利用支持向量机和高斯过程回归测定水库诱发的地震(英文)

水库诱发地震震级(M)的预测是在地震工程中的一项重要任务。本文采用支持向量机(SVM)和高斯过程回归(GPR)模型根据水库的参数预测了水库诱发地震震级(M)。综合参数(E)和最大的水库深度(H)作为支持向量机和高斯过程回归模型的输入参数。我们给出一个方程确定水库诱发地震震级(M)。将本文开发的支持向量机和建立的高斯过程回归方法与人工神经网络(ANN)方法相比。结果表明,本文研发的支持向量机和高斯过程回归方法是预测水库诱发地震震级(M)的有效工具。     

利用斜井钻孔破裂观测值约束深井和深矿中的全应力张量——在两个复杂实例中的应用

经过15年时间我们发展出一套技术,即利用钻孔井壁的非致命性破裂,包括压性破裂、钻探诱发的张性破裂以及与切穿井孔断层的滑动有关的应力扰动观测值,来确定任意向井和钻孔中的全应力张量。这些技术已延伸应用到石油工业中,也应用到矿山开采的钻孔岩芯取样中,以取得开采区周围应力集中影响的区域内外的应力状态。条件允许时,可用水压致裂法估计最小主应力值,但不能估计最大水平主应力值。作者在文中先回顾了这套方法的概念,然后对两个复杂实例进行了研究。第1个实例涉及到圣安德烈斯断层深部观测站（San Andreas Fault Observatory at Depth,SAFOD）计划第1阶段钻探应力状态的确定,SAFOD计划是一个钻穿加州中部圣安德烈斯断层的科学钻井计划。第2个实例涉及到确定南非一个极深矿周围的地壳应力状态。这些研究表明,在相当大的深度范围内,斜井钻孔破裂观测值与应力大小和方向是一致的。     

Machine learning techniques applied to prediction of residual strength of clay

Stability with first time or reactivated landslides depends upon the residual shear strength of soil. This paper describes prediction of the residual strength of soil based on index properties using two machine learning techniques. Different Artificial Neural Network (ANN) models and Support Vector Machine (SVM) techniques have been used. SVM aims at minimizing a bound on the generalization error of a model rather than at minimizing the error on the training data only. The ANN models along with their generalizations capabilities are presented here for comparisons. This study also highlights the capability of SVM model over ANN models for the prediction of the residual strength of soil. Based on different statistical parameters, the SVM model is found to be better than the developed ANN models. A model equation has been developed for prediction of the residual strength based on the SVM for practicing geotechnical engineers. Sensitivity analyses have been also performed to investigate the effects of different index properties on the residual strength of soil.     

Reliability Analysis of Quick Sand Condition

First order reliability method (FORM) is generally used for reliability analysis in geotechnical engineering. This article adopts generalized regression neural network (GRNN) based FORM, Gaussian process regression (GPR) based FORM and multivariate adaptive regression spline (MARS) based FORM for reliability analysis of quick sand condition. GRNN is related to the radial basis function (RBF) network. GPR is developed based on probabilistic framework. MARS is a nonparametric regression technique. A comparative study has been carried out between the developed models. The performance of GPR based FORM and MARS based FORM match well with the FORM. This article gives the alternative methods for reliability analysis of quick sand condition.     

Modeling of tensile strength of rocks materials based on support vector machines approaches

In the predicting of geological variables, artificial neural networks (ANNs) have some drawbacks including possibility of getting trapped in local minima, over training, subjectivity in the determining of model parameters and the components of its complex structure. Recently, support vector machines (SVM) has been found to be popular in prediction studies due to its some advantages over ANNs. Because the least squares SVM (LS‐SVM) provides a computational advantage over SVM by converting quadratic optimization problem into a system of linear equations, LS‐SVM method is also tried in study. The main purpose of this study is to examine the capability of these two SVM algorithms for the prediction of tensile strength of rock materials and to compare its performance with ANN and linear regression (MLR) models. Total porosity, sonic velocity, slake durability index and aggregate impact value were used as input in modeling applications. Favorite performance evaluation measures were employed to assess developed models. The results determined in study indicate that the SVM, LS‐SVM and ANN methods are successful tools for prediction of tensile strength variable and can give good prediction performances than MLR model. Although these three methods are powerful artificial intelligence techniques, LS‐SVM makes the running time considerably faster with the higher accuracy. In terms of accuracy, the LS‐SVM model resulted in error reductions relative to that of the other models. Copyright © 2012 John Wiley & Sons, Ltd.     

Determination of liquefaction susceptibility of soil: a least square support vector machine approach

This article adopts least square support vector machine (LSSVM) for determination of liquefactions susceptibility of soil based on standard penetration test data. Two models (Models I and II) have been developed. For Model I, input variables are cyclic stress ratio and standard penetration test value (N). Model II employs peak ground acceleration and N as input variables. The developed LSSVM models (Models I and II) give equations for determination of liquefaction susceptibility of soil. The performances of Models I and II are the same. The developed LSSVM gives probabilistic output. The results of LSSVM have been compared with the artificial neural network model. This article shows that N and the peak ground acceleration are sufficient input parameters for determination of liquefaction susceptibility of soil. Copyright © 2012 John Wiley & Sons, Ltd.     

Prediction of Rock Strain Using Hybrid Approach of Ann and Optimization Algorithms

Geotechnical and Geological Engineering - Prediction of strain is one of the important factors for assessment of characteristics of rock material. Rock strata are mostly more brittle in nature....     

Forecasting monthly precipitation using sequential modelling

In the hydrological cycle, rainfall is a major component and plays a vital role in planning and managing water resources. In this study, new generation deep learning models, recurrent neural network (RNN) and long short-term memory (LSTM), were applied for forecasting monthly rainfall, using long sequential raw data for time series analysis. “All-India” monthly average precipitation data for the period 1871–2016 were taken to build the models and they were tested on different homogeneous regions of India to check their robustness. From the results, it is evident that both the trained models (RNN and LSTM) performed well for different homogeneous regions of India based on the raw data. The study shows that a deep learning network can be applied successfully for time series analysis in the field of hydrology and allied fields to mitigate the risks of climatic extremes.     

Utilization of Gaussian Process Regression for Determination of Soil Electrical Resistivity

Soil electrical resistivity (R_E) is an important parameter for geotechnical engineering projects. This article employs Gaussian process regression (GPR) for prediction of R_E of soil based on soil thermal resistivity (R_T), percentage sum of the gravel and sand size fractions (F), and degree of saturation (S_r). GPR is derived from the perspective of Bayesian nonparametric regression. Two models (Model I and Model II) have been developed. The developed GPR has been compared with the artificial neural network. It gives the variance of the predicted R_E. The results show the developed GPR is an efficient tool for prediction of R_E of soil.