The cross-validation technique is a popular method to assess and improve the quality of prediction by least squares collocation (LSC). We present a formula for direct estimation of the vector of cross-validation errors (CVEs) in LSC which is much faster than element-wise CVE computation. We show that a quadratic form of CVEs follows Chi-squared distribution. Furthermore, a posteriori noise variance factor is derived by the quadratic form of CVEs. In order to detect blunders in the observations, estimated standardized CVE is proposed as the test statistic which can be applied when noise variances are known or unknown. We use LSC together with the methods proposed in this research for interpolation of crustal subsidence in the northern coast of the Gulf of Mexico. The results show that after detection and removing outliers, the root mean square (RMS) of CVEs and estimated noise standard deviation are reduced about 51 and 59%, respectively. In addition, RMS of LSC prediction error at data points and RMS of estimated noise of observations are decreased by 39 and 67%, respectively. However, RMS of LSC prediction error on a regular grid of interpolation points covering the area is only reduced about 4% which is a consequence of sparse distribution of data points for this case study. The influence of gross errors on LSC prediction results is also investigated by lower cutoff CVEs. It is indicated that after elimination of outliers, RMS of this type of errors is also reduced by 19.5% for a 5 km radius of vicinity. We propose a method using standardized CVEs for classification of dataset into three groups with presumed different noise variances. The noise variance components for each of the groups are estimated using restricted maximum-likelihood method via Fisher scoring technique. Finally, LSC assessment measures were computed for the estimated heterogeneous noise variance model and compared with those of the homogeneous model. The advantage of the proposed method is the reduction in estimated noise levels for those groups with the fewer number of noisy data points. 相似文献
The distribution of fractures and its dependence on lithology and petrophysical properties of rock in the Asmari Formation were examined using three wells data of one of the largest oil fields of southwestern Iran. Fractures were measured on cut cores. Mineral content and petrophysical data were obtained through thin section study and core plug measurement respectively. Influence of mineral composition and petrophysical property of rocks on fracture density was explored statistically. Increasing quartz (sand) and anhydrite content of rocks decrease and dolomite increases the threshold of fracture densities, however no significant relation was observed between calcite content of rock and fracture density. Increasing porosity and permeability of rock decrease the threshold of fracture density in some of the defined lithology groups. There are significant differences between the lithology groups in terms of fracture density, although the results in the three wells are not the same. In whole data, the highest fracture density can be observed in dolostone. Limestone and impure carbonates hold broader spaced fractures and sandstones display the least fracture density. The average fracture densities in the wells are strictly different. These differences are the result of the structural position of the wells and also the trend of the well and fractures. The distribution of fractures in most lithology groups can be explained by the function: , where F is relative frequency, D is fracture density and a, b, and c are constants. 相似文献
Although traditional cellular automata (CA)‐based models can effectively simulate urban land‐use changes, they typically ignore the spatial evolution of urban patches, due to their use of cell‐based simulation strategies. This research proposes a new patch‐based CA model to incorporate a spatial constraint based on the growth patterns of urban patches into the conventional CA model for reducing the uncertainty of the distribution of simulated new urban patches. In this model, the growth pattern of urban patches is first estimated using a developed indicator that is based on the local variations in existing urban patches. The urban growth is then simulated by integrating the estimated growth pattern and land suitability using a pattern‐calibrated method. In this method, the pattern of new urban patches is gradually calibrated toward the dominant growth pattern through the steps of the CA model. The proposed model is applied to simulate urban growth in the Tehran megalopolitan area during 2000–2006–2012. The results from this model were compared with two common models: cell‐based CA and logistic‐patch CA. The proposed model yields a degree of patch‐level agreement that is 23.4 and 7.5% higher than those of these pre‐existing models, respectively. This reveals that the patch‐based CA model simulates actual development patterns much better than the two other models. 相似文献
A primary concern of the mining industry is to meet production targets, which are required and defined by customers. Deviations from these targets, in terms of quality and quantity, highly affect the economical aspect. Recently, an efficient resource model updating framework concept has been proposed aiming for the improvement of raw material quality control and process efficiency in any type of mining operation. The concept integrates online sensor measurements, obtained during production, into the resource model. In this way, due to the spatial variability, quality attributes of the blocks that will be produced in the next days or weeks are being updated based on real-time measurements. The concept has been applied in a lignite field with the aim of identifying local impurities in a lignite seam and to improve the prediction of coal quality attributes in neighbouring blocks. This paper investigates the added value of using the resource model updating framework by using the value of information analysis. The expected benefit of additional information (integration of the online sensor measurements into the resource model) is compared to a case where there is no additional information integrated into the process. These benefits are evaluated based on the economic impact determined by applying the resource model updating framework in mine planning.
Exploring for groundwater in crystalline rocks in semiarid areas is a challenge because of their complex hydrogeology and low potential yields. An integrated approach was applied in central western Mozambique, in an area covered by Precambrian crystalline basement rocks. The approach combined a digital elevation model (DEM), remote sensing, and a ground-based geophysical survey. The aim was to identify groundwater zones with high potential and to identify geological structures controlling that potential. Lineaments were extracted from the DEM that had been enhanced using an adaptive-tilt, multi-directional, shading technique and a non-filtering technique to characterize the regional fracture system. The shallowness and amount of stored groundwater in the fracture zones was assessed using vegetation indices derived from Landsat 8 OLI images. Then, 14 transient electromagnetic (TEM) survey profiles were taken in different geological settings across continuous lineaments that were considered to be aligned along inferred faults. In the central lineament zones, the TEM soundings gave resistivity values of less than 300 Ωm at a depth of 20–80 m. The values varied with location. Conversely, values greater than 400 Ωm were observed at the sites away from the central zones. This contrast is probably caused by the differences in permeability and degree of weathering along the fractured zones. These differences could be key factors in determining groundwater occurrence. By integrating five water-related factors (lineament density, slope, geology, vegetation index, and proximity to lineaments), high groundwater potential zones were located in the vicinity of the lineaments. In these zones, vegetation remains active regardless of the season. 相似文献
Theoretical and Applied Climatology - Recently in agricultural and industrial sectors, researchers have started to classify the climate of a region using empirical methods and clustering. This... 相似文献
The slope instability is connected to a large diversity of causative and triggering factors, ranging from inherent geological structure to the environmental conditions. Thus, assessment and prediction of slope failure hazard is a difficult and complex multi-parametric problem. In contrast to the analytic approaches, the systems approaches are able to consider infinite number of affecting parameters and assess the interactions of each couple of the parameters in the system. This paper presents a complete application of the rock engineering systems approach in prediction of the instability potential of rock slopes in 15 stations along a 20?km section of the Khosh-Yeylagh Main Road, Iran as the case study of the research. In this research, the main objective has been defining the principal causative and triggering factors responsible for the manifestation of slope instability phenomena, quantify their interactions, obtain their weighted coefficients, and calculate the slope instability index, which refers to the inherent potential instability of each slope of the examined region. The final results have been mapped to highlight the rock slopes susceptible to instability. Finally, as a preliminary validation on the utilization of systems approach in the study region, the stability of investigated rock slopes were analyzed using an empirical method and the results were compared. The comparisons showed a rather good coincidence between the given classes of two methods. 相似文献