中国智力资源空间分布及其影响因素研究

现代社会已步入知识经济时代。知识经济显著特点就在于信息、知识等各种类型的智力资源取代工业经济的资本上升成为新的、决定性的终极资源和核心生产要素。在研究智力资源涵义的基础上，选取了投入、产出、环境支持、智力资源4个系统30个不同指标，运用数理统计方法中的因子分析来定量研究中国智力资源空间分布特点，从而进一步分析出影响中国智力资源的空间分布的4个主要因素：经济发展程度、行政区标准问题、历史因素和区域文化模式不同。     

A decision support system for geotechnical applications

A knowledge-based system shell called ‘Confound’ has been developed for the production of decision support systems. It was initially used for developing a system for conceptual foundation design but has subsequently been used for retaining wall, expansive soil and ground investigation applications. It uses a hierarchical object-oriented knowledge base where each object represents a decision in the decision making process. A simple rule-based approach is used to provide advice on the suitability of each potential choice, based on project specific information that is provided by the user. Confound has the ability to work with incomplete information and uses a system of confidence levels so that the user is aware of the confidence with which each piece of advice is given. Conflicting information in areas where knowledge is poorly defined can also be handled. Confound is designed to leave the user in full control. It simply provides comments on suitability (with an indication of confidence) and leaves the final decision to the user.     

Sub-basalt imaging problems and the application of Artificial Neural Networks

In many areas of the world, the presence of shallow high velocity, highly heterogeneous layers complicate seismic imaging of deeper reflectors. Of particular economic interest are areas where potentially hydrocarbon-bearing strata are obscured by layers of basalt. Basalt layers are highly reflective and heterogeneous. Using reflection seismic, top basalt is typified by a high-amplitude, coherent reflector with poor resolution of reflectors below the basalt, and even bottom basalt. Here, we present a new approach to the imaging problem using the pattern recognition abilities of a back-propagation Artificial Neural Network (ANN). ANNs are computational systems that attempt to mimic natural biological neural networks. They have the ability to recognize patterns and develop their own generalizations about a given data set. Back-propagation neural networks are trained on data sets for which the solution is known and tested on the data that are not previously presented to the ANN in order to validate the network result. We show that Artificial Neural Networks, due to their pattern recognition capabilities, can invert the medium statistics based on the seismic character. We produce statistically defined models involving a basalt analogous layer, and calculate full wavefield finite difference synthetic seismograms. We vary basalt layer thickness and source frequency to generate a synthetic model that produces seismic that is similar to real sub-basalt seismic, i.e. high amplitude top basalt reflector and the absence of base basalt and sub-basalt events. Using synthetic shot gathers, generated in a synthetic representation of the sub-basalt case, we can invert the velocity medium standard deviation by using an ANN. By inverting the velocity medium standard deviation, we successfully identified the transition from basalt to sub-basalt on the synthetic shot gathers. We also show that ANNs are capable of identifying the basalt to sub-basalt transition in the presence of incoherent noise. This is important for any future applications of this technique to the real-world seismic data, as this data is never completely noise-free. There is always a certain level of residual (noise remaining after initial noise filtering) environmental/ambient noise present on the recorded seismics, hence, neural network training with noise-free synthetic seismic is less than optimal.     

Using an Artificial Intelligence Method to Recognize Seismic Precursory Anomaly (SPA)

In this work,we seek for a new way to recognize and analyze the Seismic Precursory Anomaly(SPA)quantitatively.For further study of the mechanism of SPA,an artificial intelligence method,the Machine Discovery(MD)method,is proposed to process the Seismic Precursor Observation(SPO)data and to fit an analytic function to them.The analytic function is used as a criterion to identify the SPA.The algorithm of MD is worked out based on the theory of Recursive Function(RF).The data of Amplitude Factor of Solid Tide(7 value)are processed by the proposed method.We find that the subsequent data fit to the extrapolation of the discovered function during the quiet period,whereas they deviate from it before a strong earthquake.Finally,the feasibility of quantitative analysis of the SPA is testified.     

人工神经网络在地球科学中的应用综述

人工神经网络（ANN）是一种动态信息处理系统，它具有联想记忆、自组织、自适应、自学习和容错性等特性。ANN可对地质环境和各类地质对象进行判别分类；可根据地震及测井中的各种波谱曲线进行地质构造模式识别，并实现自动编辑和解释；可对各种遥感信息进行图像处理和地质构造解译；在勘查地球物理方面，可解决某些反演问题；在矿产勘查中可用于矿产资源定量预测与评价勘查过程中的组合优化及多目标决策等问题。     

基于移动Agent的分布式地理信息检索系统研究

如何从海量的空间信息中快速准确地找出用户感兴趣的信息,是地理信息空间检索服务所要解决的主要问题。为此,本文提出了一种基于移动Agent的分布式地理信息检索框架,并对其中的关键技术进行了研究。移动Agent技术的引入使得空间检索更加智能化,能有效解决网络带宽问题和离线计算问题,同时极大地提高了检索效率,为分布式GIS系统建设提供了一种新的思路和解决方法。     

面向叙事结构的地理空间情报可视分析方法

以地理空间情报服务于反恐为需求牵引,采用全球恐怖主义数据库(GTD)为数据源,将时空叙事结构作为分析模型,以时间要素和空间要素作为线索,进行地理空间叙事,对事件和对象的多维属性进行可视化,借助多视图人机交互式分析方法和交互技术,构建了基于JavaScript的地理空间情报可视分析平台,并根据分析任务进行了案例分析,为基于活动的地理空间情报研究探索一条新的思路。     

人工智能技术发展概述

近年来,以深度学习为核心的人工智能技术,取得了一系列重大突破.本文将就人工智能的产业化热潮,主要研究流派及发展历史,以深度学习为核心的成功应用,以及存在的一些问题和今后的可能研究方向做一个介绍.     

Superintelligence and Natural Resources: Morality and Technology in a Brave New World

A growing majority of artificial intelligence (AI) scientists and engineers believe human-level intelligence will be attained before the dawn of the 22nd century, while a large number believe this will happen much sooner. At this point, the elevation to “superintelligence,” or systems that have superhuman levels of general intelligence, would likely proceed very rapidly. With this in mind, this commentary has three goals: (1) to provide an overview of how a leading scholar, Nick Bostrom, conceives of superintelligence, with its potential and risks, (2) to bring attention to a burgeoning form of common pool resource extraction, space mining, that may benefit from superintelligence management, and (3) to suggest that environmental social scientists should play a key role in programming seed AI, infusing it not only with our existing collective knowledge, but also with just values.     

高光谱遥感图像混合像元分解的群智能算法

近年来,通过群智能算法求解组合优化或连续优化问题以实现高光谱图像混合像元分解方面取得了重要进展和显著成果.本文首先回顾了高光谱图像混合像元分解的研究背景和群智能算法的特点,然后梳理了光谱混合模型及对应的最优化模型,进而介绍了基于群智能算法的端元提取和丰度反演方法,最后通过2组实验比较了群智能算法和其他传统算法在端元提取和丰度反演方面的精度,对基于群智能算法的混合像元分解效果进行了评价.另外,本文也对群智能算法在高光谱图像信息提取中应用的优势和存在的问题进行了总结.