Dispersive Particle Transport: Identification of Macroscale Behavior in Heterogeneous Stratified Subsurface Flows

Dispersive mass transport processes in naturally heterogeneous geological formations (porous media) are investigated based on a particle approach to mass transport and on its numerical implementation using LPT3D, a Lagrangian Particle Tracking 3D code. We are currently using this approach for studying microscale and macroscale space–time behavior (advection, diffusion, dispersion) of tracer plumes, solutes, or miscible fluids, in 1,2,3-dimensional heterogeneous and anisotropic subsurface formations (aquifers, petroleum reservoirs). Our analyses are based on a general advection-diffusion model and numerical scheme where concentrations and fluxes are discretized in terms of particles. The advection-diffusion theory is presented in a probabilistic framework, and in particular, a numerical analysis is developed for the case of advective transport and rotational flows (numerical stability of the explicit Euler scheme). The remainder of the paper is devoted to the behavior of concentration, mass flux density, and statistical moments of the transported tracer plume in the case of heterogeneous steady flow fields, where macroscale dispersion occurs due to geologic heterogeneity and stratification. We focus on the case of perfectly stratified or multilayered media, obtained by generating many horizontal layers with a purely random transverse distribution of permeability and horizontal velocity. In this case, we calculate explicitly the exact mass concentration field C(x, t), mass flux density field f(x, t), and moments. This includes spatial moments and dispersion variance ² _x (t) on a finite domain L, and temporal moments on a finite time scale T, e.g., the mass variance of arrival times ² _T (x). The moments are related to flux concentrations in a way that takes explicitly into account finite space–time scales of analysis (time-dependent tracer mass; spatially variable flow through mass). The multilayered model problem is then used in numerical experiments for testing different ways of recovering information on tracer plume migration, dispersion, concentration and flux fields. Our analyses rely on a probabilistic interpretation that emerges naturally from the particle approach; it is based on spatial moments (particle positions), temporal moments (mass weighted arrival times), and probability densities (both concentrations and fluxes). Finally, as an alternative to direct estimations of the flux and concentration fields, we formulate and study the Moment Inverse Problem. Solving the MIP yields an indirect method for estimating the space–time distribution of flux concentrations based on observed or estimated moments of the plume. The moments may be estimated from field measurements, or numerically computed by particle tracking as we do here.     

Toward a unified theoretical framework for photogrammetry

ABSTRACT

The objective of photogrammetry is to extract information from imagery. With the increasing interaction of sensing and computing technologies, the fundamentals of photogrammetry have undergone an evolutionary change in the past several decades. Numerous theoretical progresses and practical applications have been reported from traditionally different but related multiple disciplines, including computer vision, photogrammetry, computer graphics, pattern recognition, remote sensing and machine learning. This has gradually extended the boundary of traditional photogrammetry in both theory and practice. This paper introduces a new, holistic theoretical framework to describe various photogrammetric tasks and solutions. Under this framework, photogrammetry is generally regarded as a reversed imaging process formulated as a unified optimization problem. Depending on the variables to be determined through optimization, photogrammetric tasks are mostly divided into image space tasks, image-object space tasks and object space tasks, each being a special case of the general formulation. This paper presents representative solution approaches for each task. With this effort, we intend to advocate an imminent and necessary paradigm change in both research and learning of photogrammetry.     

Mapping multiple tree species classes using a hierarchical procedure with optimized node variables and thresholds based on high spatial resolution satellite data

ABSTRACT

Tree species distribution mapping using remotely sensed data has long been an important research area. However, previous studies have rarely established a comprehensive and efficient classification procedure to obtain an accurate result. This study proposes a hierarchical classification procedure with optimized node variables and thresholds to classify tree species based on high spatial resolution satellite imagery. A classification tree structure consisting of parent and leaf nodes was designed based on user experience and visual interpretation. Spectral, textural, and topographic variables were extracted based on pre-segmented images. The random forest algorithm was used to select variables by ranking the impact of all variables. An iterating approach was used to optimize variables and thresholds in each loop by comprehensively considering the test accuracy and selected variables. The threshold range for each selected variable was determined by a statistical method considering the mean and standard deviation for two subnode types at each parent node. Classification of tree species was implemented using the optimized variables and thresholds. The results show that (1) the proposed procedure can accurately map the tree species distribution, with an overall accuracy of over 86% for both training and test stages; (2) critical variables for each class can be identified using this proposed procedure, and optimal variables of most tree plantation nodes are spectra related; (3) the overall forest classification accuracy using the proposed method is more accurate than that using the random forest (RF) and classification and regression tree (CART). The proposed approach provides results with 3.21% and 7.56% higher overall land cover classification accuracy and 4.68% and 10.28% higher overall forest classification accuracy than RF and CART, respectively.     

A methodology for simple and fast streamflow modelling

ABSTRACT

During the last few decades, hydrological models have become very powerful, capable of spatially analysing the hydrological information and accurately representing the geomorphological characteristics of the studied area. However, one of the drawbacks of this heightened intricacy is the amount of time required to set up a hydrological model. In this study, a simple methodology that requires only a minimum set-up time is presented. This methodology employs linear regression to combine the outputs of simple hydrological models to simulate hydrological responses. Two kinds of simple hydrological models are employed. The first one represents the characteristics of the streamflow attributed to overland flow, and the second the characteristics of the streamflow attributed to interflow and baseflow. The methodology was tested in 4 case studies, and the results were encouraging. The best performance was achieved in the case study with data of fine time step with significant length.     

长江三峡地区盐关微地震群的成因机制

盐关微地震群的活动局限在矿区内,地震都是微震和极微震。地震分两种类型:一种是微震活动,初动呈象限分布的双力偶滑动型震源机制,S波能量高于P波能量。另一种是极微震,初动几乎全负,P波能量高于S波能量,呈非双力偶的张破裂向内爆炸型的震源机制。两种地震的成因都是采矿诱发地震,不能做为仙女山断层向北延伸的证据     

前兆地震活动图象及其在地震预报中的作用

本文根据长、中和短期预报研究对主要地震活动图象加以归类和简述。结果表明:(1)利用地震活动图象进行中、短期预报,预报时间量程可能达到1、2年,甚至几个月,但要进一步缩短预报的时间尺度就十分困难,除非有明显的前震序列活动;(2)地震活动图象分析,对于辨认未来主震(尤其是强主震)的位置是有效的;(3)强震和中强震前的图象特点是有区别的,据此,可以大概估计未来主震的震级。不过图象的空间尺度与主震震级无明显的相关性;(4)信号震、前兆震群和前震(包括早期前震)的确定应考虑到异常的地震活动时空图象。     

湘黔地区“卡林型”金矿中金的赋存形式

对矿石的化学物相分析结果表明，湖南─贵州─带“卡林型”金矿原生矿石中的金主要呈微粒状单质金被包裹于硫化物及以绢云母为主的粘土矿物中，反映了金的矿化作用与黄铁矿化和绢云母化等是同时发生的。不同的“卡林型”金矿床之间，由于形成的物理化学条件的差异，其主要载金矿物的种类也不相同。表生氧化带中，载金矿物硫化物和绢云母发生了氧化和分解作用，使矿石中的大部分金转化为游离态形式存在，提高了矿石的可选性。     

PRELIMINARY STUDY OF VARIATION OF SO_2 AND AEROSOL PARTICLE BACKGROUND CONCENTRATIONS IN THE EASTERN CHINA

The average concentrations of sulphur dioxide,sulfate aerosol and TSP were about 8—10 ppb,15.08 μg m~(-3),and241.40 μg m~(-3) respectively,which were measured at the Lin'an regional background station during August—November,1991.The higher concentrations of SO_2 and SO_4~(2-) maybe acidify the rainfall.It has a great influence uponthe human health and ecosystem.The simulated results indicate that the distributions of SO_2 and SO_4~(2-) were deter-mined by local emission sources.Average aerosol particle number density was 2.0×10~4 cm~(-3).It shows that social devel-opment and human activities strongly affect the atmospheric background level.