深层搅拌桩在某住宅楼地基加固中的应用

以桂林某工地为例阐述了深层搅拌桩在深厚软弱地基中的设计、施工方法以及应用效果。     

CSAMT的静态效应校正及应用

CSAMT方法在地下近地表存在电性横向不均匀时,在双对数坐标系中视电阻率曲线会出现沿着视电阻率轴平行移动的现象,称之为静态效应.静态效应对深部电性结构处理解释结果影响重大,需对其进行合适的校正.本文介绍了目前通常使用的几种静态校正方法,并且分析了各方法的优缺点.在新疆某地进行的CSAMT铜矿勘探中,应用了曲线平移法进行静态校正,取得了较好的效果.     

数字强震仪在大坝安全监测中的应用研究

介绍了一种新型的数字强震仪（GDQJ-2型数字强震仪）,该强震仪以其独特的结构原理和技术性能, 在强震观测、大坝安全监测等地震领域得到广泛的应用.介绍了大坝安全监测的重要意义以及安全监测设备中地震监测设备的进展,介绍了GDQJ-2型数字强震仪的结构原理、技术指标以及在大坝安全监测中的应用,为大坝安全规范设计提出直接依据.     

基于融媒体技术的地震专业知识服务系统设计与应用

基于融媒体技术的地震专业知识服务系统是服务社会工程科技、地震及相关行业的专业信息平台,具有信息集成、科学技术创新、集管理与决策于一体的特点。本文论述了该服务系统的设计模式特点、服务内容及对象、系统技术架构和功能,并对其未来应用及发展方向进行讨论。     

模拟退火算法在江苏及邻区地震定位中的应用

使用模拟退火算法,以2019年4月6日江苏溧水M_L 3.3地震为典型震例,进行地震定位反演试算,确定适用于江苏及邻区地震定位的最佳初始温度、降温策略和扰动函数。设置初始温度为25,降温策略为T = 0.96T,选用效率较高的局部收敛加强型方法产生随机模型,对2019年江苏及周边地区105次M_L≥1.8地震进行重新定位,结果表明,76%的事件震中经、纬度定位误差绝对值在0.1°以内,但深度定位误差较大。鉴于该算法对P波到时信息及地震台站空间分布的依赖性,认为模拟退火算法在江苏及邻区陆地地震定位中效果较好,对于海域地震,则定位误差较大。     

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.     

A modified ISBA surface scheme for modeling the hydrology of Athabasca River Basin with GCM-scale data

A soil–vegetation–atmosphere transfer model (SVAT), interactions between the soil–biosphere–atmosphere (ISBA) of Météo France, is modified and applied to the Athabasca River Basin (ARB) to model its water and energy fluxes. Two meteorological datasets are used: the archived forecasts from the Meteorological Survey of Canada’s Global Environmental Multiscale Model (GEM) and the European Centre for Mid-range Weather Forecasts global re-analysis (ERA-40), representing spatial scales typical of a weather forecasting model and a global circulation model (GCM), respectively. The original treatment of soil moisture and rainfall in ISBA (OISBA) is modified to statistically account for sub-grid heterogeneity of soil moisture and rainfall to produce new, highly non-linear formulations for surface and sub-surface runoff (MISBA). These new formulations can be readily applied to most existing SVATs. Stand alone mode simulations using the GEM data demonstrate that MISBA significantly improves streamflow predictions despite requiring two fewer parameters than OISBA. Simulations using the ERA-40 data show that it is possible to reproduce the annual variation in monthly, mean annual, and annual minimum flows at GCM scales without using downscaling techniques. Finally, simulations using a simple downscaling scheme show that the better performance of higher resolution datasets can be primarily attributed to improved representation of local variation of land cover, topography, and climate.     

Time and space nonlocalities underlying fractional-derivative models: Distinction and literature review of field applications

We investigate the spatiotemporal nonlocality underlying fractional-derivative models as a possible explanation for regional-scale anomalous dispersion with heavy tails. Properties of four fractional-order advection–dispersion equation (fADE) models were analyzed and compared systematically, including the space fADEs with either maximally positive or negative skewness, the time fADE with a temporal fractional-derivative 0<γ<1$0<\gamma <1$, and the extension of the time fADE with 1<γ<2$1<\gamma <2$. Space fADEs describe the dependence of local concentration change on a wide range of spatial zones (i.e., the space nonlocality), while time fADEs describe dynamic mass exchange between mobile and multiple immobile phases and therefore record the temporal history of concentration “loading” (i.e., the time-nonlocality). We then applied the fADEs as models of anomalous dispersion to four extensively-studied, regional-scale, natural systems, including a hillslope composed of fractured soils, a river with simultaneous active flow zones and various dead-zones, a relatively homogeneous glaciofluvial aquifer dominated by stratified sand and gravel, and a highly heterogeneous alluvial aquifer containing both preferential flowpaths and abundant aquitards. We find that the anomalous dispersion observed at each site might not be characterized reasonably or sufficiently by previous studies. In particular, the use of the space fADE with less than maximally positive skewness implies a spatial dependence on downstream concentrations that may not be physically realistic for solute transport in watershed catchments and rivers (where the influence of dead-zones on solute transport can be described by a temporal, not spatial, fractional model). Field-scale transport studies show that large ranges of solute displacement can be described by a space nonlocal, fractional-derivative model, and long waiting times can be described efficiently by a time-nonlocal, fractional model. The unknown quantitative relationship between the nonlocal parameters and the heterogeneity, and the similarity in concentration profiles that are solutions to the different nonlocal transport models, all demonstrate the importance of distinguishing the representative nonlocality (time and/or space) for any given regional-scale anomalous dispersion process.     

地震前兆数据的管理与服务

简要介绍了中国地震局全国地震前兆台网中心及其前兆数据来源和特点，重点分析了该中心的数据管理、数据服务及应用。全国地震前兆台网中心在剖析地震前兆数据的结构和总结其特点的基础上，选择了自行研制的基于双表数据结构的CAPOmen平台来管理和维护各种前兆数据，并枚举了该平台所具有的一些特点，前兆台网中心还运用Web和FTP等网络传输手段与用户及研究单位进行各种数据交换和共享，提供以CAPOmen 平台为后台数据库管理的基于浏览器的前兆数据查询、浏览、下载及其他服务等。并对地震前兆数据的管理提出几点建议。     

ERA-Interim和CMFD气象驱动数据在新疆额尔齐斯河流域的适用性评价

气象驱动数据质量是影响流域水文过程模拟精度的一个重要因素。基于新疆额尔齐斯河流域及周边区域8个气象站记录的数据,对ERA-Interim再分析资料和中国区域地面气象要素驱动数据集（CMFD）在流域的适用性进行了评价,并对比了ERA-Interim和CMFD气象要素年均值在流域的空间分布。结果表明：ERA-Interim和CMFD记录气温、相对湿度、向下短波辐射和向下长波辐射数据与观测数据具有较高的一致性,但降水和风速数据与观测数据的一致性比较差。小时尺度上ERA-Interim记录的气温、相对湿度、降水量、向下短波辐射准确度略高于CMFD数据,而日尺度上CMFD记录的所有气象要素的准确度均高于ERA-Interim数据,结合Noah-MP模型的模拟结果,认为CMFD数据在新疆额尔齐斯河流域的适用性整体优于ERA-Interim数据。从两种驱动数据获取的流域气象要素空间分布来看,ERA-Interim和CMFD获取的年平均气温、风速、相对湿度、降水量、向下长波辐射在流域空间具有高度一致性,但向下短波辐射空间分布差别较大。