Double parameterized regularization inversion method for migration velocity analysis in transversely isotropic media with a vertical symmetry axis

Simultaneous estimation of velocity gradients and anisotropic parameters from seismic reflection data is one of the main challenges in transversely isotropic media with a vertical symmetry axis migration velocity analysis. In migration velocity analysis, we usually construct the objective function using the l₂ norm along with a linear conjugate gradient scheme to solve the inversion problem. Nevertheless, for seismic data this inversion scheme is not stable and may not converge in finite time. In order to ensure the uniform convergence of parameter inversion and improve the efficiency of migration velocity analysis, this paper develops a double parameterized regularization model and gives the corresponding algorithms. The model is based on the combination of the l₂ norm and the non‐smooth l₁ norm. For solving such an inversion problem, the quasi‐Newton method is utilized to make the iterative process stable, which can ensure the positive definiteness of the Hessian matrix. Numerical simulation indicates that this method allows fast convergence to the true model and simultaneously generates inversion results with a higher accuracy. Therefore, our proposed method is very promising for practical migration velocity analysis in anisotropic media.     

Suboptimal Rayleigh damping coefficients in seismic analysis of viscously-damped structures

An optimization method for the consistent evaluation of two Rayleigh damping coefficients is proposed. By minimizing an objective function such as an error term of the peak displacement of a structure, the two coefficients can be determined with response spectral analysis. The optimization method degenerates into the conventional method used in current practices when only two modes of vibration are included in the objective function. Therefore, the proposed method with all significant modes included for simplicity in practical applications results in suboptimal damping coefficients. The effects of both spatial distribution and frequency content of excitations as well as structural dynamic characteristics on the evaluation of Rayleigh damping coefficients were investigated with a five-story building structure. Two application examples with a 62-story high-rise building and a 840 m long cable-stayed bridge under ten earthquake excitations demonstrated the accuracy and effectiveness of the proposed method to account for all of the above effects.     

波动方程反演的全局优化方法研究

复杂介质波动方程反演是地球物理研究中的重要问题,通常表述为特定目标函数最优化,难点是多参数、非线性和不适定性.局部和全局优化方法都不能实现快速全局优化.本文概述了地震波勘探反演问题的理论基础和研究进展,阐述了反演中优化问题的解决方法和面临的困难,并提出了一种确定性全局优化的新方法.通过在优化参数空间识别并划分局部优化解及其附近区域,只需有限次参数空间划分过程就能发现所有局部解（集合）;基于复杂目标函数多尺度结构分析,提出多尺度参数空间分区优化方法的研究方向.该方法收敛速度快,优化结果不依赖初始解的选取,是对非线性全局优化问题的一个新探索.     

Non-monotone spectral projected gradient method applied to full waveform inversion

The seismic inversion problem is a highly non‐linear problem that can be reduced to the minimization of the least‐squares criterion between the observed and the modelled data. It has been solved using different classical optimization strategies that require a monotone descent of the objective function. We propose solving the full‐waveform inversion problem using the non‐monotone spectral projected gradient method: a low‐cost and low‐storage optimization technique that maintains the velocity values in a feasible convex region by frequently projecting them on this convex set. The new methodology uses the gradient direction with a particular spectral step length that allows the objective function to increase at some iterations, guarantees convergence to a stationary point starting from any initial iterate, and greatly speeds up the convergence of gradient methods. We combine the new optimization scheme as a solver of the full‐waveform inversion with a multiscale approach and apply it to a modified version of the Marmousi data set. The results of this application show that the proposed method performs better than the classical gradient method by reducing the number of function evaluations and the residual values.     

核磁共振共轭梯度解谱方法研究

核磁共振（NMR）解谱技术是核磁共振资料应用的基础和关键.本文将核磁共振解谱的混定线性反演问题转化为求目标函数极小值的最优化问题,然后利用共轭梯度算法具有二次终止性、收敛速度快的特点解决上述最优化问题.将该方法应用于无噪声和不同信噪比的理论数据解谱以及岩心NMR实验解谱并与真谱及实验室国外软件解谱结果对比表明：在信噪比SNR≥5时解谱结果和真谱符合得很好,谱线光滑连续;随着信噪比的降低对初始点的要求随之提高;两块岩心解谱结果与实验室结果符合得很好,利用解谱结果计算的核磁孔隙度与实验室氦孔隙度绝对误差分别为0.78%和0.57%.因此,本文方法有效、实用,具有较强的抗噪能力,对初始点的要求不高,能够应用于生产和科研实践中.     

Parameter estimation in ensemble based snow data assimilation: A synthetic study

Estimating erroneous parameters in ensemble based snow data assimilation system has been given little attention in the literature. Little is known about the related methods’ effectiveness, performance, and sensitivity to other error sources such as model structural error. This research tackles these questions by running synthetic one-dimensional snow data assimilation with the ensemble Kalman filter (EnKF), in which both state and parameter are simultaneously updated. The first part of the paper investigates the effectiveness of this parameter estimation approach in a perfect-model-structure scenario, and the second part focuses on its dependence on model structure error. The results from first part research demonstrate the advantages of this parameter estimation approach in reducing the systematic error of snow water equivalent (SWE) estimates, and retrieving the correct parameter value. The second part results indicate that, at least in our experiment, there is an evident dependence of parameter search convergence on model structural error. In the imperfect-model-structure run, the parameter search diverges, although it can simulate the state variable well. This result suggest that, good data assimilation performance in estimating state variables is not a sufficient indicator of reliable parameter retrieval in the presence of model structural error. The generality of this conclusion needs to be tested by data assimilation experiments with more complex structural error configurations.     

Hydraulic Gradient Comparison Method to Estimate Aquifer Hydraulic Parameters Under Steady-State Conditions

The hydraulic gradient comparison method is an inverse method for estimation of aquifer hydraulic conductivity (or trans-missivity) and boundary conductance for a ground water flow model under steady-state conditions. This method, following formal optimization techniques, defines its objective function to minimize differences between interpreted (observed) and simulated hydraulic gradients, which results in minimization of differences between observed and simulated hydraulic heads. The key features of this method are that (1) the derived optimality conditions have an explicit form with a clear hydrology concept that is con-sistent with Darcy's law, and (2) the derived optimality conditions are spatially independent as they are a function of only local hydraulic conductivity and local hydraulic gradient. This second feature allows a multidimensional optimization problem to be solved by many one-dimensional optimization procedures simultaneously, which results in a substantial reduction in computation time. The results of the numerical performance testing on a heterogeneous hypothetical case confirm that minimizing gradient residuals in the entire model domain leads to minimizing head residuals. Application of the method in real-world projects requires rigorous conceptual model development, use of a global calibration target, and an iterative calibration proess. The conceptual model development includes interpretation of a potentiometric surface and estimation of other hydrologic parameters. This method has been applied to a wide range of real-world modeling projects, including the Rocky Mountain Arsenal and Rocky Flats sites in Colorado, which demonstrates that the method is efficient and practical.     

Sensitivity Kernel for the Weighted Norm of the Frequency-Dependent Phase Correlation

Full-3D waveform tomography (F3DT) is often formulated as an optimization problem, in which an objective function defined in terms of the misfit between observed and model-predicted (i.e., synthetic) waveforms is minimized by varying the earth structure model from which the synthetic waveforms are calculated. Because of the large dimension of the model space and the computational cost for solving the 3D seismic wave equation, it is often mandatory to use Newton-type local optimization algorithms; in which case, spurious local optima in the objective function can prevent the global convergence of the descent algorithm if the initial estimate of the structure model is not close enough to the global optimum. By appropriate design of the objective function, it is possible to enlarge the attraction domain of the global optimum so that Newton-type local optimization algorithms can achieve global convergence. In this article, an objective function based on a weighted L ₂ norm of the frequency-dependent phase correlation between observed and synthetic waveforms is proposed and studied, and its full-3D Fréchet kernel is constructed using the adjoint state method. The relation between the proposed objective function and the conventional frequency-dependent group-delay is analyzed and illustrated using numerical examples. The methodology has been successfully applied on a set of ambient-noise Green’s function observations collected in northern California to derive a full-3D crustal structure model.     

Practical aspects of demand and capacity evaluation of RC members in the context of EC8‐3

The structural safety assessment procedures proposed in Part 3 of Eurocode 8 (EC8‐3) for the case of reinforced concrete structures are addressed. The practical evaluation of the member chord rotation demand according to EC8‐3 is examined in detail along with several alternative formulations. The need for these formulations is demonstrated by presenting example situations where the EC8‐3 proposal is difficult to apply. The effectiveness of the proposed approaches is assessed through an example application and recommendations for their practical use are defined. Given the importance of the shear span in this context, a sensitivity analysis of the EC8‐3 capacity models with respect to this parameter is carried out and discussed in a later section. This analysis aims to assess the validity of the simplifications proposed by a previous research study for the quantification of the EC8‐3 capacity values. Copyright © 2009 John Wiley & Sons, Ltd.     

Placement of sensors/actuators on civil structures using genetic algorithms

The optimal design and placement of controllers at discrete locations is an important problem that will have impact on the control of civil engineering structures. Though algorithms exist for the placement of sensor/actuator systems on continuous structures, the placement of controllers on discrete civil structures is a very difficult problem. Because of the nature of civil structures, it is not possible to place sensors and actuators at any location in the structure. This usually creates a non‐linear constrained mixed integer problem that can be very difficult to solve. Using genetic algorithms in conjunction with gradient‐based optimization techniques will allow for the simultaneous placement and design of an effective structural control system. The introduction of algorithms based on genetic search procedures should increase the rate of convergence and thus reduce the computational time for solving the difficult control problem. The newly proposed method of simultaneously placing sensors/actuators will be compared to a commonly used method of sensors/actuators placement where sensors/actuators are placed sequentially. The savings in terms of energy requirements and cost will be discussed. Copyright © 2001 John Wiley & Sons, Ltd.     

Automatic cross-well tomography by semblance and differential semblance optimization: theory and gradient computation

A technique for automatic cross-well tomography based on semblance and differential semblance optimization is presented. Given a background velocity, the recorded seismic data traces are back-propagated towards the source, i.e. shifted towards time zero using the modelled traveltime between the source and the receiver and corrected for the geometrical spreading. Therefore each back-propagated trace should be a pulse, close to time zero. The mismatches between the back-propagated traces indicate an error in the velocity model. This error can be measured by stacking the back-propagated traces (semblance optimization) or by computing the norm of the difference between adjacent traces (differential semblance optimization).
It is known from surface seismic reflection tomography that both the semblance and differential semblance functional have good convexity properties, although the differential semblance functional is believed to have a larger basin of attraction (region of convergence) around the true velocity model. In the case of the cross-well transmission tomography described in this paper, similar properties are found for these functionals.
The implementation of this automatic method for cross-well tomography is based on the high-frequency approximation to wave propagation. The wavefronts are constructed using a ray-tracing algorithm. The gradient of the cost function is computed by the adjoint-state technique, which has the same complexity as the computation of the functional. This provides an efficient algorithm to invert cross-well data. The method is applied to a synthetic data set to demonstrate its efficacy.     

Finite element model updating for structures with parametric constraints

This paper presents a finite element (FE) model updating procedure applied to complex structures using an eigenvalue sensitivity‐based updating approach. The objective of the model updating is to reduce the difference between the calculated and the measured frequencies. The method is based on the first‐order Taylor‐series expansion of the eigenvalues with respect to some structural parameters selected to be adjusted. These parameters are assumed to be bounded by some prescribed regions which are determined according to the degrees of uncertainty that exist in the parameters. The changes of these parameters are found iteratively by solving a constrained optimization problem. The improvement of the current study is in the use of an objective function that is the sum of a weighted frequency error norm and a weighted perturbation norm of the parameters. Two weighting matrices are introduced to provide flexibility for individual tuning of frequency errors and parameters' perturbations. The proposed method is applied to a 1/150 scaled suspension bridge model. Using 11 measured frequencies as reference, the FE model is updated by adjusting ten selected structural parameters. The final updated FE model for the suspension bridge model is able to produce natural frequencies in close agreement with the measured ones. Copyright © 2000 John Wiley & Sons, Ltd.     

Minimum‐cost optimization of nonlinear fluid viscous dampers and their supporting members for seismic retrofitting

This paper presents an effective approach for achieving minimum‐cost designs for seismic retrofitting using nonlinear fluid viscous dampers. The damping coefficients of the dampers and the stiffness coefficients of the supporting braces are designed by an optimization algorithm. A realistic retrofitting cost function is minimized subject to constraints on inter‐story drifts at the peripheries of frame structures. The cost function accounts for costs related to both the topology and the sizes of the dampers. The behavior of each damper‐brace element is defined by the Maxwell model, where the force–velocity relation of the nonlinear dampers is formulated with a fractional power law. The optimization problem is first posed and solved as a mixed integer problem. For the reduction of the computational effort required in the optimization, the problem is then reformulated with continuous variables only and solved with a gradient‐based algorithm. Material interpolation techniques, which have been successfully applied in topology optimization and in multi‐material optimization, play a key role in achieving practical final design solutions with a reasonable computational effort. Promising results attained for 3‐D irregular frames are presented and discussed. Copyright © 2017 John Wiley & Sons, Ltd.     

AN ALTERNATIVE STRATEGY FOR NON-LINEAR INVERSION OF SEISMIC WAVEFORMS1

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.     

Applications of hybrid genetic algorithms in seismic tomography

Almost all earth sciences inverse problems are nonlinear and involve a large number of unknown parameters, making the application of analytical inversion methods quite restrictive. In practice, most analytical methods are local in nature and rely on a linearized form of the problem equations, adopting an iterative procedure which typically employs partial derivatives in order to optimize the starting (initial) model by minimizing a misfit (penalty) function. Unfortunately, especially for highly non-linear cases, the final model strongly depends on the initial model, hence it is prone to solution-entrapment in local minima of the misfit function, while the derivative calculation is often computationally inefficient and creates instabilities when numerical approximations are used. An alternative is to employ global techniques which do not rely on partial derivatives, are independent of the misfit form and are computationally robust. Such methods employ pseudo-randomly generated models (sampling an appropriately selected section of the model space) which are assessed in terms of their data-fit. A typical example is the class of methods known as genetic algorithms (GA), which achieves the aforementioned approximation through model representation and manipulations, and has attracted the attention of the earth sciences community during the last decade, with several applications already presented for several geophysical problems.In this paper, we examine the efficiency of the combination of the typical regularized least-squares and genetic methods for a typical seismic tomography problem. The proposed approach combines a local (LOM) and a global (GOM) optimization method, in an attempt to overcome the limitations of each individual approach, such as local minima and slow convergence, respectively. The potential of both optimization methods is tested and compared, both independently and jointly, using the several test models and synthetic refraction travel-time date sets that employ the same experimental geometry, wavelength and geometrical characteristics of the model anomalies. Moreover, real data from a crosswell tomographic project for the subsurface mapping of an ancient wall foundation are used for testing the efficiency of the proposed algorithm. The results show that the combined use of both methods can exploit the benefits of each approach, leading to improved final models and producing realistic velocity models, without significantly increasing the required computation time.     

Estimating unsaturated soil hydraulic parameters using ant colony optimization

Although models are now routinely used for addressing environmental problems, both in research and management applications, the problem of obtaining the required parameters remains a major challenge. An attractive procedure for obtaining model parameters in recent years has been through inverse modeling. This approach involves obtaining easily measurable variables (model output), and using this information to estimate a set of unknown model parameters. Inverse procedures usually require optimization of an objective function. In this study we emulate the behavior of a colony of ants to achieve this optimization. The method uses the fact that ants are capable of finding the shortest path from a food source to their nest by depositing a trail of pheromone during their walk. Results obtained with the ant colony parameter optimization method are very promising; in eight different applications we were able to estimate the `true' parameters to within a few percent. One such study is reported in this paper plus an application to estimating hydraulic parameters in a lysimeter experiment. Despite the encouraging results obtained thus far, further improvements could still be made in the parameterization of the ant colony optimization for application to estimation of unsaturated flow and transport parameters.     

Obtaining refined first‐order predictive models of linear structural systems

This study presents an effective method for identifying predictive models and the underlying modal parameters of linear structural systems using only measured output and excitation time histories obtained from dynamic testing. The system under examination is modelled as a first‐order multi‐input multi‐output time‐invariant system, and the structural model is realized using the Eigensystem Realization Algorithm together with the Observer/Kalman filter IDentification algorithm. The identified state‐space model is further refined using a non‐linear optimization technique based on sequential quadratic programming. The numerical examples show that the developed methodology performs very well even in the presence of inadequate instrumentation and measurement noise, and that the methodology is highly capable of creating realistic predictive models of structural systems, as well as estimating their underlying modal parameters. Copyright © 2002 John Wiley & Sons, Ltd.     

地震数据互相关驱动的多道反演方法

地震反演是储层定量描述和地震油气识别的关键技术,反演结果在复杂构造区域的横向连续性和保真性是影响地震资料定量解释精度的重要因素.基于此,本文发展了地震数据互相关驱动的多道反演方法.考虑地层反射系数与地震数据在结构上具有相似性的特点,基于地震数据互相关描述地层反射系数的结构特征,并将其作为多道地震反演的横向约束条件;此外,为改善地震数据本身横向连续性差对反演结果的影响,在目标泛函的惩罚项中引入局部优化算子,构建了一个易于求解的多道地震反演目标泛函.与常规多道地震反演方法相比,本文方法能够设计更合理、更符合实际情况的横向约束算子,提高反演结果的横向连续性,并且能有效降低地震资料质量对反演结果的影响.模型测试和实际应用验证了本方法的可靠性和稳定性.     

Towards realistic minimum-cost optimization of viscous fluid dampers for seismic retrofitting

This paper presents an effective approach for achieving minimum cost designs for seismic retrofitting using viscous fluid dampers. A new and realistic retrofitting cost function is formulated and minimized subject to constraints on inter-story drifts at the peripheries of frame structures. The components of the new cost function are related to both the topology and to the sizes of the dampers. This constitutes an important step forward towards a realistic definition of the optimal retrofitting problem. The optimization problem is first posed and solved as a mixed-integer problem. To improve the efficiency of the solution scheme, the problem is then re-formulated and solved by nonlinear programming using only continuous variables. Material interpolation techniques, that have been successfully applied in topology optimization and in multi-material optimization, play a key role in achieving practical final design solutions with a reasonable computational effort. Promising results attained for 3-D irregular frames are presented and compared with those achieved using genetic algorithms.     

Finite element response sensitivity analysis of three-dimensional soil-foundation-structure interaction (SFSI) systems

The nonlinear finite element (FE) analysis has been widely used in the design and analysis of structural or geotechnical systems. The response sensitivities (or gradients) to the model parameters are of significant importance in these realistic engineering problems. However the sensitivity calculation has lagged behind, leaving a gap between advanced FE response analysis and other research hotspots using the response gradient. The response sensitivity analysis is crucial for any gradient-based algorithms, such as reliability analysis, system identification and structural optimization. Among various sensitivity analysis methods, the direct differential method (DDM) has advantages of computing efficiency and accuracy, providing an ideal tool for the response gradient calculation. This paper extended the DDM framework to realistic complicated soil-foundation-structure interaction (SFSI) models by developing the response gradients for various constraints, element and materials involved. The enhanced framework is applied to three-dimensional SFSI system prototypes for a pile-supported bridge pier and a pile-supported reinforced concrete building frame structure, subjected to earthquake loading conditions. The DDM results are verified by forward finite difference method (FFD). The relative importance (RI) of the various material parameters on the responses of SFSI system are investigated based on the DDM response sensitivity results. The FFD converges asymptotically toward the DDM results, demonstrating the advantages of DDM (e.g., accurate, efficient, insensitive to numerical noise). Furthermore, the RI and effects of the model parameters of structure, foundation and soil materials on the responses of SFSI systems are investigated by taking advantage of the sensitivity analysis results. The extension of DDM to SFSI systems greatly broaden the application areas of the d gradient-based algorithms, e.g. FE model updating and nonlinear system identification of complicated SFSI systems.