东准噶尔造山带非史密斯地层类型划分及等级体制建立

在研究东准噶尔造带类型及发展历史的基础上建立区域构造格架、组合形式 ,划分出构造 -地层体类、构造 -岩层体类、构造 -混杂体类的非史密斯地层。按照所划分类型的各自特征 ,能够满足造山带 1∶ 2 5万填图精度的要求 ,对全部无序或局部无序的地 (岩 )层建立“岩群、岩组、岩段、岩层”等级体制 ;对那些总体有序、局部无序或总体无序、局部有序的地层 ,能通过各种手段恢复原始层序 ,服从史密斯地层层序律的 ,按“群、组、段、层”建立等级体制     

油气田（井）生产动态预测的灰色预测模型

本文针对辽河油田荣３７块天然气藏（井）的生产动态预测中,生产数据的离散状态及不均衡的实际,较完整、系统地介绍了所引入的几个使用效果好,拟合精度高的一般灰色系统ＧＭ（１,１）模型及为解决油气田（井）生产过程中因关井、生产间断、或因工程上的与地质上的原因生产出现随机性急剧变化而引入两个改进的ＧＭ（１,１）模型－非等间距和阶跃模型。通过几种模型特征的分析对比及在辽河油田荣３７块全气藏及气井的动态拟合预测     

骨架矿物—粘土矿物碱耗协同效应研究

选择了克拉玛依油田二中区Ｔ２ｋ１砂砾岩储层中的六种储层矿物,石英,正长石,微斜长石,Ｃａ－蒙脱石,高岭石,伊利石。在不同的条件下研究了这六种矿物的碱耗量,还研究骨架矿物与粘土矿物按照一定的质量配比而成的复配矿物的碱耗量,探讨了矿物碱耗的协同效应,结果表明单矿物的碱耗序列为,蒙脱石（Ｓ）〉高岭石（Ｋ）〉伊利石（Ｉ）〉微斜长石（Ｍ）〉石英（Ｑ）〉正长石（Ｊ）,复配物的碱耗总体上受粘土矿物控制,尤其是蒙     

测高数据在计算海平面时的取权

根据卫星测高数据分布所具有的低纬时稀疏、高纬时密集的特点,提出了计算平均海平面时非交叉点数据的解析定权公式和交叉点数据的解析定权公式。     

中国东部降水的随机动力预测初步研究

利用一个具有门限的非线性随机动力气候模式,对中国东部江淮流域的降水进行了预报试验。结果表明：模式能对大范围环流降水作出较为准确的形势预报。还比较了海气耦合模式与外强迫模式的预报效果,表明耦合模式的预报效果略好于外强迫模式。     

中国东部降水年际变化的随机动力诊断

利用一个具有门限的非线性随机动力气候模式对中国东部江淮流域的降水进行了分析。结果表明：在不同的门限域中,降水对外部强迫的响应有明显差异,呈现出一定的非线性特征;模式能较好地拟合降水的变化趋势。     

成矿系统的结构与聚矿功能

成矿系统结构对成矿动力学条件的约束与促进,引起系统固有聚矿功能的巨大变异。成矿系统动力学包括：系统运转的动力组合、系统物质运动总方向和轨迹、矿质活动空间规模形态及单位空间中成矿微过程的频度。系统结构定性分析表明：系统结构的完善程度和成矿动力学条件的优劣与系统聚矿功能的强度正相关。系统结构完善程度可由结构紧致度（Ｉ）表征,系统聚矿功能可用标准富集量（Ｑｓ）反映,它是矿床储量（Ｑ０）经成矿难度系数（Ｃ）变换的归一化指标。经结构定性半定量分析,对成矿系统聚矿功能作出初步排序,具强烈非线性成矿特征的花岗岩浆成矿系统和环流热液成矿系统的聚矿功能最高     

Nonlinear Effect of Wave Propagation in Shallow Water

—In this paper,a nonlinear model is presented to describe wave transformation in shallow wat-er with the zero-vorticity equation of wave-number vector and energy conservation equation.Thenonlinear effect due to an empirical dispersion relation(by Hedges)is compared with that of Dalrymple＇sdispersion relation.The model is tested against the laboratory measurements for the case of a submergedelliptical shoal on a slope beach,where both refraction and diffraction are significant.The computation re-sults,compared with those obtained through linear dispersion relation.show that the nonlinear effect ofwave transformation in shallow water is important.And the empirical dispersion relation is suitable for re-searching the nonlinearity of wave in shallow water.     

Succession,palaeoecology, evolution,and speciation of Pennsylvanian non‐marine bivalves,Northern Appalachian Basin,USA

Seventeen horizons of non‐marine bivalves are described within the Appalachian succession from the base of the Pottsville Group of Westphalian A‐B age to the Uniontown coal of Stephanian C age at the top of the Carboniferous System. A new highly variable fauna of Anthraconaia from the roof shales of the Upper Freeport coal near Kempton, west Maryland, dates from late Westphalian D or very early Cantabrian time, on the evidence of non‐marine shells and megafloras. Below this horizon, the Appalachian sequence reveals zones of Anthraconauta phillipsii and Anthraconauta tenuis in the same order as in Britain, whereas faunas of Anthraconaia of these zones are less common and differ from those of Britain. In all horizons above the Upper Freeport coal all non‐marine bivalve faunas consist of stages in the sequences of two natural species, the groups of Anthraconaia prolifera and Anthraconaia puella‐saravana. The first shows evidence of having lived in well‐oxygenated, probably shallow, fresh water conditions of relatively wide extent. The second group lived preferentially in a plant‐rich environment of relatively stagnant fresh water. Both groups are found in horizons associated with coal seams and may be seen together in the same habitats, but diagrams of variation (pictographs) suggest that there was no interbreeding between the two groups in either the Northern Appalachians or in southern Germany where the species split was first recognized. In the northern Spanish coalfields of Guardo‐Valderrueda and Central Asturia, facies evidence suggests how an initial split may have taken place in the same morphological directions and into the same palaeoenvironments as the later split into two species. Appalachian deposition was generally slow and intermittent with frequent palaeosols. There is also evidence of erosion and of small palaeontological breaks in the sequence, especially near the eastern edge of the Northern Appalachian Basin in western Maryland. The amount of accumulated sediment was less than one‐tenth of that of western Europe when basin centre deposition is compared. We found no evidence of a major palaeontological break representing Westphalian D strata overlain by Stephanian C strata. We figure non‐marine bivalve faunas of Stephanian B age in association with the Pittsburgh and the Little Pittsburgh coals. Two new species of non‐marine bivalves are described: Anthraconaia anthraconautiformis sp. nov. and Anthraconaia extrema sp. nov. Copyright © 2003 John Wiley & Sons, Ltd.     

A model‐based data‐driven dictionary learning for seismic data representation

Planar waves events recorded in a seismic array can be represented as lines in the Fourier domain. However, in the real world, seismic events usually have curvature or amplitude variability, which means that their Fourier transforms are no longer strictly linear but rather occupy conic regions of the Fourier domain that are narrow at low frequencies but broaden at high frequencies where the effect of curvature becomes more pronounced. One can consider these regions as localised “signal cones”. In this work, we consider a space–time variable signal cone to model the seismic data. The variability of the signal cone is obtained through scaling, slanting, and translation of the kernel for cone‐limited (C‐limited) functions (functions whose Fourier transform lives within a cone) or C‐Gaussian function (a multivariate function whose Fourier transform decays exponentially with respect to slowness and frequency), which constitutes our dictionary. We find a discrete number of scaling, slanting, and translation parameters from a continuum by optimally matching the data. This is a non‐linear optimisation problem, which we address by a fixed‐point method that utilises a variable projection method with ?₁ constraints on the linear parameters and bound constraints on the non‐linear parameters. We observe that slow decay and oscillatory behaviour of the kernel for C‐limited functions constitute bottlenecks for the optimisation problem, which we partially overcome by the C‐Gaussian function. We demonstrate our method through an interpolation example. We present the interpolation result using the estimated parameters obtained from the proposed method and compare it with those obtained using sparsity‐promoting curvelet decomposition, matching pursuit Fourier interpolation, and sparsity‐promoting plane‐wave decomposition methods.