Effects of land use and cultivation time on soil organic and inorganic carbon storage in deep soils

The vertical distribution and exchange mechanisms of soil organic and inorganic carbon(SOC, SIC) play an important role in assessing carbon(C) cycling and budgets. However, the impact of land use through time for deep soil C(below 100 cm) is not well known. To investigate deep C storage under different land uses and evaluate how it changes with time, we collected soil samples to a depth of 500 cm in a soil profile in the Gutun watershed on the Chinese Loess Plateau(CLP); and determined SOC, SIC, and bulk density. The magnitude of SOC stocks in the 0–500 cm depth range fell into the following ranking: shrubland(17.2 kg m~(-2)) grassland(16.3 kg m~(-2)) forestland(15.2 kg m~(-2)) cropland(14.1 kg m~(-2)) gully land(6.4 kg m~(-2)). The ranking for SIC stocks were: grassland(104.1 kg m~(-2)) forestland(96.2 kg m~(-2)) shrubland(90.6 kg m~(-2)) cropland(82.4 kg m~(-2)) gully land(50.3 kg m~(-2)). Respective SOC and SIC stocks were at least 1.6-and 2.1-fold higher within the 100–500 cm depth range, as compared to the 0–100 cm depth range. Overall SOC and SIC stocks decreased significantly from the 5 th to the 15 th year of cultivation in croplands, and generally increased up to the 70 th year. Both SOC and SIC stocks showed a turning point at 15 years cultivation, which should be considered when evaluating soil C sequestration. Estimates of C stocks greatly depends on soil sampling depth, and understanding the influences of land use and time will improve soil productivity and conservation in regions with deep soils.     

A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes

ABSTRACT

The spatio-temporal residual network (ST-ResNet) leverages the power of deep learning (DL) for predicting the volume of citywide spatio-temporal flows. However, this model, neglects the dynamic dependency of the input flows in the temporal dimension, which affects what spatio-temporal features may be captured in the result. This study introduces a long short-term memory (LSTM) neural network into the ST-ResNet to form a hybrid integrated-DL model to predict the volumes of citywide spatio-temporal flows (called HIDLST). The new model can dynamically learn the temporal dependency among flows via the feedback connection in the LSTM to improve accurate captures of spatio-temporal features in the flows. We test the HIDLST model by predicting the volumes of citywide taxi flows in Beijing, China. We tune the hyperparameters of the HIDLST model to optimize the prediction accuracy. A comparative study shows that the proposed model consistently outperforms ST-ResNet and several other typical DL-based models on prediction accuracy. Furthermore, we discuss the distribution of prediction errors and the contributions of the different spatio-temporal patterns.     

硫铟铜矿在福建紫金山铜金矿床的发现及深部找矿意义

硫铟铜矿（CuInS2）是一种罕见的铟独立矿物,在中国未曾报道过。在研究福建紫金山铜金矿床深部矿石矿物组成时,首次发现了硫铟铜矿。硫铟铜矿通常见于中高温热液矿床,紫金山铜金矿床东南矿段铜矿体中出现了硫钨锡铜矿、硫钼锡铜矿、硫铁锡铜矿、硫砷锡铜矿、锡砷硫钒铜矿、似黄锡矿、辉钼矿等高温矿物,指示紫金山矿床深部成矿温度较高,成矿流体中In、Sn、Pb、Zn、Mo、W含量较高,具有斑岩型等中高温热液成矿系统的找矿潜力。     

2022年1月8日青海门源6.9级地震机器学习地震预警震级估计与现地阈值报警的回溯验证

2022年1月8日1时45分青海省海北州门源县发生6.9级地震, 周边地区普遍有感, 并导致多条高铁线路临时停运. 本文利用这次地震获取的大量烈度计加速度记录, 基于正在进行系统研发的机器学习地震预警方法模块, 对地震预警震级估计与现地阈值报警进行了回溯验证. 结果表明: 在地震发生后3.1 s, 震级估计为6.5级, 且震级估计误差不受信噪比和震中距变化的影响, 随着首台触发后时间的增加, 震级估计逐渐接近实际震级. 对于现地地震动速度峰值PGV(Peak Ground Velocity)预测, 各个台站在P波到达后3 s时, 预测PGV与观测PGV呈现1:1线性关系, 随着P波到达后时间窗的增加, 预测PGV逐步接近观测PGV, 且PGV预测误差不受信噪比和震中距变化的影响. 现地台站仪器烈度阈值设置为Ⅵ度时, 报警成功、误报、漏报的百分比分别为99.53%、0%、0.47%, 平均预警时间为19.62 s, 且地震烈度Ⅵ度区内没有发生误报和漏报; 现地台站仪器烈度阈值设置为Ⅶ度时, 报警成功、误报、漏报的百分比分别为99.77%、0%、0.23%, 平均预警时间为9.69 s, 且地震烈度Ⅶ度区内没有发生误报和漏报. 此次回溯验证结果表明: 机器学习方法在这次地震中可以得到鲁棒的震级估计和现地阈值报警结果, 并为该方法的在线测试以及中国地震预警系统升级提供可行性依据; 其次, 在这次地震事件中, 烈度计可为预警提供额外的作用, 这也为烈度计在未来地震预警的研究和应用中提供了更多的可能性.

     

郯庐断裂带中南段重磁特征与深部构造

郯庐断裂带是中国东部地球物理场的分界线。它在磁场上呈现出一条NNE向线性正磁异常带,它的重力场特征表现为规模较大的布格重力异常梯度带。在断裂带的两侧,其区域磁场、区域重力场无论是场值大小,还是异常规模以及各异常的展布形态等诸方面都具有明显的区别。地球物理特征揭示郯庐断裂带深部构造,为研究重点矿产分布规律及地震灾害预报具有重要的实际意义。     

Prediction of Stellar Atmospheric Parameters using Instance-Based Machine Learning and Genetic Algorithms

In this article we present a method for the automated prediction of stellar atmospheric parameters from spectral indices. This method uses a genetic algorithm (GA) for the selection of relevant spectral indices and prototypical stars and predicts their properties, using the k-nearest neighbors method (KNN). We have applied the method to predict the effective temperature, surface gravity, metallicity, luminosity class and spectral class of stars from spectral indices. Our experimental results show that the feature selection performed by the genetic algorithm reduces the running time of KNN up to 92%, and the predictive accuracy error up to 35%. This revised version was published online in July 2006 with corrections to the Cover Date.     

成人英语学习动机行为调查与教学对策研究

动机是激励人们行动的心理动因.英语学习动机是推动英语学习的内部动因,成人英语学习动机是复杂的.了解他们的英语学习动机特点有利于教师改进教学手段和策略,激发与培养其学习的主动性和积极性,发挥其学习潜能,从而提高英语学习的效果.运用定量研究的方法,在年轻干部学员中进行学习动机调查,分析了其学习动机的特点,并提出成人英语教学的对策.     

基于ST-ConvLSTM的南海海表面CO2分压的空间和时间序列预测

海表面二氧化碳分压(pCO₂)的未来变化趋势,对统计评估全球碳收支以及理解全球气候变化背景下的海洋酸化现象至关重要。目前传统的海面pCO₂预测方法大部分基于有限的实测数据,然而实测数据存在着时间和地理方面的制约,且计算成本较高。近年来,随着时空观测数据的爆炸性增长,基于深度学习的数据驱动模型在海表面pCO₂预测方面中表现出良好的潜力。然而,由于多种环境因素与海表面pCO₂之间的关系错综复杂,到目前为止尚无十分简单有效的相关模型来对海表面pCO₂进行预测。为应对这一挑战,利用时空卷积长短时记忆神经网络(ST-ConvLSTM)模型,通过海面温度(sea surface temperature, SST)、海面盐度(sea surface salinity, SSS)、叶绿素a浓度(chl a)和海面pCO₂数据,预测南海的海面pCO₂,并将2019年1～12月的数据作为测试集对模型的表现进行了验证。结果显示, ST-ConvLSTM模型...