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.     

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.     

Deepwater helium in Lake Baikal as an earthquake precursor

In this paper, attention is paid to the importance of short-term prognosis of earthquakes. The variability of determination methods is noted. One of the geochemical methods, based on study of the helium content in deep water of Lake Baikal, is considered; such a method has not been used for open deep-water basins within the zones of high seismic danger. It is established that in the period of earthquake preparation, variations in the helium content deep underwater in Lake Baikal are recorded. A sharp decrease in the helium content two days before the earthquake was recorded first time for a long period of observation, as well as the consequent increase. Further study of the helium content deep underwater in Lake Baikal is recommended, and, should these data be proved, it is recommended as a short-term precursor of earthquakes.     

Evidence of precursor phenomena in the Kobe earthquake obtained from atmospheric radon concentration

Atmospheric ²²²Rn concentrations were determined over a 10a period, which included the date of the Kobe, Japan earthquake, on January 17th 1995. It was found that the seismically related ²²²Rn anomaly was higher than the 99% confidence limits for the residual value of atmospheric ²²²Rn which had been observed 2 months before. The residual ²²²Rn concentration, in which residual values of the daily minimum are the difference between each normal ²²²Rn concentration (calculated from January 1984 to December 1993) and the daily minimum ²²²Rn concentration (January 1994 to January 1995), was calculated by applying the exponential smoothing method to the residual values for each day. It was found that the fluctuations of the residual values can be fitted very well to a log-periodic oscillation model. The real residual values stopped increasing at 1994.999 (December 31st 1994), which corresponds with the critical point (t_c) of best fit model. This anomalous ²²²Rn variation can be seen as the result of local stresses, not primary stresses which directly lead to the Kobe earthquake. On the other hand, when the critical exponent (z) and the radial frequency (ω) of the model were simultaneously fixed 0.2 ⩽ z ⩽ 0.6 and 6 ⩽ ω ⩽ 12, t_c (critical point) was between January 13th 1995 and January 27th 1995. The Kobe earthquake occurrence date (January 17th 1995) is within this range. Therefore this anomalous ²²²Rn variation can also be seen as the result of primary stresses which possibly led to the Kobe earthquake. There is a distinct possibility that similar statistical oscillations will be detected in other measurements such as microseismicity, tectonic strain, fluctuation in the ground level, or changes in groundwater elevations and composition.     

Application of neural network and ANFIS model for earthquake occurrence in Iran

This study examined the spatial-temporal variations in seismicity parameters for the September 10^th, 2008 Qeshm earthquake in south Iran. To this aim, artificial neural networks and Adaptive Neural Fuzzy Inference System (ANFIS) were applied. The supervised Radial Basis Function (RBF) network and ANFIS model were implemented because they have shown the efficiency in classification and prediction problems. The eight seismicity parameters were calculated to analyze spatial and temporal seismicity pattern. The data preprocessing that included normalization and Principal Component Analysis (PCA) techniques was led before the data was fed into the RBF network and ANFIS model. Although the accuracy of RBF network and ANFIS model could be evaluated rather similar, the RBF exhibited a higher performance than the ANFIS for prediction of the epicenter area and time of occurrence of the 2008 Qeshm main shock. A proper training on the basis of RBF network and ANFIS model might adopt the physical understanding between seismic data and generate more effective results than conventional prediction approaches. The results of the present study indicated that the RBF neural networks and the ANFIS models could be suitable tools for accurate prediction of epicenteral area as well as time of occurrence of forthcoming strong earthquakes in active seismogenic areas.     

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.     

应用一种改进BP神经网络算法预测密度曲线

利用测井数据与地震数据二者相结合进行综合分析,是地震勘探工作者的重要工作。可以通过分析井位处的地震数据与测井数据,提取地震的多个属性,建立一个与测井属性的统计关系。选取已改进的三层网络结构BP神经网络算法,在应用一个实际例子后表明,该算法的主要特点是收敛速度快、计算简单,同时还具有跳出局部最小的能力。应用此神经网络算法对某油田的二维地震数据进行了处理,提取了多种地震属性,并在井位置建立了地震属性与密度曲线的非线性关系,成功预测了剖面密度曲线,为了解储层状况提供了有益的资料。     

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。     

基于GA的混凝土坝抗震可靠度预测的神经网络模型

针对BP人工神经网络具有易陷入局部极小等缺陷,提出了将遗传算法与神经网络结合,同时优化网络结构的权值与阈值的思想,建立了基于遗传算法的混凝土坝抗震可靠度预测的神经网络模型。该模型分别对混凝土坝抗滑稳定可靠度、抗压可靠度和抗拉可靠度进行了预测,并与BP神经网络预测结果进行比较。结果表明,遗传神经网络模型可靠,预测精度高,在岩土工程中利用该方法进行可靠性问题预测是有效及可行的。     

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。     

Storm surge prediction using an artificial neural network model and cluster analysis

In this study, an artificial neural network model was developed to predict storm surges in all Korean coastal regions, with a particular focus on regional extension. The cluster neural network model (CL-NN) assessed each cluster using a cluster analysis methodology. Agglomerative clustering was used to determine the optimal clustering of 21 stations, based on a centroid-linkage method of hierarchical clustering. Finally, CL-NN was used to predict storm surges in cluster regions. In order to validate model results, sea levels predicted by the CL-NN model were compared with results using conventional harmonic analysis and the artificial neural network model in each region (NN). The values predicted by the NN and CL-NN models were closer to observed data than values predicted using harmonic analysis. Data such as root mean square error and correlation coefficient varied only slightly between CL-NN and NN model results. These findings demonstrate that cluster analysis and the CL-NN model can be used to predict regional storm surges and may be used to develop a forecast system.     

Determination and application of the weights for landslide susceptibility mapping using an artificial neural network

The purpose of this study is the development, application, and assessment of probability and artificial neural network methods for assessing landslide susceptibility in a chosen study area. As the basic analysis tool, a Geographic Information System (GIS) was used for spatial data management and manipulation. Landslide locations and landslide-related factors such as slope, curvature, soil texture, soil drainage, effective thickness, wood type, and wood diameter were used for analyzing landslide susceptibility. A probability method was used for calculating the rating of the relative importance of each factor class to landslide occurrence. For calculating the weight of the relative importance of each factor to landslide occurrence, an artificial neural network method was developed. Using these methods, the landslide susceptibility index (LSI) was calculated using the rating and weight, and a landslide susceptibility map was produced using the index. The results of the landslide susceptibility analysis, with and without weights, were confirmed from comparison with the landslide location data. The comparison result with weighting was better than the results without weighting. The calculated weight and rating can be used to landslide susceptibility mapping.     

Evaluation of the compression index of soils using an artificial neural network

The compression index is a one of the important soil parameters that is essential to geotechnical designs. As the determination of the compression index from consolidation tests is relatively time-consuming, empirical formulas based on soil parameters can be useful. Over the decades, a number of empirical formulas have been proposed to relate the compressibility to other soil parameters, such as the natural water content, liquid limit, plasticity index, specific gravity, and others. Each of the existing empirical formulas yields good results for a particular test set, but cannot accurately or reliably predict the compression index from various test sets. In this study, an alternative approach, an artificial neural network (ANN) model, is proposed to estimate the compression index with numerous consolidation test sets. The compression index was modeled as a function of seven variables including the natural water content, liquid limit, plastic index, specific gravity, and soil types. Nine hundred and forty-seven consolidation tests for soils sampled at 67 construction sites in the Republic of Korea were used for the training and testing of the ANN model. The predicted results showed that the neural network could provide a better performance than the empirical formulas.     

Variations of the cosmic ray fluxes as a possible earthquake precursor

Variations of the air pressure, cosmic ray fluxes, sunspot numbers, and interplanetary magnetic field in connection with strong earthquake occurrences are studied. The results of this investigation permits one to consider the variations of the cosmic rays as one of the possible cause of air pressure variations and one of the possible earthquake precursors.     

基于遗传算法的BP神经网络计算岩溶水安全开采量

随着淮北市相山区岩溶水开采量不断增大,区内岩溶水水位降落漏斗范围不断增大,为保障岩溶水的安全开采与地质环境安全,进行本区岩溶水安全开采量计算十分必要。目前神经网络模型已被广泛应用于岩溶水水位动态计算,但由于网络全局寻优能力不理想,网络训练容易陷入局部极小值,导致网络泛化能力不理想。针对人工神经网络的不足,利用遗传算法(GA)对较为常用的BP神经网络权值、阈值进行优化,将此方法应用于相山区岩溶水水位动态的预测,并以该区岩溶水临界开采水位为控制条件,经模型计算得到相山区岩溶水多年平均安全开采量为3 001.7×10~4m~3。计算结果表明:与BP神经网络相比,GA-BP神经网络具有更高的预测精度,遗传算法可以有效提高BP网络的泛化能力。     

一种基于遗传算法和BP神经网络的选星方法

在BDS/GPS组合定位中,选择空间位置最优的卫星组合是第一步,也是十分重要的一步.传统选星算法在选取最佳卫星组合过程中涉及大量的矩阵乘法和求逆运算,计算量大,定位实时性低.为解决快速选星定位问题,综合考虑定位精度和实时性要求,提出一种新的选星算法,该算法将BP神经网络和遗传算法相结合,并将几何精度因子(GDOP)作为判断定位精度高低的依据.将应用该算法得到的GDOP和运算时间与最小几何精度因子法所得对应结果进行比较,实验结果表明,该算法在大大减小了计算量的同时保证了定位精度,具有良好的实时性和可行性.     

Well-log correlation using a back-propagation neural network

We present a back-propagation neural network with an input layer in the form of a tapped delay line wich can be trained effectively on one or several well logs to recognize a particular geological marker. Subsequently, the neural network proposes locations of this marker on other wells in the field. Another neural network, similar in architecture to the first one, performs the same task for secondary markers using, in addition to the well logs, a depth reference function to the first marker. This method is shown to have better performance and better discrimination than standard cross-correlation techniques. It lends itself well for an interactive implementation on a workstation.     

Landslide detection using visualization techniques for deep convolutional neural network models

Natural Hazards - Landslides occur when masses of rock, earth, and other debris move down a slope. The slope of an area is directly responsible for the magnitude of the landslide. Being...     

The artificial neural network as a tool for assessing geotechnical properties

Geotechnical properties are controlled by factors such as mineralogy, fabric and pore water: dynamic properties which can change in response to the environment and human intervention. Their interactions are difficult to establish by statistical methods alone due to their interdependence. Based on the application of an artificial neural network, a methodology has been developed for predicting geotechnical properties utilising their Relative Strength of Effect (RSE) and Potential Relative Strength of Effect (PRSE). The PRSE reveals the trend in the neighbourhood of the focus point and has been found to be less sensitive to any noise existing within the data set. An application is illustrated using sandstone data published by Hawkins and McConnel (1991) whereby the possible influence of petrological characteristics on their geotechnical properties has been assessed.     

Rock mass excavatability estimation using artificial neural network

One important decision in design of surface mine is the selection of mine equipment and plant. Demand for mechanical excavation is growing in mining industry because of its high productivity and excavation in large scale with lower costs. Several models have been developed over the years to evaluate the ease of excavation and machine performance against rock mass properties. Due to complexity of excavation process and large number of effective parameters, approaches made for this purpose are essentially empirical. There are many uncertainties in results of these models. An attempt is made in this paper to revise the exisiting models. Neural network models for estimation of rock mass excavatability and production rate of VASM-2D excavating machine at Limestone quarry in Retznei, Austria, is presented. Input parameters of this model are Uniaxial compressive strength, tensile strength and discontinuities spacing of rocks. Output is the specific excavation rate per power consumption (bcm/Kwh) as the productivity indicator. Average of deviation between actual data and results estimated by neural network model was only 15% which is in an acceptable range.