基于模型空间压缩的大地电磁三维反演研究

本文提出了一种基于模型空间压缩技术的大地电磁三维反演方法.该方法在传统大地电磁三维反演理论的基础上,通过小波变换将待反演的空间域模型参数映射到小波域进行反演,获得小波域更新模型后再通过小波逆变换得到空间域反演模型.由于小波变换具有压缩特性和多尺度分辨能力,本文反演方法可在一定程度上提高反演分辨率.为了提高反演效率,我们针对基于L_1范数的模型约束求解不易收敛的反演问题,提出了一种基于模型粗糙度的简单有效的预条件处理技术.为验证本文算法的有效性,本文首先对经典的"棋盘"模型进行三维反演测试.反演结果表明本文算法的反演效率与传统方法相当,但对于深部异常体具有更好的分辨能力.最后,我们通过对实测数据反演进一步验证本文算法的有效性.     

Analyse multi-échelle par ondelettes des contacts géologiques : application à la carte gravimétrique du Maroc nord-oriental

The multiscale wavelet analysis has been applied to the gravity data from northeastern Morocco to map the major geological contacts, such us faults. Hence, the faults affecting the survey area were outlined with their importance level and dip direction. Seismic data confirm these results and testify the efficiency of this method, particularly in studying structure of plains. The structural map established is a very useful document in the planning of natural resources investigations (i.e. water, mines) to be undertaken in the area of study. To cite this article: D. Khattach et al., C. R. Geoscience 338 (2006).     

Localized multiscale energy and vorticity analysis: I. Fundamentals

A new methodology, multiscale energy and vorticity analysis (MS-EVA), is developed to investigate the inference of fundamental processes from oceanic or atmospheric data for complex dynamics which are nonlinear, time and space intermittent, and involve multiscale interactions. Based on a localized orthogonal complementary subspace decomposition through the multiscale window transform (MWT), MS-EVA is real problem-oriented and objective in nature. The development begins with an introduction of the concepts of scale and scale window and the decomposition of variables on scale windows. We then derive the evolution equations for multiscale kinetic and available potential energies and enstrophy. The phase oscillation reflected on the horizontal maps from Galilean transformation is removed with a 2D large-scale window synthesis. The resulting energetic terms are analyzed and interpreted. These terms, after being carefully classified, provide four types of processes: transport, transfer, conversion, and dissipation/diffusion. The key to this classification is the transfer–transport separation, which is made possible by looking for a special type of transfer, the so-called perfect transfer. The intricate energy source information involved in perfect transfers is differentiated through an interaction analysis.The transfer, transport, and conversion processes form the basis of dynamical interpretation for GFD problems. They redistribute energy in the phase space, physical space, and space of energy types. These processes are all referred to in a context local in space and time, and therefore can be easily applied to real ocean problems. When the dynamics of interest is on a global or duration scale, MS-EVA is reduced to a classical Reynolds-type energetics formalism.     

地震波形多尺度反演的一点讨论

基于在反演过程中对初始模型依赖性强、易陷入局部极值等问题,本文引入小波分析,提出多尺度地震波形反演方法,从而将参数反演问题转化到小波域中重要系数优化问题。利用多尺度之间的内在联系及小波域中重要系数的稀疏性,有将改进了局部极值、计算量等问题。并对几种多尺度反演策略进行了比较讨论。基于波动方程正演及褶积模型的两种反演方法的数值实便结果显示了本方法良好的效果。     

Development and implementation of a multiscale biomass model using hyperspectral vegetation indices for winter wheat in the North China Plain

Crop monitoring during the growing season is important for regional management decisions and biomass prediction. The objectives of this study were to develop, improve and validate a scale independent biomass model. Field studies were conducted in Huimin County, Shandong Province of China, during the 2006–2007 growing season of winter wheat (Triticum aestivum L.). The field design had a multiscale set-up with four levels which differed in their management, such as nitrogen fertilizer inputs and cultivars, to create different biomass conditions: small experimental fields (L1), large experimental fields (L2), small farm fields (L3), and large farm fields (L4). L4, planted with different winter wheat varieties, was managed according to farmers’ practice while L1 through L3 represented controlled field experiments. Multitemporal spectral measurements were taken in the fields, and biomass was sampled for each spectral campaign. In addition, multitemporal Hyperion data were obtained in 2006 and 2007. L1 field data were used to develop biomass models based on the relation between the winter wheat spectra and biomass: several published vegetation indices, including NRI, REP, OSAVI, TCI, and NDVI, were investigated. A new hyperspectral vegetation index, which uses a four-band combination in the NIR and SWIR domains, named GnyLi, was developed. Following the multiscale concept, the data of higher levels (L2 through L4) were used stepwise to validate and improve the models of the lower levels, and to transfer the improved models to the next level. Lastly, the models were transferred and validated at the regional scale using Hyperion images of 2006 and 2007. The results showed that the GnyLi and NRI models, which were based on the NIR and SWIR domains, performed best with R² > 0.74. All the other indices explained less than 60% model variability. Using the Hyperion data for regionalization, GnyLi and NRI explained 81–89% of the biomass variability. These results highlighted that GnyLi and NRI can be used together with hyperspectral images for both plot and regional level biomass estimation. Nevertheless, additional studies and analyses are needed to test its replicability in other environmental conditions.     

地球物理复杂信号的多尺度熵分析方法

为了研究地球物理观测信号在多尺度空间中的复杂性,本文提出了一种新的广义熵谱（General Entropy Spectral）概念,其在零频率下的值正好给出了传统意义的信息熵。同时,还提出了一种新的局部多尺度熵（Local Multiscale Entropy）分析方法,该方法结合了信息熵和多尺度方法,可用于对数据特征、信息复杂度进行多尺度表达。上述这些新方法适用于分析各种复杂的地球物理信号,以便深入挖掘更多的信号特征和信息。     

多尺度地震资料联合反演方法研究

常规三维地面地震反演不可避免的存在多解性和分辨率不高的缺陷,而油藏地球物理阶段丰富的多尺度地震资料为减小多解性、提高分辨率提供了可能.基于贝叶斯反演理论,通过联合概率分布建立新的似然函数,将三维地面地震、VSP和井间地震三种多尺度资料有机地融合在一起,完善了多尺度地震资料联合反演框架及反演流程.模型测试及实际资料处理表明,联合反演算法有效地引入了小尺度地震资料中的高频信息对大尺度资料进行约束,反演结果在保留大尺度地震资料特征的基础上提高了分辨率,降低了多解性,同时促进了多种地震资料之间的相互匹配.     

A multiscale adjoint method to compute sensitivity coefficients for flow in heterogeneous porous media

A multiscale adjoint (MSADJ) method is developed to compute high-resolution sensitivity coefficients for subsurface flow in large-scale heterogeneous geologic formations. In this method, the original fine-scale problem is partitioned into a set of coupled subgrid problems, such that the global adjoint problem can be efficiently solved on a coarse grid. Then, the coarse-scale sensitivities are interpolated to the local fine grid by reconstructing the local variability of the model parameters with the aid of solving embedded adjoint subproblems. The approach employs the multiscale finite-volume (MSFV) formulation to accurately and efficiently solve the highly detailed flow problem. The MSFV method couples a global coarse-scale solution with local fine-scale reconstruction operators, hence yielding model responses that are quite accurate at both scales. The MSADJ method is equally efficient in computing the gradient of the objective function with respect to model parameters. Several examples demonstrate that the approach is accurate and computationally efficient. The accuracy of our multiscale method for inverse problems is twofold: the sensitivity coefficients computed by this approach are more accurate than the traditional finite-difference-based numerical method for computing derivatives, and the calibrated models after history matching honor the available dynamic data on the fine scale. In other words, the multiscale based adjoint scheme can be used to history match fine-scale models quite effectively.