直流电阻率法的深部结构分辨——三维模型的二维反演

利用三维有限差分方法进行深部三维结构的电阻率三维数值模拟,对计算得到的单极-单极和对称四极装置视电阻率剖面进行电阻率二维反演研究。结果显示:单极-单极装置在横向上只要三维异常体中心的水平间距不小于两者中心埋深之和,就可以基本将2个三维结构分辨出来;相同情况下,对称四极装置在横向上的分辨效果优于单极-单极装置;单极-单极装置在垂向上的分辨要优于对称四极装置。     

基于GA-BP神经网络算法的高密度电法非线性反演

高密度电法技术在煤矿地质灾害勘探中发挥着重要的作用。近年来,以BP（Backpropagation）神经网络为代表的一类非线性反演方法被广泛运用到高密度电法的反演中。针对BP神经网络方法在高密度电法反演中存在的易陷入局部极小、收敛缓慢、反演精度差等问题,将BP神经网络算法与遗传算法（Genetic Algorithm,简称GA算法）联合演算,实现高密度电法的二维非线性反演。通过典型地电模型对该方法进行验证,结果表明遗传算法能有效优化BP神经网络的权值和阈值,提高了算法的全局寻优性。     

Estimation of effective stress parameter of unsaturated soils by using artificial neural networks

Great efforts are required for determination of the effective stress parameter χ, applying the unsaturated testing procedure, since unsaturated soils that have the three‐phase system exhibit complex mechanical behavior. Therefore, it seems more reasonable to use the empirical methods for estimation of χ. The objective of this study is to investigate the practicability of using artificial neural networks (ANNs) to model the complex relationship between basic soil parameters, matric suction and the parameter χ. Five ANN models with different input parameters were developed. Feed‐forward back propagation was applied in the analyses as a learning algorithm. The data collected from the available literature were used for training and testing the ANN models. Furthermore, unsaturated triaxial tests were carried out under drained condition on compacted specimens. ANN models were validated by a part of data sets collected from the literature and data obtained from the current study, which were not included in the training phase. The analyses showed that the results obtained from ANN models are in satisfactory agreement with the experimental results and ANNs can be used as reliable tool for prediction of χ. Copyright © 2007 John Wiley & Sons, Ltd.     

Effects of galvanic distortions on magnetotelluric data: Interpretation and its correction using deep electrical data

The non-inductive galvanic disturbances due to surficial bodies, lying smaller than high frequency skin depth, cause serious interpretational errors in magnetotelluric data. These frequency independent distortions result in a quasi-static shift between the apparent resistivity curves known as static shift. Two-dimensional modelling studies, for the effects of surficial bodies on magnetotelluric interpretation, show that the transverse electric (TE) mode apparent resistivity curves are hardly affected compared to the transverse magnetic (TM) mode curves, facilitating the correction by using a curve shifting method to match low frequency asymptotes. But in the case of field data the problem is rather complicated because of the random distribution of geometry and conductivity of near surface inhomogeneities. Here we present the use of deep resistivity sounding (DRS) data to constrain MT static shift. Direct current sensitivity studies show that the behaviour of MT static shift can be estimated using DC resistivity measurements close to the MT sounding station to appreciable depths. The distorted data set is corrected using the MT response for DRS model and further subject to joint inversion with DRS data. Joint inversion leads to better estimation of MT parameters compared to the separate inversion of data sets.     

Modeling the cyclic swelling pressure of mudrock using artificial neural networks

The stochastic nature of the cyclic swelling behavior of mudrock and its dependence on a large number of interdependent parameters was modeled using Time Delay Neural Networks (TDNNs). This method has facilitated predicting cyclic swelling pressure with an acceptable level of accuracy where developing a general mathematical model is almost impossible. A number of total pressure cells between shotcrete and concrete walls of the powerhouse cavern at Masjed–Soleiman Hydroelectric Powerhouse Project, South of Iran, where mudrock outcrops, confirmed a cyclic swelling pressure on the lining since 1999. In several locations, small cracks are generated which has raised doubts about long term stability of the powerhouse structure. This necessitated a study for predicting future swelling pressure. Considering the complexity of the interdependent parameters in this problem, TDNNs proved to be a powerful tool. The results of this modeling are presented in this paper.     

Applicability of artificial neural networks for obtaining velocity models from synthetic seismic data

Seismic velocity analysis is a crucial part of seismic data processing and interpretation which has been practiced using different methods. In contrast to time consuming and complicated numerical methods, artificial neural networks (ANNs) are found to be of potential applicability. ANN ability to establish a relationship between an input and output space is considered to be appropriate for mapping seismic velocity corresponding to travel times picked from seismograms. Accordingly a preliminary attempt is made to evaluate the applicability of ANNs to determine velocity and dips of dipping layered earth models corresponding to travel time data. The study is based on synthetic data generated using inverse modeling approach for three earth models. The models include a three-layer structure with same dips and same directions, a three-layer model with different dips and same directions, as well as a two-layer model with different dips and directions. An ANN structure is designed in three layers, namely, input, output, and hidden ones. The training and testing process of the ANN is successfully accomplished using the synthetic data. The evaluation of the applicability of the trained ANN to unknown data sets indicates that the ANN can satisfactorily compute velocity and dips corresponding to travel times. The error intervals between the desired and calculated velocity and dips are shown to be acceptably small in all cases. The applicability of the trained ANN in extrapolating is also evaluated using a number of data outside of the range already known to ANN. The results indicate that the trained ANN acceptably approximates the velocity and dips. Furthermore, the trained ANN is also evaluated in terms of capability of handling deficiency in input data where acceptable results were also achieved in velocity and dip calculations. Generally, this study shows that velocity analysis using ANNs can promisingly tackle the challenge of retrieving an initial velocity model from the travel time hyperbolas of seismic data.     

Normalized impedance function and the straightforward inversion scheme for magnetotelluric data

This paper investigates the performance of normalized response function obtained by normalizing the Cagniard impedance function by a suitable factor and then rotating the phase by 45‡ to make it purely real for homogeneous half-space and equal to the square root of the half-space resistivity. Two apparent resistivity functions based on respectively the real and imaginary parts of this response function are proposed. The apparent resistivity function using the real part contains almost the same information as that yielded by the Cagniard expression while the one using the imaginary part qualitatively works as an indicator of the number of interfaces in the earth model. The linear straightforward inversion scheme (SIS), developed by the authors employing the concept of equal penetration layers, has been used to validate the proposed apparent resistivity functions. For this purpose, several synthetic and field models have been examined. Five synthetic models are studied to establish the veracity of the new functions and two well-studied published field data sets are inverted through SIS for comparison. We noticed that the new function and SIS compliment each other and lead to better understanding of the data information and model resolution.     

Estimation of soil compaction parameters by using statistical analyses and artificial neural networks

This study presents the application of different methods (simple–multiple analysis and artificial neural networks) for the estimation of the compaction parameters (maximum dry unit weight and optimum moisture content) from classification properties of the soils. Compaction parameters can only be defined experimentally by Proctor tests. The data collected from the dams in some areas of Nigde (Turkey) were used for the estimation of soil compaction parameters. Regression analysis and artificial neural network estimation indicated strong correlations (r ² = 0.70–0.95) between the compaction parameters and soil classification properties. It has been shown that the correlation equations obtained as a result of regression analyses are in satisfactory agreement with the test results. It is recommended that the proposed correlations will be useful for a preliminary design of a project where there is a financial limitation and limited time.     

Predicting standard penetration test N-value from cone penetration test data using artificial neural networks

Standard Penetration Test(SPT) and Cone Penetration Test(CPT) are the most frequently used field tests to estimate soil parameters for geotechnical analysis and design.Numerous soil parameters are related to the SPT N-value.In contrast,CPT is becoming more popular for site investigation and geotechnical design.Correlation of CPT data with SPT N-value is very beneficial since most of the field parameters are related to SPT N-values.A back-propagation artificial neural network(ANN) model was developed to predict the N6o-value from CPT data.Data used in this study consisted of 109 CPT-SPT pairs for sand,sandy silt,and silty sand soils.The ANN model input variables are:CPT tip resistance(q_c),effective vertical stress(σ'_v),and CPT sleeve friction(f_s).A different set of SPT-CPT data was used to check the reliability of the developed ANN model.It was shown that ANN model either under-predicted the N_(60)-value by 7-16%or over-predicted it by 7-20%.It is concluded that back-propagation neural networks is a good tool to predict N_(60)-value from CPT data with acceptable accuracy.     

Prediction of ground subsidence in Samcheok City,Korea using artificial neural networks and GIS

This study shows the construction of a hazard map for presumptive ground subsidence around abandoned underground coal mines (AUCMs) at Samcheok City in Korea using an artificial neural network, with a geographic information system (GIS). To evaluate the factors governing ground subsidence, an image database was constructed from a topographical map, geological map, mining tunnel map, global positioning system (GPS) data, land use map, digital elevation model (DEM) data, and borehole data. An attribute database was also constructed by employing field investigations and reinforcement working reports for the existing ground subsidence areas at the study site. Seven major factors controlling ground subsidence were determined from the probability analysis of the existing ground subsidence area. Depth of drift from the mining tunnel map, DEM and slope gradient obtained from the topographical map, groundwater level and permeability from borehole data, geology and land use. These factors were employed by with artificial neural networks to analyze ground subsidence hazard. Each factor’s weight was determined by the back-propagation training method. Then the ground subsidence hazard indices were calculated using the trained back-propagation weights, and the ground subsidence hazard map was created by GIS. Ground subsidence locations were used to verify results of the ground subsidence hazard map and the verification results showed 96.06% accuracy. The verification results exhibited sufficient agreement between the presumptive hazard map and the existing data on ground subsidence area. An erratum to this article can be found at     

Prediction of surface crown pillar stability using artificial neural networks

A relatively novel technique, artificial neural networks (ANN), is used in predicting the stability of crown pillars left over large excavations. Data for the training and verification of the networks were obtained from the literature. Four artificial networks, based on two different architectures, were used. The networks used different numbers of input parameters to predict the stability or failure of crown pillars. Multi‐layer perceptron networks using mine type, dip of orebody, overburden thickness, pillar thickness, pillar length, stope height, backfill height, Rock Mass Rating (RMR) of the host rock and RMR of the orebody showed excellent performance in training and verification. Adding three more variables, namely pillar width, rock density and pillar thickness to width ratio, showed symptoms of over‐learning without degrading performance significantly. Radial basis function networks were capable of predicting crown pillar behaviour on the basis of few input functions. It was shown that mine type, dip and pillar thickness to width ratio can be used for a preliminary estimation of stability. Copyright © 2006 John Wiley & Sons, Ltd.     

长断面高密度电阻率数据处理解释

为了提高同一测线多个相邻断面的高密度电阻率数据处理解释的效率,这里通过将多个相邻的数据断面进行数据拼接、二维插值及反演处理,最终输出整个断面的电阻率二维反演数据文件,并采用Surfer软件绘图,使得数据分析和解释方便快捷,工作效率大大提高。通过对实测数据进行处理,验证了编制的处理软件可以在实际中推广使用。     

Modeling and prediction of ventilation methane emissions of U.S. longwall mines using supervised artificial neural networks

Methane emissions from a longwall ventilation system are an important indicator of how much methane a particular mine is producing and how much air should be provided to keep the methane levels under statutory limits. Knowing the amount of ventilation methane emission is also important for environmental considerations and for identifying opportunities to capture and utilize the methane for energy production.Prediction of methane emissions before mining is difficult since it depends on a number of geological, geographical, and operational factors. This study proposes a principle component analysis (PCA) and artificial neural network (ANN)-based approach to predict the ventilation methane emission rates of U.S. longwall mines.Ventilation emission data obtained from 63 longwall mines in 10 states for the years between 1985 and 2005 were combined with corresponding coalbed properties, geographical information, and longwall operation parameters. The compiled database resulted in 17 parameters that potentially impacted emissions. PCA was used to determine those variables that most influenced ventilation emissions and were considered for further predictive modeling using ANN. Different combinations of variables in the data set and network structures were used for network training and testing to achieve minimum mean square errors and high correlations between measurements and predictions. The resultant ANN model using nine main input variables was superior to multilinear and second-order non-linear models for predicting the new data. The ANN model predicted methane emissions with high accuracy. It is concluded that the model can be used as a predictive tool since it includes those factors that influence longwall ventilation emission rates.     

起伏地形下偶极-偶极激电测深二维反演软件开发及应用

在有限元点源二维正演模拟中,采用第二类齐次边界条件结合相应的网格剖分技术,并将二维正演模拟程序引入到偶极-偶极激电测深二维反演中,编制相应反演程序并进行集成,形成集数据输入、反演计算、数据成图和结果输出为一体的二维反演软件。通过对起伏地形下的两个理论模型合成数据及一例实测数据的反演,反演结果表明反演软件有效。     

电测深法岩溶路基注浆质量检测模型试验研究

针对岩溶路基注浆质量检测的难度,基于现场典型地质断面,建立室内裂隙灰岩模型,进行注浆前后多测试断面的电测深检测试验数据解释分析,并对比模型中实际浆液扩散情况。试验结果表明:水泥浆液注入后,模型视电阻率较注浆前明显降低,当注浆后视电阻率降低幅度大于等于15%时,注浆效果较好。模型注浆后的合理检测时间为注浆完7天后。现场检测试验基本能够验证模型试验结论,但现场检测试验受地下水位的影响较大,会造成局部评判失真。     

Prediction of uniaxial compression PFC3D model micro‐properties using artificial neural networks

Artificial neural networks are used to predict the micro‐properties of particle flow code in three dimensions (PFC3D) models needed to reproduce macro‐properties of cylindrical rock samples in uniaxial compression tests. Data for the training and verification of the networks were obtained by running a large number of PFC3D models and observing the resulting macro‐properties. Four artificial networks based on two different architectures were used. The networks used different numbers of input parameters to predict the micro‐properties. Multi‐layer perceptron networks using Young's modulus, Poisson's ratio, uniaxial compressive strength, model particle resolution and the maximum‐to‐minimum particle ratio showed excellent performance in both training and verification. Adding one more variable—namely, minimum particle radius—showed degrading performance. Copyright © 2009 John Wiley & Sons, Ltd.     

自适应BP算法及其在河道洪水预报上的应用

提出一种改进的BP算法,即自适应BP算法。该方法采用两种策略:一是在权重修改公式中加动量项;二是学习率随总误差的变化作自适应调整,亦即总误差增加时,学习率将减小,反之学习率增大。以上两种策略能有效的抑制网络陷于局部极小并缩短了学习时间。实例研究表明,该算法用于河道洪水的预报,能取得令人满意的结果。     

不同结构深度神经网络的时间域航空电磁数据成像性能分析

时间域航空电磁系统采样密集,数据量大,所以在该领域较为实用的数据处理方法主要为一维反演和电阻率成像法。笔者从成像问题出发,建立了庞大的数据模型训练集,研究并分析了不同结构的神经网络的成像精度。通过对比分析测试结果,获得了在一定条件下适用于航空电磁成像的最优网络模型结构,包含其神经元个数和层数等信息。本文采用早停法训练神经网络,压制数据中噪声对成像结果的影响。     

Forecasting shield pressures at a longwall face using artificial neural networks

Leg pressure data from all shields of a longwall face are monitored and recorded in the surface computer. An algorithm is developed to detect peak pressures or periodic roof weightings from these pressure data. The intensities and locations of periodic roof weighting are further analyzed using artificial neural network for forecasting of forthcoming shield pressures. The network was trained using data 153 m (500 ft) of face advance. Shield pressures are forecasted for the successive nine mining cycles or approximately 9 m of face advancement. The results obtained validate the efficacy of the developed model.     

起伏地形条件下电阻率法三维反演

地形起伏下的三维反演问题是当前电阻率法研究的一个难题.为了更好地解决上述问题,采用加权正则化共轭梯度法实现起伏地形三维电阻率反演算法.该方法引入了加权正则化思想,显著降低了迭代时目标函数发散的问题,提高了反演稳定性.对比了两种消除反演中地形影响的方法,结果表明,直接带地形的电阻率法三维反演具有更好的分辨率,能有效地消除地形所造成的误差,但在起伏角度偏大,如河流、堤坝等接近垂直角度时,使用此方法会使得反演发散得不到满意的结果;此时采用基于地形校正的方法有一定的效果.