Using Artificial Neural Networks to Predict the Presence of Overpressured Zones in the Anadarko Basin,Oklahoma

Many sedimentary basins throughout the world exhibit areas with abnormal pore-fluid pressures (higher or lower than normal or hydrostatic pressure). Predicting pore pressure and other parameters (depth, extension, magnitude, etc.) in such areas are challenging tasks. The compressional acoustic (sonic) log (DT) is often used as a predictor because it responds to changes in porosity or compaction produced by abnormal pore-fluid pressures. Unfortunately, the sonic log is not commonly recorded in most oil and/or gas wells. We propose using an artificial neural network to synthesize sonic logs by identifying the mathematical dependency between DT and the commonly available logs, such as normalized gamma ray (GR) and deep resistivity logs (REID). The artificial neural network process can be divided into three steps: (1) Supervised training of the neural network; (2) confirmation and validation of the model by blind-testing the results in wells that contain both the predictor (GR, REID) and the target values (DT) used in the supervised training; and 3) applying the predictive model to all wells containing the required predictor data and verifying the accuracy of the synthetic DT data by comparing the back-predicted synthetic predictor curves (GRNN, REIDNN) to the recorded predictor curves used in training (GR, REID). Artificial neural networks offer significant advantages over traditional deterministic methods. They do not require a precise mathematical model equation that describes the dependency between the predictor values and the target values and, unlike linear regression techniques, neural network methods do not overpredict mean values and thereby preserve original data variability. One of their most important advantages is that their predictions can be validated and confirmed through back-prediction of the input data. This procedure was applied to predict the presence of overpressured zones in the Anadarko Basin, Oklahoma. The results are promising and encouraging.     

潜在地震灾害预测和评价神经网络系统及其若干问题

结合人工神经网络自身的特性和地震灾害预测研究的特点,本文应用神经网络模型,建立了潜在地震灾害预测和评价系统。针对网络模型参数设置、数据归一化、中间层神经元最优数目以及泛化分类评价指标等若干实际问题给出了实际可行的解决方案。通过大样本数据对网络的训练,形成了有识别和记忆功能的非线性预测和评价系统。对网络的测试和检验,论证了该系统在预测潜在地震灾害上的可行性和有效性。同时,从测试精度出发,探讨了这种预测网络存在的不足,并给出了相应的改进建议,为开展进一步的研究工作提供了参考。     

利用系统工程促进地震预报工作的快速发展

地震一直是影响人类生存的天灾,尤其是大的地震。30年前的唐山大地震记忆犹新,2008年的汶川特大地震更是震惊中外,其损失之大、牺牲之惨烈、可歌可泣的事迹让我们流泪、让我们感动、终身难忘。痛定思痛,我们一定要切实加强地震预报工作。由于地震预报工作涉及的学科很多,涉及的面又相当广,例如,这次电磁会议上所报告的,在地震前是有一些预兆的,它是个复杂系统,需要有系统的思想,并可以利用系统工程。     

人工神经网络在地震中短期预报中的应用

本文将ＢＰ神经网络用于地震中短期预报。作者把一些常用的地震学指标作为神经网络的输入,而将ＢＰ神经网络的输出作为表征地震活动平静的特征参数Ｗｑ,井将其用于华北地区进行空间扫描,结果表明中强地震前１年左右或稍长时间,未来震中周围一般都开始出现Ｗｑ值的中短期异常区,证明本方法具有限好的中短期预报效果。     

Climatological Tools for Low Visibility Forecasting

Forecasters need climatological forecasting tools because of limitations of numerical weather prediction models. In this article, using Finnish SYNOP observations and ERA-40 model reanalysis data, low visibility cases are studied using subjective and objective analysis techniques. For the objective analysis, we used an AutoClass clustering algorithm, concentrating on three Finnish airports, namely, the Rovaniemi in northern Finland, Kauhava in western Finland, and Maarianhamina in southwest Finland. These airports represent different climatological conditions. Results suggested that combining of subjective analysis with an objective analysis, e.g., clustering algorithms such as the AutoClass method, can be used to construct climatological guides for forecasters. Some higher level subjective “meta-clustering” was used to make the results physically more reasonable and easier to interpret by the forecasters.     

Performance of an artificial neural network on forecasting the daily occurrence and annual depth of rainfall at a tropical site

Performance of a feed‐forward back‐propagation artificial neural network on forecasting the daily occurrence and annual depth of rainfall at a single meteorological station is presented. Both short‐term and long‐term forecasting was attempted, with ground level data collected by the meteorological station in Colombo, Sri Lanka (79° 52′E, 6° 54′N) during two time periods, 1994–2003 and 1869–2003. Two neural network models were developed; a one‐day‐ahead model for predicting the rainfall occurrence of the next day, which was able to make predictions with a 74·3% accuracy, and one‐year‐ahead model for yearly rainfall depth predictions with an 80·0% accuracy within a ± 5% error bound. Each of these models was extended to make predictions several time steps into the future, where accuracies were found to decrease rapidly with the number of time steps. The success rates and rainfall variability within the north‐east and south‐west monsoon seasons are also discussed. Copyright © 2007 John Wiley & Sons, Ltd.     

Forecasting flows in Apalachicola River using neural networks

Forecasting river flow is important to water resources management and planning. In this study, an artificial neural network (ANN) model was successfully developed to forecast river flow in Apalachicola River. The model used a feed‐forward, back‐propagation network structure with an optimized conjugated training algorithm. Using long‐term observations of rainfall and river flow during 1939–2000, the ANN model was satisfactorily trained and verified. Model predictions of river flow match well with the observations. The correlation coefficients between forecasting and observation for daily, monthly, quarterly and yearly flow forecasting are 0·98, 0·95, 0·91 and 0·83, respectively. Results of the forecasted flow rates from the ANN model were compared with those from a traditional autoregressive integrated moving average (ARIMA) forecasting model. Results indicate that the ANN model provides better accuracy in forecasting river flow than does the ARIMA model. Copyright © 2004 John Wiley & Sons, Ltd.     

Neural network methods in hydrodynamic yield estimation

Hydrodynamic theory allows us to use the speed of a shock wave front to determine the yield of an explosion. On the basis of this theory we developed a neural network to estimate a yield of underground explosions from the shock wave radius versus time (RVT) data, as measured by continuous reflectometry for radius versus time experiments (CORRTEX). The proposed method not only replaces the subjective elements of conventional algorithms, but produces significantly improved yield estimates. The network was trained with real hydrodynamic data and its performance on unseen test events was studied. A backpropagation network was employed; the architecture consisted of ten input units, a hidden layer with eleven hidden units, and one output unit. The network was trained with thousands of input-output measurement vectors, the feasible input set, derived from the RVT data from only four other training or standard events (selected on the basis of the given RVT data from the unknown event). The feasible input vectors were propagated through the trained network and the network output was used to select the optimum yield estimate. Elements of the input vector were: center of energy (COE) offsets, shock front radii, and time onset and interval of analysis for both the standard and unknown events. We studied the performance of the proposed system using 24 Nevada Test Site (NTS) events that were located in the geologic medium tuff. Sensitivity analysis of the proposed method to the assumed nominal COE offset is discussed. Variations of the proposed system that might lead to further improvements in performance are suggested.     

Estimation of Mining Tremor Occurrence by Using Neural Networks

—Changes of the primary strain-stress state (caused by interaction between natural conditions and mining activity) can result, under special circumstances, to the origin of seismic induced events. The question of induced seismic activity prediction was treated as a problem of time series extrapolation of maximum cumulative amplitudes and numbers of seismic events recorded per day. The treatment was carried out by means of Multilayered Perceptron Neural Networks (MLP NN). The application to mining tremor prediction has been tested and methodological conditions have been obtained. It was proved that the prediction of the number of mining tremors per day is more precise than the prediction of future energy (maximum amplitudes). Further advance, based on the processing of seismo-acoustic activity series, is introduced.     

Long‐lead seasonal rainfall forecasting using time‐delay recurrent neural networks: a case study

A temporal artificial neural network‐based model is developed and applied for long‐lead rainfall forecasting. Tapped delay lines and recurrent connections are two different components that are used along with a static multilayer perceptron network to design a time‐delay recurrent neural network. The proposed model is, in fact, a combination of time‐delay and recurrent neural networks. The model is applied in three case studies of the Northwest, West, and Southwest basins of Iran. In addition, an autoregressive moving average with exogenous inputs (ARMAX) model is used as a baseline in order to be compared with the time‐delay recurrent neural networks developed in this study. Large‐scale climate signals, such as sea‐level pressure, that affect the rainfall of the study area are used as the predictors in the models, as well as the persistence between rainfall data. The results of winter‐spring rainfall forecasts are discussed thoroughly. It is demonstrated that in all cases the proposed neural network results in better forecasts in comparison with the statistical ARMAX model. Moreover, it is found that in two of three case studies the time‐delay recurrent neural networks perform better than either recurrent or time‐delay neural networks. The results demonstrate that the proposed method can significantly improve the long‐lead forecast by utilizing a non‐linear relationship between climatic predictors and rainfall in a region. Copyright © 2007 John Wiley & Sons, Ltd.     

基于深度卷积神经网络的剪切波分裂质量检测

剪切波分裂是分析地震各向异性的一种重要手段,常规方法是利用网格搜索获取分裂参数,再通过不同方法的测量结果对比测量结果进行质量检测,这一过程会耗费大量计算时间。本文针对这一问题提出了一种利用深度卷积神经网络对剪切波分裂进行质量检测的新方法,对使用了Resnet残差结构的深度神经网络进行训练,直接对二分量剪切波波形数据的质量进行分类。整个过程为:神经网络通过卷积层提取波形特征,计算损失函数后反向传播训练模型参数,完成迭代训练后的模型对输入波形数据正向计算自动输出类型。本文利用川西台站接收到的实际数据以及随机生成的合成数据分别对该网络进行训练,均可以获得准确的分类结果。相比于通过多种剪切波分裂方法对比测量结果的质量检测方法,基于神经网络的方法可以省略网格搜索的计算过程直接判断质量类型,在运算速度上的优势明显,并可继续通过训练提高模型的精度,为提升剪切波分裂方法在数据处理过程中的操作效率提供帮助。     

使用Mf值,C值,D值异常资料进行地震短期预测的神经网络方法

介绍了神经网络的一些基本概念，ＢＰ神经网络及其算法，使用地震强度因子Ｍｆ值，地震空间集中度Ｃ值，地震危险度Ｄ值对华北地区１９７２￣１９９２年期间进行空间扫描的中期和短期异常资料，通过ＢＰ神经网络进行学习并进行地震短期预测。研究结果表明：利用这３类资料的多项因子进行短期预测的效果较为理想。文章还对使用ＢＰ神经网络的一些具体问题进行了讨论。     

爆破震害预测的神经网络模型

人工神经网络是近期发展最快的人工智能领域研究成果之一.通过分析国内外爆破震害预测研究现状和不足,提出一种基于BP神经网络模型的爆破地震效应预测方法,该方法能克服基于最小二乘法的回归公式的局限性,可选取影响爆破振动的多个影响因素作为输入层参数,达到爆破峰值和主频同步预测之目的.利用该方法对实际爆破监测数据进行预测,结果表明人工神经网络方法在爆破地震效应预测中应用是可行的并且是有效的.这为爆破震害预测研究提供了新途径.     

Genetic programming model for forecast of short and noisy data

Though forecasting of river flow has received a great deal of attention from engineers and researchers throughout the world, this still continues to be a challenging task owing to the complexity of the process. In the last decade or so, artificial neural networks (ANNs) have been widely applied, and their ability to model complex phenomena has been clearly demonstrated. However, the success of ANNs depends very crucially on having representative records of sufficient length. Further, the forecast accuracy decreases rapidly with an increase in the forecast horizon. In this study, the use of the Darwinian theory‐based recent evolutionary technique of genetic programming (GP) is suggested to forecast fortnightly flow up to 4‐lead. It is demonstrated that short lead predictions can be significantly improved from a short and noisy time series if the stochastic (noise) component is appropriately filtered out. The deterministic component can then be easily modelled. Further, only the immediate antecedent exogenous and/or non‐exogenous inputs can be assumed to control the process. With an increase in the forecast horizon, the stochastic components also play an important role in the forecast, besides the inherent difficulty in ascertaining the appropriate input variables which can be assumed to govern the underlying process. GP is found to be an efficient tool to identify the most appropriate input variables to achieve reasonable prediction accuracy for higher lead‐period forecasts. A comparison with ANNs suggests that though there is no significant difference in the prediction accuracy, GP does offer some unique advantages. Copyright © 2006 John Wiley & Sons, Ltd.     

地震黄土滑坡滑距预测的BP神经网络模型

地震滑坡的滑距与重力滑坡的滑距有着显著的不同,科学预测地震发生时黄土地区滑坡的滑动距离是合理评估黄土地区滑坡风险和减轻滑坡灾害的有效方式之一。基于海原特大地震诱发黄土滑坡的400组野外调查数据,通过引入BP神经网络算法,论证了BP神经网络模型用于预测黄土地震滑坡滑距的适宜性和可行性;建立了地震诱发黄土滑坡滑距的BP神经网络预测模型,并通过67组数据进行了验证。BP神经网络算法和传统多元线性回归、多元非线性回归结果的对比显示,BP神经网络的预测更接近真实情况,具有较为理想的预测效果,可以用于黄土地震滑坡滑距的预测,并为圈定较为可靠的致灾范围提供依据。     

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Abstract

A reliable flood warning system depends on efficient and accurate forecasting technology. A systematic investigation of three common types of artificial neural networks (ANNs) for multi-step-ahead (MSA) flood forecasting is presented. The operating mechanisms and principles of the three types of MSA neural networks are explored: multi-input multi-output (MIMO), multi-input single-output (MISO) and serial-propagated structure. The most commonly used multi-layer feed-forward networks with conjugate gradient algorithm are adopted for application. Rainfall—runoff data sets from two watersheds in Taiwan are used separately to investigate the effectiveness and stability of the neural networks for MSA flood forecasting. The results indicate consistently that, even though the MIMO is the most common architecture presented in ANNs, it is less accurate because its multi-objectives (predicted many time steps) must be optimized simultaneously. Both MISO and serial-propagated neural networks are capable of performing accurate short-term (one- or two-step-ahead) forecasting. For long-term (more than two steps) forecasts, only the serial-propagated neural network could provide satisfactory results in both watersheds. The results suggest that the serial-propagated structure can help in improving the accuracy of MSA flood forecasts.     

An ANN‐based model for spatiotemporal groundwater level forecasting

This paper evaluates the feasibility of using an artificial neural network (ANN) methodology for estimating the groundwater levels in some piezometers placed in an aquifer in north‐western Iran. This aquifer is multilayer and has a high groundwater level in urban areas. Spatiotemporal groundwater level simulation in a multilayer aquifer is regarded as difficult in hydrogeology due to the complexity of the different aquifer materials. In the present research the performance of different neural networks for groundwater level forecasting is examined in order to identify an optimal ANN architecture that can simulate the piezometers water levels. Six different types of network architectures and training algorithms are investigated and compared in terms of model prediction efficiency and accuracy. The results of different experiments show that accurate predictions can be achieved with a standard feedforward neural network trained usung the Levenberg–Marquardt algorithm. The structure and spatial regressions of the ANN parameters (weights and biases) are then used for spatiotemporal model presentation. The efficiency of the spatio‐temporal ANN (STANN) model is compared with two hybrid neural‐geostatistics (NG) and multivariate time series‐geostatistics (TSG) models. It is found in this study that the ANNs provide the most accurate predictions in comparison with the other models. Based on the nonlinear intrinsic ANN approach, the developed STANN model gives acceptable results for the Tabriz multilayer aquifer. Copyright © 2008 John Wiley & Sons, Ltd.     

Daily reservoir inflow forecasting using artificial neural networks with stopped training approach

In this paper, an early stopped training approach (STA) is introduced to train multi-layer feed-forward neural networks (FNN) for real-time reservoir inflow forecasting. The proposed method takes advantage of both Levenberg–Marquardt Backpropagation (LMBP) and cross-validation technique to avoid underfitting or overfitting on FNN training and enhances generalization performance. The methodology is assessed using multivariate hydrological time series from Chute-du-Diable hydrosystem in northern Quebec (Canada). The performance of the model is compared to benchmarks from a statistical model and an operational conceptual model. Since the ultimate goal concerns the real-time forecast accuracy, overall the results show that the proposed method is effective for improving prediction accuracy. Moreover it offers an alternative when dynamic adaptive forecasting is desired.