This paper presents gas compositions and H-, O-isotope compositions of sulfide- and quartz-hosted fluid inclusions, and S-, Pb-isotope compositions of sulfide separates collected from the principal Stage 2 ores in Veins 3 and 210 of the Jinwozi lode gold deposit, eastern Tianshan Mountains of China. Fluid inclusions trapped in quartz and sphalerite are dominantly primary. H-and O-isotopic compositions of pyrite-hosted fluid inclusions indicate two major contributions to the ore-forming fluid that include the degassed magma and the meteoric-derived but rock 18O-buffered groundwater. However, H- and O-isotopic compositions of quartz-hosted fluid inclusions essentially suggest the presence of groundwater. Sulfide-hosted fluid inclusions show considerably higher abundances of gaseous species CO2, N2, H2S, etc. than quartz-hosted ones. The linear trends among inclusion gaseous species reflect the mixing tendency between the gas-rich magmatic fluid and the groundwater. The relative enrichment of gaseous species in sulfide-hosted fluid inclusions, coupled with the banded ore structure indicating alternate precipitation of quartz with sulfide minerals, suggests that the magmatic fluid has been inputted to the ore-forming fluid in pulsation. Sulfur and lead isotope compositions of pyrite and galena separates indicate an essential magma derivation for sulfur but the multiple sources for metallic materials from the mantle to the bulk crust.
Debris flow is one of the most destructive phenomena of natural hazards. Recently, major natural haz-ard, claiming human lives and assets, is due to debris flow in the world. Several practical methods for forecasting de-bris flow have been proposed, however, the accuracy of these methods is not high enough for practical use because of the stochastic and non-linear characteristics of debris flow. Artificial neural network has proven to be feasible and use-fill in developing models for nonlinear systems. On the other hand, predicting the future behavior based on a time se-ries of collected historical data is also an important tool in many scientific applications. In this study we present a three-layer feed-forward neural network model to forecast surge of debris flow according to the time series data collect-ed in the Jiangjia Ravine, situated in north part of Yunnan Province of China. The simulation and prediction of debris flow using the proposed approach shows this model is feasible, however, further studies are needed. 相似文献
We analyze the magnetic configurations of three super active regions, NOAA 10484, 10486 and 10488, observed by the Huairou Multi-Channel Solar Telescope (MCST) from 2003 October 18 to November 4. Many energetic phenomena, such as flares (including a X-28 flare) and coronal mass ejections (CMEs), occurred during this period. We think that strong shear and fast emergence of magnetic flux are the main causes of these events. The question is also of great interest why these dramatic eruptions occurred so close together in the descending phase of the solar cycle. 相似文献