共查询到20条相似文献，搜索用时 95 毫秒
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地震数据交换标准（ＳＥＥＤ）是国际地震数据交换的一种标准格式。本文简要介绍了台站磁带的ＳＥＥＤ格式，详细分析了带有可变头段节的数据记录的结构，并给出了从带有可变头段节的数据记录中提取地震数据的实例。 相似文献
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珠江三角洲地区未来地震危险性预测 总被引：4，自引：1，他引：3
珠江三角洲地区的基岩地质、新构造运动、航磁和重力资料都反映ＮＥＥ向是最主要的构造方向,ＮＥＥ向的恩平－海丰一线把研究区分成南北两区。在地震相对活跃阶段,Ｍｓ≥５级地震和地震相对平静阶段的ＭＬ≥２．０级地震都具有南、北分区的特点。未来的地震活动仍然是南区活动较强北区活动较弱。推测认为,近５年内不论南区抑或北区,都将会发生ＭＬ≥４．０级地震,但两区发生ＭＬ≥５级地震的可能性不大。 相似文献
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通过对运行地震目录管理软件ＣＡＴＭＡＮＡ．ＥＳＥ，挑出一守时空范围的地震时出现的错误结果进行分析，得出了正确使用ＣＡＴＭＡＮＡ．ＥＸＥ程序的前提条件以及实现这一条件的方法和计算程序。 相似文献
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１９３２年昌马地震构造力学特征探讨郭万武，张范民，邢成起，温增平（国家地震局兰州地震研究所，兰州）ＤＩＳＣＵＳＳＩＯＮＯＮＴＨＥＴＥＣＴＯＮＯＭＥＣＨＡＮＩＣＦＥＡＴＵＲＥＳＯＦ１９３２ＣＨＡＮＧＭＡＥＡＲＴＨＱＵＡＫＥＭｓ７．６￥ＧｕｏＷａｎｗｕ；... 相似文献
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本文分析了我国１９００年以来Ｍ＿ｓ≥７．０级地震前与断层面总面积有关的参量ΣＥ￣（２／３）随时间的变化，探讨了运用ΣＥ￣（２／３）－ｔ曲线进行中期地震预报的可能性。通过分析某一地区历史地震的Ｅ￣（２／３）（ｔ）曲线，可以认识这一地区在过去若干年内地震的活动情况，并由此判断该区未来几年到几十年内地震活动的总状态；还可以根据Ｅ￣（２／３）（ｔ）曲线外推估计一个地区目前地震活动的可能强度。本文对Ｅ￣（２／３）（ｔ）曲线的基本形态作了粗略的分类，并试图对一个地区历史地震系列的Ｅ￣（２／３）（ｔ）曲线，用灾变论的观点确定该曲线的灾变条件和时间，以建立一种可应用的地震预报方法。 相似文献
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全球震中只读光盘数据库，向科研各单位，私人组织和政府部门提供地震资料。ＥＰＩＣ程序把历史地震数据存入一个系统，可以通过微型计算机进行地震震中数据存取，使用屏幕菜单，ＥＰＩＣ指导用户选择和检索。 相似文献
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ABSTRACTMaintaining a positive cost–benefit ratio for hydrologic data collection is challenging for public water management and regulatory agencies given budgetary constraints. We examine and discuss the evolution of a government agency in the last 20 years to identify the best practices for collecting, managing, and disseminating hydrologic data. The best practices include (1) developing in-house field data collection systems and integrating them with commercial, off-the-shelf software for the enterprise database, (2) standardizing hardware and data acquisition software, (3) collaborating with other agencies and tracking their data collection efforts to avoid redundancy, (4) utilizing open-source programming languages for data quality assurance, (5) rationalizing data collection network, and (6) taking a proactive approach with data collection to better prepare for hydroclimatic variability. Transforming data into easily understandable information and its near-real-time dissemination can add value to the data (e.g. providing inundation instead of water level above mean sea level) in the future. 相似文献
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禁核试北京国家数据中心（NDC）是我国与CTBTO/IDC进行数据交换的唯一节点,完成汇集、保存和处理我境内各IMS台站的监测数据,同时向国内用户提供境内IMS台站监测数据。NDC目前主要接入、处理、转发CD数据,但国内履约技术支持单位和协作单位多采用miniSEED数据,并使用JOPENS系统的流服务来交换数据,为了与其进行数据交换或向其分发监测数据,NDC需要具备分发miniSEED数据流的能力。文中设计的软件,是NDC自主开发的应用工具软件之一,可作为JOPENS系统流服务器的本地或远程仪器适配器,将CD数据流准实时转换为miniSEED流或将历史CD数据转换为miniSEED数据流,并通过JOPENS流服务器分发境内IMS台站数据,扩展了NDC的实时数据服务能力。 相似文献
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《水文科学杂志》2013,58(4)
Abstract The issue of data size (length) requirement for correlation dimension estimation continues to be the nucleus of criticisms on the (low) correlation dimensions reported for hydrological series. The present study addresses this issue from the viewpoints of both the existing theoretical guidelines and the practical reality. For this purpose, correlation dimension analysis is carried out for various data sizes from each of three types of series: (a) stochastic series (artificially generated using a random number generation technique); (b) chaotic series (artificially generated using the Henon map equation); and (c) hydrological series (real flow data observed on the Göta River in Sweden). The outcomes of the analysis of the (artificial) stochastic and chaotic series are used as a basis for interpreting the outcomes of the hydrological series. It is found that reliable dimension results for the stochastic and chaotic series are obtained even when the data size is only a few hundred points (i.e. no underestimation of dimension for small data sizes is visible), with no significant change in the scaling regimes (of the dimension plots) with respect to data size. This implies that the dimension results obtained for the hydrological series even with a few hundred points are also close to the actual ones. The insignificant difference in the scaling regimes for the various data sizes further supports this point. These results lead to the conclusions that: (1) the issue of data size requirement for correlation dimension estimation is more of a myth than reality; (2) the dimension estimates reported thus far for hydrological series could indeed be close to the actual ones (unless influenced by factors other than data size, e.g. delay time, noise, zeros, intermittency). 相似文献
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大地电磁全信息资料三维共轭梯度反演研究(英文) 总被引：7，自引：2，他引：5
在对张量阻抗数据、倾子数据和共轭梯度算法深入分析的基础上,我们实现了大地电磁全信息资料三维共轭梯度反演算法。基于全信息资料的三维共轭梯度反演研究,探讨了同时利用五个电磁场分量整理得到的大地电磁资料进行三维反演定量解释的方法以及全信息数据在三维反演中的作用。理论模型合成数据的反演结果表明,在三维反演中使用张量阻抗和倾子数据结合的全信息数据的反演结果优于只使用张量阻抗数据(或只使用倾子数据)的反演结果,提高了反演结果的分辨率和可信度。合成数据的反演算例也验证了所实现的大地电磁全信息资料三维共轭梯度反演算法的正确性和稳定性。 相似文献
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用于地震预警的通用数据采集系统构建 总被引：1，自引：0，他引：1
地震预警作为一种能够有效减轻地震灾害的手段已经被越来越多的国家和地区研究, 并在实际应用中取得了显著的减灾实效。 但是, 这些地震预警系统中都是以秒为单位组织数据包进行传输的, 使得在使用P波前三秒数据进行τc-Pd计算时, 会出现一定的延迟, 尤其是当P波开始位置处于数据包的前半部分时, 延迟会超过0.5 s以上。 另外, 由于现有的地震数据采集系统在同一时刻只能输出一种采样率, 采集到的数据无法在同一时刻用于多种研究目的。 针对这两种情况, 研制了一种适用于地震预警的低延迟数据采集系统, 以100 ms的延迟进行实时数据的传输, 有效提升了预警时间, 最多可增加0.8 s。 同时, 在系统中增加了同步多采样率输出, 使得采集到的数据在同一时刻既可用于全球地震学研究(1 Hz), 又可用于常规地震台站观测(50/100/200 Hz), 提高地震计产出数据的利用率。 相似文献
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A geographic data model for representing ground water systems 总被引：4，自引：0，他引：4
The Arc Hydro ground water data model is a geographic data model for representing spatial and temporal ground water information within a geographic information system (GIS). The data model is a standardized representation of ground water systems within a spatial database that provides a public domain template for GIS users to store, document, and analyze commonly used spatial and temporal ground water data sets. This paper describes the data model framework, a simplified version of the complete ground water data model that includes two-dimensional and three-dimensional (3D) object classes for representing aquifers, wells, and borehole data, and the 3D geospatial context in which these data exist. The framework data model also includes tabular objects for representing temporal information such as water levels and water quality samples that are related with spatial features. 相似文献
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城市地震应急指挥技术系统数据管理研究 总被引：5，自引：1，他引：5
以城市地震应急指挥技术系统(UEECTS)数据管理为研究背景,将空间地理信息系统技术引入数据管理领域,为UEECTS系统设计并开发了数据管理子软件系统,在实现对UEECTS系统数据空间可视化展示的基础上,实现了对系统数据的查询、添加、删除及修改数据管理操作,为城市UEECTS系统建设提供了良好的数据支持。针对UEECTS系统的数据规划以及数据管理子软件系统的开发与实现做了详细讨论与阐述,以期为全国范围内UEECTS系统数据库的建设以及数据管理子软件系统的开发应用提供通用性模型。 相似文献
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Environmental data are commonly constrained by a detection limit (DL) because of the restriction of experimental apparatus. In particular due to the changes of experimental units or assay methods, the observed data are often cut off by more than one DL. Measurements below the DLs are typically replaced by an arbitrary value such as zeros, half of DLs, or DLs for convenience of analysis. However, this method is widely considered unreliable and prone to bias. In contrast, maximum likelihood estimation (MLE) method for censored data has been developed for better performance and statistical justification. However, the existing MLE methods seldom address the multivariate context of censored environmental data especially for water quality. This paper proposes using a mixture model to flexibly approximate the underlying distribution of the observed data due to its good approximation capability and generation mechanism. In particular, Gaussian mixture model (GMM) is mainly focused in this study. To cope with the censored data with multiple DLs, an expectation–maximization (EM) algorithm in a multivariate setting is developed. The proposed statistical analysis approach is verified from both the simulated data and real water quality data. 相似文献
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Abstract The effect of data pre-processing while developing artificial intelligence (AI) -based data-driven techniques, such as artificial neural networks (ANN), model trees (MT) and linear genetic programming (LGP), is studied for Pawana Reservoir in Maharashtra, India. The daily one-step-ahead inflow forecasts are compared with flows generated from a univariate autoregressive integrated moving average (ARIMA) model. For the full-year data series, a large error is found mainly due to the occurrence of zero values, since the reservoir is located in an intermittent river. Hence, all the techniques are evaluated using two data series: 18 years of daily full-year inflow data (from 1 January to 31 December); and 18 years of daily monsoon season inflow data (from 1 June to 31 October) to take into account the intermittent nature of the data. The relevant range of inputs for each category is selected based on autocorrelation and partial autocorrelation analyses of the inflow series. Conventional pre-processing methods, such as transformation and/or normalization of data, do not perform well because of the large variation in magnitudes, as well as the many zero values (65% of the full-year data series). Therefore, the input data are pre-processed into un-weighted moving average (MA) series of 3 days, 5 days and 7 days. The 3-day MA series performs better, maintaining the peak inflow pattern as in the actual data series, while the coarser-scale (5-day and 7-day) MA series reduce the peak inflow pattern, leading to more errors in peak inflow prediction. The results indicate that AI methods are powerful tools for modelling the daily flow time series with appropriate data pre-processing, in spite of the presence of many zero values. The time-lagged recurrent network (TLRN) ANN modelling technique applied in this study maps the inflow forecasting in a better way than the standard multilayer perceptron (MLP) neural networks, especially in the case of the seasonal data series. The MT technique performs equally well for low and medium inflows, but fails to predict the peak inflows. However, LGP outperforms the other AI models, and also the ARIMA model, for all inflow magnitudes. In the LGP model, the daily full-year data series with more zero inflow values performs better than the daily seasonal models. Citation Jothiprakash, V. & Kote, A. S. (2011) Improving the performance of data-driven techniques through data pre-processing for modelling daily reservoir inflow. Hydrol. Sci. J. 56(1), 168–186. 相似文献