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

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Petroleum Science 2024年第2期21卷 918-935页

作者： Fábio Júnior Damasceno Fernandes Leonardo Teixeira Antonio Fernando Menezes Freire Wagner Moreira Lupinacci Exploratory Interpretation and Reservoir Characterization Group(GIECAR) Department of Geology and GeophysicsFederal Fluminense UniversityNiter oiRJ24210-346Brazil Petrobras Rio de JaneiroRJ20231-030Brazil National Institute of Science and Technology of Petroleum Geophysics(INCT-GP/CNPQ) Niter oiRJ24210-346Brazil

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.

关键词： Stochastic inversion bayesian classification Porosity modeling Carbonate reservoirs Igneous rocks

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Liver Hydatid CT Image Segmentation Using Smoothed bayesian classification Method and Modified Parametric Active Contour Model

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Chinese Journal of Biomedical Engineering(English Edition) 2010年第4期19卷 139-147,155页

作者： HU Yan-ting HAMIT· Murat CHEN Jian-jun SUN Jing JI Jin-hu KONG De-wei College of Medical Engineer Technology Xinjiang Medical University Urumqi 830011 China Department of Radiology First Affiliated Hospital of Xinjiang Medical University Urumqi 830054 China

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.

关键词： liver hydatid disease CT image segmentation bayesian classification active contour model

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Agricultural Product Quality Mining Based on bayesian classification

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Agricultural Science & Technology 2011年第2期12卷 157-158,162页

作者：陈晨董倩吴玉洁河北农业大学信息科学与技术学院河北保定071001

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 *** with a randomly selected sample gro... 详细信息

关键词： Data mining bayesian classification Agricultural applications

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Haze removal for UAV reconnaissance images using layered scattering model

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Chinese Journal of Aeronautics 2016年第2期29卷 502-511页

作者： Huang Yuqing Ding Wenrui Li Hongguang School of Electronic and Information Engineering Beihang University Beijing 100083 China Research Institute of Unmanned Aerial Vehicle Beihang University Beijing 100083 China Collaborative Innovation Center of Geospatial Technology Wuhan 430079 China

During the unmanned aerial vehicles （UAV） reconnaissance missions in the middle-low troposphere, the reconnaissance images are blurred and degraded due to the scattering process of aerosol under fog, haze and other weather conditions, which reduce the image contrast and color fidelity. Considering the characteristics of UAV itself, this paper proposes a new algorithm for dehazing UAV reconnaissance images based on layered scattering model. The algorithm starts with the atmosphere scattering model, using the imaging distance, squint angle and other metadata acquired by the UAV. Based on the original model, a layered scattering model for dehazing is proposed. Considering the relationship between wave-length and extinction coefficient, the airlight intensity and extinction coefficient are calculated in the model. Finally, the restored images are obtained. In addition, a classification method based on bayesian classification is used for classifica- tion of haze concentration of the image, avoiding the trouble of manual working. Then we evaluate the haze removal results according to both the subjective and objective criteria. The experimental results show that compared with the origin image, the comprehensive index of the image restored by our method increases by 282.84%, which proves that our method can obtain excellent dehazing effect.

关键词： Atmosphere scatteringmodel bayesian classification Haze concentration Image restoration Layered scattering model UAV

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Fully automatic identification and discrimination of sperm’s parts in microscopic images of stained human semen smear

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Journal of Biomedical Science and Engineering 2012年第7期5卷 384-395页

作者： Ahmad Bijar Antonio Penalver Benavent Mohammad Mikaeili Rasoul Khayati Department of Biomedical Engineering Shahed UniversityTehranIran Center of Operations Research University Institute Miguel Hernandez University of Elche ElcheSpain

In the last years, digital image processing and analysis are used for computer assisted evaluation of semen quality with therapeutic goals or to estimate its fertility by means of spermatozoid motility and morphology. Sperm morphology is assessed routinely as part of standard laboratory analysis in the diagnosis of human male infertility. Nowadays assessments of sperm morphology are mostly done based on subjective criteria. In order to avoid subjectivity, numerous studies that incorporate image analysis techniques in the assessment of sperm morphology have been proposed. The primary step of all these methods is segmentation of sperm’s parts. In this paper, we have proposed a new method for segmentation of sperm’s Acrosome, Nucleus, Mid-piece and identification of sperm’s tail through some points which are placed on the sperm’s tail, accurately. These estimated points could be used to verify the morphological characteristics of sperm’s tail such as length, shape and etc. At first, sperm’s Acrosome, Nucleus and Mid-piece are segmented through a method based on a bayesian classifier which utilizes the entropy based expectation–maximization (EM) algorithm and Markov random field (MRF) model to obtain and upgrade the class conditional probability density function (CCPDF) and the apriori probability of each class. Then, a pixel at the end of sperm’s Mid-piece, is selected as an initial point. To find other pixels which are placed on the sperm’s tail, structural similarity index (SSIM) is used in an iterative scheme. In order to stop the algorithm automatically at the end of sperm’s tail, local entropy is estimated and used as a feature to determine if a point is located on the sperm’s tail or not. To compare the performance of the proposed approach with those of previous approaches including manual segmentation, the Accuracy, Sensitivity and Specificity were calculated.

关键词： Sperm Segmentation Rotating Calipers bayesian classification Entropy Based EM Algorithm Structural Similarity Index(SSIM) Entropy

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An Improved bayesian with Application to Anti-Spam Email

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Journal of Electronic Science and Technology of China 2005年第1期3卷 30-33页

作者：詹川卢显良周旭侯孟书 School of Computer Science and Engineering UESTC Chengdu 610054 China

Along with the wide application of e-mail nowadays, many spam e-mails flood into people’s email-boxes and cause catastrophes to their study and life. In anti-spam e-mails campaign, we depend on not only legal measures but also technological approaches. The bayesian classifier provides a simple and effective approach to discriminate classification. This paper presents a new improved bayesian-based anti-spam e-mail filter. We adopt a way of attribute selection based on word entropy, use vector weights which are represented by word frequency, and deduce its corresponding formula. It is proved that our filter improves total performances apparently in our experiment.

关键词： word entropy bayesian classification anti-spam e-mail filter attribute selection vector

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A bayesian Framework for Target Tracking in Sensor Networks

A Bayesian Framework for Target Tracking in Sensor Networks

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第二十六届中国控制会议

作者： Cheng Yuanguo 1,2,Li Guohui 2 1.Electrical Engineering College,Navy Engineering University,Wuhan 430033,P.R.China 2.Computer College,Huazhong University of Science and Technology,Wuhan 430047,P.R.China

The work described in this paper presents a framework to track a moving target in sensor *** framework employs a method based on bayesian classification to calculate the detection probabilities and proposes a tracking algorithm by defining the moving scope of a target,where the sensors have the higher values of detection *** re-sults show this scheme is effective.

关键词： Sensor Networks Target Tracking bayesian classification

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Enjoy Driving from Thought in a Virtual City

Enjoy Driving from Thought in a Virtual City

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第36届中国控制会议

作者： Xin Pan Zhijun Zhang Jun Qu Chunhui Zhao Yuanqing Li The School of Automation Science and Engineering South China University of Technology IEEE

ISBN: (纸本)9781538629185

Virtual reality（VR） technology allows users to communicate or interact with a virtual artifact in an immersive simulated *** of the existing virtual reality platform uses standard input devices such as a keyboard and mouse,or through multimodal devices such as a wired *** this paper,a novel electroencephalo-graph（EEG） based virtual driving（EEG-VD） prototype is proposed,investigated,and *** can enjoy driving a virtual car in a simulated and realistic city only through brain ***’s EEG signals are obtained,analyzed,and recognized into control commands,which are used to control a virtual car in a three-dimensional（3D） city *** paradigm display of P300 in the proposed EEGVD system is embedded in the display of VR,to realize closely combination of VR and *** guarantees the verisimilitude of the *** addition,asynchronous BCI paradigm based on P300 signals used in the proposed EEG-VD system realizes a high degree of freedom,which can offer appropriate number of control *** order to verify the effectiveness and performance of the designed EEG-VD system,eight volunteers are asked to drive the car in the 3D *** experiment result shows that the accuracy of P300 recognition rate is larger than 90%.Most of the users（62.5%） can drive the car without any collisions in the first virtual environment,and all users can drive the car without any collisions in the second virtual *** designed system can not only be used as a tool to train a subject of BCI but also be used as a game for enjoying ***,the system can promote the research of BCI applications.

关键词： Brain-computer interface(BCI) P300 virtual reality(VR) bayesian classification

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