Application of time- and magnitude-predictable model for long-term earthquake prediction in Iran

The regional time- and magnitude-predictable model has been applied successfully in diverse regions of the world to describe the occurrence of main shocks. In the current study, the model has been calibrated against the historical and instrumental catalog of Iranian earthquakes. The Iranian plateau is divided into 15 seismogenic provinces; then, the interevent times for strong main shocks have been determined for each one. The empirical relations reported by Papazachos et al. (Tectonophysics 271:295–323, 1997a) for the Alpine–Himalayan belt (including Iran) were adopted except for the constant terms that were calculated separately for every seismotectonic area. By using the calibrated equations developed for the study area and taking into account the occurrence time and magnitude of the last main shocks in each seismogenic source, the time-dependent conditional probabilities of occurrence P(?t) of the next main shocks during next 10, 20, 30, 40 and 50 years as well as the magnitude of the expected main shocks (M _f) have been estimated. The immediate probability (within next 10 years) of a large main shock is estimated to be high and moderate (>35 %) in all regions except zones 9 (M _f = 5.8) and 15 (M _f = 6.1). However, it should be noted that the probabilities have been estimated for different M _f values in 15 regions. Comparing the model predictions with the actual earthquake occurrence rates shows the good performance of the model for Iranian plateau.     

模糊神经网络在矿震预测中的应用

矿震同天然地震一样会给矿山生产及人身安全等带来重大灾难。也是目前尚不能准确预测的矿山灾害现象之一。根据现有的研究成果可知，矿震是一个多输入、多干扰、单输出的复杂系统。由于干扰项的存在，使利用建模、神经网络等手段对系统进行预测时会导致很大误差。模糊神经网络系统在建立对象输入、输出关系时与传统数学方法不同。即可以建立在无模型基础上，并利用其较强的学习训练特性，自动获取对象的输入、输出关系表达；可以将专家的评价语言作为系统的干扰项引入。这在一定程度上缓解了人为因素对预测结果的影响，且平滑了观测数据的随机性。文章利用改进的模糊神经网络及抚顺老虎台矿的矿震资料，对矿震最大震级的预测方法进行了探索。‘初步探讨了改进的模糊神经网络在矿震预测中的应用。得出在运用模糊神经网络进行预测时，为减小预测误差，应综合多种因素并提高专家评判语言的精确度的结论。指出在建立矿震系统预测模型时，利用干扰项将人为因素引入系统是必需的。通过实际应用证明其可行性。     

人工神经网络模型在石油资源预测中的应用

锦270井区大凌河油层是盆地大幅度沉降条件下的产物,以深陷湖环境为主,发育湖底扇浊积岩.准确预测大凌河油层的砂体分布和泥质含量,是勘探目标选择的关键.由于钻井资料少,不能反映储层横向变化.为了准确预测储层的横向变化,综合钻井地质资料和地震勘探资料,并为了反映地震资料的多个参数与储层横向变化间的非线性相关关系,采用人工神经网络模型进行预测,计算了大凌河油层砂层厚度和泥质含量的平面分布.依据计算结果,分析了有利油气区的分布,并给出了几点结论.     

A new model for the prediction of earthquake ground-motion duration in Iran

The paper proposes a new empirical model to estimate earthquake ground-motion duration, which significantly influences the damage potential of an earthquake. The paper is concerned with significant duration parameters that are defined as the time intervals between which specified values of Arias intensity are reached. In the proposed model, significant duration parameters have been expressed as a function of moment magnitude, closest site-source distance, and site condition. The predictive model has been developed based on a database of earthquake ground-motion records in Iran, containing 286 records up to the year 2007, and a random-effect regression procedure. The result of the proposed model has been compared with that of other published models. It has been found that the proposed model can predict earthquake ground-motion duration in Iran with adequate accuracy.     

Integrated model for earthquake risk assessment using neural network and analytic hierarchy process:Aceh province,Indonesia

Catastrophic natural hazards,such as earthquake,pose serious threats to properties and human lives in urban areas.Therefore,earthquake risk assessment(ERA)is indispensable in disaster management.ERA is an integration of the extent of probability and vulnerability of assets.This study develops an integrated model by using the artificial neural network–analytic hierarchy process(ANN–AHP)model for constructing the ERA map.The aim of the study is to quantify urban population risk that may be caused by impending earthquakes.The model is applied to the city of Banda Aceh in Indonesia,a seismically active zone of Aceh province frequently affected by devastating earthquakes.ANN is used for probability mapping,whereas AHP is used to assess urban vulnerability after the hazard map is created with the aid of earthquake intensity variation thematic layering.The risk map is subsequently created by combining the probability,hazard,and vulnerability maps.Then,the risk levels of various zones are obtained.The validation process reveals that the proposed model can map the earthquake probability based on historical events with an accuracy of 84%.Furthermore,results show that the central and southeastern regions of the city have moderate to very high risk classifications,whereas the other parts of the city fall under low to very low earthquake risk classifications.The findings of this research are useful for government agencies and decision makers,particularly in estimating risk dimensions in urban areas and for the future studies to project the preparedness strategies for Banda Aceh.     

Seismic human loss estimation for an earthquake disaster using neural network

In Iran, earthquakes cause enormous damage to the people and economy. If there is a proper estimation of human losses in an earthquake disaster, it could be appropriately responded and its impacts and losses will be decreased. Neural networks can be trained to solve problems involving imprecise and highly complex nonlinear data. Based on the different earthquake scenarios and diverse kind of constructions, it is difficult to estimate the number of injured people. With respect to neural network’s capabilities, this paper describes a back propagation neural network method for modeling and estimating the severity and distribution of human loss as a function of building damage in the earthquake disaster. Bam earthquake data in 2003 were used to train this neural network. The final results demonstrate that this neural network model can reveal much more accurate estimation of fatalities and injuries for different earthquakes in Iran and it can provide the necessary information required to develop realistic mitigation policies, especially in rescue operation.     

BP神经网络在桩基支护方式中的应用

文章以人工神经网中BP模型为基本工具，利用其强大的非线性映射能力，在综合考虑土体的物理性质指标的基础上，确定桩基支护方式。经网络学习和检测表明，利用此模型确定的桩基支护方式与工程实际设计方式基本吻合，为软土深基坑桩基支护方式提供了新的途径。     

人工神经网络模型在地下水水质评价分类中的应用

人工神经网络(Artificial Neural Network以下简称ANN)是一种行之有效的数据处理和分析方法,它的应用领域不断扩大并逐渐完善,本文在传统ANN方法基础上进行了进一步的探讨,立足于BP算法,通过调整ANN输出结构,提高其鲁棒性能,从而使其更具有适应性.将改进后的ANN应用于地下水水质评价分类,并和模糊综合评判评价结果进行了比较,分类结果令人满意.     

Probabilistic neural network algorithm for using radon emanations as an earthquake precursor

The investigation throughout the world in past two decades provides evidence which indicate that significance variation of radon and other soil gases occur in association with major geophysical events such as earthquake. The traditional statistical algorithm includes regression to remove the effect of the meteorological parameters from the raw radon and anomalies are calculated either taking the periodicity in seasonal variations or periodicity computed using Fast Fourier Transform. In case of neural networks the regression step is avoided. A neural network model can be found which can learn the behavior of radon with respect to meteorological parameter in order that changing emission patterns may be adapted to by the model on its own. The output of this neural model is the estimated radon values. This estimated radon value is used to decide whether anomalous behavior of radon has occurred and a valid precursor may be identified. The neural network model developed using Radial Basis function network gave a prediction rate of 87.7%. The same was accompanied by huge false alarms. The present paper deals with improved neural network algorithm using Probabilistic Neural Networks that requires neither an explicit step of regression nor use of any specific period. This neural network model reduces the false alarms to zero and gave same prediction rate as RBF networks.     

大坝变形预测的ANFIS模型

大坝变形预报时,存在影响因素多且各因素之间的相互关系复杂,常规的变形预测方法难以满足大坝安全监控的要求。自适应神经模糊系统(ANFIS)兼备神经网络的自学习、自适应能力,以及模糊系统良好的知识表达性能。在系统分析大坝变形主要影响因素的基础上,以水库库水位、温度及时间效应为影响因子,建立基于自适应模糊神经系统的大坝变形预测模型,并以三峡二期围堰为例进行实证分析。研究表明,该模型计算简便,适用性强,精度高,为大坝变形预报提供了新的思路。     

Commensurability of earthquake occurrence

During recent years huge earthquakes frequently occurred and caused surprise attack on many places of the globe. Frequent exceptional strong disasters of earthquakes remind that we must strengthen our research on cause of formation, mechanism, prediction and forecast of earthquakes, and achieve the goal of advancing the development of Earth science and mitigation of seismic disasters. The commensurability of earthquake occurrences has been studied by means of the commensurability revealed by the Titius–Bode law in the paper. The studied results show that the earthquakes basically all occur at the commensurable point of its time axis, respectively. It also shows that occurrence of the earthquakes is not accidental, showing certain patterns and inevitability, and the commensurable value is different for earthquakes occurring in different areas.     

A stochastic model of earthquake occurrence and the accompanying horizontal land deformation

A large-scale earthquake is believed to be associated with a release of strain energy accumulated in the crust, probably by the motion of upper-mantle lithosphere. Such an earthquake mechanism is well simulated by a belt-conveyer model proposed by Utsu (1972). The probability of earthquake occurrence can be estimated on the assumption that the motion of a slider on the belt-conveyer is mathematically formulated as a Markov process.In the probabilistic expressions, the results of Mogi's (1962) rock-fracture experiments are applied to the hazard-rate function of earthquake occurrence. The hazard-rate function has two coefficients, A and B, to be determined by the experiments. It is concluded that, when B is small, a number of small-scale earthquakes occur in the early time after the accumulation of crustal strain energy starts, but that the accumulated strain energy changes catastrophically into a single large-scale earthquake, when B is large.     

砂土地震液化的神经网络预测

在简要分析BP算法的基础上,应用BP网络的理论与方法,选取砂土的平均粒径(d50／mm)、相对密度(Dr／％)、标准贯入击数(N63.5／击)、上覆有效应力(σv／kPa)、地震烈度(I0)作为指标,预测砂土在地震作用下液化的可能性,取得了较好的预测效果。     

High-precision chaotic radial basis function neural network model:Data forecasting for the Earth electromagnetic signal before a strong earthquake

The Earth’s natural pulse electromagnetic field data consists typically of an underlying variation tendency of intensity and irregularities.The change tendency may be related to the occurrence of earthquake disasters.Forecasting of the underlying intensity trend plays an important role in the analysis of data and disaster monitoring.Combining chaos theory and the radial basis function neural network,this paper proposes a forecasting model of the chaotic radial basis function neural network to conduct underlying intensity trend forecasting by the Earth’s natural pulse electromagnetic field signal.The main strategy of this forecasting model is to obtain parameters as the basis for optimizing the radial basis function neural network and to forecast the reconstructed Earth’s natural pulse electromagnetic field data.In verification experiments,we employ the 3 and 6 days’data of two channels as training samples to forecast the 14 and 21-day Earth’s natural pulse electromagnetic field data respectively.According to the forecasting results and absolute error results,the chaotic radial basis function forecasting model can fit the fluctuation trend of the actual signal strength,effectively reduce the forecasting error compared with the traditional radial basis function model.Hence,this network may be useful for studying the characteristics of the Earth’s natural pulse electromagnetic field signal before a strong earthquake and we hope it can contribute to the electromagnetic anomaly monitoring before the earthquake.     

大坝安全诊断的混沌优化神经网络模型

为了提高大坝变形的预测精度,采用小波变换和分形理论对大坝位移观测数据的非线性动力学特性进行了分析,揭示了其具有低维混沌动力特性,这为大坝变形预测模型的建立提供了理论依据和先验知识。基于低维混沌动力特性,设计了能捕获大坝位移观测数据全局动力特性,兼具神经网络模型结构优化和动力机制时新的混沌优化神经网络大坝变形预测模型。在工程实例中,由多个度量指标组成量化评价体系,对模型预测性能进行综合评价,结果表明,所建模型比传统BP神经网络和ARMA模型具有更高的预测精度。     

神经网络模型在坝基扬压力影响因子量化分析中的应用

本文应用改进的BP网络模型定量分析坝基扬压力的影响因子,赋于网络不同的权值来表示网络的输入变量(水位、温度、时效等因子)对网络的输出变量(扬压力)的影响程度,从而确定各影响因子分量对扬压力的影响比例.采用Lvenberg-Marquardt算法训练网络,网络达到一定的次数后收敛.实例计算结果表明,该模型具有计算精度高、简便实用等特点.因而认为,把神经网络模型应用于探讨诸如环境量对于效应量影响程度的一类问题,具有好的前景.     

Stein estimation for SIR-predictable models of earthquake occurrence

It is usually accepted that a time-predictable model of earthquake occurrences is better than the so-called slip-predictable model. Here a size-interval relation (SIR)-predictable model is proposed which combines the features of the time-predictable and slip-predictable models. Unlike a constant, and hence nonpredictive, relation between the size of the next earthquake and the inter-event interval, given by the so-called slip-predictable model, the SIR-predictable model prescribes such a relation contingent upon the size of the previous earthquake. Unlike the time-predictable model, instead of predicting the time interval, it proposes a size-interval relation. Using data about a seismogenic source called Cephalonia in Greece, the superiority of the SIR-predictable model over the time-predictable model is illustrated. The SIR-predictable model can be made more efficient by employing two-stage nonlinear estimation procedures motivated by the initial work by Stein. Introducing these procedures to seismologists is an independent objective of this paper. Also, Stein estimators have a dimensionality threshold. This work discusses two techniques of threshold extension.     

Estimation of radon as an earthquake precursor: A neural network approach

An artificial neural networks (ANN) approach combined with Fourier Transform based selection of time period in the time series Radon Emission Data has been presented and shown to improve event prediction rates and reduce false alarms in Earthquake Event Identification over the traditional multiple linear regression techniques. The paper presents a neural networks system using radial basis function (RBF) network as an alternative to traditional statistical regression technique in isolating Radon Emission Anomaly caused by seismic activities. The RBF model has been developed to accept and predict earthquakes events based on a known data set of Radon Emanation, Metrological parameters and actual earthquake events. Subsequently, the model was tested and evaluated on a future data set and a prediction rate of 87.8%, if a reduced false alarm was achieved, the results obtained are better than the traditional techniques.     

南江滑坡群体积的BP神经网络模型与预测

基于南江县境内244个典型土质滑坡统计样本,利用BP神经网络模型,采用3种不同的方案(基于不同的评价参数)对滑坡体积进行预测。方案一选取坡高、坡度、坡向、高程、植被覆盖率、岩层倾向、岩层倾角等7项评价参数;方案二选取坡高、坡度、坡向、岩层倾向、岩层倾角等5参数;方案三选取坡高、坡度、坡向等3参数。研究结果表明:3种方案建立的BP神经网络模型都具有较高的可靠性,其预测结果都可以较好地逼近真实滑坡体积值,BP神经网络能有效应用到滑坡体积预测中;3种方案预测值与实际值基本吻合,且两者间的相关系数分别为0.87083,0.90826,0.86119,评价参数的合理选择对滑坡体积预测的准确性有着重要的影响;方案二的相关系数最高,其预测准确性最好,这表明坡高、坡度、坡向、岩层倾向、岩层倾角是影响滑坡体积的重要因素,植被覆盖率和高程为其次要影响因素。