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相关概念视频

Survival Tree01:19

Survival Tree

87
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
87

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Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
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一个基于集体的机器学习解决方案,用于在石质学日志生成过程中不平衡的多类数据集.

Mohammad Saleh Jamshidi Gohari1, Mohammad Emami Niri2, Saeid Sadeghnejad3

  • 1Department of Petroleum Engineering, Kish International Campus, University of Tehran, Tehran, Iran.

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|December 7, 2023
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概括

这项研究引入了一种用于高分辨率石质学日志的新组合方法,改进了地下地质结构分析. 增强的加权平均组合显著提高了对具有挑战性的,不平衡的光电场数据的预测准确性.

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科学领域:

  • 地质地质地质地质地质地
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 石矿学日志对于理解地下地质结构和关联钻探数据至关重要.
  • 由于多类数据不平衡和地质异质性,准确地预测石灰岩状况具有挑战性.

研究的目的:

  • 开发一种新的工作流程,用于高分辨率的石质学日志生成,使用增强的加权平均总体方法.
  • 为了应对在地下地质结构中多类不平衡的石灰岩分布所带来的挑战.

主要方法:

  • 实施了加权平均组合方法,结合了纠错输出代码 (ECOC) 和成本敏感学习 (CSL).
  • 使用了机器学习算法,包括支持向量机 (SVM),随机森林 (RF),决策树 (DT),后勤回归 (LR) 和极端梯度提升 (XGBoost) 作为基线分类器.
  • 训练模型对来自四个油井的井日志数据进行了训练,并对来自中东油田的盲井进行了评估.

主要成果:

  • 增强的加权平均组合,特别是基于RF和SVM,在预测灯形状方面表现出卓越的表现.
  • 在盲井数据上,Kappa平均统计数据为84.50% (几乎完美一致),平均F-测量值为91.04%.
  • 开发的工作流程被证明是强大的和准确的,用于高分辨率的石质学日志生产.

结论:

  • 这种基于组合的新型工作流程有效地克服了与不平衡的 lithofacies 数据相关的挑战.
  • 增强的加权平均组合显著提高了石质学日志解释的准确性和可靠性.
  • 这种方法为详细的地下地质分析和资源勘探提供了有价值的工具.