成像光谱仪影像的几种处理方法

针对成像光谱仪影像的专题分析，介绍了沿用常规遥感影像分析方法时的原理和新的算法，提出了影像融合方法的应用，说明了这些方法在学术思想上的归属，并简要介绍了各种方法的实验效果。     

计算样本相似性在油气预测中的应用

介绍了计算样本相似性方法在油气预测中的应用的基本原理和技术。提出了利用参数的显著性进行参数优化和加权的方法。该方法简单、实用，并在塔北某区的应用取得了满意结果。由此证明了该方法的有效性及在综合信息预测工作中进行参数优化和加权工作的重要性。     

变差函数参数的计算

为了求出地质统计学中变差函数理论模型的参数，首先把相应模型化成线性函数的形式，多面手应用线性方程组非负解的理论计算出它的参数。     

Fuzzy set approaches to classification of rock masses

Rock mass classification is analogous to multi-feature pattern recognition problem. The objective is to assign a rock mass to one of the pre-defined classes using a given set of criteria. This process involves a number of subjective uncertainties stemming from: (a) qualitative (linguistic) criteria; (b) sharp class boundaries; (c) fixed rating (or weight) scales; and (d) variable input reliability. Fuzzy set theory enables a soft approach to account for these uncertainties by allowing the expert to participate in this process in several ways. Hence, this study was designed to investigate the earlier fuzzy rock mass classification attempts and to devise improved methodologies to utilize the theory more accurately and efficiently. As in the earlier studies, the Rock Mass Rating (RMR) system was adopted as a reference conventional classification system because of its simple linear aggregation.

The proposed classification approach is based on the concept of partial fuzzy sets representing the variable importance or recognition power of each criterion in the universal domain of rock mass quality. The method enables one to evaluate rock mass quality using any set of criteria, and it is easy to implement. To reduce uncertainties due to project- and lithology-dependent variations, partial membership functions were formulated considering shallow (<200 m) tunneling in granitic rock masses. This facilitated a detailed expression of the variations in the classification power of each criterion along the corresponding universal domains. The binary relationship tables generated using these functions were processed not to derive a single class but rather to plot criterion contribution trends (stacked area graphs) and belief surface contours, which proved to be very satisfactory in difficult decision situations. Four input scenarios were selected to demonstrate the efficiency of the proposed approach in different situations and with reference to the earlier approaches.     

基于SuperMap的图形处理技术

本文首先阐述了SuperMap DeskPro的基本功能，然后对利用SuperMap DeskPro进行常规图形处理的技巧进行了详细介绍，具体包括地图配准、地图拼接、数据集重新整合、对象标注、文本数据集到属性字段的转换和地图格网生成。通过这些技巧的运用，可以有效提高数据的处理能力。     

Penny-shaped fractures revisited

All methods of seismic characterization of fractured reservoirs are based on effective media theories that relate geometrical and material properties of fractures and surrounding rock to the effective stiffnesses. In exploration seismology, the first-order theory of Hudson is the most popular. It describes the effective model caused by the presence of a single set of thin, aligned vertical fractures in otherwise isotropic rock. This model is known to be transversely isotropic with a horizontal symmetry axis (HTI). Following the theory, one can invert the effective anisotropy for the crack density and type of fluid infill of fractures, the quantities of great importance for reservoir appraisal and management.Here I compute effective media numerically using the finite element method. I deliberately construct models that contain a single set of vertical, ellipsoidal, non-intersecting and non-interconnected fractures to check validity of the first-order Hudson’s theory and establish the limits of its applicability. Contrary to conventional wisdom that Hudson’s results are accurate up to crack density e ≈ 0.1, I show that they consistently overestimate the magnitudes of all effective anisotropic coefficients ε^(V), δ^(V), and γ^(V). Accuracy of theoretically derived anisotropy depends on the type of fluid infill and typically deteriorates as e grows. While the theory gives | ε^(V)|, |δ^(V)|, |γ^(V)| and close to the upper bound of the corresponding numerically obtained values for randomly distributed liquid-filled fractures, theoretical predictions of ε^(V), δ^(V) are not supported by numerical computations when the cracks are dry. This happens primarily because the first-order Hudson’s theory makes no attempt to account for fracture interaction which contributes to the final result much stronger for gas- than for liquid-filled cracks. I find that Mori-Tanaka’s theory is superior to Hudson’s for all examined crack densities and both types of fluid infill.The paper was presented at the 11th International Workshop on Seismic Anisotropy (11IWSA) held in St. John’s, Canada in 2004.     

A variable precision rough set approach to knowledge discovery in land cover classification

This paper presents a granular computing approach to spatial classification and prediction of land cover classes using rough set variable precision methods. In particular, it presents an approach to characterizing large spatially clustered data sets to discover knowledge in multi-source supervised classification. The evidential structure of spatial classification is founded on the notions of equivalence relations of rough set theory. It allows expressing spatial concepts in terms of approximation space wherein a decision class can be approximated through the partition of boundary regions. The paper also identifies how approximate reasoning can be introduced by using variable precision rough sets in the context of land cover characterization. The rough set theory is applied to demonstrate an empirical application and the predictive performance is compared with popular baseline machine learning algorithms. A comparison shows that the predictive performance of the rough set rule induction is slightly higher than the decision tree and significantly outperforms the baseline models such as neural network, naïve Bayesian and support vector machine methods.     

Integrating cellular automata,artificial neural network,and fuzzy set theory to simulate threatened orchards: application to Maragheh,Iran

Urbanization processes challenge the growth of orchards in many cities in Iran. In Maragheh, orchards are crucial ecological, economical, and tourist sources. To explore orchards threatened by urban expansion, this study first aims to develop a new model by coupling cellular automata (CA) and artificial neural network with fuzzy set theory (CA–ANN–Fuzzy). While fuzzy set theory captures the uncertainty associated with transition rules, the ANN considers spatial and temporal nonlinearities of the driving forces underlying the urban growth processes. Second, the CA–ANN–Fuzzy model is compared with two existing approaches, namely a basic CA and a CA coupled with an ANN (CA–ANN). Third, we quantify the amount of orchard loss during the last three decades as well as for the upcoming years up to 2025. Results show that CA–ANN–Fuzzy with 83% kappa coefficient performs significantly better than conventional CA (with 51% kappa coefficient) and CA–ANN (with 79% kappa coefficient) models in simulating orchard loss. The historical data shows a considerable loss of 26% during the last three decades, while the CA–ANN–Fuzzy simulation reveals a considerable future loss of 7% of Maragheh’s orchards in 2025 due to urbanization. These areas require special attention and must be protected by the local government and decision-makers.     

基于主成分分析的青藏高原多年冻土区高寒草地土壤质量评价

土壤质量评价是提高对土壤质量理解的关键环节。为了了解青藏高原多年冻土区高寒草地土壤质量的基本情况,在青藏高原腹地西大滩至安多地区,根据不同海拔梯度和植被盖度共采集了154个土壤样品。通过主成分分析（PCA）法确定了影响青藏高原多年冻土区高寒草地土壤质量的最小数据集（MDS）：全氮、全磷、全钾。根据影响土壤质量的最小数据集对青藏高原多年冻土区高寒草地土壤质量进行评价,得出了不同海拔、不同植被盖度下的土壤质量指数（SQI）。通过对不同海拔、不同植被盖度的土壤质量指数进行对比研究表明：随着海拔的升高,SQI呈增加的趋势,即海拔4 300～4 600 m（0.270±0.043） < 海拔4 600～4 900 m（0.326±0.061） < 海拔4 900～5 200 m（0.410±0.075）;随着植被盖度的增加,SQI也呈现增加的变化趋势,即植被盖度小于50%（0.262～0.265） < 植被盖度大于50%（0.336～0.344）。在分别考虑了有机质、盐分、土壤水分对土壤质量的影响下得出的土壤质量指数值与基于最小数据集得到的土壤质量指数相一致,说明基于主成分分析的最小数据集可以对青藏高原多年冻土区高寒草地土壤质量做出较准确的评价。