Combined with a digital bored photography system and in-situ statistics concerning the joints and fissures of both ore-body and surrounding rock,a 2D discrete model was constructed using UDEC.The stress field and disp...Combined with a digital bored photography system and in-situ statistics concerning the joints and fissures of both ore-body and surrounding rock,a 2D discrete model was constructed using UDEC.The stress field and displacement field changes of different sublevel stoping systems were also studied.Changes in the overlying rock strata settlement pattern has been analyzed and validated by in-situ monitoring data.The results show that:in the caving process,there exists an obvious delay and jump for the overlying rock strata displacement over time,and a stable arch can be formed in the process of caving,which leads to hidden goafs.Disturbed by the mining activity,a stress increase occurred in both the hanging wall and the foot wall,demonstrating a hump-shaped distribution pattern.From the comparison between simulation results and in-situ monitoring results,land subsidence shows a slow-development,suddenfailure,slow-development cycle pattern,which leads eventually to a stable state.This pattern validates the existence of balanced arch and hidden goafs.展开更多
The sea surface salinity(SSS) is a key parameter in monitoring ocean states. Observing SSS can promote the understanding of global water cycle. This paper provides a new approach for retrieving sea surface salinity fr...The sea surface salinity(SSS) is a key parameter in monitoring ocean states. Observing SSS can promote the understanding of global water cycle. This paper provides a new approach for retrieving sea surface salinity from Soil Moisture and Ocean Salinity(SMOS) satellite data. Based on the principal component regression(PCR) model, SSS can also be retrieved from the brightness temperature data of SMOS L2 measurements and Auxiliary data. 26 pair matchup data is used in model validation for the South China Sea(in the area of 4?–25?N, 105?–125?E). The RMSE value of PCR model retrieved SSS reaches 0.37 psu(practical salinity units) and the RMSE of SMOS SSS1 is 1.65 psu when compared with in-situ SSS. The corresponding Argo daily salinity data during April to June 2013 is also used in our validation with RMSE value 0.46 psu compared to 1.82 psu for daily averaged SMOS L2 products. This indicates that the PCR model is valid and may provide us with a good approach for retrieving SSS from SMOS satellite data.展开更多
This paper proves the error reduction property （saturation property）, convergence and optimality of an adaptive mixed finite element method （AMFEM） for the Poisson equation. In each step of AMFEM, the local refine...This paper proves the error reduction property （saturation property）, convergence and optimality of an adaptive mixed finite element method （AMFEM） for the Poisson equation. In each step of AMFEM, the local refinement is performed basing on simple either edge-oriented residuals or edge-oriented data oscillations, depending only on the marking strategy, under some restriction of refinement. The main tools used here are the strict discrete local efficiency property given by Carstensen and Hoppe （2006） and the quasi-orthogonality estimate proved by Chen, Holst, and Xu （2009）. Numerical experiments fully confirm the theoretical analysis.展开更多
基金financially supported by the National Natural Science Foundation of China(No.51374033)the Doctoral Program of Higher Education Research Fund(No.20120006110022)the Chenchao Iron Mine and the technical support of Itasca
文摘Combined with a digital bored photography system and in-situ statistics concerning the joints and fissures of both ore-body and surrounding rock,a 2D discrete model was constructed using UDEC.The stress field and displacement field changes of different sublevel stoping systems were also studied.Changes in the overlying rock strata settlement pattern has been analyzed and validated by in-situ monitoring data.The results show that:in the caving process,there exists an obvious delay and jump for the overlying rock strata displacement over time,and a stable arch can be formed in the process of caving,which leads to hidden goafs.Disturbed by the mining activity,a stress increase occurred in both the hanging wall and the foot wall,demonstrating a hump-shaped distribution pattern.From the comparison between simulation results and in-situ monitoring results,land subsidence shows a slow-development,suddenfailure,slow-development cycle pattern,which leads eventually to a stable state.This pattern validates the existence of balanced arch and hidden goafs.
基金supported by the National Natural Science Foundation of China under project 41275013the National High-Tech Research and development program of China under project 2013AA09A506-4the National Basic Research Program under project 2009CB723903
文摘The sea surface salinity(SSS) is a key parameter in monitoring ocean states. Observing SSS can promote the understanding of global water cycle. This paper provides a new approach for retrieving sea surface salinity from Soil Moisture and Ocean Salinity(SMOS) satellite data. Based on the principal component regression(PCR) model, SSS can also be retrieved from the brightness temperature data of SMOS L2 measurements and Auxiliary data. 26 pair matchup data is used in model validation for the South China Sea(in the area of 4?–25?N, 105?–125?E). The RMSE value of PCR model retrieved SSS reaches 0.37 psu(practical salinity units) and the RMSE of SMOS SSS1 is 1.65 psu when compared with in-situ SSS. The corresponding Argo daily salinity data during April to June 2013 is also used in our validation with RMSE value 0.46 psu compared to 1.82 psu for daily averaged SMOS L2 products. This indicates that the PCR model is valid and may provide us with a good approach for retrieving SSS from SMOS satellite data.
基金supported in part by the Natural Science Foundation of China under Grant No.10771150the National Basic Research Program of China under Grant No.2005CB321701the Natural Science Foundation of Chongqing City under Grant No.CSTC,2010BB8270
文摘This paper proves the error reduction property （saturation property）, convergence and optimality of an adaptive mixed finite element method （AMFEM） for the Poisson equation. In each step of AMFEM, the local refinement is performed basing on simple either edge-oriented residuals or edge-oriented data oscillations, depending only on the marking strategy, under some restriction of refinement. The main tools used here are the strict discrete local efficiency property given by Carstensen and Hoppe （2006） and the quasi-orthogonality estimate proved by Chen, Holst, and Xu （2009）. Numerical experiments fully confirm the theoretical analysis.
