Artificial neural network forward modelling and inversion of electrokinetic logging data

An artificial neural network method is proposed as a computationally economic alternative to numerical simulation by the Biot theory for predicting borehole seismoelectric measurements given a set of formation properties. Borehole seismoelectric measurements are simulated using a finite element forward model, which solves the Biot equations together with an equation for the streaming potential. The results show that the neural network method successfully predicts the streaming potentials at each detector, even when the input pressures are contaminated with 10% Gaussian noise. A fast inversion methodology is subsequently developed in order to predict subsurface material properties such as porosity and permeability from streaming potential measurements. The predicted permeability and porosity results indicate that the method predictions are more accurate for the permeability predictions, with the inverted permeabilities being in excellent agreement with the actual permeabilities. This approach was finally verified by using data from a field experiment. The predicted permeability results seem to predict the basic trends in permeabilities from a packer test. As expected from synthetic results, the predicted porosity is less accurate. Investigations are also carried out to predict the zeta potential. The predicted zeta potentials are in agreement with values obtained through experimental self potential measurements.     

核电站扩建工程基坑开挖爆破振动安全控制

结合秦山核电二期扩建工程,在已运行核电站附近新扩建核电站的大型基坑开挖过程中进行爆破振动的监测与爆破振动的控制。通过理论分析与监测等多种手段对爆破振动进行有效的控制,确保了正在运行核电站的安全。所总结的减震措施经验对其他核电站的爆破振动控制具有借鉴作用。     

玉环干江栈头公路隧道的爆破开挖技术

根据栈头公路隧道的工程特点及隧道施工的复杂性,阐述了隧道开挖的减振爆破控制技术和隧道开挖的方法,以及施工量测和振速监测技术在隧道开挖过程中的应用。     

爆心距对爆破震动信号频谱的影响分析

结合临沂某一隧洞工程,利用matlab分析软件分析了不同爆心距下的爆破震动信号的频谱变化情况。得出：在爆破中近区,震动信号的主频在垂直方向的分量较水平方向大,随着爆心距的增加地面震动峰值速度的主震方向会由水平方向向垂直方向转化,总体呈衰减趋势,震动主频趋向于低频;在爆破参数和爆区特征类似于本工程的工程爆破现场其中低频震动信号在水平方向上的分量所占能量比例高于垂直分量的能量比例,在距爆源较远的位置也不能忽略爆破震动对建筑结构的影响,尤其是要加强水平方向的抗震设防。     

A neurocomputing approach to the forecasting of monthly maximum temperature over Kolkata, India using total ozone concentration as predictor

The association between the monthly total ozone concentration and monthly maximum temperature over Kolkata (22.56° N, 88.30° E), India, has been explored in this paper. For this, the predictability of monthly maximum temperature based on the total ozone as predictor is investigated using Artificial Neural Network. The presence of persistence and similar cyclic patterns are revealed through autocorrelation and cross-correlation coefficients. Common cycles of length 12 and 6 have been identified through periodogram. Hence, a predictive model has been generated by Artificial Neural Network in the form of Multi Layer Perceptron (MLP) using scaled conjugate gradient learning with sigmoid non-linearity. After training and testing the network, an MLP with total ozone of month n as predictor and maximum temperature of month (n + 1) as the target output is found as the best model. Performance of the model has been judged statistically. Finally, the MLP model has been compared with linear and non-linear regressions and the efficiency of MLP has been established over the regression models.     

神经网络敏感性分析及其在遥感影像分类中的应用

针对基于连接权的神经网络敏感性分析方法中求取敏感性系数的不稳定性,提出一种优化连接权的神经网络敏感性分析方法。首先采用遗传算法根据误差最小化原则对神经网络进行优化,在优化的神经网络模型上进行基于连接权的敏感性分析。以1个数值模拟实例和华盛顿广场地区的遥感图像地物分类为例,验证所提方法的有效性。实验结果表明,所提方法求取输入变量的敏感性系数是稳定有效的,能有效筛选出遥感图像中对分类贡献较大的特征波段,达到降维的同时提高分类精度。     

控制水稻分蘖角度对群体生态特性的影响

控制分蘖角度对群体温度、群体相对湿度、群体CO2浓度和光合有效辐射均会产生一定影响。选用2006年水稻有关研究数据,分析水稻分蘖角度对群体生态特征的影响。结果表明：各生育时期群体温度在07:00－19:00处理大于CK。分蘖高峰期到孕穗期CK白天群体相对湿度大于处理,齐穗期到灌浆期处理群体相对湿度大于CK。群体CO2浓度在拔节期和孕穗期均为处理大于CK,其他生育期差异不显著。光合有效辐射垂直分布是处理前期和后期上层截获的光能均小于CK,群体内消光系数小。控制分蘖角度形成了有较高温度、较低湿度、高CO2浓度和适宜光分布的群体,可为获得高产奠定基础。     

The effect of discontinuity frequency on ground vibrations produced from bench blasting: A case study

A good number of empirical formulae and methods dealing with the analysis of the effects of blast-induced ground vibrations have been developed. The most common approach suggested for estimating the attenuation of particle velocity on the ground is to scale the distance (scaled distance, SD). This approach makes it possible to estimate the peak particle velocity when the amount of explosive charge or the distance or both are altered.Many parameters known to have an influence on particle velocity have been used for particle velocity prediction equations. Some of these parameters are maximum charge per delay, the distance between the station and shot location, burden, inelastic attenuation factor and site factors. However, the impacts of the discontinuities existing on the benches where blasts are detonated on the propagation velocity of seismic waves have not been taken into consideration in these equations.This study aims to examine the impacts of the discontinuity frequency parameter derived through geological measurements carried out on the blasting benches or nearby in a quarry mine (Supren, Eskisehir) in Turkey on the propagation of blast-induced ground vibrations. Developed based on the geological observations carried out on the benches, the model was formed by adding discontinuity frequency parameter to the particle velocity prediction model suggested by Nicholls et al. [Nicholls HR, Johnson CF, Duvall WI. Blasting vibrations and their effects on structures. Bulletin no. 656. Washington, DC: US Bureau of Mines; 1971]. In order to research the effect of the discontinuity frequency in the bench on the blast-induced ground vibrations, the relationship between the recorded peak particle velocity, scaled distance and discontinuity frequency was statistically evaluated for the site. The established relationship and the results of the study are presented.     

人工神经网络在爆破块度预测中的应用研究

利用人工神经网络模型对爆破块度进行预测,实验结果表明,该方法是完全可行的。通过对实验样本数据进行归一化处理后再对人工神经网络模型进行训练和预测,其预测精度会得到大大提高。     

Neural network modelling of planform geometry of headland-bay beaches

The shoreline of beaches in the lee of coastal salients or man-made structures, usually known as headland-bay beaches, has a distinctive curvature; wave fronts curve as a result of wave diffraction at the headland and in turn cause the shoreline to bend. The ensuing curved planform is of great interest both as a peculiar landform and in the context of engineering projects in which it is necessary to predict how a coastal structure will affect the sandy shoreline in its lee. A number of empirical models have been put forward, each based on a specific equation. A novel approach, based on the application of artificial neural networks, is presented in this work. Unlike the conventional method, no particular equation of the planform is embedded in the model. Instead, it is the model itself that learns about the problem from a series of examples of headland-bay beaches (the training set) and thereafter applies this self-acquired knowledge to other cases (the test set) for validation. Twenty-three headland-bay beaches from around the world were selected, of which sixteen and seven make up the training and test sets, respectively. As there is no well-developed theory for deciding upon the most convenient neural network architecture to deal with a particular data set, an experimental study was conducted in which ten different architectures with one and two hidden neuron layers and five training algorithms – 50 different options combining network architecture and training algorithm – were compared. Each of these options was implemented, trained and tested in order to find the best-performing approach for modelling the planform of headland-bay beaches. Finally, the selected neural network model was compared with a state-of-the-art planform model and was shown to outperform it.