早、中更新世东亚冬季风强度的快速变化

对黄土高原中部洛川黄土堆积进行了系统的粒度和磁化率测量,以黄土－古土壤中的粗颗粒组分,即＞３０μｍ颗粒的百分含量作为冬季风强度代用指标,根据一个独立的时间标尺,发现在１４５～１６５、２４０～２８０、３２０～３５０、３９０～４４０、６００～６４０、８６０～８９０、９００～９３０和１３３０～１４００ｋａＢ．Ｐ．东亚冬季风强度存在千年尺度快速变化的特征,冬季风加强事件的持续时间在１．４～７．２ｋａ之间,变化幅度也不相同;周期分析结果表明,这些古气候变化存在１．８９～４．０ｋａ之间的周期,由于时间标尺的精度还有待于进一步提高,在这里没有把冬季风强度变化与北大西洋沉积物记录的古气候事件进行对比,但是,实验结果证明在早、中更新世的某些阶段东亚冬季风强度存在快速变化的现象     

Possible controlling factors in the development of seasonal sand wedges on the Ordos Plateau,North China

Wedge-like structures filled with silty sand penetrate Quaternary fluvial and aeolian sediments and, in places, Tertiary bedrock on the Ordos Plateau, North China. The wedges reflect thermal contraction cracking of either permafrost or seasonal frost during the Late Pleistocene and early Holocene. Wedges of about 1 m in depth form polygonal nets of 2-3 m in diameter(type B). They contrast with wedges of 3-4 m in depth that form polygons of 10-15 m in diameter(type A).This review focuses upon the highly variable size of the inferred polygon nets and discusses the problem of differentiating between seasonally and perennially frozen ground, or between seasonal frost and permafrost.     

A multiscale method for subsurface inverse modeling: Single-phase transient flow

High-resolution geologic models that incorporate observed state data are expected to effectively enhance the reliability of reservoir performance prediction. One of the major challenges faced is how to solve the large-scale inverse modeling problem, i.e., to infer high-resolution models from the given observations of state variables that are related to the model parameters according to some known physical rules, e.g., the flow and transport partial differential equations. There are typically two difficulties, one is the high-dimensional problem and the other is the inverse problem. A multiscale inverse method is presented in this work to attack these problems with the aid of a gradient-based optimization algorithm. In this method, the model responses (i.e., the simulated state data) can be efficiently computed from the high-resolution model using the multiscale finite-volume method. The mismatch between the observations and the multiscale solutions is then used to define a proper objective function, and the fine-scale sensitivity coefficients (i.e., the derivatives of the objective function with respect to each node’s attribute) are computed by a multiscale adjoint method for subsequent optimization. The difficult high-dimensional optimization problem is reduced to a one-dimensional one using the gradient-based gradual deformation method. A synthetic single-phase transient flow example problem is employed to illustrate the proposed method. Results demonstrate that the multiscale framework presented is not only computationally efficient but also can generate geologically consistent models. By preserving spatial structure for inverse modeling, the method presented overcomes the artifacts introduced by the multiscale simulation and may enhance the prediction ability of the inverse-conditional realizations generated.     

Aeolian origin and palaeoclimatic implications of the ‘red clay’ (north China) as evidenced by grain‐size distribution

The ‘Red Clay’ is an important deposit underlying the Quaternary loess–palaeosol sequence in the Chinese Loess Plateau, being regarded as an excellent record of palaeoclimate changes in the late Tertiary. Several properties of the ‘Red Clay’ have been measured previously in order to derive climatic information. However, the sedimentary processes involved and the origin of the materials remain controversial. Here we present results of grain‐size analyses of the ‘Red Clay’ from four representative sites in the Chinese Loess Plateau. In particular their grain‐size distribution is compared with that of typical Quaternary aeolian loess–palaeosol, as well as lacustrine and fluvial sediments. It appears from the sedimentological evidence that the major part of the ‘Red Clay’ is of aeolian origin. It is rather similar in some of its properties to the Quaternary loessic palaeosols. The dust forming the ‘Red Clay’ was transported by a wind system that was weaker than that involved in the accretion of the Quaternary loess. Furthermore, the ‘Red Clay’ sediment has been modified by post‐depositional weathering. Copyright © 2001 John Wiley & Sons, Ltd.     

Quantifying geological uncertainty for flow and transport modeling in multi-modal heterogeneous formations

In this work, we address the problem of characterizing the heterogeneity and uncertainty of hydraulic properties for complex geological settings. Hereby, we distinguish between two scales of heterogeneity, namely the hydrofacies structure and the intrafacies variability of the hydraulic properties. We employ multiple-point geostatistics to characterize the hydrofacies architecture. The multiple-point statistics are borrowed from a training image that is designed to reflect the prior geological conceptualization. The intrafacies variability of the hydraulic properties is represented using conventional two-point correlation methods, more precisely, spatial covariance models under a multi-Gaussian spatial law. We address the different levels and sources of uncertainty in characterizing the subsurface heterogeneity, and explore their effect on groundwater flow and transport predictions. Typically, uncertainty is assessed by way of many images, termed realizations, of a fixed statistical model. However, in many cases, sampling from a fixed stochastic model does not adequately represent the space of uncertainty. It neglects the uncertainty related to the selection of the stochastic model and the estimation of its input parameters. We acknowledge the uncertainty inherent in the definition of the prior conceptual model of aquifer architecture and in the estimation of global statistics, anisotropy, and correlation scales. Spatial bootstrap is used to assess the uncertainty of the unknown statistical parameters. As an illustrative example, we employ a synthetic field that represents a fluvial setting consisting of an interconnected network of channel sands embedded within finer-grained floodplain material. For this highly non-stationary setting we quantify the groundwater flow and transport model prediction uncertainty for various levels of hydrogeological uncertainty. Results indicate the importance of accurately describing the facies geometry, especially for transport predictions.     

Image transforms for determining fit-for-purpose complexity of geostatistical models in flow modeling

Increased diversity of water or energy resources has led to an increased complexity in models aimed at representing accurately dynamic behavior and geological variability in such systems. In terms of variability of properties at least, simple layered models have mostly been replaced with more complex geostatistical models. The newest trend is to replace covariance-based models with geologically more realistic models such as Boolean, multiple-point, surface-, or process-based models. In this paper, we address the following question: given some design purpose or a set of flow-based decision variables, does adding more complexity increase predictability and ultimately improve decisions based on such models? In this paper, we do not attempt to make any generalizing statements or answer this question with yes/no, but provide some initial ideas on practical workflows to discover the needed complexity. We do treat complexity only in the context of decision making under uncertainty. Two workflows are proposed: complexifying versus simplifying. In these workflows, we attempt to extract, using image transforms, relevant features of the variability between geostatistical realizations that are related to uncertainty in flow dynamics. A simple distance-based calibration between the static variability and dynamic variability provides a means to decide on what the relevant complexity of geostatistical models should be for the given purpose.     

Direct Pattern-Based Simulation of Non-stationary Geostatistical Models

Non-stationary models often capture better spatial variation of real world spatial phenomena than stationary ones. However, the construction of such models can be tedious as it requires modeling both statistical trend and stationary stochastic component. Non-stationary models are an important issue in the recent development of multiple-point geostatistical models. This new modeling paradigm, with its reliance on the training image as the source for spatial statistics or patterns, has had considerable practical appeal. However, the role and construction of the training image in the non-stationary case remains a problematic issue from both a modeling and practical point of view. In this paper, we provide an easy to use, computationally efficient methodology for creating non-stationary multiple-point geostatistical models, for both discrete and continuous variables, based on a distance-based modeling and simulation of patterns. In that regard, the paper builds on pattern-based modeling previously published by the authors, whereby a geostatistical realization is created by laying down patterns as puzzle pieces on the simulation grid, such that the simulated patterns are consistent (in terms of a similarity definition) with any previously simulated ones. In this paper we add the spatial coordinate to the pattern similarity calculation, thereby only borrowing patterns locally from the training image instead of globally. The latter would entail a stationary assumption. Two ways of adding the geographical coordinate are presented, (1) based on a functional that decreases gradually away from the location where the pattern is simulated and (2) based on an automatic segmentation of the training image into stationary regions. Using ample two-dimensional and three-dimensional case studies we study the behavior in terms of spatial and ensemble uncertainty of the generated realizations.