The Truncated Discovery Process Model

The purpose of this paper is to describe a mathematical and computer model that has been developed by TransCanada for TransCanada Pipelines (TCPL) and for the Canadian Gas Potential Committee (CGPC) for future Canadian natural gas resource assessment work. It was developed in the contexts of developing a gas supply model for forecasting future Canadian gas supply for TCPL and of improving the CGPC's assessment methodologies and software. The model developed in this paper integrates two discovery process models used by the CGPC and solves the economic truncation problem for semi-mature to mature plays. It is based on applying techniques developed by G. M. Kaufman and J. D. Fuller and colleagues in new ways which extends the work of these authors. This model has a number of important advantages including: fast computational speed, integration of parametric and nonparametric statistical approaches, integration of the Kaufman and others model and the Arps-Roberts model, overcoming a PETRIMES limitation of a maximum of 1000 samples, providing a finding rate forecast for economic analysis and providing a fresh look at solving the economic truncation problem. The details of the new Modified Arps-Roberts Model (now known as the Truncated Discovery Process Model or TDPM for short) are discussed in this paper.     

Combining machine learning and ontological data handling for multi-source classification of nature conservation areas

Manual field surveys for nature conservation management are expensive and time-consuming and could be supplemented and streamlined by using Remote Sensing (RS). RS is critical to meet requirements of existing laws such as the EU Habitats Directive (HabDir) and more importantly to meet future challenges. The full potential of RS has yet to be harnessed as different nomenclatures and procedures hinder interoperability, comparison and provenance. Therefore, automated tools are needed to use RS data to produce comparable, empirical data outputs that lend themselves to data discovery and provenance. These issues are addressed by a novel, semi-automatic ontology-based classification method that uses machine learning algorithms and Web Ontology Language (OWL) ontologies that yields traceable, interoperable and observation-based classification outputs. The method was tested on European Union Nature Information System (EUNIS) grasslands in Rheinland-Palatinate, Germany. The developed methodology is a first step in developing observation-based ontologies in the field of nature conservation. The tests show promising results for the determination of the grassland indicators wetness and alkalinity with an overall accuracy of 85% for alkalinity and 76% for wetness.     

Identifying tropical dry forests extent and succession via the use of machine learning techniques

Information on ecosystem services as a function of the successional stage for secondary tropical dry forests (TDFs) is scarce and limited. Secondary TDFs succession is defined as regrowth following a complete forest clearance for cattle growth or agriculture activities. In the context of large conservation initiatives, the identification of the extent, structure and composition of secondary TDFs can serve as key elements to estimate the effectiveness of such activities. As such, in this study we evaluate the use of a Hyperspectral MAPper (HyMap) dataset and a waveform LIDAR dataset for characterization of different levels of intra-secondary forests stages at the Santa Rosa National Park (SRNP) Environmental Monitoring Super Site located in Costa Rica. Specifically, a multi-task learning based machine learning classifier (MLC-MTL) is employed on the first shortwave infrared (SWIR1) of HyMap in order to identify the variability of aboveground biomass of secondary TDFs along a successional gradient. Our paper recognizes that the process of ecological succession is not deterministic but a combination of transitional forests types along a stochastic path that depends on ecological, edaphic, land use, and micro-meteorological conditions, and our results provide a new way to obtain the spatial distribution of three main types of TDFs successional stages.     

Measuring visual enclosure for street walkability: Using machine learning algorithms and Google Street View imagery

One major limitation currently with studying street level urban design qualities for walkability is the often inconsistent and unreliable measures of streetscape features across different field surveyors even with costly training due to lack of more objective processes, which also make large scale study difficult. The recent advances in sensor technologies and digitization have produced a wealth of data to help research activities by facilitating improved measurements and conducting large scale analysis. This paper explores the potential of big data and big data analytics in the light of current approaches to measuring streetscape features. By applying machine learning algorithms on Google Street View imagery, we generated objectively three measures on visual enclosure. The results showed that sky areas were identified fairly well for the calculation of proportion of sky. The three visual enclosure measures were found to be correlated with pedestrian volume and Walk Score. This method allows large scale and consistent objective measures of visual enclosure that can be done reproducibly and universally applicable with readily available Google Street View imagery in many countries around the world to help test their association with walking behaviors.     

Slope stability analysis: a support vector machine approach

Artificial Neural Network (ANN) such as backpropagation learning algorithm has been successfully used in slope stability problem. However, generalization ability of conventional ANN has some limitations. For this reason, Support Vector Machine (SVM) which is firmly based on the theory of statistical learning has been used in slope stability problem. An interesting property of this approach is that it is an approximate implementation of a structural risk minimization (SRM) induction principle that aims at minimizing a bound on the generalization error of a model, rather than minimizing only the mean square error over the data set. In this study, SVM predicts the factor of safety that has been modeled as a regression problem and stability status that has been modeled as a classification problem. For factor of safety prediction, SVM model gives better result than previously published result of ANN model. In case of stability status, SVM gives an accuracy of 85.71%.     

西藏措勤县尼雄矽卡岩型铁铜矿田的发现及地质特征与找矿意义

西藏措勤县尼雄铁铜矿田位于冈底斯地块的北带,由尼雄超大型富铁矿和日阿中型铜矿构成,是在青藏高原首轮国土资源大调查1∶25万措麦区幅区域地质调查过程中发现的。它形成于板块俯冲、碰撞造陆到青藏高原新生代板内造山的重大构造转折过程中,成矿期为晚白垩世,是冈底斯北带新发现的最具有找矿潜力的超大型铁矿床。笔者以西藏措勤—昂仁地区富铁矿、铜矿评价资料为基础,分析了措勤县尼雄铁铜矿田的地质特征及其找矿意义。     

1.26米红外望远镜电控系统软件设计与实现

2011年,国家天文台兴隆基地1.26 m红外望远镜进行了全面升级改造。主要讲述望远镜电控系统软件的设计与实现,用于实现望远镜各种观测策略和运动方式的自动化操作。为了提高稳定性和可靠性,软件基于有限状态机原理设计,定义了望远镜的状态集和动作集,以及各个状态间的状态转换图;同时给出了望远镜常见异常及其处理方式,并在本地控制的基础上提供远程控制接口,使得望远镜可方便纳入兴隆基地望远镜集中控制系统。该软件及其设计思想可推广至我国其他中小口径望远镜。     

西昆仑山北缘下更新统西域组中上部泥石流沉积 的发现及其意义

报道了在西昆仑山北缘的恰哈、桑株、柯克亚等地的西域组中上部地层中发现数十层泥石流沉积。容重测量表明,泥石流以粘性泥石流为主,典型沉积相组合为AC、AA1C、DAA1BC、AA1BC、AA1B、A1BC,反映泥石流沉积从上游向下游由扇顶向扇缘环境演化,泥石流沉积时代对比为早更新世早中期。青藏高原西北缘西域组中上部泥石流沉积的发现,为分析该区西域组沉积时期的构造背景、地貌条件、气候特征提供了重要的新资料,为研究青藏高原隆升及其引发的地质灾害效应提供了新的证据,对泥石流地质灾害的防治提出了预警。     

普遍计算环境中的移动中间件

网络基础结构NI(Network Infrastructure)如Internet和Grid通常由静态计算节点组成。这些节点支持高性能计算和通信，还拥有许多嵌入资源。同时移动设备虽然计算能力较弱，但却具有NI所没有的灵活性和移动性。因而，如何将NI与移动设备的优势结合起来，有效利用NI中的资源是一个重要的课题。普遍计算环境中的移动中间件提供了解决这一问题的途径，并且具有环境敏感性，能自动发现NI中的服务资源。     

A data mining based approach to predict spatiotemporal changes in satellite images

The interpretation of remotely sensed images in a spatiotemporal context is becoming a valuable research topic. However, the constant growth of data volume in remote sensing imaging makes reaching conclusions based on collected data a challenging task. Recently, data mining appears to be a promising research field leading to several interesting discoveries in various areas such as marketing, surveillance, fraud detection and scientific discovery. By integrating data mining and image interpretation techniques, accurate and relevant information (i.e. functional relation between observed parcels and a set of informational contents) can be automatically elicited.