Stochastic seismic inversion and Bayesian facies classification applied to porosity modeling and igneous rock identification

We apply stochastic seismic inversion and Bayesian facies classification for porosity modeling and igneous rock identification in the presalt interval of the Santos Basin. This integration of seismic and well-derived information enhances reservoir characterization. Stochastic inversion and Bayesian classification are powerful tools because they permit addressing the uncertainties in the model. We used the ES-MDA algorithm to achieve the realizations equivalent to the percentiles P10, P50, and P90 of acoustic impedance, a novel method for acoustic inversion in presalt. The facies were divided into five: reservoir 1,reservoir 2, tight carbonates, clayey rocks, and igneous rocks. To deal with the overlaps in acoustic impedance values of facies, we included geological information using a priori probability, indicating that structural highs are reservoir-dominated. To illustrate our approach, we conducted porosity modeling using facies-related rock-physics models for rock-physics inversion in an area with a well drilled in a coquina bank and evaluated the thickness and extension of an igneous intrusion near the carbonate-salt interface. The modeled porosity and the classified seismic facies are in good agreement with the ones observed in the wells. Notably, the coquinas bank presents an improvement in the porosity towards the top. The a priori probability model was crucial for limiting the clayey rocks to the structural lows. In Well B, the hit rate of the igneous rock in the three scenarios is higher than 60%, showing an excellent thickness-prediction capability.

Quantitative Method of Classification and Discrimination of a Porous Carbonate Reservoir Integrating K-means Clustering and Bayesian Theory

Reservoir classification is a key link in reservoir evaluation.However,traditional manual means are inefficient,subjective,and classification standards are not uniform.Therefore,taking the Mishrif Formation of the Western Iraq as an example,a new reservoir classification and discrimination method is established by using the K-means clustering method and the Bayesian discrimination method.These methods are applied to non-cored wells to calculate the discrimination accuracy of the reservoir type,and thus the main reasons for low accuracy of reservoir discrimination are clarified.The results show that the discrimination accuracy of reservoir type based on K-means clustering and Bayesian stepwise discrimination is strongly related to the accuracy of the core data.The discrimination accuracy rate of TypeⅠ,TypeⅡ,and TypeⅤreservoirs is found to be significantly higher than that of TypeⅢand TypeⅣreservoirs using the method of combining K-means clustering and Bayesian theory based on logging data.Although the recognition accuracy of the new methodology for the TypeⅣreservoir is low,with average accuracy the new method has reached more than 82%in the entire study area,which lays a good foundation for rapid and accurate discrimination of reservoir types and the fine evaluation of a reservoir.

Agricultural Product Quality Mining Based on Bayesian Classification

Combined with the characteristics of crop growth and environmental data and the basic principle of Bayesian algorithm,the crop product quality is analyzed and forecasted in this study.Test with a randomly selected sample group ensures high forecasting accuracy,which shows that the algorithm is effective.

Modulation classification of MPSK signals based on nonparametric Bayesian inference

A nonparametric Bayesian method is presented to classify the MPSK （M-ary phase shift keying） signals. The MPSK signals with unknown signal noise ratios （SNRs） are modeled as a Gaussian mixture model with unknown means and covariances in the constellation plane, and a clustering method is proposed to estimate the probability density of the MPSK signals. The method is based on the nonparametric Bayesian inference, which introduces the Dirichlet process as the prior probability of the mixture coefficient, and applies a normal inverse Wishart （NIW） distribution as the prior probability of the unknown mean and covariance. Then, according to the received signals, the parameters are adjusted by the Monte Carlo Markov chain （MCMC） random sampling algorithm. By iterations, the density estimation of the MPSK signals can be estimated. Simulation results show that the correct recognition ratio of 2/4/8PSK is greater than 95% under the condition that SNR 〉5 dB and 1 600 symbols are used in this method.

Improvement of X-Band Polarization Radar Melting Layer Recognition by the Bayesian Method and ITS Impact on Hydrometeor Classification

Using melting layer(ML)and non-melting layer(NML)data observed with the X-band dual linear polarization Doppler weather radar(X-POL)in Shunyi,Beijing,the reflectivity(ZH),differential reflectivity(ZDR),and correlation coefficient(CC)in the ML and NML are obtained in several stable precipitation processes.The prior probability density distributions(PDDs)of the ZH,ZDR and CC are calculated first,and then the probabilities of ZH,ZDR and CC at each radar gate are determined(PBB in the ML and PNB in the NML)by the Bayesian method.When PBB>PNB the gate belongs to the ML,and when PBB<PNB the gate belongs to the NML.The ML identification results with the Bayesian method are contrasUsing melting layer(ML)and non-melting layer(NML)data observed with the X-band dual linear polarization Doppler weather radar(X-POL)in Shunyi,Beijing,the reflectivity(ZH),differential reflectivity(ZDR),and correlation coefficient(CC)in the ML and NML are obtained in several stable precipitation processes.The prior probability density distributions(PDDs)of the ZH,ZDR and CC are calculated first,and then the probabilities of ZH,ZDR and CC at each radar gate are determined(PBB in the ML and PNB in the NML)by the Bayesian method.When PBB>PNB the gate belongs to the ML,and when PBB<PNB the gate belongs to the NML.The ML identification results with the Bayesian method are contrasted under the conditions of the independent PDDs and joint PDDs of the ZH,ZDR and CC.The results suggest that MLs can be identified effectively,although there are slight differences between the two methods.Because the values of the polarization parameters are similar in light rain and dry snow,it is difficult for the polarization radar to distinguish them.After using the Bayesian method to identify the ML,light rain and dry snow can be effectively separated with the X-POL observed data.ted under the conditions of the independent PDDs and joint PDDs of the ZH,ZDR and CC.The results suggest that MLs can be identified effectively,although there are slight differences between the two methods.Because the values of the polarization parameters are similar in light rain and dry snow,it is difficult for the polarization radar to distinguish them.After using the Bayesian method to identify the ML,light rain and dry snow can be effectively separated with the X-POL observed data.

A Survey on Acute Leukemia Expression Data Classification Using Ensembles

Acute leukemia is an aggressive disease that has high mortality rates worldwide.The error rate can be as high as 40%when classifying acute leukemia into its subtypes.So,there is an urgent need to support hematologists during the classification process.More than two decades ago,researchers used microarray gene expression data to classify cancer and adopted acute leukemia as a test case.The high classification accuracy they achieved confirmed that it is possible to classify cancer subtypes using microarray gene expression data.Ensemble machine learning is an effective method that combines individual classifiers to classify new samples.Ensemble classifiers are recognized as powerful algorithms with numerous advantages over traditional classifiers.Over the past few decades,researchers have focused a great deal of attention on ensemble classifiers in a wide variety of fields,including but not limited to disease diagnosis,finance,bioinformatics,healthcare,manufacturing,and geography.This paper reviews the recent ensemble classifier approaches utilized for acute leukemia gene expression data classification.Moreover,a framework for classifying acute leukemia gene expression data is proposed.The pairwise correlation gene selection method and the Rotation Forest of Bayesian Networks are both used in this framework.Experimental outcomes show that the classification accuracy achieved by the acute leukemia ensemble classifiers constructed according to the suggested framework is good compared to the classification accuracy achieved in other studies.

Liver Hydatid CT Image Segmentation Using Smoothed Bayesian Classification Method and Modified Parametric Active Contour Model

Liver hydatid disease is a common parasitic disease in farm and pastoral areas, which seriously influences people's health. Based on CT imaging features of this disease, an iterative approach for liver segmentation and hydatid lesion extraction simultaneously is proposed. In each iteration, our algorithm consists of two main steps: 1) according to the user-defined pixel seeds in the liver and hydatid lesion, Gaussian probability model fitting and smoothed Bayesian classification are applied to get initial segmentation of liver and lesion; 2) the parametric active contour model using priori shape force field is adopted to refine initial segmentation. We make subjective and objective evaluation on the proposed algorithm validity by the experiments of liver and hydatid lesion segmentation on different patients' CT slices. In comparison with ground-truth manual segmentation results, the experimental results show the effectiveness of our method to segment liver and hydatid lesion.

Naive Bayesian Classifier在遥感影像分类中的应用研究

将Naive Bayesian Classifier(简单贝叶斯网络分类器)用于遥感影像的分类,并对其主要问题如特征选择和后验概率推理等展开研究。使用K2结构学习算法选出具有类别可分性的波段,进一步利用互信息测试对遥感波段之间的相关性做分析,去除冗余信息。特征(波段)的条件独立性假设简化了联合概率的计算,以较小的计算代价获得后验概率。在此基础上,将Naive Bayesian Classifier用于多光谱和高光谱影像的分类,获得很好的性能和相当高的稳健性。

Accuracies and Training Times of Data Mining Classification Algorithms:An Empirical Comparative Study

Two important performance indicators for data mining algorithms are accuracy of classification/ prediction and time taken for training. These indicators are useful for selecting best algorithms for classification/prediction tasks in data mining. Empirical studies on these performance indicators in data mining are few. Therefore, this study was designed to determine how data mining classification algorithm perform with increase in input data sizes. Three data mining classification algorithms—Decision Tree, Multi-Layer Perceptron (MLP) Neural Network and Na&iuml;ve Bayes— were subjected to varying simulated data sizes. The time taken by the algorithms for trainings and accuracies of their classifications were analyzed for the different data sizes. Results show that Na&iuml;ve Bayes takes least time to train data but with least accuracy as compared to MLP and Decision Tree algorithms.

Energy enhanced tissue texture in spectral computed tomography for lesion classification

Tissue texture reflects the spatial distribution of contrasts of image voxel gray levels,i.e.,the tissue heterogeneity,and has been recognized as important biomarkers in various clinical tasks.Spectral computed tomography(CT)is believed to be able to enrich tissue texture by providing different voxel contrast images using different X-ray energies.Therefore,this paper aims to address two related issues for clinical usage of spectral CT,especially the photon counting CT(PCCT):(1)texture enhancement by spectral CT image reconstruction,and(2)spectral energy enriched tissue texture for improved lesion classification.For issue(1),we recently proposed a tissue-specific texture prior in addition to low rank prior for the individual energy-channel low-count image reconstruction problems in PCCT under the Bayesian theory.Reconstruction results showed the proposed method outperforms existing methods of total variation(TV),low-rank TV and tensor dictionary learning in terms of not only preserving texture features but also suppressing image noise.For issue(2),this paper will investigate three models to incorporate the enriched texture by PCCT in accordance with three types of inputs:one is the spectral images,another is the cooccurrence matrices(CMs)extracted from the spectral images,and the third one is the Haralick features(HF)extracted from the CMs.Studies were performed on simulated photon counting data by introducing attenuationenergy response curve to the traditional CT images from energy integration detectors.Classification results showed the spectral CT enriched texture model can improve the area under the receiver operating characteristic curve(AUC)score by 7.3%,0.42%and 3.0%for the spectral images,CMs and HFs respectively on the five-energy spectral data over the original single energy data only.The CM-and HF-inputs can achieve the best AUC of 0.934 and 0.927.This texture themed study shows the insight that incorporating clinical important prior information,e.g.,tissue texture in this paper,into the medical imaging,such as the upstream image reconstruction,the downstream diagnosis,and so on,can benefit the clinical tasks.

Classification and Prediction on Rural Property Mortgage Data with Three Data Mining Methods

The Farmers Property Mortgage Policy is a strategic financial policy in western China, a relatively underdeveloped region. Many contradictions and conflicts exist in the process between the strong demand for the loans by farmers and the strict risk control by the financial institutions. The rural finance corporations should use scientific analysis and investigation of the potential households for overall evaluation of the customers. These include historical credit rating, present family situation, and other related information. Three different data mining methods were applied in this paper to the specifically-collected household data. The objective was to study which factor could be the most important in determining loan demand for households, and in the meanwhile, to classify and predict the possibility of loan demand for the potential customers. The results obtained from the three methods indicated the similar outputs, income level, land area, the way of loan, and the understanding of policy were four main factors which decided the probability of one specific farmer applying for a credit loan. The results also embodied the difference within the three methods for classifying and predicting the loan anticipation for the testing households. The artificial neural network model had the highest accuracy of 91.4 which is better than the other two methods.

Bayesian Deep Learning Enabled Sentiment Analysis on Web Intelligence Applications

In recent times,web intelligence(WI)has become a hot research topic,which utilizes Artificial Intelligence(AI)and advanced information technologies on theWeb and Internet.The users post reviews on social media and are employed for sentiment analysis(SA),which acts as feedback to business people and government.Proper SA on the reviews helps to enhance the quality of the services and products,however,web intelligence techniques are needed to raise the company profit and user fulfillment.With this motivation,this article introduces a new modified pigeon inspired optimization based feature selection(MPIO-FS)with Bayesian deep learning(BDL),named MPIOBDL model for SA on WI applications.The presented MPIO-BDL model initially involved preprocessing and feature extraction take place using Term Frequency—Inverse Document Frequency(TF-IDF)technique to derive a useful set of information from the user reviews.Besides,the MPIO-FS model is applied for the selection of optimal feature subsets,which helps to enhance classification accuracy and reduce computation complexity.Moreover,the BDL model is employed to allocate the proper class labels of the applied user review data.A comprehensive experimental results analysis highlighted the improved classification efficiency of the presented model.

基于贝叶斯超参数优化的鲜香菇机器视觉图像分级识别

针对鲜香菇分级机械化程度低,精度不高等问题,本文提出1种基于贝叶斯超参数优化技术的鲜香菇机器视觉图像识别方法。利用摄像头拍摄鲜香菇图像,按人工分级标准对采样图像进行正反面标记分级,获取并标记了5级的鲜香菇图像,利用仿射变换和对比度变换的方法对获取的数据集进行扩充,建立各等级鲜香菇图像数据集;基于深度卷积神经网络,对3种预训练网络模型(AlexNet、GoogLeNet、ResNet-18)分别进行迁移学习,3种模型分别记为XGu_Ale、XGu_Goo和XGu_Res-18;使用贝叶斯优化算法对3种模型的香菇正反面数据集进行超参数优化,并分析了各个网络模型的测试结果。分析可知鲜香菇正面图像等级模型以Z-XGu_Res-18模型的识别准确率最高,鲜香菇反面图像等级模型以F-XGu_Res-18模型的识别准确率最高,准确率分别为98.73%和99.15%,选择以上2个模型可满足鲜香菇的分级要求,对正反面识别结果进行加权组合得到鲜香菇分级识别的最终等级。

基于客户消费习惯的贝叶斯网络的分类算法

面对大量用户的通信消费数据,如何对其进行挖掘从而获得有价值的信息,对客户进行分类,并制定不同的服务策略,是当前通信企业面临的一个普遍问题。本文介绍了几种常用的文本分类算法,通过分析用户的消费行为数据,选取了朴素贝叶斯分类预测算法、贝叶斯网络分类预测算法和决策树分类预测算法对用户进行分类,实验结果表明贝叶斯网络分类预测算法对于用户通信消费数据具有较好的分类效果。

基于ERF和BO-SVC的交流接触器触头故障识别方法

针对交流接触器各状态样本不均衡导致故障状态识别精度低和特征冗余度高的问题,文中提出一种基于嵌入式随机森林(embedded random forest,ERF)和贝叶斯优化非线性支持向量机(Bayesian optimization-support vector classification,BO-SVC)的复合识别方法。首先,通过交流接触器全寿命试验平台提取接触器状态特征,并针对各状态样本间不均衡导致识别精度低现象,提出一种基于权重法的样本均衡处理策略。然后,使用ERF对均衡后样本进行特征选择和降维,提取最能表征触头状态变化规律的最优特征。最后,将最优特征输入到BO-SVC识别模型,与另外2种代表性模型作为对比,以精确率、召回率和F1-分数3个指标对各模型性能进行评估。在3个指标上,文中方法的结果分别达到95.22%、98.91%和97.01%,均高于对比模型。以F1-分数为指标,在4组样本上对各模型性能进行测试,结果表明文中方法的F1-分数平均高出对比模型0.56%和27.28%,验证文中研究有效解决了交流接触器特征冗余和故障识别精度低的问题。

基于人脸表情特征的高校课堂教学质量在线评估模型

针对高校课堂教学质量在线评估模型识别率较低,评估过程主观性较强的问题,提出基于人脸表情特征的高校课堂教学质量在线评估模型。提取高校课堂人脸表情特征,利用图像层、S1层、C1层、S2层和C2层进行特征匹配和评选,使用贝叶斯分类模型对特征图像的平滑参数进行优化,确定使用率先验概率,判断学生的状态,评估课堂质量。实验结果表明,提出评估模型的评估率优于传统评估模型,在10~30 min内,学生的听课率最高,因此可以将重点问题和难点问题在第10~30 min内讲解,提高教学质量。

基于Naive Bayes Classifiers的航空影像纹理分类

在Naive Bayes Classifiers模型中,要求父节点下的子节点(特征变量)之间相对独立,然而在现实世界中,特征与特征之间是非独立的、相关的。提出一种预处理方法,实验结果表明,该方法明显地提高了分类精度。

产生“Tuned”模板的Bayesian Networks方法

介绍了Bayesian Networks(简称BNs)产生“Tuned”模板新方法的基本原理以及BNs法与蚁群行为仿真技术和单纯形法组合的方法。通过实际航空影像的实验结果表明,新方法对纹理影像的识别率是令人满意的,同时还将新方法与遗传算法的结果作了对比,结果表明新方法是很有应用前景的。

基于Tree Augmented Naive Bayes Classifier的影像纹理分类

提出了一种松弛方法,允许类别节点下的相邻子节点之间存在相关关系(有向边),这种方法称为树增强型简单贝叶斯分类器(tree augmented naive Bayes classifier,TAN)。实验结果表明,TAN比简单贝叶斯分类器(naive Bayes classifier,NBC)可以获得更高的分类精度。