雷云荷电模型量子反演

雷电物理学的发展和雷电防护新理论与新技术的研究需要对雷云荷电结构进行深入探索.利用地面电场观测数据对雷云荷电模型进行地球物理学反演是一个可行的研究途径.实际雷云荷电结构复杂多变,反演目标函数高度非线性,传统的反演方法往往显得无能为力,利用量子反演方法可尝试解决此问题.在总结分析近年发展比较成熟的量子遗传算法(QGA)、量子退火算法(QA)和量子粒子群算法(QPSO)的基础上,针对Amoruso和Lattarulo提出的带电圆盘雷云荷电模型建立反演模型,分别用三种改进的量子反演算法对理论模型计算结果进行了反演实验,发现QA对此模型的反演准确度最高,而QGA的全局收敛速度最快.通过用QGA对一组实际观测数据分别进行的三层、四层、五层带电圆盘模型的反演,对比分析了不同模型结构对实际反演结果的影响.     

量子退火反演的原理和实现

现有的非线性反演方法,大都存在着求解的局部收敛性和计算速度慢的问题. 在与模拟退火方法进行比较的基础上,本文研究了基于量子跃迁过程中量子隧道效应的量子退火反演算法. 数值试验结果表明,量子退火方法应用于地球物理反问题的求解是成功的,它适合于非线性、多极值的地球物理反演问题,与模拟退火反演相比,量子退火反演在退火收敛速度和避免陷入局部极小等方面有着一定的优势,该方法也适用于其他领域非线性最优化问题的求解,具有较强的普适性.     

基于初至波走时层析成像的Tikhonov正则化与梯度优化算法

初至波走时层析成像是利用地震初至波走时和其传播的射线路径来反演地下介质速度的技术.该问题本质上是一个不适定问题,需要使用正则化方法并辅之以适当的最优化技巧.本文从数值优化的角度介绍了初至波走时层析成像的反演原理,建立了Tikhonov正则化层析成像反演模型并提出求解极小化问题的加权修正步长的梯度下降算法.该方法可以从速度模型的可行域中迭代找到一个最优解.数值试验表明,该方法是可行和有应用前景的.     

基于BP神经网络的波阻抗反演及应用

人工神经网络是近期发展最快的人工智能领域研究成果之一.本文在介绍BP神经网络的有关原理的基础上,提出一种基于BP神经网络模型的波阻抗反演方法,该方法克服了常规基于模型的波阻抗反演方法严重依赖于初始模型的选择和易陷入局部最优等局限性.利用该方法对实际地震剖面进行了波阻抗参数反演处理,结果表明人工神经网络方法在波阻抗反演中的应用是可行的并且是有效的.     

跨孔雷达全波形反演成像方法的研究

跨孔雷达全波形反演是一种使用全波形信息反演两钻孔之间地下信息的层析成像技术.常规的层析成像反演大部分采用射线追踪方法,其中基于初至时的射线追踪方法可以反演出速度剖面（介电常数剖面）,基于最大振幅的层析成像可以反演出衰减剖面（电导率剖面）.常规射线追踪方法有许多不足,究其原因是该方法仅使用了小部分的信号信息.为了进一步提高成像分辨率,本文全面推导了全波形跨孔雷达层析成像反演方法,该方法利用雷达波全幅度相位信息能够反演出地下高分辨率的介电常数和电导率图像.本文通过基于局域网的分布式并行算法,有效地解决了巨量数据正演计算问题.文中首先建立了基于单轴各向异性介质完全匹配层的时间域有限差分二维正演算法,进而通过应用包括时间维度在内的全波场信息与残场逆向传播的全波场信息乘积来计算梯度方向,通过求取以步长为自变量的目标函数的极值确定步长公式,并提出以第一次介电常数反演作为同步反演的初始模型,能够有效提高收敛速度.本文对多组模型进行成像实验,取得了较好的反演效果.     

基于波数域梯度场分解的多尺度波形反演方法

全波形反演是一种建立高精度速度模型的有力工具,是偏移模式和层析模式的联合.然而,当初始模型较差、数据缺失低频成分和大偏移距数据缺失时,常规波形反演的层析成分更新较弱.因此,反演过程以偏移模式为主,容易导致反演快速陷入局部极小值.本文发展了基于波数域梯度场分解的多尺度波形反演方法(WGDFWI),从梯度场中分离出层析成分,在反演的初期主要依赖层析分量更新背景速度场,为常规全波形反演建立良好的初始模型.首先,基于一种高效的隐式波场分离方法,将梯度场分解为层析成分和偏移成分.然后在层析梯度上应用二维波数域滤波器,以缓解偏移成分泄露的问题,并利用多尺度反演策略,增强反演的稳定性.利用双层模型和Marmousi模型进行试算的结果表明,该方法可以有效重构背景速度模型,为常规波形反演提供良好的初始模型,有效提高反演精度.     

基于子空间的二维大地电磁量子遗传反演法研究

量子遗传算法作为一种高效的优化算法,仍存在容易陷入局部极值的缺点.为提高算法的高效性,并探讨将算法应用于大地电磁二维反演的可行性和有效性,本文对算法进行了改进,并通过一维两层D型和四层HK型模型数值试验验证了改进的有效性.然后将改进后的算法引入二维大地电磁反演,在引入滑动子空间思想,同时只考虑最简化反演条件的前提下,对...     

多极子阵列声波测井数据的纵横波慢度联合反演方法

多极子阵列声波测井仪器采集的单极和偶极数据受到地层、井孔、仪器测量系统的影响.在处理实际声波测井数据时,必须考虑多极子模式波的频散效应,以及测井仪器在其中的影响.根据仪器等效理论和相位匹配方法,本文提出了一种从多极子阵列声波测井数据中同时获得纵、横波慢度的联合反演方法.这种方法的关键在于利用相同仪器-地层模型计算多极子模式波频散曲线,以此来匹配频域内纵波与横波数据的相位.相对于将泄漏纵波和弯曲波频散效应分开处理的其他方法,该方法不仅可以减少纵横波速度反演的不确定性,而且还避免了从声波数据中提取频散数据的繁琐过程.通过理论分析和现场数据处理证明了本文联合反演方法的准确性和有效性.     

时间二阶积分波场的全波形反演

通过对波场的时间二阶积分运算以增强地震数据中的低频成分,提出了一种可有效减小对初始速度模型依赖性的地震数据全波形反演方法—时间二阶积分波场的全波形反演方法.根据散射理论中的散射波场传播方程,推导出时间二阶积分散射波场的传播方程,再利用一阶Born近似对时间二阶积分散射波场传播方程进行线性化.在时间二阶积分散射波场传播方程的基础上,利用散射波场反演地下散射源分布,再利用波场模拟的方法构建地下入射波场,然后根据时间二阶积分散射波场线性传播方程中散射波场与入射波场、速度扰动间的线性关系,应用类似偏移成像的公式得到速度扰动的估计,以此建立时间二阶积分波场的全波形迭代反演方法.最后把时间二阶积分波场的全波形反演结果作为常规全波形反演的初始模型可有效地减小地震波场全波形反演对初始模型的依赖性.应用于Marmousi模型的全频带合成数据和缺失4Hz以下频谱成分的缺低频合成数据验证所提出的全波形反演方法的正确性和有效性,数值试验显示缺失4Hz以下频谱成分数据的反演结果与全频带数据的反演结果没有明显差异.     

基于强震动资料的破裂过程快速反演及其自动化的可行性

破裂过程快速反演是目前快速获取地震灾害特征的主要手段之一,是震后应急工作的重要内容.近十年来基于远震资料开展的手动快速反演工作取得了长足进步,但在响应时间方面存在固有的局限,阻碍了反演效率的持续提升.我们根据新近发展的IDS （Iterative Deconvolution and Stacking）自动反演方法,尝试反演近场强震动资料确定破裂过程,探讨破裂过程反演自动化的可行性.对近几年国内发生的强震——包括2013年芦山M_W6.6地震、2016年青海门源M_W5.9地震和2016年新疆阿克陶M_W6.6地震——的应用结果表明,采用IDS方法反演强震数据可以得到稳定可靠的破裂模型,且反演计算时间都控制在几十秒内.此外,以2008年汶川M_W7.9地震为例,测试了不同子断层尺度、截止频率和地壳速度结构模型对反演结果的影响,发现滑动分布主要特征不强烈依赖于反演参数和地壳模型,证实了自动反演的稳定性和很强的适应能力.这一研究表明,基于强震动资料的自动反演可能是破裂过程快速反演的主要发展方向.特别地,在未来强震动台网持续发展、强震动数据的质量和共享速度都得到进一步提高之后,这一工作可望纳入到地震参数的常规自动测定工作中,为震后应急和海域地震的海啸预警提供急需的震源模型.     

基于群体搜索的串行蒙特卡罗反演方法的并行算法(英文)

随着并行计算技术的发展,非线性反演计算效率在不断提高,但对于基于单点搜索的非线性反演方法,其并行算法的实现则是一个难题。本文将群体搜索的思想引入到基于单点搜索的非线性反演方法,构建了并行算法,以量子蒙特卡罗方法为例进行了二维地震波速度反演及实际资料波阻抗反演,并测试了使用不同节点数进行计算的效率。计算结果表明:该并行算法在理论和实际资料反演中是可行的和有效的,具有很好的通用性;算法计算效率随着使用节点数的增加而提高,但算法计算效率的提高幅度随着使用节点数的增加逐渐减小。     

Non‐linear prestack seismic inversion with global optimization using an edge‐preserving smoothing filter

Estimating elastic parameters from prestack seismic data remains a subject of interest for the exploration and development of hydrocarbon reservoirs. In geophysical inverse problems, data and models are in general non‐linearly related. Linearized inversion methods often have the disadvantage of strong dependence on the initial model. When the initial model is far from the global minimum, inversion iteration is likely to converge to the local minimum. This problem can be avoided by using global optimization methods. In this paper, we implemented and tested a prestack seismic inversion scheme based on a quantum‐behaved particle swarm optimization (QPSO) algorithm aided by an edge‐preserving smoothing ( EPS) operator. We applied the algorithm to estimate elastic parameters from prestack seismic data. Its performance on both synthetic data and real seismic data indicates that QPSO optimization with the EPS operator yields an accurate solution.     

量子遗传算法在大地电磁反演中的应用

量子遗传算法（QGA）以量子理论为基础,通过利用量子位编码代替经典遗传算法的二进制位编码,利用量子旋转门定向更新种群来代替传统方法中种群的选择、交叉和变异过程,使得算法具有一定的内在并行运算能力和量子的隧道效应,从而加快了搜索速度,改善了收敛速度,并具有更强的全局寻优能力.本文针对地球物理反演问题的非线性、多极值特点提出一套实现方案,通过理论模型和实测数据试验对比研究,表明量子遗传方法在大地电磁反演中的寻优质量和效果明显优于传统遗传算法.     

JOINT INVERSION OF SEISMIC AND GEOELECTRIC DATA RECORDED IN AN UNDERGROUND COAL MINE1

Until the present time the ‘ rock-coal-rock’ layer sequence and offsets in coal-seams in underground coal mines have been detected with the aid of seismic waves and geoelectric measurements. In order to determine the geometrical and petrophysical parameters of the coal-seam situation, the data recorded using seismic and geoelectric methods have been inverted independently. In consequence, the inversion of partially inaccurate data resulted in a certain degree of ambiguity. This paper presents the first results of a joint inversion scheme to process underground vertical seismic profiling data, geolectric resistivity and resistance data. The joint inversion algorithm makes use of the damped least-squares method and its weighted version to solve the linearized set of equations for the seismic and geolectric unknowns. In order to estimate the accuracy and reliability of the derived geometrical and petrophysical layer parameters, both a model covariance matrix and a correlation matrix are calculated. The weighted least-squares algorithm is based on the method of most frequent values (MFV). The weight factors depend on the difference between measured data and those calculated by an iteration process. The joint inversion algorithm is tested by means of synthetic data. Compared to the damped least-squares algorithm, the MFV inversion leads to smaller estimation errors as well as lower sensitivities due to the choice of the initial model. It is shown that, compared to an independent inversion, the correlation between the model parameters is definitely reduced, while the accuracy of the parameter estimation is appreciably increased by the joint inversion process. Thus the ambiguity is significantly reduced. Finally, the joint inversion algorithm using the MFV method is applied to underground field data. The model parameters can be derived with a sufficient degree of accuracy, even in the case of noisy data.     

Two-dimensional joint inversion of radiomagnetotelluric and direct current resistivity data

An algorithm for the two-dimensional (2D) joint inversion of radiomagnetotelluric and direct current resistivity data was developed. This algorithm can be used for the 2D inversion of apparent resistivity data sets collected by multi-electrode direct current resistivity systems for various classical electrode arrays (Wenner, Schlumberger, dipole-diplole, pole-dipole) and radiomagnetotelluric measurements jointly. We use a finite difference technique to solve the Helmoltz and Poisson equations for radiomagnetotelluric and direct current resistivity methods respectively. A regularized inversion with a smoothness constrained stabilizer was employed to invert both data sets. The radiomagnetotelluric method is not particularly sensitive when attempting to resolve near-surface resistivity blocks because it uses a limited range of frequencies. On the other hand, the direct current resistivity method can resolve these near-surface blocks with relatively greater accuracy. Initially, individual and joint inversions of synthetic radiomagnetotelluric and direct current resistivity data were compared and we demonstrated that the joint inversion result based on this synthetic data simulates the real model more accurately than the inversion results of each individual method. The developed 2D joint inversion algorithm was also applied on a field data set observed across an active fault located close to the city of Kerpen in Germany. The location and depth of this fault were successfully determined by the 2D joint inversion of the radiomagnetotelluric and direct current resistivity data. This inversion result from the field data further validated the synthetic data inversion results.     

A joint inversion algorithm to process geoelectric and sutface wave seismic data. Part I: basic ideas1

For the exploration of near-surface structures, seismic and geoelectric methods are often applied. Usually, these two types of method give, independently of each other, a sufficiently exact model of the geological structure. However, sometimes the inversion of the seismic or geoelectric data fails. These failures can be avoided by combining various methods in one joint inversion which feads to much better parameter estimations of the model than the independent inversions. A suitable seismic method for exploring near-surface structures is the use of dispersive surface waves: the dispersive characteristics of Rayleigh and Love surface waves depend strongly on the structural and petrophysical (seismic velocities) features of the near-surface Underground. Geoelectric exploration of the structure Underground may be carried out with the well-known methods of DC resistivity sounding, such as the Schlumberger, the radial-dipole and the two-electrode arrays. The joint inversion algorithm is tested by means of synthetic data. It is demonstrated that the geoelectric joint inversion of Schlumberger, radial-dipole and two-electrode sounding data yields more reliable results than the single inversion of a single set of these data. The same holds for the seismic joint inversion of Love and Rayleigh group slowness data. The best inversion result is achieved by performing a joint inversion of both geoelectric and surface-wave data. The effect of noise on the accuracy of the solution for both Gaussian and non-Gaussian (sparsely distributed large) errors is analysed. After a comparison between least-square (LSQ) and least absolute deviation (LAD) inversion results, the LAD joint inversion is found to be an accurate and robust method.     

飞行高度同时反演的固定翼航空瞬变电磁-维反演

航空电磁测量记录中,不仅感生电动势测量数据有观测误差,而且高度计测量数据也有误差,直接进行常规反演往往导致反演结果不可靠,研究飞行高度数据有误差下的反演算法具有实际意义.本文以层状模型的固定翼时间域航空电磁多分量理论响应数据为例,提出了两种针对飞行高度计记录数据有误差时的正则化反演算法,一个是自适应正则化反演方法,另一...     

利用偏移进行视反射率估计的初步研究

视反射率估计是地震数据处理解释中的一项重要内容,通常采用反演的方法得到.本文以地震偏移和地震线性反演理论相结合为基础,并利用保幅单程波传播算子和保幅波动方程叠前偏移算法以及成像空间中的角度域波动方程偏移成像和照明补偿等方法技术,提出了一种利用单程波波动方程偏移进行地下反射面视反射率估计方法,并进行了理论模型的数值试验.这种估计方法得到的视反射率估计是一种近法向入射的小角度反射率.     

Appraisal of a new 1D weighted joint inversion of ground based and helicopter‐borne electromagnetic data

In order to couple spatial data from frequency‐domain helicopter‐borne electromagnetics with electromagnetic measurements from ground geophysics (transient electromagnetics and radiomagnetotellurics), a common 1D weighted joint inversion algorithm for helicopter‐borne electromagnetics, transient electromagnetics and radiomagnetotellurics data has been developed. The depth of investigation of helicopter‐borne electromagnetics data is rather limited compared to time‐domain electromagnetics sounding methods on the ground. In order to improve the accuracy of model parameters of shallow depth as well as of greater depth, the helicopter‐borne electromagnetics, transient electromagnetics, and radiomagnetotellurics measurements can be combined by using a joint inversion methodology. The 1D joint inversion algorithm is tested for synthetic data of helicopter‐borne electromagnetics, transient electromagnetics and radiomagnetotellurics. The proposed concept of the joint inversion takes advantage of each method, thus providing the capability to resolve near surface (radiomagnetotellurics) and deeper electrical conductivity structures (transient electromagnetics) in combination with valuable spatial information (helicopter‐borne electromagnetics). Furthermore, the joint inversion has been applied on the field data (helicopter‐borne electromagnetics and transient electromagnetics) measured in the Cuxhaven area, Germany. In order to avoid the lessening of the resolution capacities of one data type, and thus balancing the use of inherent and ideally complementary information content, a parameter reweighting scheme that is based on the exploration depth ranges of the specific methods is proposed. A comparison of the conventional joint inversion algorithm, proposed by Jupp and Vozoff ( 1975 ), and of the newly developed algorithm is presented. The new algorithm employs the weighting on different model parameters differently. It is inferred from the synthetic and field data examples that the weighted joint inversion is more successful in explaining the subsurface than the classical joint inversion approach. In addition to this, the data fittings in weighted joint inversion are also improved.