A number of criteria based on kriging variance calculations may be used for infill sampling design in geologic site characterization. Searching for the best new sample locations from a set of candidate locations can result in excessive computation time if these criteria and the naive rekriging are used. The relative updated kriging estimate and variance for universal kriging estimation are demonstrated as a simple kriging estimate and variance, respectively. The updated kriging variance is demonstrated as the multiplication of two kriging variances. Using these updated kriging variance equations can increase the computational speed for selecting the best new sample locations. The application results for oil rock thickness in an oilfield indicate that minimizing the average relative updated kriging variance is a useful alternative to the other criteria based on kriging variance in optimal infill sampling design for geologic site characterization. 相似文献
Experiments involving the gradual drying out of controlled mixtures of soil and organic lake sediment during storage at room temperature show that this leads to a loss of magnetic susceptibility and isothermal remanence greatly in excess of the initial values for the sediment components of the mixtures. We conclude that during storage in the moist state, soil-derived, fine-grained, ferrimagnetic iron oxides (magnetite and/or maghemite) are transformed to paramagnetic and/or imperfect antiferrimagnetic minerals. The imperfect anti-ferromagnetic component of the initial mixtures, which probably includes goethite, appears to survive and may even increase during storage. The experimental results compare well with the previously documented effects of storing wet sediment from the site, Peckforton Mere, Cheshire, U.K., over a comparable time interval. We conclude that transformation of fine grained ferrimagnets during storage diagenesis may be responsible for many of the examples of loss of magnetic susceptibility and remanence attributed by other authors solely to the oxidation of an iron sulphide such as greigite. Only where greigite is positively identified is it valid to infer a contribution from it to the magnetic properties of lake sediments: loss of susceptibility or remanence during storage is not alone a sufficient basis for such an inference. Early drying of samples will normally avoid the effects of storage diagenesis; and recent sediment samples so treated will, where greigite formation, bacterial magnetite and magnetite dissolution are insignificant, provide a valid basis for source identification on the basis of magnetic properties. 相似文献
The Support Vector Machine (SVM) is an increasingly popular learning procedure based on statistical learning theory, and involves a training phase in which the model is trained by a training dataset of associated input and target output values. The trained model is then used to evaluate a separate set of testing data. There are two main ideas underlying the SVM for discriminant-type problems. The first is an optimum linear separating hyperplane that separates the data patterns. The second is the use of kernel functions to convert the original non-linear data patterns into the format that is linearly separable in a high-dimensional feature space. In this paper, an overview of the SVM, both one-class and two-class SVM methods, is first presented followed by its use in landslide susceptibility mapping. A study area was selected from the natural terrain of Hong Kong, and slope angle, slope aspect, elevation, profile curvature of slope, lithology, vegetation cover and topographic wetness index (TWI) were used as environmental parameters which influence the occurrence of landslides. One-class and two-class SVM models were trained and then used to map landslide susceptibility respectively. The resulting susceptibility maps obtained by the methods were compared to that obtained by the logistic regression (LR) method. It is concluded that two-class SVM possesses better prediction efficiency than logistic regression and one-class SVM. However, one-class SVM, which only requires failed cases, has an advantage over the other two methods as only “failed” case information is usually available in landslide susceptibility mapping. 相似文献
The criterion used to select infill sample locations should depend on the sampling objective. Minimizing the global estimation variance is the most widely used criterion and is suitable for many problems. However, when the objective of the sampling program is to partition an area of interest into zones of high values and zones of low values, minimizing the expected cost of classification errors is a more appropriate criterion. Unlike the global estimation variance, the cost of classification errors incorporates both the sample locations and the sample values into an objective infill-sampling design criterion. 相似文献