共查询到18条相似文献，搜索用时 32 毫秒
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《地球物理学进展》2015,(1)
目前瞬变电磁测深资料的反演基本局限于一维,且多数是基于最小二乘法,实际上是将非线性问题做线性化处理,再利用迭代的方法逐步逼近最优解,所以在一定程度上会丢失许多细节信息,同时极度依赖初始模型,易陷入局部最优解,而非全局最优解.为提高瞬变电磁测深法的反演解释精度,本文以瞬变电磁测深的一维正演理论为基础,同时针对野外实测资料的特点,建立非线性反演问题的目标函数,考虑到现有非线性反演算法存在的易陷入局部优化和过学习等问题,首次引入了一种当前优化领域的十分新颖且性能优良的智能优化算法——ABC(人工蜂群)算法,来求解反演问题.ABC算法对求解多极值非线性问题优势明显,它不需要了解待求解问题特殊信息,只需要对问题进行优劣的比较,通过单独人工蜂的局部寻优的行为,最终将全局最优值突显出来,具有很快的收敛速度.通过在Matlab平台下对算法参数的详细试算研究,最终确定了一套最佳反演参数,并以二、三、四层的地电模型为例进行了验证.在野外实测资料的反演解释中,经过与实地钻孔资料及其他反演方法的对比分析,表明利用ABC算法建立的瞬变电磁测深一维反演系统具有明显的理论优势和实用价值. 相似文献
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可控源音频大地电磁法在资源勘探等领域中发挥着重要的作用.我们把有限差分数值模拟方法用于可控源音频大地电磁三维正演,结合正则化反演方案和共轭梯度反演的思路,将反演中的雅可比矩阵计算问题转为求解两次"拟正演"问题,得到模型参数的更新步长,形成反演迭代,实现了可控源音频大地电磁三维共轭梯度反演算法.该反演算法可用于对有限长度电偶源激发下采集到的可控源音频大地电磁全区(近区、过渡区和远区)视电阻率和相位资料进行三维反演定量解释,获得地下三维模型的电阻率结构.理论模型合成数据的反演算例验证了所实现的可控源音频大地电磁三维共轭梯度反演算法的有效性和稳定性. 相似文献
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文中在于鹏等提出的电阻率和速度随机分布的大地电磁与地震联合反演方法的基础上,将Tikhonov正则化思想引入到联合反演中,加入先验信息进行模型约束,以最小模型为稳定器,采用L曲线方法来确定近似最佳的正则化因子。考虑到线性寻优算法容易陷入局部极小,文中采用非线性的模拟退火方法来实现大地电磁与地震的同步联合反演。通过模型试验的对比分析,我们认为加入有效模型约束的正则化联合反演可以比单纯考虑数据拟合的联合反演和单独反演方法更有效地提高解的稳定性和计算效率,获得更接近实际而且稳定的解。 相似文献
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为进一步提高大地电磁非线性反演的稳定性、运算效率及准确度,将遗传神经网络算法引入大地电磁反演.首先针对大地电磁二维地电模型建立BP（Back Propagation）神经网络基本框架进行学习训练,网络输入为已知地电模型的视电阻率参数,输出为该地电模型参数;再利用遗传算法对神经网络学习训练过程进行优化,计算出多种地电模型网络连接权值和阈值的最优解;最后将最优连接权值和阈值对未知模型进行反演测试,网络输入为未知地电模型的视电阻率参数,输出为该地电模型参数.模型实验表明：遗传神经网络算法充分结合了遗传算法的全局寻优性和神经网络的局部寻优性,相比单一神经网络算法,在网络学习训练中提高了解的收敛成功率和计算速度,在反演测试中能更准确地逼近真实模型.将遗传神经网络算法与最小二乘正则化反演进行对比,理论模型和实测数据都验证了遗传神经网络算法在大地电磁反演中的可行性和有效性. 相似文献
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对于迭代方式的参数化反演方法,如何使反演结果稳定地收敛到整体极小仍是目前大地电磁（MT）反演中急需解决的问题．本文利用小波变换理论中的多尺度分析方法将大地电磁反问题分解为依赖于尺度变量的反问题序列,然后按尺度从大到小的次序依次求解,求解过程中前一个尺度反问题的解作为下一个尺度反问题的初始模型,直到来出对应于尺度为0的原反问题的解为止．该方法称为多尺度反演方法．数值试验和实际资料的反演结果表明,该方法可有效改善传统广义逆反演方法易陷入局部极小的弊端． 相似文献
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Mohammad Israil 《Acta Geophysica》2006,54(2):126-141
Two techniques have been presented for the delineation of boundaries from smooth models obtained by smooth inversion techniques
of geoelectrical sounding data, such as straightforward inversion scheme, Occam’s and Zohdy’s methods. The smooth model consists
of a large number of equally spaced layers, wherein the real geological boundaries are missing. The techniques proposed here
suppress the geologically irrelevant boundaries and support the real structural boundaries present in the geoelectrical data.
In the first technique, solution of linear inverse problem is improved iteratively through weighted minimum norm inverse,
the weight being taken from the current solution. The technique is referred as Iterative Straightforward Inversion Scheme.
The second method is analytical, based on the application of smoothing filter, referred in the literature as edge-preserving
smoothing. A few examples of theoretical magnetotelluric, dc resistivity and field sounding data have been presented to demonstrate
the capabilities of the techniques. The methodologies also reduce the conspicuous oscillations in the smooth solutions caused
by the conversion of sharp boundaries to the smooth ones. 相似文献
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Hopfield neural networks are massive parallel automata that support specific models and are adept in solving optimization problems. They suffer from a ‘rough’ solution space and convergence properties that are highly dependent on the starting model or prior. These detractions may be overcome by introducing regularization into the network in the form of local feedback smoothing. Application of regularized Hopfield networks to over 50 optimization test cases has yielded successful results, even with uniform (minimal information) priors. In particular, the non-linear, one- and two-dimensional magnetotelluric inverse problems have been solved by means of these regularized networks. The solutions compare favourably with those produced by other methods. Such regularized networks, with either hardware or programmed, parallel-computer implementation, can be extended to the problem of three-dimensional magnetotelluric inversion. Because neural networks are natural analog-to-digital converters, it is predicted that they will be the basic building blocks of future magnetotelluric instrumentation. 相似文献
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A method of approximate magnetotelluric sounding (MTS) data inversion is developed on the basis of the representation of the inverse operator by an artificial neural network in classes of geoelectric structures. A methodology of the neural network inversion of magnetotelluric data is proposed for a family of classes of geoelectric structures and the uncertainty of the inferred results is estimated. A neural network algorithm of MTS data inversion is tested using synthetic 2-D data. 相似文献
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地球物理反问题线性化处理之后, 各种反演算法归结为对病态线性方程组的求解. 为了快速准确地计算出地球物理参数, 本文提出了一种全新的基于LSQR算法的混合差分进化算法(Hybrid Differential Evolution Algorithm, HDE). 该算法利用LSQR算法给出DE算法的初始种群, 提高DE算法的计算速度和稳定性. 在不同噪声水平下, 对四种正则化方法Tikhonov、TSVD、LSQR和HDE的反演结果进行详细比较. 理论模型和实际数据反演的结果都表明: 改进的HDE算法应用于地球物理反问题的求解是成功的: 反演结果与原设定模型具有较高的相关性, 在稳定性和准确性上较常规的反演算法都具有一定的优势; 而且不需要给定正则化参数, 具有更强的实用性. 相似文献
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将遗传算法和单纯形算法相结合,得到了一种高效、健全的2D混合地震走时反演方法.把速度场划分为不同的空间尺度,定义网格节点上的速度作为待反演参数,采用双三次样条函数模型参数化,正问题采用有限差分走时计算方法,反问题采用多尺度混合反演方法.首先在较大的空间尺度内反演,然后减小空间尺度,将大尺度的反演结果作为次一级尺度反问题的初始模型,再进行混合反演,如此类推逐次逼近全局最优解.一个低速度异常体的数值模拟试验和抗走时扰动试验表明该方法是有效和健全的.我们将该方法应用到青藏高原东北缘阿尼玛卿缝合带东段上部地壳速度结构研究中,并与前人的成果进行了对比. 相似文献
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Doug Oldenburg 《Surveys in Geophysics》1990,11(2-3):231-270
This paper explores some of the newer techniques for acquiring and inverting electromagnetic data. Attention is confined primarily to the 2d magnetotelluric (MT) problem but the inverse methods are applicable to all areas of EM induction. The basis of the EMAP technique of Bostick is presented along with examples to illustrate the efficacy of that method in structural imaging and in overcoming the deleterious effects of near-surface distortions of the electric field. Reflectivity imaging methods and the application of seismic migration techniques to EM problems are also explored as imaging tools. Two new approaches to the solution of the inverse problem are presented. The AIM (Approximate Inverse Mapping) inversion of Oldenburg and Ellis uses a new way to estimate a perturbation in an iterative solution which does not involve linearization of the equations. The RRI (Rapid Relaxation Inverse) of Smith and Booker shows how approximate Fréchet derivatives and sequences of 1d inversions can be used to develop a practical inversion algorithm. The overview is structured to provide insight about the latest inversion techniques and also to touch upon most areas of the inverse problem that must be considered to carry out a practical inversion. These include model parameterization, methods of calculating first order sensitivities, and methods for setting up a linearized inversion. 相似文献