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31.
George Christakos 《Mathematical Geology》1988,20(2):111-133
Recursive algorithms for estimating states of nonlinear physical systems are presented. Orthogonality properties are rediscovered and the associated polynomials are used to linearize state and observation models of the underlying random processes. This requires some key hypotheses regarding the structure of these processes, which may then take account of a wide range of applications. The latter include streamflow forecasting, flood estimation, environmental protection, earthquake engineering, and mine planning. The proposed estimation algorithm may be compared favorably to Taylor series-type filters, nonlinear filters which approximate the probability density by Edgeworth or Gram-Charlier series, as well as to conventional statistical linearization-type estimators. Moreover, the method has several advantages over nonrecursive estimators like disjunctive kriging. To link theory with practice, some numerical results for a simulated system are presented, in which responses from the proposed and extended Kalman algorithms are compared. 相似文献
32.
The upper part of the Limestone Coal Group (Pendleian E1), between the major marine transgressions of the Black Metals and the Index Limestone, represents an early example of ‘coal measures’ facies. It comprises a distal and a proximal facies association. The distal facies association, which was subject to relatively strong marine influences and included both deltaic and fluvial elements, is characterized by a regular ‘layer-cake’ succession with laterally-persistent lithological members. By contrast, the proximal association, which was more subject to fluvial influences and is typified by the variable Bannockburn Main Complex, is characterized by abrupt lateral changes in lithofacies. The distal facies association is dominant in the west of the Kilsyth Trough, but proximal facies intercalations increase eastwards, so that they constitute a fairly high proportion of the succession in the Kincardine Basin, particularly in areas of locally-increased tectonic subsidence that were frequently occupied by major channel belts. Some of the sheet sandstones within the distal association have a lower, upward-coarsening portion succeeded by a coarser-grained, erosive-based, upward-fining portion. The resulting ‘two-storey’ profile may reflect deltaic sand bodies having been suceeded by fluvial sand bodies, following a general fall in base level. Linear regression lines showing the relationship between the number of horizons colonized by vegetation and net subsidence, suggest that local autocyclic, tectonosedimentary processes, such as delta switching, channel migration, and avulsion, were superimposed upon a widespread allocyclic, probably glacial-eustatic, process. The former processes were most effective within the proximal facies association and in the Kincardine Basin and the latter in the distal association and the Kilsyth Trough. 相似文献
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The distribution of chemical elements at and near the Earth's surface, the so-called critical zone, is complex and reflects the geochemistry and mineralogy of the original substrate modified by environmental factors that include physical, chemical and biological processes over time.Geochemical data typically is illustrated in the form of plan view maps or vertical cross-sections, where the composition of regolith, soil, bedrock or any other material is represented. These are primarily point observations that frequently are interpolated to produce rasters of element distributions. Here we propose the application of environmental or covariate regression modelling to predict and better understand the controls on major and trace element geochemistry within the regolith. Available environmental covariate datasets (raster or vector) representing factors influencing regolith or soil composition are intersected with the geochemical point data in a spatial statistical correlation model to develop a system of multiple linear correlations. The spatial resolution of the environmental covariates, which typically is much finer (e.g. ∼90 m pixel) than that of geochemical surveys (e.g. 1 sample per 10-10,000 km2), carries over to the predictions. Therefore the derived predictive models of element concentrations take the form of continuous geochemical landscape representations that are potentially much more informative than geostatistical interpolations.Environmental correlation is applied to the Sir Samuel 1:250,000 scale map sheet in Western Australia to produce distribution models of individual elements describing the geochemical composition of the regolith and exposed bedrock. As an example we model the distribution of two elements – chromium and sodium. We show that the environmental correlation approach generates high resolution predictive maps that are statistically more accurate and effective than ordinary kriging and inverse distance weighting interpolation methods. Furthermore, insights can be gained into the landscape processes controlling element concentration, distribution and mobility from analysis of the covariates used in the model. This modelling approach can be extended to groups of elements (indices), element ratios, isotopes or mineralogy over a range of scales and in a variety of environments. 相似文献
36.
《地学前缘(英文版)》2020,11(3):871-883
Landslides are abundant in mountainous regions.They are responsible for substantial damages and losses in those areas.The A1 Highway,which is an important road in Algeria,was sometimes constructed in mountainous and/or semi-mountainous areas.Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results.In this research,we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor.To do this,an important section at Ain Bouziane(NE,Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods,namely,random forest(RF),support vector machine(SVM),and boosted regression tree(BRT).First,an inventory map and nine input factors were prepared for landslide susceptibility mapping(LSM) analyses.The three models were constructed to find the most susceptible areas to this phenomenon.The results were assessed by calculating the receiver operating characteristic(ROC) curve,the standard error(Std.error),and the confidence interval(CI) at 95%.The RF model reached the highest predictive accuracy(AUC=97.2%) comparatively to the other models.The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section.In addition,their application gives an improvement of the accuracy of LSMs near the road corridor.The machine learning models may become an important prediction tool that will identify landslide alleviation actions. 相似文献
37.
Estimating concentrations or flow rates along a stream network requires specific models. Two classes of models, recently proposed in the literature, are generalized, to the intrinsic case in particular. We present a global construction by ‘streams’, i.e. on the whole set of paths between sources and outlet. Combining stationary or intrinsic one-dimensional random functions leads to stationary or intrinsic models on segments, with discontinuities at the forks. A construction from outlet to sources, leads to stationary or intrinsic models on each stream, without any discontinuity at the forks. The linear variogram is found as a particular case. The extension to the linear model of coregionalization is immediate, allowing a multivariate modelling of concentrations. To cite this article: C. de Fouquet, C. Bernard-Michel, C. R. Geoscience 338 (2006). 相似文献
38.
This study addresses the phenomenon of the critical scale of fluctuation (SOF) for active lateral force (Pa) in undrained clay when there is a spatial variability in the clay. The phenomenon is significant under shear strength (τf) random fields but is insignificant under unit weight (γ) random fields. It is found that the phenomenon of the critical SOF is connected to the nature of the spatial averaging, which is “line averaging” under τf random fields and is “area averaging” under γ random fields. The former averaging effect (line) is significantly weaker than the latter (area), so the tendency for the critical slip plane to seek for a favorable location is stronger for the τf random field than for the γ random field. Hence, the phenomenon of the critical SOF is more pronounced under τf random fields than under γ random fields. The underlying mechanisms for the phenomenon of the critical SOF will be explored in this paper. 相似文献
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