Viticulturists traditionally have a keen interest in studying the relationship between the biochemistry of grapevines’ leaves/petioles and their associated spectral reflectance in order to understand the fruit ripeni...Viticulturists traditionally have a keen interest in studying the relationship between the biochemistry of grapevines’ leaves/petioles and their associated spectral reflectance in order to understand the fruit ripening rate, water status, nutrient levels, and disease risk. In this paper, we implement imaging spectroscopy (hyperspectral) reflectance data, for the reflective 330 - 2510 nm wavelength region (986 total spectral bands), to assess vineyard nutrient status;this constitutes a high dimensional dataset with a covariance matrix that is ill-conditioned. The identification of the variables (wavelength bands) that contribute useful information for nutrient assessment and prediction, plays a pivotal role in multivariate statistical modeling. In recent years, researchers have successfully developed many continuous, nearly unbiased, sparse and accurate variable selection methods to overcome this problem. This paper compares four regularized and one functional regression methods: Elastic Net, Multi-Step Adaptive Elastic Net, Minimax Concave Penalty, iterative Sure Independence Screening, and Functional Data Analysis for wavelength variable selection. Thereafter, the predictive performance of these regularized sparse models is enhanced using the stepwise regression. This comparative study of regression methods using a high-dimensional and highly correlated grapevine hyperspectral dataset revealed that the performance of Elastic Net for variable selection yields the best predictive ability.展开更多
In this paper,we introduce the censored composite conditional quantile coefficient(cC-CQC)to rank the relative importance of each predictor in high-dimensional censored regression.The cCCQC takes advantage of all usef...In this paper,we introduce the censored composite conditional quantile coefficient(cC-CQC)to rank the relative importance of each predictor in high-dimensional censored regression.The cCCQC takes advantage of all useful information across quantiles and can detect nonlinear effects including interactions and heterogeneity,effectively.Furthermore,the proposed screening method based on cCCQC is robust to the existence of outliers and enjoys the sure screening property.Simulation results demonstrate that the proposed method performs competitively on survival datasets of high-dimensional predictors,particularly when the variables are highly correlated.展开更多
当前,步态识别的主流方法常依赖堆叠卷积层来逐步扩大感受野,以融合局部特征,这种方法大多采用浅层网络,在提取步态图像的全局特征时存在一定的局限性,并缺乏对时序周期特征信息的关注。因此提出一种融合Transformer和3D卷积的深层神经...当前,步态识别的主流方法常依赖堆叠卷积层来逐步扩大感受野,以融合局部特征,这种方法大多采用浅层网络,在提取步态图像的全局特征时存在一定的局限性,并缺乏对时序周期特征信息的关注。因此提出一种融合Transformer和3D卷积的深层神经网络算法(3D convolutional gait recognition network based on adaptFormer and spect-conv,3D-ASgaitNet)。首先,初始残差卷积层将二进制轮廓数据转换为浮点编码特征图,以提供密集的低级结构特征;在此基础上,光谱层通过频域和时域的联合处理增强特征提取能力,并使用伪3D残差卷积模块进一步提取高级时空特征;最后融合AdaptFormer模块,通过轻量级的下采样-上采样网络结构,以适应不同的数据分布和任务需求,提供灵活的特征变换能力。3D-ASgaitNet分别在4个公开的室内数据集(CASIA-B、OU-MVLP)、室外数据集(GREW、Gait3D)上进行,分别取得99.84%、87.83%、45.32%、72.12%的识别准确率。实验结果表明,所提出方法在CASIA-B、Gait3D数据集中的识别准确率接近SOTA性能。展开更多
We consider the fourth-order nonlinear Schr?dinger equation(4NLS)(i?t+εΔ+Δ2)u=c1um+c2(?u)um-1+c3(?u)2um-2,and establish the conditional almost sure global well-posedness for random initial data in Hs(Rd)for s∈(sc-...We consider the fourth-order nonlinear Schr?dinger equation(4NLS)(i?t+εΔ+Δ2)u=c1um+c2(?u)um-1+c3(?u)2um-2,and establish the conditional almost sure global well-posedness for random initial data in Hs(Rd)for s∈(sc-1/2,sc],when d≥3 and m≥5,where sc:=d/2-2/(m-1)is the scaling critical regularity of 4NLS with the second order derivative nonlinearities.Our proof relies on the nonlinear estimates in a new M-norm and the stability theory in the probabilistic setting.Similar supercritical global well-posedness results also hold for d=2,m≥4 and d≥3,3≤m<5.展开更多
High dimensional data clustering,with the inherent sparsity of data and the existence of noise,is a serious challenge for clustering algorithms.A new linear manifold clustering method was proposed to address this prob...High dimensional data clustering,with the inherent sparsity of data and the existence of noise,is a serious challenge for clustering algorithms.A new linear manifold clustering method was proposed to address this problem.The basic idea was to search the line manifold clusters hidden in datasets,and then fuse some of the line manifold clusters to construct higher dimensional manifold clusters.The orthogonal distance and the tangent distance were considered together as the linear manifold distance metrics. Spatial neighbor information was fully utilized to construct the original line manifold and optimize line manifolds during the line manifold cluster searching procedure.The results obtained from experiments over real and synthetic data sets demonstrate the superiority of the proposed method over some competing clustering methods in terms of accuracy and computation time.The proposed method is able to obtain high clustering accuracy for various data sets with different sizes,manifold dimensions and noise ratios,which confirms the anti-noise capability and high clustering accuracy of the proposed method for high dimensional data.展开更多
Next Generation Sequencing (NGS) provides an effective basis for estimating the survival time of cancer patients, but it also poses the problem of high data dimensionality, in addition to the fact that some patients d...Next Generation Sequencing (NGS) provides an effective basis for estimating the survival time of cancer patients, but it also poses the problem of high data dimensionality, in addition to the fact that some patients drop out of the study, making the data missing, so a method for estimating the mean of the response variable with missing values for the ultra-high dimensional datasets is needed. In this paper, we propose a two-stage ultra-high dimensional variable screening method, RF-SIS, based on random forest regression, which effectively solves the problem of estimating missing values due to excessive data dimension. After the dimension reduction process by applying RF-SIS, mean interpolation is executed on the missing responses. The results of the simulated data show that compared with the estimation method of directly deleting missing observations, the estimation results of RF-SIS-MI have significant advantages in terms of the proportion of intervals covered, the average length of intervals, and the average absolute deviation.展开更多
This article develops a procedure for screening variables, in ultra high-di- mensional settings, based on their predictive significance. This is achieved by ranking the variables according to the variance of their res...This article develops a procedure for screening variables, in ultra high-di- mensional settings, based on their predictive significance. This is achieved by ranking the variables according to the variance of their respective marginal regression functions (RV-SIS). We show that, under some mild technical conditions, the RV-SIS possesses a sure screening property, which is defined by Fan and Lv (2008). Numerical comparisons suggest that RV-SIS has competitive performance compared to other screening procedures, and outperforms them in many different model settings.展开更多
In this paper, the following contaminated linear model is considered:y i=(1-ε)x τ iβ+z i, 1≤i≤n,where r.v.'s ｛ y i ｝ are contaminated with errors ｛ z i ｝. To assume that the errors have the fin...In this paper, the following contaminated linear model is considered:y i=(1-ε)x τ iβ+z i, 1≤i≤n,where r.v.'s ｛ y i ｝ are contaminated with errors ｛ z i ｝. To assume that the errors have the finite moment of order 2 only. The non parametric estimation of contaminated coefficient ε and regression parameter β are established, and the strong consistency and convergence rate almost surely of the estimators are obtained. A simulated example is also given to show the visual performance of the estimations.展开更多
The curse of high-dimensionality has emerged in the statistical fields more and more frequently.Many techniques have been developed to address this challenge for classification problems. We propose a novel feature scr...The curse of high-dimensionality has emerged in the statistical fields more and more frequently.Many techniques have been developed to address this challenge for classification problems. We propose a novel feature screening procedure for dichotomous response data. This new method can be implemented as easily as t-test marginal screening approach, and the proposed procedure is free of any subexponential tail probability conditions and moment requirement and not restricted in a specific model structure. We prove that our method possesses the sure screening property and also illustrate the effect of screening by Monte Carlo simulation and apply it to a real data example.展开更多
On-orbit service is important for maintaining the sustainability of the space environment.A space-based visible camera is an economical and lightweight sensor for situational awareness during on-orbit service.However,...On-orbit service is important for maintaining the sustainability of the space environment.A space-based visible camera is an economical and lightweight sensor for situational awareness during on-orbit service.However,it can be easily affected by the low illumination environment.Recently,deep learning has achieved remarkable success in image enhancement of natural images,but it is seldom applied in space due to the data bottleneck.In this study,we first propose a dataset of BeiDou navigation satellites for on-orbit low-light image enhancement(LLIE).In the automatic data collection scheme,we focus on reducing the domain gap and improving the diversity of the dataset.We collect hardware-in-the-loop images based on a robotic simulation testbed imitating space lighting conditions.To evenly sample poses of different orientations and distances without collision,we propose a collision-free workspace and pose-stratified sampling.Subsequently,we develop a novel diffusion model.To enhance the image contrast without over-exposure and blurred details,we design fused attention guidance to highlight the structure and the dark region.Finally,a comparison of our method with previous methods indicates that our method has better on-orbit LLIE performance.展开更多
In this paper, we study the sure independence screening of ultrahigh-dimensional censored data with varying coefficient single-index model. This general model framework covers a large number of commonly used survival ...In this paper, we study the sure independence screening of ultrahigh-dimensional censored data with varying coefficient single-index model. This general model framework covers a large number of commonly used survival models. The property that the proposed method is not derived for a specific model is appealing in ultrahigh dimensional regressions, as it is difficult to specify a correct model for ultrahigh dimensional predictors.Once the assuming data generating process does not meet the actual one, the screening method based on the model will be problematic. We establish the sure screening property and consistency in ranking property of the proposed method. Simulations are conducted to study the finite sample performances, and the results demonstrate that the proposed method is competitive compared with the existing methods. We also illustrate the results via the analysis of data from The National Alzheimers Coordinating Center(NACC).展开更多
This paper proposes a new sure independence screening procedure for high-dimensional survival data based on censored quantile correlation(CQC).This framework has two distinctive features:1)Via incorporating a weightin...This paper proposes a new sure independence screening procedure for high-dimensional survival data based on censored quantile correlation(CQC).This framework has two distinctive features:1)Via incorporating a weighting scheme,our metric is a natural extension of quantile correlation(QC),considered by Li(2015),to handle high-dimensional survival data;2)The proposed method not only is robust against outliers,but also can discover the nonlinear relationship between independent variables and censored dependent variable.Additionally,the proposed method enjoys the sure screening property under certain technical conditions.Simulation results demonstrate that the proposed method performs competitively on survival datasets of high-dimensional predictors.展开更多
This study guides the discovery of sustainable high-performance polymer membranes for organic binary solvent separations.We focus on solvent diffusivity in polymers,a key factor in quantifying solvent transport.Tradit...This study guides the discovery of sustainable high-performance polymer membranes for organic binary solvent separations.We focus on solvent diffusivity in polymers,a key factor in quantifying solvent transport.Traditional experimental and computational methods for determining diffusivity are time-and resource-intensive,while current machine learning(ML)models often lack accuracy outside their training domains.To overcome this,we fuse experimental and simulated diffusivity data to train physics-enforced multi-task ML models,achieving more robust predictions in unseen chemical spaces and outperforming single-task models in data-limited scenarios.Next,we address the challenge of identifying optimal membranes for a model toluene-heptane separation,identifying polyvinyl chloride(PVC)as the optimal membrane among 13,000 polymers,consistent with literature findings,thereby validating our methodology.Expanding our search,we screen 1 million publicly available and 7 million chemically recyclable polymers,identifying greener halogen-free alternatives to PVC.This capability is expected to advance membrane design for solvent separations.展开更多
造斜率的准确预测是进行井眼轨迹调控的基础,直接影响定向井钻井效率,但由于井下力学行为的复杂性,传统预测方法存在一定限制,难以实现精确预测。为此,提出了一种力学-智能模型融合的造斜率预测方法。利用力学模型计算钻头侧向力、钻头...造斜率的准确预测是进行井眼轨迹调控的基础,直接影响定向井钻井效率,但由于井下力学行为的复杂性,传统预测方法存在一定限制,难以实现精确预测。为此,提出了一种力学-智能模型融合的造斜率预测方法。利用力学模型计算钻头侧向力、钻头转角和极限造斜率并作为主控因素,通过自动化机器学习框架联合其他参数进行拟合预测,从而取代传统方法反演经验系数的过程,使其充分发挥力学模型宏观规律描述准确和智能模型非线性拟合能力强的优势。利用新疆玛湖区块14口井数据进行训练和测试。结果显示,融合力学参数后,模型造斜率最大误差、均方根误差和平均绝对误差分别下降了17%、12%和8%,其中均方根误差和平均绝对误差均小于每30 m 1.00°,表明该方法能够有效提升造斜率预测精度,尤其在造斜率急剧变化的井段表现出更优的预测性能。研究结果可为造斜率的准确预测提供新的思路,同时也可为井眼轨迹的精确调控提供一定的技术支撑。展开更多
文摘Viticulturists traditionally have a keen interest in studying the relationship between the biochemistry of grapevines’ leaves/petioles and their associated spectral reflectance in order to understand the fruit ripening rate, water status, nutrient levels, and disease risk. In this paper, we implement imaging spectroscopy (hyperspectral) reflectance data, for the reflective 330 - 2510 nm wavelength region (986 total spectral bands), to assess vineyard nutrient status;this constitutes a high dimensional dataset with a covariance matrix that is ill-conditioned. The identification of the variables (wavelength bands) that contribute useful information for nutrient assessment and prediction, plays a pivotal role in multivariate statistical modeling. In recent years, researchers have successfully developed many continuous, nearly unbiased, sparse and accurate variable selection methods to overcome this problem. This paper compares four regularized and one functional regression methods: Elastic Net, Multi-Step Adaptive Elastic Net, Minimax Concave Penalty, iterative Sure Independence Screening, and Functional Data Analysis for wavelength variable selection. Thereafter, the predictive performance of these regularized sparse models is enhanced using the stepwise regression. This comparative study of regression methods using a high-dimensional and highly correlated grapevine hyperspectral dataset revealed that the performance of Elastic Net for variable selection yields the best predictive ability.
基金Outstanding Youth Foundation of Hunan Provincial Department of Education(Grant No.22B0911)。
文摘In this paper,we introduce the censored composite conditional quantile coefficient(cC-CQC)to rank the relative importance of each predictor in high-dimensional censored regression.The cCCQC takes advantage of all useful information across quantiles and can detect nonlinear effects including interactions and heterogeneity,effectively.Furthermore,the proposed screening method based on cCCQC is robust to the existence of outliers and enjoys the sure screening property.Simulation results demonstrate that the proposed method performs competitively on survival datasets of high-dimensional predictors,particularly when the variables are highly correlated.
文摘当前,步态识别的主流方法常依赖堆叠卷积层来逐步扩大感受野,以融合局部特征,这种方法大多采用浅层网络,在提取步态图像的全局特征时存在一定的局限性,并缺乏对时序周期特征信息的关注。因此提出一种融合Transformer和3D卷积的深层神经网络算法(3D convolutional gait recognition network based on adaptFormer and spect-conv,3D-ASgaitNet)。首先,初始残差卷积层将二进制轮廓数据转换为浮点编码特征图,以提供密集的低级结构特征;在此基础上,光谱层通过频域和时域的联合处理增强特征提取能力,并使用伪3D残差卷积模块进一步提取高级时空特征;最后融合AdaptFormer模块,通过轻量级的下采样-上采样网络结构,以适应不同的数据分布和任务需求,提供灵活的特征变换能力。3D-ASgaitNet分别在4个公开的室内数据集(CASIA-B、OU-MVLP)、室外数据集(GREW、Gait3D)上进行,分别取得99.84%、87.83%、45.32%、72.12%的识别准确率。实验结果表明,所提出方法在CASIA-B、Gait3D数据集中的识别准确率接近SOTA性能。
基金supported by the NationalNatural Science Foundation of China(12001236)the Natural Science Foundation of Guangdong Province(2020A1515110494)。
文摘We consider the fourth-order nonlinear Schr?dinger equation(4NLS)(i?t+εΔ+Δ2)u=c1um+c2(?u)um-1+c3(?u)2um-2,and establish the conditional almost sure global well-posedness for random initial data in Hs(Rd)for s∈(sc-1/2,sc],when d≥3 and m≥5,where sc:=d/2-2/(m-1)is the scaling critical regularity of 4NLS with the second order derivative nonlinearities.Our proof relies on the nonlinear estimates in a new M-norm and the stability theory in the probabilistic setting.Similar supercritical global well-posedness results also hold for d=2,m≥4 and d≥3,3≤m<5.
基金Project(60835005) supported by the National Nature Science Foundation of China
文摘High dimensional data clustering,with the inherent sparsity of data and the existence of noise,is a serious challenge for clustering algorithms.A new linear manifold clustering method was proposed to address this problem.The basic idea was to search the line manifold clusters hidden in datasets,and then fuse some of the line manifold clusters to construct higher dimensional manifold clusters.The orthogonal distance and the tangent distance were considered together as the linear manifold distance metrics. Spatial neighbor information was fully utilized to construct the original line manifold and optimize line manifolds during the line manifold cluster searching procedure.The results obtained from experiments over real and synthetic data sets demonstrate the superiority of the proposed method over some competing clustering methods in terms of accuracy and computation time.The proposed method is able to obtain high clustering accuracy for various data sets with different sizes,manifold dimensions and noise ratios,which confirms the anti-noise capability and high clustering accuracy of the proposed method for high dimensional data.
文摘Next Generation Sequencing (NGS) provides an effective basis for estimating the survival time of cancer patients, but it also poses the problem of high data dimensionality, in addition to the fact that some patients drop out of the study, making the data missing, so a method for estimating the mean of the response variable with missing values for the ultra-high dimensional datasets is needed. In this paper, we propose a two-stage ultra-high dimensional variable screening method, RF-SIS, based on random forest regression, which effectively solves the problem of estimating missing values due to excessive data dimension. After the dimension reduction process by applying RF-SIS, mean interpolation is executed on the missing responses. The results of the simulated data show that compared with the estimation method of directly deleting missing observations, the estimation results of RF-SIS-MI have significant advantages in terms of the proportion of intervals covered, the average length of intervals, and the average absolute deviation.
文摘This article develops a procedure for screening variables, in ultra high-di- mensional settings, based on their predictive significance. This is achieved by ranking the variables according to the variance of their respective marginal regression functions (RV-SIS). We show that, under some mild technical conditions, the RV-SIS possesses a sure screening property, which is defined by Fan and Lv (2008). Numerical comparisons suggest that RV-SIS has competitive performance compared to other screening procedures, and outperforms them in many different model settings.
文摘In this paper, the following contaminated linear model is considered:y i=(1-ε)x τ iβ+z i, 1≤i≤n,where r.v.'s ｛ y i ｝ are contaminated with errors ｛ z i ｝. To assume that the errors have the finite moment of order 2 only. The non parametric estimation of contaminated coefficient ε and regression parameter β are established, and the strong consistency and convergence rate almost surely of the estimators are obtained. A simulated example is also given to show the visual performance of the estimations.
基金supported by Graduate Innovation Foundation of Shanghai University of Finance and Economics of China (Grant Nos. CXJJ-2014-459 and CXJJ-2015-430)National Natural Science Foundation of China (Grant No. 71271128), the State Key Program of National Natural Science Foundation of China (Grant No. 71331006), the State Key Program in the Major Research Plan of National Natural Science Foundation of China (Grant No. 91546202)+1 种基金National Center for Mathematics and Interdisciplinary Sciences, Key Laboratory of Random Complex Structures and Data Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences (Grant No. 2008DP173182)Innovative Research Team in Shanghai University of Finance and Economics (Grant No. IRT13077)
文摘The curse of high-dimensionality has emerged in the statistical fields more and more frequently.Many techniques have been developed to address this challenge for classification problems. We propose a novel feature screening procedure for dichotomous response data. This new method can be implemented as easily as t-test marginal screening approach, and the proposed procedure is free of any subexponential tail probability conditions and moment requirement and not restricted in a specific model structure. We prove that our method possesses the sure screening property and also illustrate the effect of screening by Monte Carlo simulation and apply it to a real data example.
基金Project supported by the National Natural Science Foundation of China(Nos.62403242 and 61973167)the Fundamental Research Funds for the Central Universities(No.30924010932)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX23_0481)。
文摘On-orbit service is important for maintaining the sustainability of the space environment.A space-based visible camera is an economical and lightweight sensor for situational awareness during on-orbit service.However,it can be easily affected by the low illumination environment.Recently,deep learning has achieved remarkable success in image enhancement of natural images,but it is seldom applied in space due to the data bottleneck.In this study,we first propose a dataset of BeiDou navigation satellites for on-orbit low-light image enhancement(LLIE).In the automatic data collection scheme,we focus on reducing the domain gap and improving the diversity of the dataset.We collect hardware-in-the-loop images based on a robotic simulation testbed imitating space lighting conditions.To evenly sample poses of different orientations and distances without collision,we propose a collision-free workspace and pose-stratified sampling.Subsequently,we develop a novel diffusion model.To enhance the image contrast without over-exposure and blurred details,we design fused attention guidance to highlight the structure and the dark region.Finally,a comparison of our method with previous methods indicates that our method has better on-orbit LLIE performance.
基金Supported by the National Natural Science Foundation of China(No.11801567)
文摘In this paper, we study the sure independence screening of ultrahigh-dimensional censored data with varying coefficient single-index model. This general model framework covers a large number of commonly used survival models. The property that the proposed method is not derived for a specific model is appealing in ultrahigh dimensional regressions, as it is difficult to specify a correct model for ultrahigh dimensional predictors.Once the assuming data generating process does not meet the actual one, the screening method based on the model will be problematic. We establish the sure screening property and consistency in ranking property of the proposed method. Simulations are conducted to study the finite sample performances, and the results demonstrate that the proposed method is competitive compared with the existing methods. We also illustrate the results via the analysis of data from The National Alzheimers Coordinating Center(NACC).
基金supported by the National Natural Science Foundation of China under Grant No.11901006the Natural Science Foundation of Anhui Province under Grant Nos.1908085QA06 and 1908085MA20。
文摘This paper proposes a new sure independence screening procedure for high-dimensional survival data based on censored quantile correlation(CQC).This framework has two distinctive features:1)Via incorporating a weighting scheme,our metric is a natural extension of quantile correlation(QC),considered by Li(2015),to handle high-dimensional survival data;2)The proposed method not only is robust against outliers,but also can discover the nonlinear relationship between independent variables and censored dependent variable.Additionally,the proposed method enjoys the sure screening property under certain technical conditions.Simulation results demonstrate that the proposed method performs competitively on survival datasets of high-dimensional predictors.
基金the Office of Naval Research through a multidisciplinary university research initiative(MURI)for their funding support,We would also like to acknowledge Dr.Kuan-Hsuan Shen for their valuable support in building the simulation pipeline.We also extend a thank you to Dr.Lihua Chen for her guidance in the initial stage of the work.This research is supported in part through research cyber-infrastructure resources and services provided by the Partnership for an Advanced Computing Environment(PACE)at the Georgia Institute of Technology and XSEDE/ACCESS for computational support through Grant No.TG-DMR080058N.
文摘This study guides the discovery of sustainable high-performance polymer membranes for organic binary solvent separations.We focus on solvent diffusivity in polymers,a key factor in quantifying solvent transport.Traditional experimental and computational methods for determining diffusivity are time-and resource-intensive,while current machine learning(ML)models often lack accuracy outside their training domains.To overcome this,we fuse experimental and simulated diffusivity data to train physics-enforced multi-task ML models,achieving more robust predictions in unseen chemical spaces and outperforming single-task models in data-limited scenarios.Next,we address the challenge of identifying optimal membranes for a model toluene-heptane separation,identifying polyvinyl chloride(PVC)as the optimal membrane among 13,000 polymers,consistent with literature findings,thereby validating our methodology.Expanding our search,we screen 1 million publicly available and 7 million chemically recyclable polymers,identifying greener halogen-free alternatives to PVC.This capability is expected to advance membrane design for solvent separations.
文摘造斜率的准确预测是进行井眼轨迹调控的基础,直接影响定向井钻井效率,但由于井下力学行为的复杂性,传统预测方法存在一定限制,难以实现精确预测。为此,提出了一种力学-智能模型融合的造斜率预测方法。利用力学模型计算钻头侧向力、钻头转角和极限造斜率并作为主控因素,通过自动化机器学习框架联合其他参数进行拟合预测,从而取代传统方法反演经验系数的过程,使其充分发挥力学模型宏观规律描述准确和智能模型非线性拟合能力强的优势。利用新疆玛湖区块14口井数据进行训练和测试。结果显示,融合力学参数后,模型造斜率最大误差、均方根误差和平均绝对误差分别下降了17%、12%和8%,其中均方根误差和平均绝对误差均小于每30 m 1.00°,表明该方法能够有效提升造斜率预测精度,尤其在造斜率急剧变化的井段表现出更优的预测性能。研究结果可为造斜率的准确预测提供新的思路,同时也可为井眼轨迹的精确调控提供一定的技术支撑。
