数字与模拟地震台网记录的震相特征与地震参数对比分析

本文对辽宁数字与模拟地震台网从1998年12月至1999年5月记录到震波特征、地震参数进行初步的比较与分析。     

邯郸遥测地震台网可能提供的华北震情信息

根据邯郸遥测地震台网所属８个子台记录的小震月频度曲线演变的起伏特点,研究了它与华北中强震活动的时程关系后,认为本台网在整个华北的中期、短期的趋势动态跟踪监视工作中可提供较为可靠的震情信息。文中对使用资料取择的物理意义和提取信息标志的具体办法等作出了具体叙述,本台网之所以具有反映华北震情信息的功能,可能与它所处的部位是在北东和北西两组构造的交汇部位有关。这样的部位无论来自何方的地震应力场强、弱演变的     

16选X地震报警器

地震报警是缩短地震速报时间的关键,本文介绍了适合遥测地震台网使用的１６选Ｘ地震报警器的工作原理和使用方法,并提出了电原理图,便于仿制,或根据需要作相应改变,以满足不同场合的要求。     

广州(珠江三角洲)数字遥测地震台网建设回顾

广州（珠江三角洲）数字无线遥测地震台网是我国第一个全套引进的数字无线遥测地震系统的数字地震台网,采用２４位数字地震采集、无线数据传输、地震数据处理等先进技术,对增强广东省特别是珠江三角洲地区的地震监测能力发挥了重要作用,为发展和推动我国数字地震观测技术进行了有益的探索和实践。系统介绍了广州台网的建设过程和技术特色。     

应用人工神经元网络方法识别近震与远震

利用人工神经元网络方法,提出了一种在实时系统中识别近震与远震的算法,并用广东省台网记录的地震波形数据进行训练和测试。结果表明,该算法能准确可靠地识别远、近震,可以应用于台网的实时处理系统。     

长短时记忆神经网络在中期地震预报中的探索——以川滇地区为例

利用机器学习方法对地震活动大数据进行挖掘,识别出一些过去认识不到的异常,提高地震预测的准确性,是一个非常具有挑战性的科学问题.本文基于川滇部分地区（24°N—32°N,98°E—106°E）地震目录,采用滑动的时空窗口,选取16个反映地震时空强度分布特征的地震预测因子,建立了长短时记忆（LSTM）神经网络,对研究区域9个子区块未来一年的最大地震震级进行预测.通过设置训练集：测试集=8：2和训练集：测试集=7：3两个不同的训练测试模型,对过往发生的地震进行了回溯性预报.结果表明：训练集：测试集=7：3模型能够利用1970年1月至2004年9月的地震目录进行学习,成功回溯性预报2008年汶川地震;训练集：测试集=8：2模型利用1970年1月至2009年5月资料进行训练,回溯性预报2010—2019年间6级以上地震的R评分为0.407,回溯性7级地震预报时准确率高达92.31%.本文还探讨性给出预测意见：2022年2月前研究区西部、中部、东部、西南部存在发生5.1~5.3级地震的潜在危险性.

     

A new scheme for numerical modelling of flow and transport processes in 3D fractured porous media

The objective of this work is to develop a new numerical approach for the three-dimensional modelling of flow and transient solute transport in fractured porous media which would provide an accurate and efficient treatment of 3D complex geometries and inhomogeneities. For this reason, and in order to eliminate as much as possible the number of degrees of freedom, the fracture network, fractures and their intersections, are solved with a coupled 2D–1D model while the porous matrix is solved independently with a 3D model. The interaction between both models is accounted for by a coupling iterative technique. In this way it is possible to improve efficiency and reduce CPU usage by avoiding 3D mesh refinements of the fractures. The approach is based on the discrete-fracture model in which the exact geometry and location of each fracture in the network must be provided as an input. The formulation is based on a multidimensional coupling of the boundary element method-multidomain (BEM-MD) scheme for the flow and boundary element dual reciprocity method-multidomain (BE-DRM-MD) scheme for the transport. Accurate results and high efficiency have been obtained and are reported in this paper.     

Performing cluster analysis and discrimination analysis of hydrological factors in one step

Based on self-organizing map, a method that can perform cluster analysis and discrimination analysis in one step is proposed in this paper. Using the proposed method, one can view the relative topological relationships of input patterns, determine the proper number of clusters, and assign unknown patterns to known clusters without losing any information of input patterns. Regarding the capability of determining the proper number of clusters, the proposed method is superior to conventional cluster analysis. The discrimination results also show that the assignments of unknown patterns to known clusters are reasonable using the proposed method. The advantages of the proposed method are also demonstrated by an application to the hydrological factors affecting low-flow duration curves in southern Taiwan.     

A conceptual model of depositional,rather than erosional,tidal channel development in the rapidly prograding Skagit River Delta (Washington,USA)

The origin and growth of blind tidal channels is generally considered to be an erosional process. This paper describes a contrasting depositional model for blind tidal channel origin and development in the Skagit River delta, Washington, USA. Chronological sequences of historical maps and photos spanning the last century show that as sediments accumulated at the river mouth, vegetation colonization created marsh islands that splintered the river into distributaries. The marsh islands coalesced when intervening distributary channels gradually narrowed and finally closed at the upstream end to form a blind tidal channel, or at mid‐length to form two blind tidal channels. Channel closure was probably often mediated through gradient reduction associated with marsh progradation and channel lengthening, coupled with large woody debris blockages. Blind tidal channel evolution from distributaries was common in the Skagit marshes from 1889 to the present, and it can account for the origin of very small modern blind tidal channels. The smallest observed distributary‐derived modern blind tidal channels have mean widths of 0·3 m, at the resolution limit of the modern orthophotographs. While channel initiation and persistence are similar processes in erosional systems, they are different processes in this depositional model. Once a channel is obstructed and isolated from distributary flow, only tidal flow remains and channel persistence becomes a function of tidal prism and tidal or wind/wave erosion. In rapidly prograding systems like the Skagit, blind tidal channel networks are probably inherited from the antecedent distributary network. Examination of large‐scale channel network geometry of such systems should therefore consider distributaries and blind tidal channels part of a common channel network and not entirely distinct elements of the system. Finally, managers of tidal habitat restoration projects generally assume an erosional model of tidal channel development. However, under circumstances conducive to progradation, depositional channel development may prevail instead. Copyright © 2006 John Wiley & Sons, Ltd.     

Integration of transfer function model and back propagation neural network for forecasting storm sewer flow in Taipei metropolis

Typhoons and storms have often brought heavy rainfalls and induced floods that have frequently caused severe damage and loss of life in Taiwan. Our ability to predict sewer discharge and forecast floods in advance during storm seasons plays an important role in flood warning and flood hazard mitigation. In this paper, we develop an integrated model (TFMBPN) for forecasting sewer discharge that combines two traditional models: a transfer function model and a back propagation neural network. We evaluated the integrated model and the two traditional models by applying them to a sewer system of Taipei metropolis during three past typhoon events (NARI, SINLAKU, and NAKR). The performances of the models were evaluated by using predictions of a total of 6 h of sewer flow stages, and six different evaluation indices of the predictions. Finally, an overall performance index was determined to assess the overall performance of each model. Based on these evaluation indices, our analysis shows that TFMBNP yields accurate results that surpass the two traditional models. Thus, TFMBNP appears to be a promising tool for flood forecasting for the Taipei metropolis sewer system. For publication in Stochastic Environmental Research and Risk Analysis.