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.     

蒸发波导对GNSS海面反射信号有效散射区域的影响

蒸发波导在海洋低空的发生概率高达90%,对舰船雷达、通信等电磁系统具有重要影响.为了分析利用GNSS卫星海面反射信号的时延-相关功率波形反演蒸发波导的可行性,本文提出了GNSS卫星海面反射信号的有效散射区域的概念,并将有效散射区域内的GNSS反射信号区分为GNSS标准反射信号和GNSS波导反射信号;然后,利用射线追踪方法,仿真分析了GNSS卫星海面反射信号的有效散射区域大小对蒸发波导的关键参数——波导高度的敏感性,并分析了在时延-相关功率波形上利用反射信号的传播时延将二者分离开的可行性.结果表明,GNSS卫星海面反射信号的有效散射区域对蒸发波导高度非常敏感,对于2～25m高的GNSS反射信号接收天线,当蒸发波导的高度由0m增加至20m时,GNSS反射信号有效散射区域半径的均值可由约14km迅速扩展至约160km;采用高度角足够大的GNSS卫星可以将有效散射区内的GNSS标准反射信号与GNSS波导反射信号在时延-相关功率波形上分离开.     

基于浮油膜双光束干涉模型的油膜厚度遥感反演理论

海面浮油膜是海洋石油污染监测与海洋油气资源勘探的共同关注对象,其厚度的遥感定量反演是研究难点之一.随着油膜厚度的变化,入射光在浮油膜中的辐射传输有所不同,是海面浮油膜厚度光学遥感反演的理论依据.本研究将均匀分布的浮油膜视为一个平行平板,分析其可见光范围内的辐射传输过程,探讨油膜反射光的双光束干涉,构建以油膜厚度及其光谱反射率为变量的油膜厚度遥感定量反演模型,并结合菲涅耳公式进行模型参数的物理意义分析,揭示油膜厚度遥感定量反演的关键在于油膜的消光特性.利用油膜光谱响应实验数据,开展理论模型的分析与验证,结果表明：油膜厚度遥感定量反演的理论模型不仅具有明确的光学物理意义,同时也具有较好的模拟精度;浮油膜光谱反射率随厚度变化响应的主要因素是油膜对入射光的分子散射作用,该散射作用与入射光波长具有显著的关系,因此油膜光学遥感散射特性研究将有助于油膜种类识别与厚度的遥感定量反演;模型参数的理论意义与数据分析表明,建立典型油种油膜的模型参数查找表,将有效提高海面浮油膜厚度遥感定量反演的效率.     

基于方位电磁波的煤岩层界面及低阻异常体探测方法（英文）

以煤岩介质和煤岩界面作为电磁波传播响应研究对象,构建与煤岩电磁参数相关的地质模型。针对煤矿井下随钻方位电磁波探测技术在煤岩地质导向中的响应特性进行深入分析,研究煤岩界面探测识别方法及其适用性。模拟仪器在煤岩层响应特性规律,建立煤层顶底板界面的方位、仪器到煤层界面距离的计算方法。利用拟牛顿法对随钻方位电磁波电阻率仪器响应理论模型进行反演计算,并对模拟数据数据进行实时反演,降低了Jacobian矩阵的计算次数,提高了反演速度并降低了对初值的依赖性。基于正反演数值仿真模拟,获得低阻含水层、异常地质体（陷落柱、夹矸层等）方位电磁波探测的信号响应规律。通过正交线圈的定向电动势模拟成像方法研究,验证了高阻煤层中方位电磁波电阻率应用的可行性、能够为煤层界面、地质异常体探测及地质导向应用提供依据。     

高频返回散射扫频电离图的反演

斜向返回探测可以对遥远地区的电离层进行连续监测,是探测电离层的主要手段之一,一般得到返回功率、群路径或时延与频率之间的关系,称为高频返回散射电离图.由于电离图包含了探测路径上电离层状态信息,可以通过对其反演获得电离层结构参数.本文提出了一种新的反演算法,使用模拟退火方法对返回散射扫频电离图前沿进行了反演,并用实际探测数...     

带先验约束的地表参数提取的有效反演方法

地球表面的各向异性特性可以用地表二向反射函数（BRDF）恰当地描述.BRDF的核心是利用线性核驱动模型,数学上表述为各向同性核、体散射核和几何光学核的线性组合.随着多角度遥感领域的发展,BRDF模型越来越被看作是可以反演重要的有关地表生物的或气候的参数,比如说叶面积指数和地表反照率.一个线性逼近的核驱动BRDF模型通常可以写成下述形式（Roujean等,1992）：fiso＋kvol（ti,tv,φ）fvol＋kgeo（ti,tv,φ）fgeo=r（ti,tv,φ）,其中r表示地表的二向反射;kvol和kgeo为通常所说的核函数,即为已知的入射和观测几何特性的函数,分别描述了体散射和几何散射（包括折射和反射）;ti是太阳方向天顶角,tv是观测方向天顶角;φ表示太阳-观测方向的相对方位角;fiso,fvol和fgeo为未知的待反演参数,可以用来拟合观测.计算过程的稳定性是由核矩阵的代数算子特征谱和观测噪音/误差来刻画的.因此为了计算地表反照率,成功反演模型参数是至关重要的环节.我们首先考虑了为计算BRDF模型反演的光滑解方法.业已知道,这是一个不适定的反问题.不适定性是由线性核驱动BRDF模型的欠定性表征的,比如说观测严重不足或观测方向范围有限,或者是观测数据高度线性相关以及噪音的污染等.例如,一次单角度观测可以导致一个欠定的系统（核算子的零空间含有非零向量）或者系统无解（系数矩阵的秩不等于增广矩阵的秩）.因此,光滑性或正则化技巧应当加以利用来压制不适定性.Li等（2001）应用先验知识把原始模型转换为一个超定的模型并求得最小二乘解.Pokrovsky等（2002）应用QR分解反演BRDF模型.Wang等（2007）考虑到了反演的正则化策略并提出了不适定地表参数反演的一个完整的正则化理论.在文中,强调从不同的空间添加先验信息于反演模型中.首先从数学物理的观点,第一次提出了?     

TOMRADE和MODTRAN 3.7模式研究紫外后向散射探测臭氧总量问题的比较

本文利用MODTRAN 3.7和TOMRADE模式分别计算了308.0 nm、312.5 nm、317.5 nm、322.3 nm、331.2 nm、360.0 nm六个紫外波段的后向散射强度并进行了比较,且利用两个模式计算得到的插值表,对EPTOMS实测资料分别进行反演,得到结论如下：1）两模式算得的后向散射强度随着太阳天顶角、卫星天顶角、地表反照率的变化特性基本一致,均能较好的反映紫外后向散射的辐射特性;2）总体而言,两模式计算的后向散射强度值相对偏差在－6％～18％之间;3）分别把MODTRAN 3.7和TOMRADE模式计算的插值表用于2003年7月30日中纬度地区EPTOMS实测资料的反演,得到的臭氧总量相对偏差在－3％～5％之间.本文对紫外波段大气辐射传输模式的研究和紫外后向散射探测臭氧总量的反演误差研究有着重要意义.     

多震相走时联合三参数同时反演成像

采用新近研制的分区多步不规则最短路径多震相地震射线追踪正演技术,结合流行的子空间反演算法,提出了一种联合多震相走时资料进行地震三参数 （速度、反射界面和震源位置） 同时反演的方法技术.数值模拟反演实例、以及与双参数 （速度和反射界面或速度和震源位置） 同时反演的对比分析表明：三参数同时反演成像结果大体接近双参数同时反演成像的结果.另外,噪声敏感性试验表明：所提算法对到时数据中可容许的随机误差并不敏感,结果说明多震相走时的联合三参数同时反演成像方法技术不失为一种提高走时成像空间分辨率、进而降低重建模型参数失真度、行之有效的方法技术.     

应用叠前反演弹性参数进行储层预测(英文)

本文是利用叠前弹性参数反演结果进行致密性含气砂岩储层预测的一个实例研究。随着油气勘探开发的发展,叠前地震数据及其反演结果的应用研究已经广泛用于实际生产中。叠前地震数据的特有属性研究,不仅包括简单的AVO特性,还包括其他的弹性属性的变化特性。本文通过对含气砂岩岩芯弹性属性参数响应特征的分析,发现特定弹性属性参数或其组合可以作为流体检测因子。因此,可以利用叠前地震反演得到不同的弹性属性参数结果,进行储层解释和储层描述。该叠前反演方法是基于Zoeppritz方程的Aki—Richard简化公式建立起来的,根据测井数据和地质解释结果建立初始反演模型,反演的地震数据为叠前时间或深度偏移的共反射点道集数据,反演结果可以是不同的弹性属性参数及其组合。通过对一实际的致密性含气砂岩储层进行叠前弹性属性参数反演,并将反演结果与其它预测结果进行对比分析发现弹性属性参数λ和λρ, λ/μ,以及K／μ能够很好地预测含气储层,而且反演结果很好展现出储层中的含气特性。     

2013年1月29日哈萨克斯坦MS6.1地震序列的震源机制解分析

北京时间2013年1月29日,哈萨克斯坦发生MS6.1地震,为了提高对地震震源机制解的认识,并进一步了解震源区的应力场特征,利用CAP方法反演了此次地震序列震源机制解.反演结果表明,MS6.1地震节面Ⅰ的参数：走向241°,倾角80°,滑动角7°;节面Ⅱ的参数：走向150°,倾角84°,滑动角170°;P轴方位为196°,倾角2°,T轴方位为105°,倾角12°;矩震级MW为6.1;矩心深度为13km;震源类型是左旋走滑型.此次地震序列破裂优势方向为NEE—SWW,倾角以30°～60°居多,滑动角以60°～120°、-60°～-120°居多;P轴方位的优势取向为近NE—SW向,接近水平的居优;T轴优势取向为近SEE—NWW向,接近垂直的居优;震源机制类型以倾向滑动型为主.反演结果与断层的分布、余震分布及哈萨克斯坦中天山（伊犁盆地西部）NEE—SWW向应力场有很好的一致性.     

Soil moisture estimation using tomographic ground penetrating radar in a MCMC–Bayesian framework

In this study, we focus on a hydrogeological inverse problem specifically targeting monitoring soil moisture variations using tomographic ground penetrating radar (GPR) travel time data. Technical challenges exist in the inversion of GPR tomographic data for handling non-uniqueness, nonlinearity and high-dimensionality of unknowns. We have developed a new method for estimating soil moisture fields from crosshole GPR data. It uses a pilot-point method to provide a low-dimensional representation of the relative dielectric permittivity field of the soil, which is the primary object of inference: the field can be converted to soil moisture using a petrophysical model. We integrate a multi-chain Markov chain Monte Carlo (MCMC)–Bayesian inversion framework with the pilot point concept, a curved-ray GPR travel time model, and a sequential Gaussian simulation algorithm, for estimating the dielectric permittivity at pilot point locations distributed within the tomogram, as well as the corresponding geostatistical parameters (i.e., spatial correlation range). We infer the dielectric permittivity as a probability density function, thus capturing the uncertainty in the inference. The multi-chain MCMC enables addressing high-dimensional inverse problems as required in the inversion setup. The method is scalable in terms of number of chains and processors, and is useful for computationally demanding Bayesian model calibration in scientific and engineering problems. The proposed inversion approach can successfully approximate the posterior density distributions of the pilot points, and capture the true values. The computational efficiency, accuracy, and convergence behaviors of the inversion approach were also systematically evaluated, by comparing the inversion results obtained with different levels of noises in the observations, increased observational data, as well as increased number of pilot points.     

基于稀疏反演理论的自动叠加速度反演方法

叠加速度分析技术是常规地震资料处理中的重要环节,也是经典的时间域速度建模方法.叠加速度分析技术主要包括速度谱计算和拾取两个步骤.至今为止,多数研究工作通过提高速度谱的分辨率以及抗噪声能力,获得高质量的速度谱从而有利于拾取.本文的目标是将叠加速度分析技术转为一个全自动化的处理流程.从参数估计的角度出发,将叠加速度估计转化为稀疏反演框架下的模型参数估计问题,并通过稀疏反演算法自动反演叠加速度,进而提高叠加速度建模的效率.为实现这一目标,首先给出了正问题的定义,即层状介质中CMP道集的预测模型,利用叠加速度、垂向双程走时(t_0)以及反射子波以及CMP道集时距关系(如双曲时距关系)可以预测CMP道集.接着,速度分析反问题可以描述为已知观测的CMP道集,估计模型参数(叠加速度及t_0时间等).利用模型参数的稀疏性作为约束条件并用L_0范数作为模型稀疏性的度量准则,叠加速度分析可以转化为L_0范数约束下的稀疏反演问题.本文提出了一种基于预测校正思想的匹配追踪算法求解上述反问题,实现了自动叠加速度建模并为后续的高精度速度反演方法提供较好的初始模型.理论和实际资料的测试结果证明了本文方法的有效性.     

COMPARISON OF FIVE LEAST-SQUARES INVERSION TECHNIQUES IN RESISTIVITY SOUNDING*

A brief history of the development of the inverse problem in resistivity sounding is presented with the development of the equations governing the least-squares inverse. Five algorithms for finding the minimum of the least-square problem are described and their speed of convergence is compared on data from two planar earth models. Of the five algorithms studied, the ridge-regression algorithm required the fewest numbers of forward problem evaluations to reach a desired minimum. Solution space statistics, including (1) parameter-standard errors, (2) parameter correlation coefficients, (3) model parameter eigenvectors, and (4) data eigenvectors are discussed. The type of weighting applied to the data affects these statistical parameters. Weighting the data by taking log₁₀ of the observed and calculated values is comparable to weighting by the inverse of a constant data error. The most reliable parameter standard errors are obtained by weighting by the inverse of observed data errors. All other solution statistics, such as dataparameter eigenvector pairs, have more physical significance when inverse data error weighting is used.     

Simultaneous identification of parameter,initial condition,and boundary condition in groundwater modelling

In this paper, we perform an inverse method to simultaneously estimate aquifer parameters, initial condition, and boundary conditions in groundwater modelling. The parameter estimation is extended to a complete inverse problem that makes the calibrated groundwater flow model more realistic. The adjoint state method, the gradient search method, and the least square error algorithm are combined to build the optimization procedure. Horizontal two‐dimensional groundwater flow in a confined aquifer is exemplified to demonstrate the correlation between unknowns, the contribution of observation, as well as the suitability of applying the inverse method. The correlation analysis shows the connection between storage coefficient and initial condition. Besides, transmissivity and boundary conditions are also highly correlated. More observations at different location and time are necessary to provide sufficient information. A time series of unsteady head is requested for estimation of storage coefficient and initial condition. Observation near boundary is very effective for boundary condition estimation. The observation at pumping well mostly contributes to the estimation of transmissivity. According to all observations, it is possible to identify parameters, initial condition, and boundary condition simultaneously. Furthermore, the results not only illustrate the traditional assumption of known boundary condition but also initial condition, which may cause an incorrect estimation. Copyright © 2009 John Wiley & Sons, Ltd.     

基于贝叶斯理论的AVO三参数波形反演

在实际的AVO反演问题中,叠前数据体中的噪声或其他因素严重影响了AVO反演问题的适定性,而采用先验地质信息作为AVO反演问题的约束条件是解决AVO反演问题不适定的一种可行方法. 文中的似然函数采用了[WTBX]ι[WTBX]p范数的解,并用Cauchy分布表示先验模型参数的分布. 以此为基础,在反演中建立了测井数据的参数协方差矩阵对反演过程进行约束,并采用了共轭梯度算法实现多参数非线性的反演过程. 同时,为了提高反演精度,避免动校正拉伸及依赖于炮检距的调谐效应对参数估计的影响,反演采用动校前地震数据进行参数估计. 从应用效果分析来看,即使叠前道集的信噪比不高,反演的结果也能较好地与实际情况相匹配,为识别储层流体性质提供了新的手段.     

BAYESIAN ESTIMATION IN SEISMIC INVERSION. PART I: PRINCIPLES1

This paper gives a review of Bayesian parameter estimation. The Bayesian approach is fundamental and applicable to all kinds of inverse problems. Its basic formulation is probabilistic. Information from data is combined with a priori information on model parameters. The result is called the a posteriori probability density function and it is the solution to the inverse problem. In practice an estimate of the parameters is obtained by taking its maximum. Well-known estimation procedures like least-squares inversion or l₁ norm inversion result, depending on the type of noise and a priori information given. Due to the a priori information the maximum will be unique and the estimation procedures will be stable except (in theory) for the most pathological problems which are very unlikely to occur in practice. The approach of Tarantola and Valette can be derived within classical probability theory. The Bayesian approach allows a full resolution and uncertainty analysis which is discussed in Part II of the paper.     

Parameter estimation of subsurface flow models using iterative regularized ensemble Kalman filter

A new parameter estimation algorithm based on ensemble Kalman filter (EnKF) is developed. The developed algorithm combined with the proposed problem parametrization offers an efficient parameter estimation method that converges using very small ensembles. The inverse problem is formulated as a sequential data integration problem. Gaussian process regression is used to integrate the prior knowledge (static data). The search space is further parameterized using Karhunen–Loève expansion to build a set of basis functions that spans the search space. Optimal weights of the reduced basis functions are estimated by an iterative regularized EnKF algorithm. The filter is converted to an optimization algorithm by using a pseudo time-stepping technique such that the model output matches the time dependent data. The EnKF Kalman gain matrix is regularized using truncated SVD to filter out noisy correlations. Numerical results show that the proposed algorithm is a promising approach for parameter estimation of subsurface flow models.     

BAYESIAN ESTIMATION IN SEISMIC INVERSION. PART II: UNCERTAINTY ANALYSIS1

A parameter estimation or inversion procedure is incomplete without an analysis of uncertainties in the results. In the fundamental approach of Bayesian parameter estimation, discussed in Part I of this paper, the a posteriori probability density function (pdf) is the solution to the inverse problem. It is the product of the a priori pdf, containing a priori information on the parameters, and the likelihood function, which represents the information from the data. The maximum of the a posteriori pdf is usually taken as a point estimate of the parameters. The shape of this pdf, however, gives the full picture of uncertainty in the parameters. Uncertainty analysis is strictly a problem of information reduction. This can be achieved in several stages. Standard deviations can be computed as overall uncertainty measures of the parameters, when the shape of the a posteriori pdf is not too far from Gaussian. Covariance and related matrices give more detailed information. An eigenvalue or principle component analysis allows the inspection of essential linear combinations of the parameters. The relative contributions of a priori information and data to the solution can be elegantly studied. Results in this paper are especially worked out for the non-linear Gaussian case. Comparisons with other approaches are given. The procedures are illustrated with a simple two-parameter inverse problem.     

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.     

基于随机抽样一致性-网格搜索方法反演断层面倾角

采用小地震的丛集性活动资料定量地研究地下断层的三维几何信息对地震危险性评估具有至关重要的意义。若所使用的资料存在高比率的离群值,将会使断层几何参数的估值产生较大偏差。为了提高断层几何参数估值的稳健性,本文将随机抽样一致性（RANSAC）与网格搜索（GS）相结合,给出了随机抽样一致性-网格搜索（RANSAC-GS）估算方法。在数值模拟试验中,对模拟观测值加入1%,5%,10%和20%的离群值,分别采用GS和RANSAC-GS两种方法估算了断层面倾角,并从反演参数的残差、反演模型计算值与观测值的密合度、目标函数及相关度等方面证明了RANSAC-GS方法即使在高比率离群值的情况下,依然能够给出稳健的参数估值。最后,利用2008年1月至2012年12月鄂尔多斯地区小震重定位结果,以震源点到断层面距离最小为准则,采用RANSAC-GS方法反演获得太谷断裂的断层面倾角为52.5°,与前人的结果有较好的一致性,在此基础上对大地测量形变观测给出合理的阐释,从而证明了本文方法的有效性。