Does a one point sample adequately characterize the lake environment for paleoenvironmental calibration studies?

A major goal of paleolimnological studies is the quantification of past environmental conditions. This is accomplished by computing transfer functions relating organism assemblages to environmental factors. The environmental data are typically comprised of a point sample of water chemistry and other environmental parameters that are collected at the same time as a surface sediment sample. We explore whether the year of sampling of the environmental variables affects the parameterization of organism-environment relations, in particular chironomids, ostracodes and diatoms. Canonical correspondence analyses revealed that the year of sampling is of secondary importance when relating the organism assemblages to environmental variables, but only with the major explanatory variables. A chironomid-inferred bottom water temperature partial least squares transfer function revealed similar performance statistics between the years. Taxon optima and tolerances were computed for both years using weighted averaging, and the results are comparable. A paired t-test computed on the proxy-inferred bottom water temperature values indicated that the results between the 2 years are not statistically different. The results of this study provide guarded optimism that the methodology of estimating transfer functions as currently applied is not entirely determined by the particular year when the data were collected, although more case studies are needed.     

Modern diatom assemblages from Chilean tidal marshes and their application for quantifying deformation during past great earthquakes

Tidal marsh sediments from south‐central Chile provide evidence for multiple great earthquakes. Diatom transfer functions, statistical models of the relationship between species preserved in the sediment and elevation, provide quantitative estimates of coseismic vertical land‐level change associated with individual earthquakes. However, in south‐central Chile, our ability to quantify land‐level change is currently limited by a lack of understanding of the environmental variables controlling the distribution of diatoms, an essential prerequisite for converting variations in fossil diatom assemblages into quantitative estimates of past elevation changes. We present a new modern diatom dataset for the region and explore the implications of the scale of the dataset used in transfer function models on the reconstructions of land‐level change. Modern training sets containing samples from a regional scale are superior to sub‐regional and local‐scale training sets, providing closer estimates for known deformation during the great 1960 Chilean earthquake, a higher proportion of good modern analogues and uncertainty terms up to 42% smaller than previously published reconstructions. Copyright © 2017 The Authors. Journal of Quaternary Science Published by John Wiley & Sons Ltd.

     

稠油热采储层非均质性及其对开发的影响——以辽河西部凹陷某试验区于楼油层为例

本文以辽河西部凹陷某试验区为例,基于7 口井岩心、镜下薄片、分析测试及400口井测井等多种资料,采取点、线、面相结合的研究思路,利用地质统计学方法,开展稠油热采储层非均质性研究。整体上,于楼油层储层非均质性较强,储层非均质性受沉积作用、成岩作用和构造作用等影响（以沉积作用为主）。单井岩电特征与层内韵律非均质性关系密切,目的层可见3种沉积韵律模式,包括正韵律、反韵律和复合韵律（其中以正韵律和复合韵律为主）。沉积微相空间上的快速变化,导致储层非均质性增强。单层之间隔层分布较稳定,隔层厚度多大于2 m。非均质性强、中、弱的区域大体呈条带状,以北西-南东向展布,与物源方向基本一致。储层非均质性直接影响到了油藏的开发效果,研究储层非均质性可为油藏开发方式的转换提供参考。     

Representing geographical objects with scale‐induced indeterminate boundaries: A neural network‐based data model

The degree of uncertainty of many geographical objects has long been known to be in intimate relation with the scale of its observation and representation. Yet, the explicit consideration of scaling operations when modeling uncertainty is rarely found. In this study, a neural network‐based data model was investigated for representing geographical objects with scale‐induced indeterminate boundaries. Two types of neural units, combined with two types of activation function, comprise the processing core of the model, where the activation function can model either hard or soft transition zones. The construction of complex fuzzy regions, as well as lines and points, is discussed and illustrated with examples. It is shown how the level of detail that is apparent in the boundary at a given scale can be controlled through the degree of smoothness of each activation function. Several issues about the practical implementation of the model are discussed and indications on how to perform complex overlay operations of fuzzy maps provided. The model was illustrated through an example of representing multi‐resolution, sub‐pixel maps that are typically derived from remote sensing techniques.     

模糊可变集合方法在降雨相似性分析中的应用

基于特定流域场次降雨具有的相似性,应用考虑区间值模糊可变的模糊可变集合方法进行场次降雨相似性分析。通过计算各场次降雨隶属于各降雨级别的特征值及与目标场次降雨之间的相似距离,从而获得流域场次降雨相似性排序,有效解决了场次降雨相似分析中的模糊性和不确定性问题。找出与实时阶段场次降雨相似的历史场次降雨,预估水库实时阶段面临的洪水过程及量级,可为水库防洪调度提供信息参考。     

Seismic characterization of reservoirs with multiple fracture sets using velocity and attenuation anisotropy data

Knowledge about the spatial distribution of the fracture density and the azimuthal fracture orientation can greatly help in optimizing production from fractured reservoirs. Frequency-dependent seismic velocity and attenuation anisotropy data contain information about the fractures present in the reservoir. In this study, we use the measurements of velocity and attenuation anisotropy data corresponding to different seismic frequencies and azimuths to infer information about the multiple fracture sets present in the reservoir. We consider a reservoir model with two sets of vertical fractures characterized by unknown azimuthal fracture orientations and fracture densities. Frequency-dependent seismic velocity and attenuation anisotropy data is computed using the effective viscoelastic stiffness tensor and solving the Christoffel equation. A Bayesian inversion method is then applied to measurements of velocity and attenuation anisotropy data corresponding to different seismic frequencies and azimuth to estimate the azimuthal fracture orientations and the fracture densities, as well as their uncertainties. Our numerical examples suggest that velocity anisotropy data alone cannot recover the unknown fracture parameters. However, an improved estimation of the unknown fracture parameters can be obtained by joint inversion of velocity and attenuation anisotropy data.     

Improvements to Remote Sensing Using Fuzzy Classification, Graphs and Accuracy Statistics

This paper puts together some techniques that have been previously developed by the authors, but separately, relative to fuzzy classification within a remote sensing setting. Considering that each image can be represented as a graph that defines proximity between pixels, certain distances between the characteristic of contiguous pixels are defined on such a graph, so a segmentation of the image into homogeneous regions can be produced by means of a particular algorithm. Such a segmentation can be then introduced as information, previously to any classification procedure, with an expected significative improvement. In particular, we consider specific measures in order to quantify such an improvement. This approach is being illustrated with its application into a particular land surface problem.     

Emergency management evaluation by a fuzzy multi-criteria group decision support system

Emergency risk management (ERM) is a process which involves dealing with risks to the community arising from emergency events. Emergency management evaluation as one of the important parts of ERM aims assessing and improving social preparedness and organizational ability in identifying, analyzing, and treating emergency risks. This study first develops an emergency management evaluation model. It then proposes an extended fuzzy multi-criteria group evaluation method, which can deal with both subjective and objective criteria under multi-levels by a group of evaluators, for emergency management evaluation. A fuzzy multi-criteria group decision support system (FMCGDSS) is then developed to implement the proposed method for the case of emergency operating center/system evaluation.     

基于粗糙集理论与遗传规划的地表下沉系数预测研究

利用粗糙集理论处理大数据量.消除冗余信息等方面的优势,计算出下沉系数各影响因素的属性重要性,约简了遗传规划训练样本集,建立了基于粗糙集和遗传规划的地表下沉系数预测模型。并与BP神经网络法预测结果进行了对比,结果表明,本模型具有精度高,收敛速度快等特点,将其应用到地表下沉系数预测中是可行的。