汶川地震断裂带科学深钻井孔附近微震观测问题及微震数据转换

为了配合汶川地震科学深钻,弄清楚钻孔附近浅层的断层面结构,中国地震局地球物理研究所分别在四川省绵竹市天池乡和四川省绵阳市南坝镇布设15套南非矿山地震研究所(IMS)生产的矿山地震仪.分析矿山地震仪天然微震监测中仪器布设、数据采集和数据处理等方面遇到的问题,介绍与该仪器对应的数据的文件格式,并实现该数据与通用地震数据格式SAC二进制格式的转换.     

三峡水库蓄水前后长江中下游流量频率分布特征及其对洪水造床作用的影响

三峡水库蓄水前后长江中下游流量特征变化及其对造床作用的影响一直受到学者的关注.采用枝城等6个水文站日均流量资料,分别统计了各站流量的经验频率分布,检验了8种概率密度函数的适用性,并讨论了三峡水库蓄水前后流量频率分布特征与造床流量的关系.研究表明:长江中下游干流洪中枯各级流量的频率分布具有分段特性,无法用皮尔逊Ⅲ型或对数...     

Erratum to: Understanding and building upon pioneering work of Nobel Prize in Physics 2021 laureates Syukuro Manabe and Klaus Hasselmann: From greenhouse effect to Earth system science and beyond

Science China Earth Sciences -     

川青藏地区的地震活动及震源特征

在分析区域地震活动和构造环境及利用ＧＤＳＮ的宽频带波形资料分析震源破裂特征的基础上,结合余震资料,讨论了１９８８～１９９０年发生在四川和青海的９次强震的震源破裂和余震分布特征,分析了四川、青海、西藏相邻区域内的地震能量迁移,并用单键群分析方法对１９７０年以来的地震活动总体图象进行了研究。结果表明,这些地震的震源性质与构造应力环境相符,但青海地震的震源都具有较大的逆掩分量,与过去的走滑震源性质不同,而四川地震的震源Ｐ轴不接近水平方向与当地的“Ｙ”字形复杂构造有关     

Geochemical constraints on the petrogenesis of basalts from eastern Jiangnan orogen, South China

The basalts crop out widely in the eastern part of late Proterozoic Jiangnan orogen. In terms of their petrographical and geochemical characteristics, they can be divided into two distinct types: low- and high-Ti. basalts. They crystallized from the magmas derived from the depleted upper mantle differing in partial melting degree. Project supported by the National Natural Science Foundation of China.     

某高层建筑模型振动台试验结果分析

本文分析了广州市某２８层高层建筑模型模拟地震振动台试验结果,对此建筑的抗震性能进行了评价,考究了设置摩擦耗能支对其动力反应的减震作用。     

腾冲火山活动监测,预测与对策研究中的若干重要问题

《膳冲火山活动监测、预测与对策研究》项目是一项集火山活动监测研究、防灾及资源综合开发与利用为一体的系统工程。需要火山地质、地震、地球化学、地球物理、地壳形变及地热等诸多相关学科的合理配置和联合攻关。在过去的３０年,众多学和研究实体对腾冲火山进行 大量的观察和研究获得一批与火山活动的相关的基础研究成果。其中也不乏说明腾冲火山存在在喷发危险性的证据。为了保证本重大项目能在有限时间内及有限的人力、财务     

Mosaic desert pavement influences water infiltration and vegetation distribution on fluvial fan surfaces

Desert pavements (DPs) are critical for maintaining ecological stability and promoting near-surface hydrological cycling in arid regions. However, few studies have focused on eco-hydrological processes of DPs in the ecological systems of fluvial fans. Although DP surfaces appear to be barren and flat, we found that the surfaces are characterized by surface mosaic patterns of desert pavement (mosaic DP) and bare ground (mosaic BG). We investigated the effects of mosaic DP on water infiltration and vegetation distribution at six sites in fluvial fans (one on a hillside and five within the sectors of fans) along a southwest belt transect in northern Linze County, in the central Hexi Corridor (China). We found significant differences in mosaic DP between the hillside and sector sites in terms of pavement thickness and vesicular horizon thickness (Av thickness), particle composition, and bulk density, although significant differences were absent for mass soil water content, gravel coverage, and surface gravel size. The mosaic DP inhibited water infiltration by the pavement layer, where the sorptivity (S), initial infiltration rate (i_int), steady-state infiltration rate (i_sat) and infiltration time (T) averaged 1.19 cm/min^-0.5, 0.64 cm/min, 0.13 cm/min and 12.76 min, respectively. Where the pavement layer was scalped, the S, i_int, and i_sat increased by 0.27 cm/min^-0.5, 0.52 cm/min, and 0.40 cm/min, respectively, and the T reduced by 7.42 min. Water infiltration was mainly controlled by the pavement layer thickness (+), Av thickness (−), surface gravel coverage (−), fine earth (+) and fine gravel (−) in the pavement layer. The DP surfaces only had a sparse covering of shrubs, but an abundance of herbs. Few shrubs were present on the mosaic DP, but a greater number of shrubs and herbs grew on the mosaic BG. It can be concluded that DPs can maintain vegetation stability for different surface mosaic patterns. This study deepens our understanding of the eco-hydrological cycle of DP landscapes in arid regions.     

A comparative study of extensive machine learning models for predicting long-term monthly rainfall with an ensemble of climatic and meteorological predictors

Rainfall prediction is of vital importance in water resources management. Accurate long-term rainfall prediction remains an open and challenging problem. Machine learning techniques, as an increasingly popular approach, provide an attractive alternative to traditional methods. The main objective of this study was to improve the prediction accuracy of machine learning-based methods for monthly rainfall, and to improve the understanding of the role of large-scale climatic variables and local meteorological variables in rainfall prediction. One regression model autoregressive integrated moving average model (ARIMA) and five state-of-the-art machine learning algorithms, including artificial neural networks, support vector machine, random forest (RF), gradient boosting regression, and dual-stage attention-based recurrent neural network, were implemented for monthly rainfall prediction over 25 stations in the East China region. The results showed that the ML models outperformed ARIMA model, and RF relatively outperformed other models. Local meteorological variables, humidity, and sunshine duration, were the most important predictors in improving prediction accuracy. 4-month lagged Western North Pacific Monsoon had higher importance than other large-scale climatic variables. The overall output of rainfall prediction was scalable and could be readily generalized to other regions.