基于空地观测的地震大数据处理系统研究

地震观测数据包括地面和空间两部分,地面地震台站能够监测形变、重力、地磁、电磁、地电、流体等多种学科参数,空间卫星能够采集电离层离子密度、温度、电磁场等多种数据,数据格式多种多样,数据采样频率长短不一,属于典型的大数据。本文分析了地震监测产生的数据特点,符合大数据的"5V"特征;给出了大数据处理的技术框架,从大数据存储、预处理、大数据分析、知识发现以及知识应用几个方面,分析了大数据处理的六个层次,阐述了不同层次需要的数据处理方法;从存储管理技术、Map Reduce技术模型、深度学习技术、信息融合与数据挖掘技术、可视化展示与虚拟现实技术等几个方面探讨了地震大数据处理的关键技术,并给出了两例地震大数据应用场景。大数据技术应用于地震监测分析将对人类战胜地震灾害具有重要意义。

动态观测数据驱动的滑坡灾害精准模拟分析方法

受全球极端气候变化和人类活动影响,复杂滑坡灾害时空演变过程复杂,其时空演变的高突变性、高隐蔽性、高动态性,以及孕育环境的时空异质性与不确定性等十分突出,复杂地形地质条件下滑坡灾害模拟分析已经成为世界性难题.为此,本文提出动态观测数据驱动的滑坡灾害精准模拟分析方法,设计了动态数据驱动的复杂滑坡灾害精准模拟分析框架,阐释了滑坡灾害过程时空变化的显式语义描述,任务驱动的空天地一体化观测数据规划与调度,计算与存储融合的复杂滑坡灾害实时数据组织与管理,变化驱动的多源动态观测数据实时接入与自主加载,多源动态观测数据在线智能处理,复杂滑坡灾害模拟模型参数智能率定等关键技术,为滑坡灾害链的全链条防灾减灾提供科学理论支撑.

Earth Observations in China and the World: History and Development in 50 Years

Remote sensing, which came into being at the first International Symposium on Remote Sensing of Environment (ISRSE) 50 years ago, has enabled people to obtain objecive and realistic spatial and temporal information through the application of Earth observation technologies and analyze and understand the macro-level changes of the Earth system from a spaial view. The technology of Earth observaion from space has incomparable advantages in the study of the Earth. This aricle introduces the 50-year development of Earth observaion in the world and the 30-year development of Earth observaion in China and reflects on the building of China's Earth observaion system.

Contrast patterns and trends of lapse rates calculated from near-surface air and land surface temperatures in China from 1961 to 2014

The near-surface lapse rate reflects the atmospheric stability above the surface.Lapse rates calculated from land surface temperature(γTs)and near-surface air temperature( γTa )have been widely used.However,γTs and γTa have different sensitivity to local surface energy balance and large-scale energy transport and therefore they may have diverse spatial and temporal variability,which has not been clearly illustrated in existing studies.In this study,we calculated and compared γTa and γTs at^2200 stations over China from 1961 to 2014.This study finds that γTa and γTs have a similar multiyear national average(0.53°C/100 m)and seasonal cycle.Nevertheless,γTs shows steeper multiyear average than γTa at high latitudes,and γTs in summer is steeper than γTa ,especially in Northwest China.The North China shows the shallowest γTa and γTs,then inhibiting the vertical diffusion of air pollutants and further reducing the lapse rates due to accumulation of pollutants.Moreover,the long-term trend signs for γTa and γTs are opposite in northern China.However,the trends in γTa and γTs are both negative in Southwest China and positive in Southeast China.Surface incident solar radiation,surface downward longwave radiation and precipitant frequency jointly can account for 80%and 75%of the long-term trends in γTa and γTs in China,respectively,which provides an explanation of trends of γTa and γTs from perspective of surface energy balance.

Mapping Soil Texture Based on Field Soil Moisture Observations at a High Temporal Resolution in an Oasis Agricultural Area

Due to the almost homogeneous topography in low relief areas, it is usually difficult to make accurate predictions of soil properties using topographic covariates. In this study, we examined how time series of field soil moisture observations can be used to estimate soil texture in an oasis agricultural area with low relief in the semi-arid region of northwest China. Time series of field-observed soil moisture variations were recorded for 132 h beginning at the end of an irrigation event during which the surface soil was saturated.Spatial correlation between two time-adjacent soil moisture conditions was used to select the factors for fuzzy c-means clustering. In each of the ten generated clusters, soil texture of the soil sample with the maximum fuzzy membership value was taken as the cluster centroid. Finally, a linearly weighted average was used to predict soil texture from the centroids. The results showed that soil moisture increased with the increase of clay and silt contents, but decreased with the increase of sand content. The spatial patterns of soil moisture changed during the entire soil drying phase. We assumed that these changes were mainly caused by spatial heterogeneity of soil texture. A total of 64 independent samples were used to evaluate the prediction accuracy. The root mean square error(RMSE)values of clay, silt and sand were 1.63, 2.81 and 3.71, respectively. The mean relative error(RE) values were 9.57% for clay, 3.77% for silt and 12.83% for sand. It could be concluded that the method used in this study was effective for soil texture mapping in the low-relief oasis agricultural area and could be applicable in other similar irrigation agricultural areas.

Comparing simulated atmospheric carbon dioxide concentration with GOSAT retrievals

Satellite observations of atmospheric carbon dioxide （CO2） provide a useful way to improve the understanding of global carbon cycling. In this paper, we present a comparison between simulated CO2 concentrations from an inversion model of the CarbonTracker Data Assimilation System （CTDAS） and satellite-based CO2 measurements of column-averaged dry air mole fraction （denoted XCO2） derived from version 3.3 Atmospheric CO2 Observations from Space retrievals of the Greenhouse Gases Observing SATellite （ACOS-GOSAT） L2 data products. We examine the differences of CTDAS and GOSAT to provide important guidance for the further investigation of CTDAS in order to quantify the corre- sponding flux estimates with satellite-based CO2 observations. We find that the mean point-by-point difference （CTDAS-GOSAT） between CTDAS and GOSAT XCO2 is -0.11 4-1.81 ppm, with a high agreement （correlation r = 0.77, P 〈 0.05） over the studied period. The latitudinal zonal variations of CTDAS and GOSAT are in general agreement with clear seasonal fluctuations. The major exception occurs in the zonal band of 0°-15°N where the difference is approximately 4 ppm, indicating that large uncertainty may exist in the assimilated CO2 for the low- latitude region of the Northem Hemisphere （NH）. Additionally, we find that the hemispherical/continental differences between CTDAS and GOSAT are typically less than 1 ppm, but obvious discrepancies occur in different hemispheres/continents, with high consistency （point-by-point correlation r = 0.79, P 〈 0.05） in the NH and a weak correlation （point-by-point correlation r = 0.65, P 〈 0.05） in the Southern Hemisphere. Overall, the difference of CTDAS and GOSAT is small, and the comparison of CTDAS and GOSAT will further instruct the inverse modeling of CO2 fluxes using GOSAT.