不同模型约束的SOTEM稳定性反演

短偏移距瞬变电磁法（简称SOTEM）理论上探测精度较高,但由于地下电性结构复杂多样,目前所采用的单一模型约束反演策略具有局限性.本文提出了一种不同模型约束的SOTEM稳定性反演方案,能够快速获得具有光滑型、聚焦型、突变型结构特点的反演结果.该方案引入了光滑泛函、总变分泛函、最小梯度支撑泛函进行不同正则化,将不同稳定泛函重新构造,在统一反演框架下获得不同模型约束反演问题的解,并对正演、迭代过程、雅可比矩阵计算、正则化参数设置等多个细节进行了优化,以提高SOTEM反演的稳定性和效率.通过不同地电模型的大量样本,考察了本文所提的多种优化方案对反演的稳定性和效率的提升效果,对比了采用不同模型约束进行正则化的反演效果,从而验证了所提SOTEM反演方案的稳定性与快速有效性.

     

光滑模型与尖锐边界结合的MT二维反演方法

如何得到快速稳定的反演结果和更清晰的地质体分界面等问题仍然是当前MT反演研究的一个重点.为了解决反演结果不能得到清晰的电性分界面的问题,本文在前人研究的基础上,基于OCCAM反演以及突出尖锐边界反演的思想,结合最小支撑梯度泛函,构建了新的反演目标函数,并利用共轭梯度法优化目标函数,实现了一种光滑模型与尖锐边界结合的MT二维反演方法.模型实验证明了该方法的准确性,通过与已发表的相关反演方法的结果进行比较,证明了该方法在光滑稳定的基础上可突出对尖锐电性边界的刻画.对广东徐闻地区的实测MT资料进行了处理,表明了该方法的适用性和效果.     

水平层状介质中双侧向测井资料的迭代Tikhonov正则化反演

根据非线性反演理论与Morozov偏差原理研究建立从双侧向测井(DLL)资料中同时重构地层原状电阻率、侵入带电阻率、侵入半径、层界面位置以及井眼泥浆电阻率的迭代正则化算法.首先利用Tikhonov正则化反演理论将双侧向测井资料的反演问题转化为含有稳定泛函的非线性目标函数的极小化问题,并利用Gauss-Newton算法确定极小化解.为得到稳定的反演结果并有效实现测井资料的最佳拟合,在迭代过程中将Morozov偏差原理和Cholesky分解技术相结合,建立了一套后验选择正则化因子的方法.最后通过理论模型和大庆油田实际测井资料的处理结果,验证了该算法能够取得更为满意的反演效果.     

Two-dimensional inversion of magnetotelluric data with consecutive use of conjugate gradient and least-squares solution with singular value decomposition algorithms

I investigated the two‐dimensional magnetotelluric data inversion algorithms in studying two significant aspects within a linearized inversion approach. The first one is the method of minimization and second one is the type of stabilizing functional used in parametric functionals. The results of two well‐known inversion algorithms, namely conjugate gradient and the least‐squares solution with singular value decomposition, were compared in terms of accuracy and CPU time. In addition, magnetotelluric data inversion with various stabilizers, such as L2‐norm, smoothing, minimum support, minimum gradient support and first‐order minimum entropy, were examined. A new inversion algorithm named least‐squares solution with singular value decomposition and conjugate gradient is suggested in seeing the outcomes of the comparisons carried out on least‐squares solutions with singular value decomposition and conjugate gradient algorithms subject to a variety of stabilizers. Inversion results of synthetic data showed that the newly suggested algorithm yields better results than those of the individual implementations of conjugate gradient and least‐squares solution with singular value decomposition algorithms. The suggested algorithm and the above‐mentioned algorithms inversion results for the field data collected along a line crossing the North Anatolian Fault zone were also compared each other and results are discussed.     

二维大地电磁尖锐边界反演研究

尖锐边界反演是大地电磁及其他地球物理反演中的一个较难解决的问题,目前有不少专家正对这方面进行研究.我们在分析了Zhdanov（2004）提出的基于最小支撑泛函聚焦反演方法的基础上,在目标函数中引入对角梯度支撑以改善倾斜电性分界面的反演效果.同时,为了提高计算效率,本文还使用互易定义计算灵敏度矩阵及使用正则化共轭梯度法优化目标函数.通过模型试验并与未引入对角梯度支撑的反演结果进行了比较,表明本文的方法反演效果更好而且算法稳定和实用.     

New advances in regularized inversion of gravity and electromagnetic data

The interpretation of potential and electromagnetic fields observed over 3D geological structures remains one of the most challenging problems of exploration geophysics. In this paper I present an overview of novel methods of inversion and imaging of gravity and electromagnetic data, which are based on new advances in the regularization theory related to the application of special stabilizing functionals, which allow the reconstruction of both smooth images of the underground geological structures and models with sharp geological boundaries. I demonstrate that sharp-boundary geophysical inversion can improve the efficiency and resolution of the inverse problem solution. The methods are illustrated with synthetic and practical examples of the 3D inversion of potential and electromagnetic field data.     

Three-dimensional inversion of full magnetic gradient tensor data based on hybrid regularization method

Rapid developments in SQUID-based technology make it possible for geophysical exploration to direct measure, inverse and interpret magnetic gradient tensor data. This contribution introduces a novel three-dimensional hybrid regularization method for inversion of magnetic gradient tensor data, which is based on the minimum support functional and total variation functional. Compared to the existing stabilizers, for example, the minimum support stabilizer, the minimum gradient support stabilizer or the total variation stabilizer, our proposed hybrid stabilizer, in association with boundary penalization, improves the revision result greatly, including higher spatial and depth resolution, more clear boundaries, more highlighted images and more evident structure depiction. Moreover, suitable selection of model parameter λ will further improve the image quality of the recovered model. We verify our proposed hybrid method with various synthetic magnetic models. Experiment results prove that this method gives more accurate results, exhibiting advantages of less computational costs even when less prior information of magnetic sources are provided. Comparison of results with different types of magnetic data with and without remanence indicates that our inversion algorithm can obtain more detailed information on the source structure based on rational estimation of total magnetization direction. Finally, we present a case study for inverting SQUID-based magnetic tensor data acquired at Da Hinggan Mountains area, inner Mongolia, China. The result also certifies that the method is reliable and efficient for real cases.     

First‐arrival traveltime tomography with modified total‐variation regularization

First‐arrival traveltime tomography is a robust tool for near‐surface velocity estimation. A common approach to stabilizing the ill‐posed inverse problem is to apply Tikhonov regularization to the inversion. However, the Tikhonov regularization method recovers smooth local structures while blurring the sharp features in the model solution. We present a first‐arrival traveltime tomography method with modified total‐variation regularization to preserve sharp velocity contrasts and improve the accuracy of velocity inversion. To solve the minimization problem of the new traveltime tomography method, we decouple the original optimization problem into the two following subproblems: a standard traveltime tomography problem with the traditional Tikhonov regularization and a L₂ total‐variation problem. We apply the conjugate gradient method and split‐Bregman iterative method to solve these two subproblems, respectively. Our synthetic examples show that the new method produces higher resolution models than the conventional traveltime tomography with Tikhonov regularization, and creates less artefacts than the total variation regularization method for the models with sharp interfaces. For the field data, pre‐stack time migration sections show that the modified total‐variation traveltime tomography produces a near‐surface velocity model, which makes statics corrections more accurate.     

Sharp and laterally constrained multitrace impedance inversion based on blocky coordinate descent

Seismic impedance inversion is a well-known method used to obtain the image of subsurface geological structures. Utilizing the spatial coherence among seismic traces, the laterally constrained multitrace impedance inversion (LCI) is superior to trace-by-trace inversion and can produce a more realistic image of the subsurface structures. However, when the traces are numerous, it will take great computational cost and a lot of memory to solve the large-scale matrix in the multitrace inversion, which restricts the efficiency and applicability of the existing multitrace inversion algorithm. In addition, the multitrace inversion methods are not only needed to consider the lateral correlation but also should take the constraints in temporal dimension into account. As usual, these vertical constraints represent the stratigraphic characteristics of the reservoir. For instance, total-variation regularization is adopted to obtain the blocky structure. However, it still limits the magnitude of model parameter variation and therefore somewhat distorts the real image. In this paper, we propose two schemes to solve these issues. Firstly, we introduce a fast algorithm called blocky coordinate descent (BCD) to derive a new framework of laterally constrained multitrace impedance inversion. This new BCD-based inversion approach is fast and spends fewer memories. Next, we introduce a minimum gradient support regularization into the BCD-based laterally constrained inversion. This new approach can adapt to sharp layer boundaries and keep the spatial coherence. The feasibility of the proposed method is illustrated by numerical tests for both synthetic data and field seismic data.     

基于一种反余切紧凑约束的电磁波走时层析研究

电磁波走时层析可用于空洞和孤石等离散块体异常的探测中.在传统电磁波走时层析中,通常采用一个低阶差分泛函来稳定反演过程,而这类泛函都具有光滑约束的性质,在成像结果中难以对此类离散块体异常进行准确的解释.本文基于Tikhonov正则化思想,提出了一种反余切泛函,该泛函具有紧凑约束的性质且无需选择一个聚焦因子.结合弯曲胖射线层析理论和重加权正则化共轭梯度反演算法,对两个理论模型进行了成像效果研究.与低阶差分泛函和最小支撑泛函的成像结果相比,该方法能够更好地突出异常的边界,成像结果中的虚假异常也要更少.同时,还分析讨论了激发电磁波的中心频率以及数据噪音对成像结果的影响.此外,针对一个地下连续墙墙体内部缺陷的现场模型,利用该方法取得了理想的成像结果,良好地揭示了缺陷的形态和位置.

     

Model Fusion and Joint Inversion

Inverse problems are inherently non-unique, and regularization is needed to obtain stable and reasonable solutions. The regularization adds information to the problem and determines which solution, out of the infinitely many, is obtained. In this paper, we review and discuss the case when a priori information exists in the form of either known structure or in the form of another inverse problem for a different property. The challenge is to include such information in the inversion process. To use existing known structure, we review the concept of model fusion, where we build a regularization functional that fuses the inverted model to a known one. The fusion is achieved by four different techniques. Joint inversion of two data sets is achieved by using iterative data fusion. The paper discusses four different methods for joint inversion. We discuss the use of correspondence maps or the petrophysics of the rocks, as well as structure. In particular, we suggest to further stabilize the well-known gradient cross product and suggest a new technique, Joint Total Variation, to solve the problem. The Joint Total Variation is a convex functional for joint inversion and, as such, has favorable optimization properties. We experiment with the techniques on the DC resistivity problem and the borehole tomography and show how model fusion and joint inversion can significantly improve over existing techniques.     

基于共轭梯度算法的重力梯度数据三维聚焦反演研究

重力梯度数据相对于传统重力数据,能够更细致、准确地描述地球浅部构造和研究矿产资源分布等信息.本文采用共轭梯度算法,在加权密度域求解重力梯度数据三维聚焦反演最优化问题,以恢复地下三维密度分布,目标函数包括数据不拟合函数和最小支撑稳定函数.首先,在推导目标函数对加权密度的一阶导数时,为了得到更合理的计算公式,我们考虑变加权函数中含有密度变量;此外,本文通过密度上下限约束,改善了传统聚焦反演中聚焦因子选取困难的问题.新算法获得的反演结果,对聚焦因子的选择约束较少,相比传统聚焦算法,能够更容易的获得理想结果.将方法应用于理论模型验证其有效性和正确性,并应用本文方法处理文顿盐丘地区的航空全张量重力梯度数据,得到了与已知地质信息匹配的密度分布,表明本文方法具有处理实际数据的能力.

     

Inversion of surface gravity data for 3-D density modeling of geologic structures using total variation regularization

We develop an inversion procedure using the total variation (TV) regularization method as a stabilizing function to invert surface gravity data to retrieve 3-D density models of geologic structures with sharp boundaries. The developed inversion procedure combines several effective algorithms to solve the TV regularized problem. First, a matrix form of the gradient vector is designed using the Kronecker product to numerically approximate the 3-D TV function. The piecewise polynomial truncated singular value decomposition (PP-TSVD) algorithm is then used to solve the TV regularized inverse problem. To obtain a density model with depth resolution, we use a sensitivity-based depth weighting function. Finally, we apply the Genetic Algorithm (GA) to select the best combination of the PP-TSVD algorithm and the depth weighting function parameters. 3-D simulations conducted with synthetic data show that this approach produces sub-surface images in which the structures are well separated in terms of sharp boundaries, without the need of a priori detailed density model. The method applied to a real dataset from a micro-gravimetry survey of Gotvand Dam, southwestern Iran, clearly delineates subsurface cavities starting from a depth of 40 m within the area of the dam reservoir.     

稳定正则化参数估计方法及其在盆地基底重力异常反演中的应用

正则化方法通过带有正则化参数的约束项,将不适定问题转换为一个适定问题.如何选取最优正则化参数一直以来都是正则化研究的难点和热点.本文通过定义解的不稳定性度量来直接估算正则化参数μ的最优值,并将这种正则化参数估计方法应用到二维沉积盆地基底重力反演中.测试该方法在通过对一次野外测量的数据加不同噪声得到的多组数据与多次野外测量中得到的多组数据这两种情况中的反演效果.最后将该方法应用到非洲西海岸的北加蓬次盆进行盆地基底反演,测试该方法的实用性.模型测试的结果显示,在这两种情况下获得的反演解非常接近且能够反演得到较为准确的模型基底深度,故该方法适用于一般情况下只进行一次野外测量的实际重力勘探情况且能得到稳定的最优反演解;实际资料的最优反演结果稳定且符合当地的地质构造背景.在模型测试与实际资料测试中,都能够确定最优正则化参数并得到最优反演结果,证明了该方法在重力反演中的正确性和实用性.

     

Modelling of the conductance distribution at the eastern margin of the European Hercynides

Magnetic variation data recorded at 143 sites in the period range of 1000–6000 seconds were used to model the conductance distribution in the eastern part of the Bohemian Massif (BM) and the West Carpathians (WCP). The region represents a contact zone of the Palaeozoic Hercynian and Tertiary Carpathian orogenic systems. Two anomalies in the distribution of the electrical conductivity were found. One with a simple two-dimensional feature is located approximately near the boundary of the Inner and Outer West Carpathians, but the other of a complicated three-dimensional character is at the eastern margin of the BM. We inverted the observed geomagnetic data for the conductance in a unimodal thin sheet embedded at a specific depth. The inversion algorithm minimises the parametric functional that sums the squared norm of the misfit and the stabilising functional, and employs conjugate gradient optimisation. To express the sharp tectonic boundaries, we employed a minimum gradient support functional, which is applied in areas with strong variations of the model parameters. The inversion results confirmed a quasi-linear character of the regional anomaly distinguishing the Carpathian plate and led to new insight into the anomaly over the eastern margin of the BM. According to the results of the modelling the anomaly is formed by several conductive belts intersecting the anomalous zone. These belts follow important faults, dividing the transition zone between the BM and the WCP into individual blocks.     

Sparse Bayesian linearized amplitude-versus-angle inversion

The technique of seismic amplitude-versus-angle inversion has been widely used to estimate lithology and fluid properties in seismic exploration. The amplitude-versus-angle inversion problem is intrinsically ill-posed and generally stabilized by the use of L2-norm regularization methods but with drawback of smoothing important boundaries between adjacent layers. In this study, we propose a sparse Bayesian linearized solution for amplitude-versus-angle inversion problem to preserve the sharp geological interfaces. In this regard, a priori constraint term with two regularization functions is presented: the sparse constraint regularization and the low-frequency model information. In addition, to obtain high-resolution reflectivity estimation, the model parameters decorrelation technique combined with dipole decomposition method is employed. We validate the applicability of the presented method by both synthetic and real seismic data from the Gulf of Mexico. The accuracy improvement of the presented method is also confirmed by comparing the results with the commonly used Bayesian linearized amplitude-versus-angle inversion.     

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.     

三维大地电磁自适应L1范数正则化反演

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.     

地震波形反演的稀疏约束正则化方法

本文考虑地震波形反演问题.为了克服传统的Tikhonov正则化方法过度光滑的弊端,引入了非线性稀疏约束正则化方法,并采用对偶方法求解稀疏约束泛函的极小点.基于二维声波方程波形反演问题进行了数值模拟,针对不同模型对稀疏约束正则化方法进行了测试.结果表明,稀疏约束正则化方法对不连续介质模型的介质边缘具有良好的识别能力.     

实数编码遗传算法在大地电磁测深二维反演中的应用

作为全局非线性优化的新方法之一的遗传算法,近年来已从生物工程流行到大地电磁测深资料解释中.然而,大地电磁反演问题具有不适定性,解的非唯一性.通过结合求解不适定问题的Tikhonov正则化方法,本文采用实数编码遗传算法求解大地电磁二维反演问题.此算法在构建目标函数时引入正则化的思想,利用遗传算法求解最优化问题.常规的基于局部线性化的最优化反演方法易使解陷入局部极小值,而且严重的依赖初始模型的选择.与传统线性化的迭代反演方法相比,实数编码遗传算法能够克服传统方法的不足且能获得更好的反演结果.通过对大地电磁测深理论模型进行计算,结果表明:该算法具有收敛速度快、解的精度高和避免出现早熟等优点,可用于大地电磁资料解释.