通过投票堆叠回归 (VStaR) 算法进行声阻逆转
Lutfi Mulyadi Surachman1, Sanlin I Kaka2, Abdullatif Al-Shuhail2
1Geoscience Department, College of Petroleum Engineering and Geoscience, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia. g201408060@kfupm.edu.sa.
Scientific reports
|July 2, 2025
概括
本研究介绍了VStaR模型,用于在地震勘探中改进声阻估计. 通过提供比现有方法更精确的预测,VStaR提高了碳化合物勘探的准确性.
科学领域:
- 地质物理学 地质物理学
- 石油地质科学 石油地质科学
背景情况:
- 声阻抗 (AI) 对于碳化合物勘探至关重要,因为砂岩和碳酸盐中的低AI通常意味着高孔度.
- 准确的AI估计对于识别潜在的碳化合物储库至关重要.
研究的目的:
- 通过使用先进的机器学习算法来完善声阻估计.
- 将新型VStaR模型的性能与VSR和BLIMP等现有方法进行比较.
主要方法:
- 采用堆叠和投票回归算法,以深度,双向旅行时间 (TWTT) 和九个地震属性作为输入.
- 使用scikit-learn实现模型,专注于VStaR模型的超参数调整.
主要成果:
- 该VStaR模型显示出优异的预测性能 (R2=0.9973) 和适配精度 (True > VStaR > VSR > BLIMP).
- 与最佳基准模型相比,VStaR减少了14.74%的根平均平方误差 (RMSE),尽管计算时间更长.
- 在平均平方误差 (MSE),RMSE和R2指标上,VStaR的表现优于VSR和BLIMP.
结论:
- VStaR方法为声阻力预测提供了一种新且卓越的方法.
- 这种改进的AI估计有潜力显著提高碳化合物勘探效率,特别是在碳酸盐数据集,如Illam.
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