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Three-dimensional shear wave velocity prediction by integrating post-stack seismic attributes and well logs: application on Asmari formation in Iran

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JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY 2024年第8-9期14卷 2399-2411页

作者： Leisi, Ahsan Manaman, Navid Shad Sahand Univ Technol Dept Min Engn Tabriz Iran

Understanding the distribution of shear wave velocity (V S ) in hydrocarbon reservoirs is a crucial concern in reservoir geophysics. This geophysical parameter is utilized for reservoir characterization, calculating elastic properties, assessing fractures, and evaluating reservoir quality. Unfortunately, not all wells have available V S data due to the expensive nature of its measurements. Hence, it is crucial to calculate this parameter using other relevant features. Therefore, over the past few decades, numerous techniques have been introduced to calculate the V S data using petrophysical logs in wells with limited information. Unfortunately, the majority of these methods have a drawback they only offer insight into the location of the wells and do not provide any details regarding the distribution of V S in the space between the wells. In this article, we employed three-dimensional post-stack seismic attributes and well-logging data integration to predict the distribution of V S in the Asmari formation in an Iranian oil field. To accomplish this objective, the model-based seismic inversion algorithm was utilized to convert the seismic section into the acoustic impedance (AI) section. Then, AI and seismic data were utilized in the cross-validation method to determine the relevant attributes for predicting the spatial distribution of V S throughout the entire reservoir area, using an artificial neural network. The proposed method was shown to provide 94% correlation and 109 m/s error between the actual and estimated V S . Also, the calculated V S section has a high correlation with the actual logs at the location of the wells.

关键词： Shear wave velocity Seismic attributes cross-validation method Data integration Asmari formation

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Computational aspects of the kNN local linear smoothing for some conditional models in high dimensional statistics

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2023年第7期52卷 2985-3005页

作者： Almanjahie, Ibrahim M. Alahmari, Wafa Mesfer Laksaci, Ali Rachdi, Mustapha King Khalid Univ Coll Sci Dept Math Abha Saudi Arabia Univ Grenoble Lab AGEIS EA 7407 UFR SHS BP 47 F-38040 Grenoble 09 France

We consider the problem of nonparametric estimation of certain conditional models when the explanatory variable is functional. The models studied are the Conditional Expectation (CE), the Conditional Distribution Function (CDF) and the Conditional Probability Density (CPD). The estimators are constructed by combining the local linear method and the k-Nearest Neighbors (kNN) smoothing approach. For each of the three models, we define an optimal estimator associated with the best number of neighbors chosen using two bandwidth selection procedures which are the cross-validation criterion and the bootstrap smoothing rule. The asymptotic properties of these optimal kNN Local Linear Estimators (k NN-LLE) are established under some standard conditions. The performances of the finite samples of these estimators are then examined and compared through a Monte-carlo study. In addition, an application on the riboflavin content in the yogurt using the Near-infrared curves is carried out to demonstrate the usefulness of the models proposed in the point-wise prediction as well as for the predictive interval.

关键词： Bootstrap smoothing cross-validation method Functional data analysis Kernel method kNN method Local linear estimation method Near-infrared curves Riboflavin content in the yogurt

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Analysis of Land Surface Performance Differences and Uncertainty in Multiple Versions of MODIS LST Products

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REMOTE SENSING 2024年第22期16卷 4255-4255页

作者： Zhao, Ruoyi Yu, Wenping Deng, Xiangyi Huang, Yajun Yang, Wen Zhou, Wei Southwest Univ Chongqing Engn Res Ctr Remote Sensing Big Data App Sch Geog Sci Chongqing Jinfo Mt Karst Ecosyst Natl Observat & R 2 Tiansheng Rd Beibei Dist Chongqing 400715 Peoples R China Southwest Univ Yibin Acad Yibin 644000 Peoples R China

Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) products are essential data sources for global and regional climate change research. Currently, several versions of the MODIS LST product have been released, yet the performance differences and uncertainties they introduce in land surface studies remain insufficiently addressed. To bridge this gap, this study focuses on four distinct versions of the LST product: MxD11A1 Collection 5 (C5), Collection 6 (C6), Collection 6.1 (C6.1), and MxD21A1 Collection 6.1 (MxD21). The spatial resolution of all product generations is 1 km, and the temporal resolution is 0.5 days. This study provides a comprehensive analysis of the errors arising from different generations of these products in various land surface process studies. The error assessment includes cross-comparisons between product versions and evaluations of the absolute errors generated. Absolute errors in evaluation data were collected from 13 surface sites within the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project during the period 2013-2018. cross-validation results show that the largest difference between C5 and C6.1 occurs over bare land, with an RMSE of approximately 1.45 K, while there is no significant change between C6 and C6.1. MOD21 shows considerable variation compared to C6.1 at night across different land cover types, with RMSE over cropland exceeding 2 K. The temperature difference between MOD21 and C6.1 is more pronounced at night (2.01 K) than during the day (0.30 K). validation results based on temperature indicate that C5 has greater uncertainty compared to C6, especially over bare land, where errors are 2.06 K and 1.06 K, respectively. Furthermore, MxD21 demonstrates significant day-night performance discrepancies, with an average bias of 0.10 K at night, while daytime errors over bare land can reach 2 K, potentially influenced by atmospheric conditions. Based on the research in this paper, i

关键词： land surface temperature cross-validation method temperature-based validation method MODIS

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Prediction of traumatic pathology by classifying thorax trauma using a hybrid method for emergency services

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IET SIGNAL PROCESSING 2020年第10期14卷 754-764页

作者： Karaci, Abdulkadir Ozkaraca, Osman Acar, Ethem Demir, Ahmet Kastamonu Univ Dept Comp Engn Kastamonu Turkey Mugla Sitki Kocman Univ Dept Informat Syst Engn Mugla Turkey Mugla Sitki Kocman Univ Dept Emergency Med Mugla Turkey

In recent years, data mining and algorithm-based methods have been used frequently for the prediction and diagnosis of various diseases. Traumas, being one of the significant health problems in the world, are also one of the most important causes of death. This study aims to predict the presence of traumatic pathology in the lung of the patients admitted to the emergency department due to blunt thorax trauma with no X-ray and computed tomography (CT) history by machine learning methods. The models developed in the study using the 5-fold cross-validation method are most accurately classified by the ensemble (voting) classifier, whether there is a pathology in X-ray (mean accuracy = 0.82) and CT (mean accuracy = 0.83). The K-nearest neighbourhood method classifies patients with pathology in X-ray by 83% accuracy, while the ensemble (voting) method classifies non-pathology patients by 94% accuracy in models. Of CT results, random forest, ensemble (voting), and ensemble (stacking) classifiers are precisely classified by 96%, while those patients with pathology are classified perspicuously by 77%. As a result, a mathematical framework using data mining methods was proposed based on estimating the X-ray and CT results for the thorax graph scan.

关键词： data mining lung computerised tomography medical image processing learning (artificial intelligence) diseases image classification nearest neighbour methods random forests traumatic pathology thorax trauma hybrid method emergency services algorithm-based methods health problems computed tomography history machine learning methods cross-validation method ensemble classifier nonpathology patients CT results data mining methods thorax graph scan K-nearest neighbourhood method random forest

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Natural Gas Demand Forecasting Model Based on LASSO and Polynomial Models and Its Application: A Case Study of China

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ENERGIES 2023年第11期16卷 4268页

作者： Liu, Huanying Liu, Yulin Wang, Changhao Song, Yanling Jiang, Wei Li, Cuicui Zhang, Shouxin Hong, Bingyuan Zhejiang Ocean Univ Sch Petrochem Engn & Environm Natl & Local Joint Engn Res Ctr Harbor Oil & Gas S Zhejiang Key Lab Petrochem Environm Pollut Control Zhoushan 316022 Peoples R China Zhejiang Ocean Univ Coll Informat & Engn Zhoushan 316022 Peoples R China China Univ Petr Natl Engn Lab Pipeline Safety MOE Key Lab Petr Engn Beijing Key Lab Urban Oil & Gas Distribut Technol Fuxue Rd 18 Beijing 102249 Peoples R China

China aims to reduce carbon dioxide emissions and achieve peak carbon and carbon neutrality goals. Natural gas, as a high-quality fossil fuel energy, is an important transition resource for China in the process of carbon reduction, so it is necessary to predict China's natural gas demand. In this paper, a novel natural gas demand combination forecasting model is constructed to accurately predict the future natural gas demand. The Lasso model and the polynomial model are used to build a combinatorial model, which overcomes the shortcomings of traditional models, which have low data dimensions and poor prediction abilities. In the modeling process, the cross-validation method is used to adjust the modeling parameters. By comparing the performance of the combinatorial forecasting model, the single forecasting model and other commonly used forecasting models, the results show that the error (2.99%) of the combinatorial forecasting model is the smallest, which verifies the high accuracy and good stability advantages of the combinatorial forecasting model. Finally, the paper analyzes the relevant data from 1999 to 2022 and predicts China's natural gas demand in the next 10 years. The results show that the annual growth rate of China's natural gas demand in the next 10 years will reach 13.33%, at 8.3 x 10(11) m(3) in 2033, which proves that China urgently needs to rapidly develop the gas supply capacity of gas supply enterprises. This study integrates the impact of multiple factors on the natural gas demand, predicts China's natural gas demand from 2023 to 2033, and provides decision-making support for China's energy structure adjustment and natural gas import trade.

关键词： natural gas demand Lasso model combined forecasting cross-validation method

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Critical assessment of steady-state kinetic models for the synthesis of methanol over an industrial Cu/ZnO/Al2O3 catalyst

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CHEMICAL ENGINEERING JOURNAL 2020年 389卷 124181-000页

作者： Slotboom, Y. Bos, M. J. Pieper, J. Vrieswijk, V. Likozar, B. Kersten, S. R. A. Brilman, D. W. F. Univ Twente Fac Sci & Technol Sustainable Proc Technol POB 217 NL-7500 AE Enschede Netherlands Natl Inst Chem Dept Catalysis & Chem React Engn Hajdrihova 19 SI-1000 Ljubljana Slovenia

In this paper a thorough comparison is made between steady state kinetic models for methanol synthesis from Graaf et al. (1988), Vanden Bussche and Froment (1996), Seidel et al. (2018), Ma et al. (2009) and Villa et al. (1985). A new experimental dataset using an industrial Cu/ZnO/Al2O3 catalyst is presented and used together with the dataset of Seidel et al. (2018) for refitting the kinetic models. The models are refitted using the statistical cross-validation (CV) method to test for predictive capabilities and model variance. A new kinetic model is proposed with the aim to reduce parameter identifiability problems. The model is derived based on physical observations from literature. This physically consistent model has ten parameters, however more experiments are needed, because the current dataset is not discriminating enough for regression of adsorption isotherms. The proposed model is further reduced to only six parameters. This model is predicting the dataset equally well or better than current higher parameter models. It is shown that the model is a good predicting model for experiments outside the training set. The model is valid for pressures from 20 to 70 bara and temperatures from 450 to 530 K with a high probability of predicting well outside these boundaries.

关键词： Methanol kinetics CO2 Syn gas Cu/ZnO/Al2O3 cross-validation method

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The optimal Mexican hat wavelet filter de-noising method based on cross-validation method

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NEUROCOMPUTING 2013年 108卷 31-35页

作者： Liu, W. Y. Han, J. G. Jiangsu Normal Univ Sch Mech & Elect Engn Xuzhou 221116 Peoples R China

A new de-noising method based on parameter optimized Mexican hat wavelet was put forward in this paper. For the similar shape to the mechanical shock vibration signal, the Mexican hat wavelet is chosen as the mother wavelet and improved by the shape parameters optimization. The noise jamming in the raw vibration signals can be filtered by the continue wavelet transform (CWT) using the improved Mexican hat wavelet as the mother wavelet. The shape parameters of the Mexican hat wavelet are optimized by the cross-validation method (CVM). In the CWT process, the optimal scale factor is also obtained by the circle CVM. The useful components can be extracted by the CWT with the optimal shape parameters and scale factor. The experimental result shows that the proposed method can not only de-noise the useless noise effectively but also extract the fault feature availably. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Wavelet transform Wavelet de-noising Optimal Mexican hat wavelet cross-validation method Fault diagnosis

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KPLS optimization approach using genetic algorithms 11

KPLS optimization approach using genetic algorithms

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11th International Conference on Ambient Systems, Networks and Technologies (ANT) / 3rd International Conference on Emerging Data and Industry 4.0 (EDI)

作者： Daniel Mello-Roman, Jorge Hernandez, Adolfo Univ Complutense Madrid Fac Ciencias Matemat Madrid 28040 Spain Univ Complutense Madrid Fac Comercio & Turismo Madrid 28003 Spain

Kernel partial least squares regression (KPLS) is a technique widely used in the construction of predictive models. However, the adjustment of both the parameter of the kernel function and the number of components supposes for the researcher an unavoidable additional task. This paper presents a procedure that optimizes the generalization capacity of KPLS multivariate models using genetic algorithms (GA), selects the values of the kernel function parameter and the number of components for which the value of the cross -validation coefficient Q(cum)(2) is maximum, adds preliminary tests to configure the GA and defines a convergence criterion in terms of dispersion in the estimates. GA has demonstrated a good performance in the task of optimizing KPLS with convergent solutions towards a global optimum. (C) 2020 The Authors. Published by Elsevier B.V.

关键词： Partial Least Squares Regression Kernel-based methods cross-validation method Genetic Algorithm

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Statistical-based approach for driving style recognition using Bayesian probability with kernel density estimation

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IET INTELLIGENT TRANSPORT SYSTEMS 2019年第1期13卷 22-30页

作者： Han, Wei Wang, Wenshuo Li, Xiaohan Xi, Junqiang Tsinghua Univ Dept Comp Sci Beijing Peoples R China Chinese Acad Sci Inst Microelect Beijing Peoples R China Beijing Inst Technol Dept Mech Engn Beijing Peoples R China Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA Tech Univ Berlin Fac Mech Engn & Transport Syst Chair Human Machine Syst Berlin Germany

Driving style recognition plays a crucial role in eco-driving, road safety, and intelligent vehicle control. This study proposes a statistical-based recognition method to deal with driver behaviour uncertainty in driving style recognition. First, the authors extract discriminative features using the conditional kernel density function to characterise path-following behaviour. Meanwhile, the posterior probability of each selected feature is computed based on the full Bayesian theory. Second, they develop an efficient Euclidean distance-based method to recognise the path-following style for new input datasets at a low computational cost. By comparing the Euclidean distance of each pair of elements in the feature vector, then they classify driving styles into seven levels from normal to aggressive. Finally, they employ a cross-validation method to evaluate the utility of their proposed approach by comparing with a fuzzy logic (FL) method. The experiment results show that the proposed statistical-based recognition method integrating with the kernel density is more efficient and robust than the FL method.

关键词： statistical analysis feature extraction probability Bayes methods estimation theory road safety driver information systems pattern classification statistical-based approach driving style recognition Bayesian probability eco-driving road safety intelligent vehicle control statistical-based recognition method driver behaviour uncertainty discriminative feature extraction conditional kernel density function path-following behaviour characterization posterior probability full Bayesian theory Euclidean distance-based method low computational cost feature vector driving style classification cross-validation method fuzzy logic method FL method

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KPLS Optimization With Nature-Inspired Metaheuristic Algorithms

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IEEE ACCESS 2020年 8卷 157482-157492页

作者： Mello-Roman, Jorge Daniel Hernandez, Adolfo Univ Complutense Madrid Fac Math Sci Madrid 28040 Spain Univ Complutense Madrid Financial & Actuarial Econ & Stat Dept Madrid 28223 Spain

Kernel partial least squares regression (KPLS) is a technique used in several scientific areas because of its high predictive ability. This article proposes a methodology to simultaneously estimate both the parameters of the kernel function and the number of components of the KPLS regression to maximize its predictive ability. A metaheuristic optimization problem was proposed taking the cumulative cross-validation coefficient as an objective function to be maximized. It was solved using nature-inspired metaheuristic algorithms: the genetic algorithm, particle swarm optimization, grey wolf optimization and the firefly algorithm. To validate the results and have a reference measure of the efficiency of the nature-inspired metaheuristic algorithms, derivative-free optimization algorithms were also applied: Hooke-Jeeves and Nelder-Mead. The metaheuristic algorithms estimated optimal values of both of the kernel function parameters and the number of components in the KPLS regression.

关键词： Kernel Optimization Genetic algorithms Prediction algorithms Particle swarm optimization Sociology Statistics Partial least squares regression kernel-based method cross-validation method nature-inspired metaheuristic algorithms

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