共查询到19条相似文献，搜索用时 829 毫秒
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常规三维大地电磁反演的正则项为L2范数,它以电阻率空间分布函数处处光滑为模型期望,弱化了算法对电性突变界面的分辨能力.本文实现了正则项为L1范数的三维大地电磁反演算法,让模型空间梯度向量更有机会取得稀疏解,在充分正则的迭代下能够有效突出模型真实电性界面.为避免L1范数零点不可导带来的求解困难,使用迭代重加权最小二乘法把原问题转换为一系列L2正则子问题迭代求解.每个子问题的极小方法使用改进型拟牛顿法,其下降方向既能保证正则项海塞矩阵的精确性,又能允许反演过程随迭代灵活更新正则因子.使用比值法或分段衰减法自适应更新正则因子以避免迭代早期陷入奇异解,从而提升反演收敛的稳定性并降低初始模型依赖度.合成的无噪数据反演表明L1正则算法的模型恢复效果优于L2正则;不同噪声水平的合成数据反演表明本文的算法具有稳健性;实测数据反演对比表明在合理的正则因子调整策略下,L1正则反演结果的模型分辨率优于L2正则.另外,不同初始模型的反演测试还表明,正则因子选取不合理时L1正则可能造成方块状假异常. 相似文献
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《应用地球物理》2015,(2)
<正>则化反演通过引入模型约束和正则化因子求解病态的地球物理反演问题,但该方法存在正则化因子选取困难和初始模型依赖的问题。针对该问题,本文提出多目标粒子群反演算法。该算法反演中不需要目标函数梯度信息和正则化因子,先同时求数据拟合和模型约束的多目标反演解集,再权衡两者的相对重要程度,最后从反演解集中优选出最终反演结果,从而起到正则化因子的作用。以二维磁测数据反演为例,进行理论模型反演试验,试验结果表明,多目标粒子群反演算法能尽可能多地保留可行解,得到反演解集;通过分析反演解集,既能深入的理解反演过程,又能灵活地从数据拟合和模型约束两方面进行权衡与选择,得到比正则化反演更合理的反演结果;该算法能同时解决正则化因子选取困难和初始模型依赖问题。 相似文献
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本文针对ASD-POCS算法中约束项权重对不同应用的多变性引起的算法鲁棒性差等问题,提出了一种基于稀疏约束的自适应正则化迭代重建算法,该算法采用一种Lagendijk型的正则化策略构造最优化问题,分别采用局部方差、图像能量估计自适应地求取加权对角矩阵和全局正则化参数。最优化问题的求解过程中,采用SART算法和共轭梯度法求解保真项和约束项最优化问题。实验结果表明,AR-SART-CG算法能更好地权衡恢复图像边缘和平滑噪声的关系,更好地调节保真项和约束项的权重,得到更高质量的重建图像。 相似文献
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针对大地电磁正则化反演中正则化因子的选取困难问题提出了自适应正则化反演算法（Adaptive Regularized Inversion Algorithm, ARIA）. 在该算法中， ①提出了一种新的数据方差处理方法：数据方差规范化，使得数据方差的大小只对数据的拟合发生影响，不对数据目标函数和模型约束目标函数的权重产生影响，从而减少了正则化因子取值的影响因素；②提出了粗糙度核矩阵的概念，并给出了由基本结构插值基函数计算粗糙度核矩阵的公式，使得模型目标函数的构建更为简便、直接；③根据数据目标函数、模型约束目标函数和正则化因子之间的关系，提出了两种正则化因子自适应调节方法. 本文详细阐述了最平缓模型约束下的大地电磁一维连续介质反演的ARIA实现，以几个算例的分析比较来说明ARIA的有效性. 相似文献
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《地球物理学进展》2016,(3)
航空电磁探测数据量大,二维、三维反演算法复杂、计算速度慢,通常采用一维反演,利用层状模型拼接描述地下复杂结构,但航空电磁数据信噪比低,容易引起一维反演结果横向连续性差等问题.本文针对上述问题,基于一维反演算法,通过整合测线观测数据,建立了测线数据整体的目标函数,并根据Tikhonov正则化反演理论,引入包含空间粗糙度和先验信息的模型参数约束项,确定了拟二维整体反演的目标函数,推导了反演迭代方程组,利用超松弛共轭梯度算法,求得由于整条测线整体反演所致的大型稀疏矩阵的极小化解,实现了对整条测线数据同时反演的固定翼航空电磁数据的拟二维整体反演算法.在反演迭代过程中,正则化因子采用线性搜索自适应迭代的方法自动选取,提高了反演结果的稳定性.对比分析了仿真数据的一维反演与拟二维整体反演结果,得出拟二维整体反演算法横向连续性较好,对高导覆盖层下的导体分辨率优于一维反演,同时受高斯噪声的影响较小.最后,将直升机飞行实测噪声加入仿真数据中,拟二维整体反演结果平均相对误差较一维反演结果降低了31.6%,进一步验证了拟二维整体反演算法的有效性. 相似文献
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《地球物理学进展》2016,(1)
本文基于重力梯度张量密度反演基本理论,建立了模型约束正则化密度反演矩阵方程.分析了代数重构算法(ART)中迭代初始值、松弛因子和终止条件三个关键参数的影响;与最小二乘求逆法对应比较分析了算法的时间和精度.结果表明:在地震、地质等地球物理手段提供初值、边界等约束较多的情况下,ART可以克服方程的不适定进行直接求解,并且合理的松弛因子和终止条件可有效提高反演效率.当初始信息不足时,添加光滑假设、深度加权等模型约束,正则化方程可以提高反演结果的可靠性.ART的行迭代可有效避免观测误差的积累和矩阵求逆的计算,从而使计算精度和速度提高数倍.最后基于GOCE地球重力场模型所得重力梯度,以地震层析成像所得速度模型为约束,对华北克拉通密度结构进行了反演,并与该区已有密度研究结果进行了对比.结果表明:利用GOCE重力场系数计算重力梯度扰动,以速度模型为约束,基于代数重构算法进行重力梯度反演所得密度模型与重力-地震联合反演所得密度模型具有很好的对应性.ART算法为重力梯度张量反演中大规模复杂问题的快速计算提供了又一种有效手段. 相似文献
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随着重力和重力梯度测量技术的日趋成熟,基于重力和重力梯度数据的反演技术得到了广泛关注.针对反演多解性严重、计算效率低和内存消耗大等难点问题,本文开展了三维重力和重力梯度数据的联合反演研究,该方法结合重力和重力梯度两种数据,将L0范数正则化项加入到目标函数中,并在数据空间下采用改进的共轭梯度算法求解反演最优化问题.同时,本文摒弃了依赖先验信息的深度加权函数,引入了自适应模型积分灵敏度矩阵,用来克服因重力和重力梯度数据核函数随深度增加而衰减引起的趋肤效应问题.为了提高反演计算效率,本文又推导出基于规则网格化的重力和重力梯度快速正演计算方法.模拟试算表明,改进的共轭梯度法可以降低反演的迭代次数,提高反演的收敛速度;自适应模型积分灵敏度矩阵,可以有效解决趋肤效应,提高反演纵向分辨能力;数据空间和改进的共轭梯度算法结合,可以更好地降低反演求解方程的维度,避免存储灵敏度矩阵,有效地降低反演计算时间和内存消耗量.野外实例表明,该算法可以在普通计算机下快速地获得地下密度分布模型,表现出较强的稳定性和适用性. 相似文献
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不规则采样地震数据的重建是地震数据分析处理的重要问题.本文给出了一种基于非均匀快速傅里叶变换的最小二乘反演地震数据重建的方法,在最小二乘反演插值方程中,引入正则化功率谱约束项,通过非均匀快速傅里叶变换和修改周期图的方式,自适应迭代修改约束项,使待插值数据的频谱越来越接近真实的频谱,采用预条件共轭梯度法迭代求解,保证了解的稳定性和收敛速度.理论模型和实际地震数据插值试验证明了本文方法能够去除空间假频,速度快、插值效果好,具有实用价值. 相似文献
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A common example of a large-scale non-linear inverse problem is the inversion of seismic waveforms. Techniques used to solve this type of problem usually involve finding the minimum of some misfit function between observations and theoretical predictions. As the size of the problem increases, techniques requiring the inversion of large matrices become very cumbersome. Considerable storage and computational effort are required to perform the inversion and to avoid stability problems. Consequently methods which do not require any large-scale matrix inversion have proved to be very popular. Currently, descent type algorithms are in widespread use. Usually at each iteration a descent direction is derived from the gradient of the misfit function and an improvement is made to an existing model based on this, and perhaps previous descent directions. A common feature in nearly all geophysically relevant problems is the existence of separate parameter types in the inversion, i.e. unknowns of different dimension and character. However, this fundamental difference in parameter types is not reflected in the inversion algorithms used. Usually gradient methods either mix parameter types together and take little notice of the individual character or assume some knowledge of their relative importance within the inversion process. We propose a new strategy for the non-linear inversion of multi-offset reflection data. The paper is entirely theoretical and its aim is to show how a technique which has been applied in reflection tomography and to the inversion of arrival times for 3D structure, may be used in the waveform case. Specifically we show how to extend the algorithm presented by Tarantola to incorporate the subspace scheme. The proposed strategy involves no large-scale matrix inversion but pays particular attention to different parameter types in the inversion. We use the formulae of Tarantola to state the problem as one of optimization and derive the same descent vectors. The new technique splits the descent vector so that each part depends on a different parameter type, and proceeds to minimize the misfit function within the sub-space defined by these individual descent vectors. In this way, optimal use is made of the descent vector components, i.e. one finds the combination which produces the greatest reduction in the misfit function based on a local linearization of the problem within the subspace. This is not the case with other gradient methods. By solving a linearized problem in the chosen subspace, at each iteration one need only invert a small well-conditioned matrix (the projection of the full Hessian on to the subspace). The method is a hybrid between gradient and matrix inversion methods. The proposed algorithm requires the same gradient vectors to be determined as in the algorithm of Tarantola, although its primary aim is to make better use of those calculations in minimizing the objective function. 相似文献
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Full waveform inversion and the truncated Newton method: quantitative imaging of complex subsurface structures 总被引：2，自引：0，他引：2
Full waveform inversion is a powerful tool for quantitative seismic imaging from wide‐azimuth seismic data. The method is based on the minimization of the misfit between observed and simulated data. This amounts to the solution of a large‐scale nonlinear minimization problem. The inverse Hessian operator plays a crucial role in this reconstruction process. Accounting accurately for the effect of this operator within the minimization scheme should correct for illumination deficits, restore the amplitude of the subsurface parameters, and help to remove artefacts generated by energetic multiple reflections. Conventional minimization methods (nonlinear conjugate gradient, quasi‐Newton methods) only roughly approximate the effect of this operator. In this study, we are interested in the truncated Newton minimization method. These methods are based on the computation of the model update through a matrix‐free conjugate gradient solution of the Newton linear system. We present a feasible implementation of this method for the full waveform inversion problem, based on a second‐order adjoint state formulation for the computation of Hessian‐vector products. We compare this method with conventional methods within the context of 2D acoustic frequency full waveform inversion for the reconstruction of P‐wave velocity models. Two test cases are investigated. The first is the synthetic BP 2004 model, representative of the Gulf of Mexico geology with high velocity contrasts associated with the presence of salt structures. The second is a 2D real data‐set from the Valhall oil field in North sea. Although, from a computational cost point of view, the truncated Newton method appears to be more expensive than conventional optimization algorithms, the results emphasize its increased robustness. A better reconstruction of the P‐wave velocity model is provided when energetic multiple reflections make it difficult to interpret the seismic data. A better trade‐off between regularization and resolution is obtained when noise contamination of the data requires one to regularize the solution of the inverse problem. 相似文献
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We present preconditioned non‐linear conjugate gradient algorithms as alternatives to the Gauss‐Newton method for frequency domain full‐waveform seismic inversion. We designed two preconditioning operators. For the first preconditioner, we introduce the inverse of an approximate sparse Hessian matrix. The approximate Hessian matrix, which is highly sparse, is constructed by judiciously truncating the Gauss‐Newton Hessian matrix based on examining the auto‐correlation and cross‐correlation of the Jacobian matrix. As the second preconditioner, we employ the approximation of the inverse of the Gauss‐Newton Hessian matrix. This preconditioner is constructed by terminating the iteration process of the conjugate gradient least‐squares method, which is used for inverting the Hessian matrix before it converges. In our preconditioned non‐linear conjugate gradient algorithms, the step‐length along the search direction, which is a crucial factor for the convergence, is carefully chosen to maximize the reduction of the cost function after each iteration. The numerical simulation results show that by including a very limited number of non‐zero elements in the approximate Hessian, the first preconditioned non‐linear conjugate gradient algorithm is able to yield comparable inversion results to the Gauss‐Newton method while maintaining the efficiency of the un‐preconditioned non‐linear conjugate gradient method. The only extra cost is the computation of the inverse of the approximate sparse Hessian matrix, which is less expensive than the computation of a forward simulation of one source at one frequency of operation. The second preconditioned non‐linear conjugate gradient algorithm also significantly saves the computational expense in comparison with the Gauss‐Newton method while maintaining the Gauss‐Newton reconstruction quality. However, this second preconditioned non‐linear conjugate gradient algorithm is more expensive than the first one. 相似文献
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In full waveform inversion (FWI), Hessian information of the misfit function is of vital importance for accelerating the convergence of the inversion; however, it usually is not feasible to directly calculate the Hessian matrix and its inverse. Although the limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) or Hessian-free inexact Newton (HFN) methods are able to use approximate Hessian information, the information they collect is limited. The two methods can be interlaced because they are able to provide Hessian information for each other; however, the performance of the hybrid iterative method is dependent on the effective switch between the two methods. We have designed a new scheme to realize the dynamic switch between the two methods based on the decrease ratio (DR) of the misfit function (objective function), and we propose a modified hybrid iterative optimization method. In the new scheme, we compare the DR of the two methods for a given computational cost, and choose the method with a faster DR. Using these steps, the modified method always implements the most efficient method. The results of Marmousi and over thrust model testings indicate that the convergence with our modified method is significantly faster than that in the L-BFGS method with no loss of inversion quality. Moreover, our modified outperforms the enriched method by a little speedup of the convergence. It also exhibits better efficiency than the HFN method. 相似文献
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Different from the stacked seismic data, pre-stack data includes abundant information about shear wave and density. Through inversing the shear wave and density information from the pre-stack data, we can determine oil-bearing properties from different incident angles. The state-of-the-art inversion methods obtain either low vertical resolution or lateral discontinuities. However, the practical reservoir generally has sharp discontinuities between different layers in vertically direction and is horizontally smooth. Towards obtaining the practical model, we present an inversion method based on the regularized amplitude-versus-incidence angle (AVA) data to estimate the piecewise-smooth model from pre-stack seismic data. This method considers subsurface stratum as a combination of two parts: a piecewise smooth part and a constant part. To fix the ill-posedness in the inversion, we adopt four terms to define the AVA inversion misfit function: the data misfit itself, a total variation regularization term acting as a sparsing operator for the piecewise constant part, a Tikhonov regularization term acting as a smoothing operator for the smooth part, and the last term to smoothly incorporate a priori information for constraining the magnitude of the estimated model. The proposed method not only can incorporate structure information and a priori model constraint, but also is able to derive into a convex objective function that can be easily minimized using iterative approach. Compared with inversion results of TV and Tikhonov regularization methods, the inverted P-wave velocity, S-wave velocity and density of the proposed method can better delineate the piecewise-smooth characteristic of strata. 相似文献
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不同类别参数间的相互耦合使多参数地震全波形反演的非线性程度显著增加,地震波速度与各向异性参数取值数量级的巨大差异也会使反演问题的性态变差.合理使用Hessian逆算子可以减弱这两类问题对反演的影响,提高多参数反演的精度,而截断牛顿法是一种可以比较准确地估计Hessian逆算子的优化方法.本文采用截断牛顿法在时间域进行了VTI介质的声波双参数同时反演的研究.不同模型的反演试验表明,在VTI介质声波双参数同时反演中,截断牛顿法比有限内存BFGS(Limited-memory Broyden-Fletcher-Goldfarb-Shanno,L-BFGS)法能更准确地估计Hessian逆算子,进而较好地平衡两类不同参数的同时更新,得到了比较精确的反演结果. 相似文献
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地震全波形反演(FWI)从理论走向实际面临着诸多难题,其中之一就是需要一个较高精度的初始模型,另一个难题就是需要一个较为精确的震源子波,初始模型和震源子波的准确程度严重影响着全波形反演的最终结果.为此,本文提出了不依赖子波、基于包络的FWI初始模型建立的方法,建立了相应的目标函数,推导出了反演的梯度,给出了伴随震源的表达式,理论上分析了不依赖子波FWI的可行性.在数值试验中,讨论了参考道的选取方式,通过分析归一化目标函数收敛速率,认为近偏移距参考道优于远偏移距参考道,在地震数据含干扰噪音时,平均道作为参考道要优于最小偏移距参考道.通过包络、包络对数、包络平方三种目标函数反演结果的比较,发现包络对数目标函数对深层的反演效果最好.通过不同子波的试验进一步验证了本方法的正确性. 相似文献
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Ho Seuk Bae Sukjoon Pyun Wookeen Chung Seung‐Goo Kang Changsoo Shin 《Geophysical Prospecting》2012,60(3):413-432
We developed a frequency‐domain acoustic‐elastic coupled waveform inversion based on the Gauss‐Newton conjugate gradient method. Despite the use of a high‐performance computer system and a state‐of‐the‐art parallel computation algorithm, it remained computationally prohibitive to calculate the approximate Hessian explicitly for a large‐scale inverse problem. Therefore, we adopted the conjugate gradient least‐squares algorithm, which is frequently used for geophysical inverse problems, to implement the Gauss‐Newton method so that the approximate Hessian is calculated implicitly. Thus, there was no need to store the Hessian matrix. By simultaneously back‐propagating multi‐components consisting of the pressure and displacements, we could efficiently extract information on the subsurface structures. To verify our algorithm, we applied it to synthetic data sets generated from the Marmousi‐2 model and the modified SEG/EAGE salt model. We also extended our algorithm to the ocean‐bottom cable environment and verified it using ocean‐bottom cable data generated from the Marmousi‐2 model. With the assumption of a hard seafloor, we recovered both the P‐wave velocity of complicated subsurface structures as well as the S‐wave velocity. Although the inversion of the S‐wave velocity is not feasible for the high Poisson's ratios used to simulate a soft seafloor, several strategies exist to treat this problem. Our example using multi‐component data showed some promise in mitigating the soft seafloor effect. However, this issue still remains open. 相似文献
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为推进大地电磁三维反演的实用化,本文实现了基于L-BFGS算法的带地形大地电磁三维反演.首先推导了大地电磁法三维反演的Tikhonov正则化目标函数以及Hessian矩阵逆矩阵近似表达式和计算方法,然后设计了一种既能保证空气电阻率固定不变又能保证模型平滑约束的协方差矩阵统一表达式,解决带地形反演问题.在反演算法中采用正则化因子冷却法以及基于Wolf条件的步长搜索策略,提升了反演的稳定性.利用开发的算法对多个带地形地电模型（山峰地形下的单个异常模型、峰-谷地形下的棋盘模型）的合成数据进行了三维反演,并与已有大地电磁三维反演程序（ModEM）进行对比,验证了本文开发的三维反演算法的正确性和可靠性.最后,利用该算法反演了华南某山区大地电磁实测数据,得到该区三维电性结构,揭示了研究区以高阻介质为基底,中间以低阻不整合面和相对低阻介质连续分布,浅部覆盖高阻介质的电性结构特征,进一步验证了本文算法的实用性. 相似文献