分布.通过对均匀半空间中赋存2-D 地质异常体模型的反演试验,获得了令人满意的结果.

全空间条件下矿井瞬变电磁法粒子群优化反演研究

煤矿井下矿井瞬变电磁法(MTEM)探测中,电磁场呈全空间分布,全空间瞬变电磁反演是复杂的非线性问题,目前反演计算中全空间响应主要由半空间响应乘以全空间响应系数来得到,导致反演结果中顶板和底板异常(或前方和后方异常)叠加在一起难以分离,造成分辨率下降.论文提出采用粒子群优化算法(PSO)进行全空间MTEM反演,通过理论分析,在常规的粒子群算法基础上提出了一种新的进化公式改进策略,提高了粒子群算法的寻优能力.基于全空间瞬变电磁场理论,编写了粒子群算法反演程序,进行全空间条件下五层含巷道的复杂模型的反演计算.结合某矿井巷道顶板、底板岩层及断层含水性的探测实例,对实测数据进行反演计算和解释,探测结果得到钻探证实.研究表明,改进的粒子群优化算法对理论模型和实际资料的反演拟合程度较高,实现了矿井顶板、底板视电阻率异常的分离,提高了全空间瞬变电磁勘探资料的解释精度和分辨率.

基于GA-BP理论的储层视裂缝密度地震非线性反演方法

基于GA-BP理论,将自适应遗传算法与人工神经网络技术(BP算法)有机地相结合,形成了一种储层裂缝自适应遗传-神经网络反演方法.这种新的方法是由编码、适应度函数、遗传操作及混合智能学习等组成,即在成像测井裂缝密度数据约束下,通过对目标问题进行编码(称染色体),然后对染色体进行选择、交叉和变异等遗传操作,使染色体不断进化,从而快速获得全局最优解.在反演执行过程中,利用地震数据和成像测井裂缝密度数据之间的非线性映射关系建立训练样本,将GA算法与BP算法有机地结合,优化三层前向网络参数;或将GA与ANFIS相结合,优化ANFIS网络参数.并采用GA算法与TS算法(Tabu Search)相结合的自适应混合学习算法,该学习算法自始至终将GA和BP两种算法按一定的概率比例进行,其概率自适应变化,以达到混合算法的均衡.这种混合算法提高了网络的收敛速度和精度.我们分别利用两个研究地区的6井和1井成像测井裂缝密度数据与地震数据之间的非线性映射关系建立训练样本,对过这两口井的测线的地震数据进行反演,获得了视裂缝密度剖面,视裂缝密度剖面上裂缝分布特征符合沉积相分布特征和岩石力学性质的变化特征.这种视裂缝密度剖面含有储层裂缝的定量信息,其误差可为油气勘探开发实际要求所允许.因此,这种新的方法优于只能作裂缝定性分析的常规裂缝地震预测方法,具有广阔的应用前景.

量子路径积分算法及其在大地电磁反演中的应用

常规非线性反演方法虽然对初始模型的依赖大为减弱,但局部收敛现象和计算速度慢仍然是瓶颈.本文提出了一种新的反演方法——量子路径积分算法(Quantum Path Integral Algorithm,简称QPIA).该方法引入量子力学的横向场、传播子等概念,并充分利用量子隧穿效应,大大提高反演的效率,具体是通过对反演目标函数的构建,并以Feynman的传播子来构成模型的接收概率来实现.在对一维大地电磁模型和实际数据进行试验后,表明该方法比常规反演方法更能够精确、稳定和快速地逼近真实模型.

基于遗传算法的改性渣土非线性本构模型参数识别

根据渣土三轴压缩实验观测数据,建立了基于遗传算法的改性渣土非线性本构模型参数反演方法。以三维有限元模拟的应变值与三轴压缩实验观测应变数据残差平方和最小为参数反演的目标函数。为解决简单遗传算法的早熟和收敛速度慢等问题,采用了浮点编码模式和基于排序的选择策略;提出了针对参数识别反问题的罚函数形式。实际应用结果表明,所提出的参数反演方法具有较快的收敛速度;预测的模型变形与实验观测值具有较高的拟合精度。

基于遗传算法的油气悬架非线性特征参数的无损评测

准确辨识油气悬架的非线性特征参数有利于提高车辆的乘坐舒适性和操纵性能。提出了一种基于遗传算法的非线性刚度和阻尼特性曲线的无损评测方法。在建立油气悬架激励与响应测试系统的基础上,采用少数离散点来表征刚度和阻尼两条非线性特征曲线,通过遗传算法来反求离散点的值,从而通过一次测试就能无损伤地整体得到油气悬架刚度和阻尼的特征曲线。

基于径基函数神经网络的精馏塔自适应控制

精馏塔是化工过程中最常用，最重要的操作单元，其本质的非线性及时变性使得对它的控制非常困难．本文提出了一种基于径基函数（RBF）网络的自适应控制方案．该方案简捷、可靠，具有很强的鲁棒性和抗干扰性能．将该方案应用在工业脱乙烷塔的控制中得到了令人满意的结果．

变臂关节式坐标测量机的参数自标定方法研究

为了有效地提高关节式坐标测量机的测量精度,变臂关节式坐标测量机通过锁定坐标测量机的前2个关节,减小前端关节转角误差的引入降低系统传递误差。经D-H方法建模,变臂关节式坐标测量机和关节式坐标测量机具有相同的数学方程,但是两者具有不同的结构参数,主要差别体现在前2个关节转角。提出了一种基于逆运动学分析方法和非线性规划遗传算法的参数自标定方法。首先,采用Robotics Toolbox中的逆运动学分析方法反求到达同一标定点的多组可能位姿;其次,根据这些反求的位姿结合测量模型计算标定点的坐标值;最后,以单点重复误差为目标函数通过遗传算法进行全局搜索并利用非线性规划算法进行局部搜索获取最优标定结果。实验结果表明,所提出的参数自标定方法使得变臂关节式坐标测量机的单点测量精度提高1 000倍,长度测量精度提高8倍。

自适应逆控制研究及其未来发展

由B.Widrow教授提出的自适应逆控制方法经过十多年的发展,已经取得了很多成果。近年来,自适应逆控制以其自身的众多优点成为一个活跃的研究领域,并被成功应用到各个领域。本文对自适应逆控制的已有研究成果做了综述。首先分析了各种自适应滤波方法,尤其是线性变步长LMS自适应滤波算法的完善过程,每种算法的性能及优缺点;其次分析了基于X-滤波、ε-滤波和U-滤波的自适应逆控制系统在噪声扰动消除上的成功应用。然后介绍了跟随非线性系统的非线性滤波器,及各种针对非线性对象的自适应逆控制系统。最后介绍了自适应逆控制新的应用,并提出了其自身存在的问题,探讨了新的研究方向。

流体饱和多孔介质参数反演的遗传算法

根据位移响应的理论合成应与实际测量数据相拟合的原则,将流体饱和多孔介质参数反演问题归结为非线性多峰函数的最优化问题。全局最优解的求解采用了遗传算法,克服了传统的优化法难于求得全局最优的困难。数值算例的实践表明了遗传算法的可行性和稳健性。