Oxygen isotope(δ~(18)O) of seawater is an excellent proxy for tracing the origins of water masses and their mixing processes. Combining with hydrographic observation, hybrid coordinate ocean model(HYCOM) analysis data, and seawater oxygen isotope, we investigated the source of the South China Sea Warm Current(SCSWC) in the southwestern Taiwan Strait and its underlying mechanism. Results show that the Kuroshio subsurface water(KSSW) can intrude the continental slope in the southwestern Taiwan Strait, and thereby climb up the continental slope coupled with upwelling. The δ~(18)O-salinity relationship further indicates that in spring, the SCSWC in the southwestern Taiwan Strait originates from the upslope deflection of the slope current formed by the KSSW intrusion into the South China Sea, rather than from the west segment of the SCSWC formed to the east of Hainan Island. In addition, the southward flowing Zhe-Min Coastal Current(ZMCC) can reach as far as the Taiwan Bank(TB) and deflects offshore over the western TB at approximately 23.5°N, to some extent affecting the SCSWC. Moreover, this study reveals that seawater δ~(18)O is exquisitely sensitive to the determination of the origin and transport of water masses as compared with traditional potential temperature-salinity plot(θ-S) and HYCOM analysis data. In addition, their coupling can more reliably interpret the mixing processes of shelf water masses. 相似文献
As the application of high-density high-efficiency acquisition technology becomes more and more wide, the areas with complex surface conditions gradually become target exploration areas, and the first-break picking work of massive low signal-to-noise ratio data is a big challenge. The traditional method spends a lot of manpower and time to interactively pick first breaks, a large amount of interactive work affects the accuracy and efficiency of picking. In order to overcome the shortcoming that traditional methods have weak anti-noise to low signal-to-noise ratio primary wave, this paper proposes a high accurate automated first-break picking method for low signal-to-noise ratio primary wave from high-density acquisition in areas with a complex surface. Firstly, this method determines first-break time window using multi-azimuth spatial interpolation technology; then it uses the improved clustering algorithm to initially pick first breaks and then perform multi-angle comprehensive quality evaluation to first breaks according to the following sequence: ‘single trace → spread → single shot → multiple shots’ to identify the abnormal first breaks; finally it determines the optimal path through the constructed evaluation function and using the ant colony algorithm to correct abnormal first breaks. Multi-azimuth time window spatial interpolation technology provides the base for accurately picking first-break time; the clustering algorithm can effectively improve the picking accuracy rate of low signal-to-noise ratio primary waves; the multi-angle comprehensive quality evaluation can accurately and effectively eliminate abnormal first breaks; the ant colony algorithm can effectively improve the correction quality of low signal-to-noise ratio abnormal first breaks. By example analysis and comparing with the commonly used Akaike Information Criterion method, the automated first-break picking theory and technology studied in this paper has high picking accuracy and the ability to stably process low signal-to-noise ratio seismic data, has a significant effect on seismic records from high-density acquisition in areas with a complex surface and can meet the requirements of accuracy and efficiency for massive data near-surface modelling and statics calculation. 相似文献
Based on the three-dimensional digital image correlation (3D-DIC) technique, the stereovision system has been applied to the improved triaxial apparatus to obtain 3D full-field deformation of the specimen during triaxial testing. Through the calibration process, the 3D-DIC technique can obtain the accurate specimen’s spatial displacement deformation. Meanwhile, a subpixel edge detection algorithm has been combined with 3D-DIC technique to calculate the radial strain and the volume strain of the specimen directly. Furthermore, a series of consolidated drained and undrained triaxial tests were carried out on Hainan (China) sand specimens and measured by the conventional and the image measurement methods. Compared to the results measured by the conventional method, the image measurement technique can obtain the more experimental data, such as the 3D displacement field of the whole specimen, the local strain distribution, and so on. The measurement results also show the conventional method would be disturbed by the end constraints in triaxial tests so that the strength of the soil would be overestimated. Meanwhile, the middle of the specimen would be selected to calculate the stress–strain relationship without the influence of the end constraints in the proposed method. Based on the image measurement results, the proposed method has the potential to be used in geotechnical tests for exploring the soil’s progressive failure behaviors, inhomogeneous deformation and mechanical characteristics.
Biomass in karst terrain has rarely been measured because the steep mountainous limestone terrain has limited the ability to sample woody plants.Satellite observation, especially at high spatial resolution, is an important surrogate for the quantification of the biomass of karst forests and shrublands. In this study, an artificial neural network(ANN) model was built using Pléiades satellite imagery and field biomass measurements to estimate the aboveground biomass(AGB) in the Houzhai River Watershed, which is a typical plateau karst basin in Central Guizhou Province, Southwestern China. A back-propagation ANN model was also developed.Seven vegetation indices, two spectral bands of Pléiades imagery, one geomorphological parameter,and land use/land cover were selected as model inputs. AGB was chosen as an output. The AGB estimated by the allometric functions in 78 quadrats was utilized as training data(54 quadrats, 70%),validation data(12 quadrats, 15%), and testing data(12 quadrats, 15%). Data-model comparison showed that the ANN model performed well with an absolute root mean square error of 11.85 t/ha, which was 9.88%of the average AGB. Based on the newly developed ANN model, an AGB map of the Houzhai River Watershed was produced. The average predicted AGB of the secondary evergreen and deciduous broadleaved mixed forest, which is the dominant forest type in the watershed, was 120.57 t/ha. The average AGBs of the large distributed shrubland,tussock, and farmland were 38.27, 9.76, and 11.69 t/ha, respectively. The spatial distribution pattern ofthe AGB estimated by the new ANN model in the karst basin was consistent with that of the field investigation. The model can be used to estimate the regional AGB of karst landscapes that are distributed widely over the Yun-Gui Plateau. 相似文献
Theoretical and Applied Climatology - The surface radiation and energy flux in the source area of the Yellow River are estimated by using the Moderate Resolution Imaging Spectroradiometer products... 相似文献