High resolution paleoenvironmental and chronological investigations of Norse landnám at Tasiusaq, Eastern Settlement, Greenland     

Kevin J. Edwards  J. Edward Schofield 《Quaternary Research》2008,69(1):1-15

High-resolution paleoenvironmental data from a peat profile with a small pollen source area are used to reconstruct the impacts of landnám on vegetation and soils at a Norse farm complex (∅2 at Tasiusaq) comprising two farms in the Eastern Settlement of Greenland. Analyses include the AMS ¹⁴C dating of plant macrofossil samples and the use of Bayesian radiocarbon calibration to construct improved age-depth models for Norse cultural horizons. The onset of a regional landnám may be indicated by the clearance of Betula pubescens woodland immediately prior to local settlement. The latter is dated to AD 950-1020 (2σ) and is characterised by possible burning of Betula glandulosa scrub to provide grassland pasture for domestic stock. Clearance and grazing resulted in accelerated levels of soil erosion at a westerly farm. This was followed by an easterly migration of settlement and agriculture. Site constraints prevent an assessment of the demise of the easterly farm, but pressures of overgrazing and land degradation may have been the major factors responsible for the abandonment of the earlier farm.  相似文献   

Description of the weathering states of building stones by fractal geometry and fuzzy inference system in the Olba ancient city (Southern Turkey)     

Kivanc Zorlu   《Engineering Geology》2008,101(3-4):124-133

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A New Probability of Detection Model for Updating Crack Distribution of Offshore Structures     

李典庆  张圣坤  唐文勇 《中国海洋工程》2003,17(3)

1 .IntroductionNondestructiveinspection (NDI)isveryimportantforensuringthereliabilityofoffshorestructuresintheirservicelives (Lauraetal.,1 996 ) .Itiswellknownthatdetectionofflawsinvolvesconsider ablestatisticaluncertainties.Asaresult,theprobabilityofdetection (POD)forallflawsofagivensizehasbeenusedintheliteraturetodefinethecapabilityofaparticularNDItechniqueinagivenen vironment.SincethedataofPODusuallyscatterlargely ,itisdifficulttodeterminewhichmodelfitstheavailabledatabest.Thismodelun…  相似文献   

预报指标自动归纳和实时自动推理预报系统     

李开乐 《海洋预报》1992,9(3):74-79

本文结合实际应用介绍一种人工智能系统。系统的重要特点是,根据预报指标经验知识的产生过程的机理性知识所设计成功的系统主程序之一的“预报指标自动归纳程序”,能够通过对多因子历史资料的分析归纳,自动生成含有较全面知识规则的规则库。有关专家只须凭感性经验提供可能因子和原始资料,系统便能帮助升华出理性的经验规则,自动建成某一专项业务的应用预报系统。对这种“母系统”的反复使用,就可以根据不同类别的资料生成相应的多种应用系统。既适用于气象上风雨暑寒各类天气预报,也适用于其他某些专业,如水文、地震等的定性预报或分析。 本文除简单介绍系统的构造、功能、设计原理之外,还给出一个应用实例,介绍了如何使用本系统去产生应用性系统的具体方法步骤。  相似文献   

An assessment of Greenland walrus populations     

Witting  Lars; Born  Erik W. 《ICES Journal of Marine Science》2005,62(2):266-284

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Variation in the catchability of yellow perch (Perca flavescens) in the fisheries of Lake Erie using a Bayesian error-in-variable approach     

Jiao  Yan; Reid  Kevin; Nudds  Tom 《ICES Journal of Marine Science》2006,63(9):1695-1704

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基于交互多模型的水下目标跟踪方法(英文)   总被引：1，自引：0，他引：1  

Weiming Xu  Yanchun Liu  Xiaodong Yin 《地球空间信息科学学报》2008,11(3):186-190

According to the requirements of real-time performance and reliability in underwater maneuvering target tracking as well as clarifying motion features of the underwater target, an interacting multiple model algorithm based on fuzzy logic inference （FIMM） is proposed. Maneuvering patterns of the target are represented by model sets, including the constant velocity model （CA）, the Singer mode~, and the nearly constant speed horizontal-turn model （HT） in FIMM technology. The simulation results show that compared to conventional IMM, the reliability and real-time performance of underwater target tracking can be improved by FIMM algorithm.  相似文献   

The Bayesian detection of discontinuities in a polynomial regression and its application to the cycle-slip problem   总被引：4，自引：1，他引：3  

Maria Clara de Lacy  Mirko Reguzzoni  Fernando Sansò  Giovanna Venuti 《Journal of Geodesy》2008,82(9):527-542

This paper deals with the problem of detecting and correcting cycle-slips in Global Navigation Satellite System (GNSS) phase data by exploiting the Bayesian theory. The method is here applied to undifferenced observations, because repairing cycle-slips already at this stage could be a useful pre-processing tool, especially for a network of permanent GNSS stations. If a dual frequency receiver is available, the cycle-slips can be easily detected by combining two phase observations or phase and range observations from a single satellite to a single receiver. These combinations, expressed in a distance unit form, are completely free from the geometry and depend only on the ionospheric effect, on the electronic biases and on the initial integer ambiguities; since these terms are expected to be smooth in time, at least in a short period, a cycle-slip in one or both the two carriers can be modelled as a discontinuity in a polynomial regression. The proposed method consists in applying the Bayesian theory to compute the marginal posterior distribution of the discontinuity epoch and to detect it as a maximum a posteriori (MAP) in a very accurate way. Concerning the cycle-slip correction, a couple of simultaneous integer slips in the two carriers is chosen by maximazing the conditional posterior distribution of the discontinuity amplitude given the detected epoch. Numerical experiments on simulated and real data show that the discontinuities with an amplitude 2 or 3 times larger than the noise standard deviation are successfully identified. This means that the Bayesian approach is able to detect and correct cycle-slips using undifferenced GNSS observations even if the slip occurs by one cycle. A comparison with the scientific software BERNESE 5.0 confirms the good performance of the proposed method, especially when data sampled at high frequency (e.g. every 1 s or every 5 s) are available.  相似文献   

Bayesian estimation of seismic hazard for two sites in Switzerland     

Erik Rüttener  Juan José Egozcue  Dieter Mayer-Rosa  Stephan Mueller 《Natural Hazards》1996,14(2-3):165-178

Seismic hazard analysis is based on data and models, which both are imprecise and uncertain. Especially the interpretation of historical information into earthquake parameters, e.g. earthquake size and location, yields ambiguous and imprecise data. Models based on probability distributions have been developed in order to quantify and represent these uncertainties. Nevertheless, the majority of the procedures applied in seismic hazard assessment do not take into account these uncertainties, nor do they show the variance of the results. Therefore, a procedure based on Bayesian statistics was developed to estimate return periods for different ground motion intensities (MSK scale).Bayesian techniques provide a mathematical model to estimate the distribution of random variables in presence of uncertainties. The developed method estimates the probability distribution of the number of occurrences in a Poisson process described by the parameter . The input data are the historical occurrences of intensities for a particular site, represented by a discrete probability distribution for each earthquake. The calculation of these historical occurrences requires a careful preparation of all input parameters, i.e. a modelling of their uncertainties. The obtained results show that the variance of the recurrence rate is smaller in regions with higher seismic activity than in less active regions. It can also be demonstrated that long return periods cannot be estimated with confidence, because the time period of observation is too short. This indicates that the long return periods obtained by seismic source methods only reflects the delineated seismic sources and the chosen earthquake size distribution law.  相似文献   

Bayesian Spatial Modelling of Gamma Ray Count Data     

Daniela Leonte  David J. Nott 《Mathematical Geology》2006,38(2):135-154

Gamma ray logging is a method routinely employed by geophysicists and environmental engineers in site geology evaluations. Modelling of gamma ray data from individual boreholes assists in the local identification of major lithological changes; modelling these data from a network of boreholes assists with lithological mapping and spatial stratigraphic correlation. In this paper we employ Bayesian spatial partition models to analyse gamma ray data spatially. In particular, a spatial partition is defined via a Voronoi tessellation and the mean intensity is assumed constant in each cell of the partition. The number of vertices generating the tessellation as well as the locations of vertices are assumed unknown, and uncertainty about these quantities is described via a hierarchical prior distribution. We describe the advantages of the spatial partition modelling approach in the context of smoothing gamma ray count data and describe an implementation that may be extended to the fitting of a more general model than a constant mean within each cell of the partition. As an illustration of the methodology we consider a data set collected from a network of eight boreholes, which is part of a geophysical study to assist in mapping the lithology of a site. Gamma ray logs are linked with geological information from cores and the spatial analysis of log data assists with predicting the lithology at unsampled locations.  相似文献