二维核磁共振变参量迭代快速反演方法

核磁共振二维谱应用在储层流体识别与评价中具有快速直观识别流体类型、储层参数估算精度高的特点,使之能高效率的完成测井资料解释工作,然而拥有稳健、可靠及计算快速的反演方法是获取高质量核磁共振二维谱的关键所在.通过对二维核磁共振子空间类型反演方法的大量研究,在奇异值截断公式上进行了分析和改进,使得反演方程组的奇异值矩阵能保留下更多的奇异值,为反演结果的保真性奠定基础,并对其进行分组处理使反演方程组的条件数降低,从而提高求解的稳定性;在迭代计算中提出变参量迭代算法,使求解更合理、快速及高效.在数值模拟中,变参量迭代快速反演方法能够准确地还原30×30、60×80扩散弛豫二维构造谱,计算用时都在1分钟内;在油水试验中,变参量迭代快速反演方法反演出的扩散—弛豫二维谱能够正确识别所测流体类型,含油饱和度估算结果的相对误差为0.6%,绝对误差为0.79%.变参量迭代快速反演方法能够快速有效得处理二维核磁共振数据,反演出的扩散一弛豫二维谱质量高,二维谱应用于解释中所得到的结论可靠性高,表明该方法具有一定应用价值.     

位场数据网格化的反插值法

位场不规则分布数据的网格化是位场数据分析处理的首要问题.本文借鉴反插值法的原理，提出利用基于预条件共轭梯度的反插值法实现位场数据的网格化.其中，插值算子采用高斯权系数，滤波算子采用Laplacian算子，预条件算子采用滤波算子的逆.通过理论模型和实际航磁数据的网格化试验分析，验证了本文的反插值法适合地球物理位场特征，网格化速度快，精度高，效果好.     

基于最小二乘正则化的自然电场场源反演成像

自然电场对多种地下污染物敏感,开展相应的场源反演成像研究可帮助有效监测污染源活动.把目标区域内的多种自然电场场源进行统一处理,利用有限单元构建二维地电模型,实现自然电位正演的高精度计算.采用最小二乘正则化反演迭代,实现自然电场场源的二维反演.在设计二维自然电场场源反演算法的基础上,利用加入高斯噪声的合成模拟数据,对反演算法进行测试.测试结果表明:基于有限单元二维模型的最小二乘正则化反演算法能有效地实现对自然电场场源的反演成像,并能准确地确定自然电位异常源的位置和深度,且算法收敛快速稳定,具有一定的抗噪声能力.     

Delaunay三角剖分插值算法在MT成图中的应用

研究并实现了一种基于Delaunay三角剖分的二维快速插值算法,并将其应用于大地电磁(MT)二维反演实时成像网格化处理中。实际资料试算结果表明,该算法具有稳定、插值效果好以及易于模拟地形数据等优点,能满足MT反演结果实时成图的要求。     

2.5维复电阻率反演及其应用试验

自复电阻率法被提出以来,其区分矿与非矿方面的潜力就引起了人们的广泛关注,但反演问题一直没有得到较好的解决,已经严重影响和制约了该方法的应用和发展.对此,本文提出并实现了利用多个排列视电阻率和视相位数据的2.5维SIP联合反演方法.算法利用最小二乘原理构建了反演目标函数,为提高反演的稳定性,在反演方程中加入了Occam法的光滑模型约束.通过借助电场的偏导数形式,推导出了灵敏度矩阵的解析表达式,并应用互换定理对其进行精确求取.反演算法充分利用了电磁感应和激电效应中的异常信息,能够同时反演出二维地质断面上所有单元的四种复电阻率参数.最后,利用该反演程序对安徽某地区的SIP实测数据进行了反演成像,通过与已知钻井资料及CSAMT反演结果的对比分析表明,该反演算法具有良好的应用效果.     

Pg回折波的上地壳三维层析成像

提出利用人工地震Ｐｇ回折波走时重建区域上地壳三维速度分布的方法。正问题利用经典最小二乘迭代求解垂向速度梯度模型的参数值及相应走时残差。反问题采用泛函空间的最小二乘准则来实现。对速度不需要预先进行参数化处理，可以计算反演区内任意点的速度值。     

复杂条件下大地电磁资料二维反演及初始模型构建研究

大地电磁资料反演方法较多,但构建二维反演初始模型常规方法主要有两种:以均匀半空间为初始模型、直接以一维反演结果为初始模型.本文利用典型实测剖面资料对这两种方法进行反复试验对比,结果表明:在地形及构造复杂地区,由于实测资料存在严重的静态效应,利用常规方法构建反演初始模型进行二维反演,难以达到理想的反演效果.基于此,本文提出了一种构建二维反演初始模型与反演新的思路方法,其核心为用于反演与构建初始模型数据为同一基础数据,但对基础数据分别进行处理,得到两个处理及校正程度不同的数据,分别用于初始模型构建与二维反演输入数据,这样得到的反演结果既能较好地保留浅部至深部有用异常信息,又能够最大程度上消除实测资料中的静态效应.本文进一步对实测剖面反演结果与已知地质信息进行对比,验证了本方法的有效性和可靠性,认为此方法为地形及地质条件复杂地区大地电磁资料处理反演提供了新的思路,具有一定的参考意义和借鉴作用.     

大地电磁法三维快速松弛反演

实现大地电磁法快速三维反演的关键在于找到快速计算灵敏度矩阵的方案. 本文在对大地电磁三维张量阻抗表达式进行深入分析的基础上，获得了三维快速松弛反演算法的灵敏度函数表达式，解决了三维快速松弛反演的核心问题；为了减小反演解的多解性，针对三维问题定义了最小构造函数，实现了求最小构造的三维快速松弛反演算法. 对二维棱柱体、三维棱柱体理论模型的大地电磁合成数据进行了反演试算，反演结果与理论模型相吻合. 对日本Kayabe地区实测资料进行了XY模式反演，反演得到的地电模型较好地反映了地热资源的分布. 反演试算结果表明大地电磁三维快速松弛反演具有计算速度快、结果稳定可靠等特点；使三维快速松弛反演在普通微机上得以实现.     

利用走时反演二维多孔介质渗透率

综合利用直接方法和优化方法研究二维多孔介质渗透率反演问题. 问题的提法与前人不同,是由区域内各点流体渗透的走时来反演渗透率. 该反问题的求解可分成两步进行,归结为两个相应的子问题进行研究. 首先由流体走时反演二维多孔介质速度场的数值算法,然后由二维多孔介质速度场反演二维多孔介质渗透率的数值算法,最后给出数值例子. 数值结果表明了所用数值方法的正确性和有效性.     

固定翼时间域航空电磁数据拟二维整体反演

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.     

A practical review of geostatistical processing applied to geophysical data: methods and applications

Nowadays, geostatistics is commonly applied for numerous gridding or modelling tasks. However, it is still under used and unknown for classical geophysical applications. This paper highlights the main geostatistical methods relevant for geophysical issues, for instance to improve the quality of seismic data such as velocity cubes or interpreted horizons. These methods are then illustrated through four examples. The first example, based on a gravity survey presents how a geostatistical interpolation can be used to filter out a global trend, in order to better define real anomalies. In the second case study, dedicated to refraction surveying, geostatistical filtering is used to filter out acquisition artefacts and identify the main geological structures. The third one is an example of porosity being integrated geostatistically with a seismic acoustic impedance map. The last example deals with geostatistical time to depth conversion; the interest of performing geostatistical simulations is finally discussed.     

Reduction of distortion and improvement of efficiency for gridding of scattered gravity and magnetic data

This paper presents a reasonable gridding-parameters extraction method for setting the optimal interpolation nodes in the gridding of scattered observed data. The method can extract optimized gridding parameters based on the distribution of features in raw data. Modeling analysis proves that distortion caused by gridding can be greatly reduced when using such parameters. We also present some improved technical measures that use human-machine interaction and multi-thread parallel technology to solve inadequacies in traditional gridding software. On the basis of these methods, we have developed software that can be used to grid scattered data using a graphic interface. Finally, a comparison of different gridding parameters on field magnetic data from Ji Lin Province, North China demonstrates the superiority of the proposed method in eliminating the distortions and enhancing gridding efficiency.     

消除探地雷达数据的子波衰减和频散的反滤波方法

消除探地雷达数据的子波衰减和频散可以很好地提高探地雷达的勘探深度和勘探分辨率.常用的消除探地雷达数据的子波衰减和频散方法为反Q滤波方法.该方法需要利用地下介质的Q参数,但是正确求取地下介质的Q参数很困难.针对这一问题,本文提出了一种消除探地雷达数据的子波衰减和频散的反滤波方法.该方法以地下介质反射系数是随机数为前提,利用地下介质等效滤波器具有最小相位这个特性,通过求取等效滤波器的振幅谱来求取等效滤波器的反滤波器.最后,利用该反滤波器对探地雷达数据进行反滤波,实现消除探地雷达数据的子波衰减和频散.     

Using data assimilation method to calibrate a heterogeneous conductivity field and improve solute transport prediction with an unknown contamination source

Hydraulic conductivity distribution and plume initial source condition are two important factors affecting solute transport in heterogeneous media. Since hydraulic conductivity can only be measured at limited locations in a field, its spatial distribution in a complex heterogeneous medium is generally uncertain. In many groundwater contamination sites, transport initial conditions are generally unknown, as plume distributions are available only after the contaminations occurred. In this study, a data assimilation method is developed for calibrating a hydraulic conductivity field and improving solute transport prediction with unknown initial solute source condition. Ensemble Kalman filter (EnKF) is used to update the model parameter (i.e., hydraulic conductivity) and state variables (hydraulic head and solute concentration), when data are available. Two-dimensional numerical experiments are designed to assess the performance of the EnKF method on data assimilation for solute transport prediction. The study results indicate that the EnKF method can significantly improve the estimation of the hydraulic conductivity distribution and solute transport prediction by assimilating hydraulic head measurements with a known solute initial condition. When solute source is unknown, solute prediction by assimilating continuous measurements of solute concentration at a few points in the plume well captures the plume evolution downstream of the measurement points.     

Optimal estimation of irrigation schedule – An example of quantifying human interferences to hydrologic processes

Reliable records of water use for irrigation are often lacking. This presents a difficulty for a qualified water use and water availability assessment. Quantification of the hydrologic cycle processes in regions of intensive agricultural practice requires irrigation as an input to hydrologic models. This paper presents a coupled forward-inverse framework to estimate irrigation schedule using remote-sensed data and data assimilation and optimization techniques. Irrigation schedule is treated as an unknown input to a hydro-agronomic simulation model. Remote-sensed data is used to assess actual crop evapotranspiration, which is used as the “observation” of the computed crop evapotranspiration from the simulation model. To handle the impact of model and observation error and the unknown biased error with irrigation inputs, a coupled forward-inverse approach is proposed, implemented and tested. The coupled approach is realized by an integrated ensemble Kalman filter (EnKF) and genetic algorithm (GA). The result from a case study demonstrates that the forward and inverse procedures in the coupled framework are complementary to each other. Further analysis is provided on the impact of model and observation errors on the non-uniqueness problem with inverse modeling and on the exactness of irrigation estimates.     

PRESTACK INVERSION OF GROUP-FILTERED SEISMIC DATA1

Three methods for least-squares inversion of receiver array-filtered seismic data are investigated: (1) point receiver inversion where array effects are neglected; (2) preprocessing of the data with an inverse array filter, followed by point receiver inversion; (3) array inversion, where the array effects are included in the forward modelling. The methods are tested on synthetic data generated using the acoustic wave equation and a horizontally stratified earth model. It is assumed that the group length and the group interval are identical. For arrays that are shorter than the minimum wavelength of the emitted wavefield, and when the data are appropriately muted, point receiver inversion (first method) gives satisfactory results. For longer arrays, array inversion (third method) should be used. The failure of the inverse array filter (second method) is due to aliasing problems in the data.     

地理坐标下网格化数据等值线的处理及编程

分析了均匀网格化数据等值线处理的具体步骤 ,介绍了非均匀网格分布数据的均匀网格化处理中的 3种方法 :按距离加权的最小二乘法 (N— P法 )、按方位取点加权法和加权最小二乘法拟合法 (M— S法 ) ,提供了 N— P法和 M— S法的 C 源程序     

RADON TRANSFORM APPLICATION TO THE IMPROVED GRIDDING OF AIRBORNE GEOPHYSICAL SURVEY DATA1

The Radon transform is applied to airborne geophysical data, which consist of parallel profiles, analogous to a seismic record. The plane-wave decomposition (PWD) thus becomes the strike-direction decomposition (SDD) since the observed spatially distributed information is represented by its strike directions in a domain achieved by the transformation. It is important that, after the SDD, we can identify anomalies and work on them according to their strikes. In particular, for gridding purposes, we may guide the second interpolation of the bi-directional gridding approach along the strike directions. In principle, the proposed Radon transform gridding method (RTGM) transforms the observed parallel profiles into a domain where information is mapped as its strike-direction ‘traces’ against its wavelengths. The number of strike directions into which the data are decomposed is equal to the number of lines to be interpolated. The Fourier spectrum of the grid is reconstructed from the strike-wavenumber domain by using the projection-slice theorem and the final square grid is obtained by performing an inverse Fourier transformation on the spectrum. The SDD is restricted to the Nyquist wavenumber bandwidth imposed by the survey line-spacing, so that there is no addition of ambiguous short wavelengths in the gridded data. A tapering window is employed to prevent any Gibb's oscillation in the final grid because of the sharp Nyquist cut-off in the reconstructed spectrum due to the survey line-spacing. The RTGM is first tested on a set of synthetic line-based data. It is also applied to aeromagnetic profile data from northern Botswana as a practical example.     

位移和加速度融合的自适应多速率Kalman滤波方法

多速率Kalman滤波方法可用于低采样率的位移和高采样率的加速度数据融合,而未知的噪声协方差信息则显著制约着多速率Kalman滤波精度.本文通过将多速率Kalman滤波转换为传统的单速率Kalman滤波,建立了Kalman滤波增益的自协方差矢量与未知的加速度谱密度和观测噪声参数间的线性函数模型,并采用最小二乘估计方法对未知的噪声协方差参数进行估计,进而有效地提高了多速率Kalman滤波精度.数值仿真和震动台实验结果验证了本文方法的正确性和有效性.