In this letter, a practical mean reflectivity model of radar land clutter (LC) for the complex system design of ground-based radars involved in low-angle targets on some typical terrains is studied using the inductive reasoning method. The functional relationships between the radar parameters and radar surface clutter backscattering are analyzed. Following the recent research work in the area of reflectivity modeling of LC in the literature, the least squares method is employed to estimate the model parameters. The model is validated using reliable practical data and shown to outperform other models in accuracy. 相似文献
Well che89, located in the Chepaizi area in the northwest margin of Junggar basin, acquires high production industrial oil flow, which is an important breakthrough in the exploration of the south foreland slope area of Junggar basin. The Chepaizi area is near two hydrocarbon generation depressions of Sikeshu and Shawan, which have sets of hydrocarbon source rock of Carboniferous to Jurassic as well as Upper Tertiary. Geological and geochemical parameters are proper for the accumulation of mixed source crude oil. Carbon isotope, group composition and biomarkers of crude oil in Upper Tertiary of well Che89 show that the features of crude oil in Upper Tertiary Shawan Formation are between that of Permian and Jurassic, some of them are similar to these two, and some are of difference, they should be the mixed source of Permian and Jurassic. Geochemical analysis and geological study show that sand extract of Lower Tertiary Wulunguhe Formation has the same source as the crude oil and sand extract of Upper Tertiary Shawan Formation, but they are not charged in the same period. Oil/gas of Wulunguhe Formation is charged before Upper Tertiary sedimentation, and suffered serious biodegradation and oxidation and rinsing, which provide a proof in another aspect that the crude oil of Upper Tertiary Shawan Formation of well Che89 is not from hydrocarbon source rock of Lower Tertiary.
The quartz vein-type gold deposits are widely hosted by the Neoproterozoic (Xiajiang Group) epimeta- morphic clastic rock series in southeastern Guizhou Province, China. The Zhewang gold deposit studied in this paper occurs in the second lithologieal member of the Pinglue Formation of the Xiajiang Group. Trace element geochemis- try of host rocks, quartz veins and arsenopyrite has revealed that ore-forming fluid was enriched in sulphophile ele- ments such as Au, Ag, As, Sb, Pb and Zn, and simultaneously concentrated some magmaphile elements such as W and Mo, which probably provides some evidence for multi-stage mineralization or overprinting of magmatic hydro- therm. Quartz veins and arsenopyrite were characterized by depletion in HFSE and enrichment in LREE. Hf/Sm, Nb/La and Th/La imply that the ore-forming fluid was probably a NaC1-H20 solution system enriched in more C1 than F; Th/U values reflect the strong reducibility of the ore-forming fluid, coincident with the sulfide assemblages. The values of Co/Ni reflect that magmatic fluids may have partly participated in the ore-forming process and Y/Ho values have proved that the ore-forming fluid was associated with metamorphism and exotic hydrotherm which has reformed former quartz veins during late mineralization. The concentrations of REE, Eu anomalies and Ce anomalies of this deposit display that ore-forming elements mainly were derived from host rocks and possibly from a mixed deep source, and the ore-forming fluid was mixed by dominant metamorphic fluid and minor other sources. The physical-chemical conditions of ore-forming fluid changed from the initial stage to the late stage. The metamorphic fluid is responsible for the mineralization. Therefore, the Zhewang gold deposit is classified as a quartz vein-type gold deposit which may have been reformed by magmatic fluids during the late stage. 相似文献
A nonlinear wavelet neural network (WNN) model with natural orthogonal expansion (NOE) and combined weights is constructed to predict the annual frequency of tropical cyclones (TCF) occurring over the coastal regions of Southern China. Combined weights are obtained by calculating categorical weights, based on the particle swarm projection pursuit, and ranking weights, based on fuzzy mathematics, followed by optimization. The global monthly mean heights at 500?hPa and sea-surface temperature fields are used as two predictors. The linear and nonlinear information of the predictors with reduced dimensions is gathered through the NOE and combined weights, respectively, and treated as the input into the WNN model. This model is first trained with the 55-year (i.e., 1950?C2004) TCF data and then used to predict annual TCFs for the subsequent 5?years (i.e., 2005?C2009). Results show that the mean absolute and relative errors are 0.6175 and 9.34?%, respectively. The impacts of the combined weights, NOE and WNN as well as the traditional multi-regression approach on the TCF prediction are examined. Results show superior performance of the WNN-based model in the annual TCF prediction. 相似文献