新疆东天山红石金矿床原生晕特征与矿体叠加模型

红石金矿床是新疆东天山康古尔塔格金矿带中的代表性矿床之一。通过对其地表、已开采中段及部分钻孔的岩石系统采样分析和计算统计,研究了各指示元素在不同位置的组合特征。结果表明:指示元素中Au、Ag密切相关,其他元素组合复杂多变,反映了成矿成晕的多期性、多阶段特点。结合实际勘查资料,采用多种计算方法,建立了矿床原生晕轴向分带模式,即矿床轴向分带(自上而下)为Bi-Mo-Hg-Zn-Pb-Ag-As-Cu-Sb-Au。利用不同中段的7个分带评价值指标确定了矿体的叠加模型。     

Calibration of the parameters for a hardening–softening constitutive model using genetic algorithms

The present paper introduces a genetic algorithm-based optimization technique to calibrate a nonlinear strain hardening–softening constitutive model for soils using five material parameters. The efficiency of the proposed technique is analyzed through the use of different GA techniques. The effects of elitism, crossover, and mutation, as well as population size, on the performance of the conventional GAs for this problem are investigated. Micro-genetic algorithms (mGAs) are chosen and tested for different population sizes. The mGAs with a population size of five yields the optimal parameter values after fewer function evaluations and capture the overall simulated or experimental behavior at every point in stress–strain and strain paths in triaxial compression. The proposed calibration technique is validated through comparison with the traditional calibration technique.     

地震纵波技术预测煤层瓦斯富集区的探讨与实践

基于国内外煤层瓦斯富集的岩石物理及地球物理响应的研究成果,提出了以煤层反射波振幅、频率及衰减等动力学特征变化为主要研究对象的地震纵波预测技术方案;探讨了利用叠后Gamma拟合声波约束反演、频谱分解属性分析和叠前AVO反演、弹性阻抗反演等纵波技术预测煤层瓦斯富集性的理论依据及实现方法。上述技术在沁水盆地进行了预测尝试,其获得的含气性预测成果与探井实测的瓦斯富集情况基本吻合,初步证明了上述纵波预测技术方法在开展煤层瓦斯富集区研究方面的可行性和有效性。     

海洋透光率仪的设计及其在初级生产力研究中的应用*

海洋浮游植物光合作用固碳在全球碳循环中扮演着极为重要的角色, 获取不同光衰减对应的海水深度, 对于采集水样用于海洋初级生产力的研究具有重要意义。本研究于国际上首次开发了无缆便携式海洋透光率仪, 特殊的光学结构设计和光谱滤光技术使得光谱响应在波段400~700nm范围内超过了同类海洋传感器, 特定光衰减下对应的深度误差小于0.5m。将本设备应用于南海中尺度涡旋初级生产力的研究中, 在采样率和自动化方面验证了仪器的便携性; 通过对涡旋中初级生产力的空间分布规律进行分析, 检验了仪器数据结果的可靠性。海洋透光率仪在价格、准确性、可靠度和操作的便携性上具有不可比拟的优势, 将在未来海洋生态环境的研究中发挥重要作用。     

Spatiotemporal variations of water use efficiency and its driving factors in Inner Mongolia from 2001 to 2020

Water use efficiency (WUE) is an important variable to explore coupled relationships in carbon and water cycles. In this study, we first compared the spatial variations of annual gross primary productivity (GPP) and evapotranspiration (ET) using four GPP and ET products. Second, we selected the products closest to the flux towers data to estimate WUE. Finally, we quantitatively analyzed the impact of climate change and soil water content on WUE. The results showed that: (1) Four GPP and ET products provided good performance, with GOSIF-GPP and FLDAS-ET exhibiting a higher correlation and the smallest errors with the flux tower data. (2) The spatial pattern of WUE is consistent with that of GPP and ET, gradually decreasing from the northeast to the southwest. Higher WUE values appeared in the northeast forest ecosystem, and lower WUE values occurred in the western Gobi Desert, with a value of 0.28 gC m^?2 mm^?1. The GPP and ET products showed an increasing trend, while WUE showed a decreasing trend (55.15%) from 2001 to 2020. (3) The spatial relationship between WUE and driving factors reveal the variations in WUE of Inner Mongolia are mainly affected by soil moisture between 0 and 10 cm (SM0-10cm), vapor pressure deficit (VPD), and precipitation, respectively. (4) In arid regions, VPD and precipitation exhibit a major influence on WUE. An increase in VPD and precipitation has a negative and positive effect on WUE, with threshold values of approximately 0.36 kPa and 426 mm, respectively. (5) In humid regions, SM0-10cm, VPD, SM10-40cm, and SM40-100cm exert a significant impact on WUE, especially SM0-10cm, and weakens with increasing soil depths, these differences may be related to physiological structure and living characteristics of vegetation types in different climate regimes. Our results emphasize the importance of VPD and soil moisture in regional variability in WUE.     

1961–2005年中国陆地生态系统净初级生产力模拟（英文）

Net primary productivity(NPP) is the most important index that represents the structure and function of the ecosystem.NPP can be simulated by dynamic global vegetation models(DGVM),which are designed to represent vegetation dynamics relative to environmental change.This study simulated the NPP of China's ecosystems based on the DGVM Integrated Biosphere Simulator(IBIS) with data on climate,soil,and topography.The applicability of IBIS in the NPP simulation of China's terrestrial ecosystems was verified first.Comparison with other relevant studies indicates that the range and mean value of simulations are generally within the limits of observations;the overall pattern and total annual NPP are close to the simulations conducted with other models.The simulations are also close to the NPP estimations based on remote sensing.Validation proved that IBIS can be utilized in the large-scale simulation of NPP in China's natural ecosystem.We then simulated NPP with climate change data from 1961 to 2005,when warming was particularly striking.The following are the results of the simulation.(1) Total NPP varied from 3.61 GtC/yr to 4.24 GtC/yr in the past 45 years and exhibited minimal significant linear increase or decrease.(2) Regional differences in the increase or decrease in NPP were large but exhibited an insignificant overall linear trend.NPP declined in most parts of eastern and central China,especially in the Loess Plateau.(3) Similar to the fluctuation law of annual NPP,seasonal NPP also displayed an insignificant increase or decrease;the trend line was within the general level.(4) The regional differences in seasonal NPP changes were large.NPP declined in spring,summer,and autumn in the Loess Plateau but increased in most parts of the Tibetan Plateau.     

The Soil Moisture and Net Primary Production Affected by CO_2 and Climate Change Using a Coupled Model

In this paper, a coupled model was used to estimate the responses of soil moisture and net primary production of vegetation （NPP） to increasing atmospheric CO2 concentration and climate change. The analysis uses three experiments simulated by the second-generation Earth System Model （CanESM2） of the Canadian Centre for Climate Modelling and Analysis （CCCma）, which are part of the phase 5 of the Coupled Model Intercomparison Project （CMIP5）. The authors focus on the magnitude and evolution of responses in soil moisture and NPP using simulations modeled by CanESM, in which the individual effects of increasing CO2 concentration and climate change and their combined effect are separately accounted for. When considering only the single effect of climate change, the soil moisture and NPP have a linear trend of 0.03 kg m^-2 yr^-1 and-0.14 gC m^- 2 yr^-2, respec- tively. However, such a reduction in the global NPP results from the decrease of NPP at lower latitudes and in the Southern Hemisphere, although increased NPP has been shown in high northern latitudes. The largest negative trend is located in the Amazon basin at -1.79 gC m^-2 yr^-2. For the individual effect of increasing CO2 concentration, both soil moisture and NPP show increases, with an elevated linear trend of 0.02 kg m^-2 yr^-1 and 0.84 gC m^-2 yr^-2, respectively. Most regions show an increasing NPP, except Alaska. For the combined effect of increasing atmospheric CO2 and climate change, the increased soil moisture and NPP exhibit a linear trend of 0.04 kg m^2 yr^-1 and 0.83 gC m^2 yr^-2 at a global scale. In the Amazon basin, the higher reduction in soil moisture is illustrated by the model, with a linear trend of-0.39 kg m^-2 yr^-1, for the combined effect. Such a change in soil moisture is caused by a weakened Walker circulation simulated by this coupled model, compared with the single effect of increasing CO2 concentration （experiment M2）, and a consequence of the reduction in NPP is also shown in this area, with a linear trend of-     

Seasonality of oceanic primary production and its interannual variability from 1998 to 2007

The seasonality of primary productivity plays an important role in nutrient and carbon cycling. We quantify the seasonality of satellite-derived, oceanic net primary production (NPP) and its interannual variability during the first decade of the SeaWiFS mission (1998 to 2007) using a normalized seasonality index (NSI). The NSI, which is based upon production half-time, t(1/2), generally becomes progressively more episodic with increasing latitude in open ocean waters, spanning from a relatively constant rate of primary productivity throughout the year (mean t(1/2) ~5 months) in subtropical waters to more pulsed events (mean t(1/2) ~3 months) in subpolar waters. This relatively gradual, poleward pattern in NSI differs from recent estimates of phytoplankton bloom duration, another measure of seasonality, at lower latitudes (~40°S–40°N). These differences likely reflect the temporal component of production assessed by each metric, with NSI able to more fully capture the irregular nature of production characteristic of waters in this zonal band. The interannual variability in NSI was generally low, with higher variability observed primarily in frontal and seasonal upwelling zones. The influence of the El Niño–Southern Oscillation on this variability was clearly evident, particularly in the equatorial Pacific, where primary productivity was anomalously episodic from the date line east to the coast of South America in 1998. Yearly seasonality and the magnitude of annual production were generally positively correlated at mid-latitudes and negatively correlated at tropical latitudes, particularly in a region bordering the Pacific equatorial divergence. This implies that increases of annual production in the former region are attained over the course of a year by shorter duration but higher magnitude NPP events, while in the latter areas it results from an increased frequency or duration of similar magnitude events. Statistically significant trends in the seasonality, both positive and negative, were detected in various patches. We suggest that NSI be used together with other phenomenological characteristics of phytoplankton biomass and productivity, such as the timing of bloom initiation and duration, as a means to remotely quantify phytoplankton seasonality and monitor the response of the oceanic ecosystem to environmental variability and climate change.     

大量气象数据小文件自适应优化传输

为满足气象实时资料传输中大量气象数据小文件的高时效传输需求,对其中的数据传输服务进行优化,提出一种基于实时网络状况的自适应数据传输优化方法。该方法采用优化网络传输协议并使用文件压缩技术,通过获取网络传输线路上的实时参数,实时调整压缩参数和网络传输参数以优化传输性能。在自适应压缩时,通过试验分析和归纳,确立了气象数据小文件标准为文件小于50 KB;根据网络实时状况,设计了基于实时网络状况自适应调整压缩等级的算法。在自适应传输参数调优中,研究了TCP缓冲区大小和TCP并发连接数在GridFTP协议中的重要性,针对实时网络状况,分别设计了自适应调整TCP缓冲区大小和TCP并发连接数的算法,算法提升传输性能65%。对以上提出各自适应参数调整算法进行试验验证表明,融合压缩和网络传输的自适应调优方法能显著提升气象小数据文件的传输性能近500倍。     

The potential of the greenness and radiation (GR) model to interpret 8-day gross primary production of vegetation

Remote sensing of vegetation gross primary production (GPP) is an important step to analyze terrestrial carbon (C) cycles in response to changing climate. The availability of global networks of C flux measurements provides a valuable opportunity to develop remote sensing based GPP algorithms and test their performances across diverse regions and plant functional types (PFTs). Using 70 global C flux measurements including 24 non-forest (NF), 17 deciduous forest (DF) and 29 evergreen forest (EF), we present the evaluation of an upscaled remote sensing based greenness and radiation (GR) model for GPP estimation. This model is developed using enhanced vegetation index (EVI) and land surface temperature (LST) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and global course resolution radiation data from the National Center for Environmental Prediction (NCEP). Model calibration was achieved using statistical parameters of both EVI and LST fitted for different PFTs. Our results indicate that compared to the standard MODIS GPP product, the calibrated GR model improved the GPP accuracy by reducing the root mean square errors (RMSE) by 16%, 30% and 11% for the NF, DF and EF sites, respectively. The standard MODIS and GR model intercomparisons at individual sites for GPP estimation also showed that GR model performs better in terms of model accuracy and stability. This evaluation demonstrates the potential use of the GR model in capturing short-term GPP variations in areas lacking ground measurements for most of vegetated ecosystems globally.