全球变化过程的数学模拟和定量预测

本文首先介绍了全球变化过程数学模拟问题的由来、主要研究内容和重要意义,进一步讨论了全球变化过程综合模拟的数学模型及其已经取得的进展和系统分析的可能应用,然后介绍了过去全球变化过程的数学模拟以及已有的部分区域性工作基础和几种数学方法的可能应用。作者综述了GCM模型的应用现状和存在问题,并讨论了全球变化过程的定最预测,分析了若干新技术的可能应用,提出了对开展此项工作的看法和建议。     

试论河北平原东部第四纪地质几个基本问题

河北平原第四纪沉积厚300至500余米,地层连续。在濒临渤海的沧州地区,又有多层海相层及火山岩分布,是研究第四纪地质有意义的地区。十余年来,为开发地下水在本区打了四百多个岩心钻孔,最大深度787.79米,范围近一万五千平方公里。在许多兄弟单位支持下完成了大量样品分析鉴定工作。本文对第四纪古气候分期,海进与海退,第四纪下限及年表等问题,提出一些探讨性意见,以期引起讨论。     

沉积数值模拟研究现状及实例

沉积数值模拟是开展沉积学定量研究的重要手段,在油气地质学、水利工程等领域中发挥着重要作用。本文基于大量国内外文献调研,结合实际研究工作,系统概述了沉积数值模拟的发展历程及模型分类,归纳了几何规则、水动力方程、扩散方程、元胞自动机和模糊逻辑等5种模拟方法,对比各种方法的基本原理、优缺点以及与其相对应的代表性模拟软件的特点;总结了沉积数值模拟在层序地层学,沉积过程及沉积机理,“源-汇”系统和油气地质学等4个研究领域中的应用现状及独特优势;并结合东营凹陷北部陡坡带断层控制背景下两种湖相重力流沉积数值模拟研究实例,进一步说明了沉积数值模拟在沉积学研究中的有效性与重要性;最后展望了探索不同沉积机制数值模拟新方法,多地质过程耦合数值模拟与地质几何建模一体化,基于人工智能驱动的沉积过程反演数值模拟以及在非常规油气资源勘探开发中深入应用是该领域未来的发展方向。     

水环境数学模拟的应用现状及存在问题

水环境中污染物的迁移转化过程模拟和预测是水生态环境健康诊断、预测、预警及控制管理的重要基础。水环境数学模拟已经在国内外有了非常广泛的应用,并且成为国内外学者研究的热点。本文介绍了水环境数学模型的基础研究和应用现状,并提出其发展趋势和国内水质数学模型存在的主要问题。     

秦岭新构造运动与成矿作用

本文在叙述地质地貌的基础上,对新构造运动与成矿作用进行了讨论。描述了3处第四纪沉积区:北带——关中盆地;中带——秦岭徽成、月河盆地;南带——汉中盆地及第四纪冰川活动。对3个矿化类型(硅灰泥岩型、花岗岩型和砂金型)进行了研究。最后对成矿模式得出结论:早期矿化形成阶段→第四纪沉积成矿阶段→风化剥蚀成矿阶段→淋滤沉积成矿阶段。     

黄骅拗陷南部沉积盆地数学模拟的初步研究

六十年代初国外有人开始用确定型的数学模拟方法来模拟沉积盆地的形成和演化。L.L.Sloss(1962)提出了一种沉积盆地的概念模型,J.W.Harbaugh等(1970)详细地讨论了这种模型,并编制了计算机程序。这种模型适用于由流经海岸平原的河流携带到海边的大陆架上的碎屑沉积物的沉积作用。本文利用这种沉积模式,对我国黄骅拗陷南部一个约1000平方公里的膏盐湖沉积盆地进行了数学模拟,并取得了一些初步成果。     

北康盆地第四纪块体搬运沉积地震反射特征及成因机制

北康盆地陆坡区地震资料显示,自第四纪以来广泛发育深水块体搬运沉积,面积约占盆地面积50%以上。为了更加精细地了解该区块体搬运沉积的地震反射特征、分布期次、规模及其成因机制,共选取30条二维高分辨率地震剖面,采用精细解释的方法,对研究区内的块体搬运沉积体的外部形态和内部结构进行了识别和刻画;同时结合北康盆地地质背景和地震反射特征,讨论了自第四纪以来块体搬运沉积广泛发育的原因。结果表明,自第四纪以来,北康盆地共发育了6次大规模块体搬运沉积,识别出3种典型外部形态和6种内部结构单元;研究区第四纪块体搬运沉积体系的广泛发育主要由陆坡坡度、沉积速率、海平面变化及水合物分解和运移4个因素控制,其中海平面变化与水合物分解和运移起关键触发作用。     

上海松江地区第四纪地层与沉积环境特征

上海地区第四纪以来沉积的松散沉积物,地层和沉积环境具有显著的空间差异。本文利用松江地区第四纪钻孔的测年、有孔虫、孢粉等测试资料,研究阐述了过渡沉积区地层发育和沉积环境特征,为地层厘定及全面认识上海地区第四纪地质演化规律提供依据,并为地层的区域对比研究提供参考。     

论地质作用中元素间的相关性及制约因素

本文把地质作用中元素间的相关性划分为曲线相关、准线性相关和线性相关三种类型;并在建立反映岩浆、沉积作用过程中元素间相关关系的数学模型及其它工作的基础上,详细讨论分配系数、源区成分和作用过程等方面对地质作用中元素间相关关系的制约作用。进而获得批式熔融、结晶分异、岩浆混合、化学沉积、碎屑沉积等过程中元素间呈线性和(?)线性关系的一般特征和应用前景。最后还给出La/Ce相关性的应用实例。     

第四纪沉积地球化学研究的若干进展

第四纪沉积地球化学是研究第四纪时期化学元素及其同位素在地壳中的分布与演化规律的科学。它着重研究元素及其同位素在不同成因类型的第四纪沉积物中分市、分配、迁移和富集的规律。距今2—3Ma以来的第四纪是地球历史的最新篇章。第四纪沉积物作为第四纪地质过程在特定的地质环境中所形成的产物,留下了许多自然环境变迁的遗迹,含有它们形     

Three-dimensional numerical modeling of Late Quaternary paleoceanography and sedimentation in the northern North Atlantic

Modelling of Late Quaternary paleoceanography and sedimentation in the northern North Atlantic (NNA) is achieved by coupling the ocean general circulation model SCINNA (Sensitivity and Circulation In the NNA) to the sedimentation models SENNA (Sedimentation In the NNA) and PATRINNA (PArticle Tracing In the NNA).SCINNA is based on the primitive equations with conservation of mass, momentum, energy, heat and salt. SENNA and PATRINNA are driven by temperature, salinity and velocity fields derived from SCINNA. The modelling includes three-dimensional circulation of the ocean, sediment transport in the water column and two-dimensional sedimentary processes in a thin bottom layer. SENNA calculates the erosion, transport and deposition of sediments, resulting in sedimentation patterns for specific time intervals. PATRINNA models the transport paths of single sediment grains corresponding to the ocean circulation.The NNA reacts in a highly sensitive manner to small forcing changes, as shown by our sensitivity experiments. From these experiments it is possible to model specific circulation regimes for glacial and interglacial periods, for melt water events and for the onset of glaciation. The different climatic stages in the circulation model produce different sediment patterns in the sedimentation models, which correspond closely to the sedimentary record.     

Laboratory simulation of diffusion in contaminated marine sediments

Wastes from offshore oil drilling activities are often discharged to the marine environment. Solid wastes that settle onto the bottom sediment may pose a health threat to marine organisms and eventually to man through the food chain. We need to understand their fate in order to predict the chemical concentration levels and life-times in the sediment and adjoining aquatic boundary layer. A laboratory simulation of selected in-bed processes that addresses contaminant leaching from the sediment is proposed. The process chosen for simulation in this study is the coupled desorption-diffusion of contaminants from the bed to the water column. A simple mathematical model of the process is also proposed. Preliminary results using organic chemicals for both the simulation and the model are presented. The results suggest that the experimental procedure represents a good way of estimating the diffusive leaching rates of hydrophobic compounds from contaminated sediments.     

Use of random-genetic models in the study of sedimentary processes

To study sedimentary phenomena, we introduce random-genetic models in which genetic hypotheses and structural random elements occur for the main part. Starting from geologic hypotheses we choose principal factors which may be random functions or random variables. These factors are: depth, nature of the facies, sedimentation rate, and subsidence. Equations of evolution link the factors. Depth is a Markov process, but generally the resultant sequence does not make a Markov chain or Markov process. Three examples of such models are given with the results of simulations.     

Mathematical analysis to discriminate two types of sandstone-shale alternations

Sedimentation processes forming a series of bipartite layers have been studied mathematically. Two types of sedimentation processes are recognized, i.e. concurrent deposition of sandstone and shale by a turbidity current (Type I) and alternate deposition of each of them (Type II). A time series of events in a sedimentation process is reasonably considered a first-order Markov chain, and the process is described with a Markov matrix including four state-variables such as deposition of sandstone, erosion of sandstone, deposition of shale, and erosion of shale. Analysis of Markov matrix yields a fixed probability vector, which for Type I process is different from that for Type II process. The vector bears a close relation to Kolmogorov's coefficient, which is the ratio of the number of beds deposited and the number of beds preserved in a given sedimentary section. This coefficient can be computed on the basis of field observations. Substitution of the computed data determined the values of the fixed probability vector for two sedimentary sections in Japan. The results permitted a theoretical conclusion as to the genesis of observed sandstone-shale alternations. This conclusion is in good agreement with the deductions from more conventional sedimentological methods.     

Markov Chain Random Fields for Estimation of?Categorical Variables

Multi-dimensional Markov chain conditional simulation (or interpolation) models have potential for predicting and simulating categorical variables more accurately from sample data because they can incorporate interclass relationships. This paper introduces a Markov chain random field (MCRF) theory for building one to multi-dimensional Markov chain models for conditional simulation (or interpolation). A MCRF is defined as a single spatial Markov chain that moves (or jumps) in a space, with its conditional probability distribution at each location entirely depending on its nearest known neighbors in different directions. A general solution for conditional probability distribution of a random variable in a MCRF is derived explicitly based on the Bayes’ theorem and conditional independence assumption. One to multi-dimensional Markov chain models for prediction and conditional simulation of categorical variables can be drawn from the general solution and MCRF-based multi-dimensional Markov chain models are nonlinear.     

Markov Chain Random Fields for Estimation of Categorical Variables

Multi-dimensional Markov chain conditional simulation (or interpolation) models have potential for predicting and simulating categorical variables more accurately from sample data because they can incorporate interclass relationships. This paper introduces a Markov chain random field (MCRF) theory for building one to multi-dimensional Markov chain models for conditional simulation (or interpolation). A MCRF is defined as a single spatial Markov chain that moves (or jumps) in a space, with its conditional probability distribution at each location entirely depending on its nearest known neighbors in different directions. A general solution for conditional probability distribution of a random variable in a MCRF is derived explicitly based on the Bayes’ theorem and conditional independence assumption. One to multi-dimensional Markov chain models for prediction and conditional simulation of categorical variables can be drawn from the general solution and MCRF-based multi-dimensional Markov chain models are nonlinear.     

The Kolmogorov model of bed-thickness distribution: an assessment based on numerical simulation and field-data analysis

Kolmogorov's model of the distribution of bed thicknesses is assessed by numerical simulations of a sedimentation process, assumed to be a random time-series of alternating depositional and erosional episodes conformable with a stationary Markov process in a state of equilibrium. The study supports the validity of the main point of the model. The random time-series process generates a succession of beds with 'positive' (preserved) and 'negative' (eroded) thicknesses, the frequency distribution of which, f(x), spans the range of positive to negative x -values. The beds with negative thicknesses are absent in the stratigraphic record, whereby the measured bed thicknesses show a frequency distribution, f*( x | x > 0), that is left-side truncated, cut off at the zero thickness value. The numerical simulations further indicate that f( x ) is a 'composite' geometrical distribution, whose actual form changes progressively with p _d, the probability of sediment deposition relative to erosion. The distribution f( x ) invariably has a maximum at x ≤0, such that the truncated distribution f*( x ) for p _d≥0.5 is a simple geometrical distribution regardless of p _d value. The f*( x ) distribution will appear to be a negative exponential distribution when based on the bed-thickness data measured in a conventional metric scale. Data sets from four different turbidite successions in the Cenozoic of Japan, each comprising a few thousand beds, show this type of distribution. However, the sandstone-capping shales in one of the turbidite successions show a truncated Gaussian distribution, attributed to a significant component of non-turbiditic mud. No universal form of bed-thickness distribution can be assumed for the Kolmogorov model. The form of bed-thickness distribution may vary with the type of the depositional process and the character of the sedimentary environment.     

沉积过程的三维计算机模拟及其应用

利用模拟沉积、剥蚀和搬运过程的数学模型和基本算法,开发了模拟软件SSSP(simulating system of sedimentary processes)。该软件能够进行主要沉积环境的大规模剥蚀、搬运和沉积过程的复杂模拟,并可控制各种参数实时显示模拟结果,包括剖面图、沉积相图、等值线图、网格图和速度矢量图等。在已知沉积环境参数的情况下,利用SSSP再现盆地的沉积演化过程、沉积构型和沉积相分布,并给出了三角洲、河道和水下台阶沉积环境模拟实例。     

长江三角洲南通地区第四纪地层马尔柯夫链综合分析

本文简要论述了马尔柯夫链的基本方法,并试图利用此法对长江三角洲地区第四纪地层剖面作综合分析。文中以南通地区两个有代表性的钻孔剖面为例,采用马尔柯夫链极限概率分析、置换分析、熵分析、旋回类型分析和地层模拟,对两钻孔剖面作了综合对比分析。结果表明,马尔柯夫链分析方法是该区第四纪地层分析的一种行之有效的方法。