双支向量回归用于表征表面重建中的不确定性
ShiCheng Yu1,2, JiaQing Miao3,4, FeiLong Qin1
1School of Big Data and Artificial Intelligence, Chengdu Technological University, Chengdu, 611730, China.
Scientific reports
|August 23, 2024
概括
这项研究引入了一种新的方法,用于使用双支持向量的回归来表征表面重建不确定性. 该方法有效地整合数据,并利用井道信息来限制不确定性,提高油气勘探等领域的准确性.
科学领域:
- 地质科学 地质科学
- 数据科学数据科学数据科学
- 工程 工程师 工程师 工程师
背景情况:
- 表面重建对于逆向工程和石油/天然气勘探至关重要.
- 数据错误和有限的表面信息在重建中造成不确定性.
- 准确的不确定性可视化对于风险评估和数据采集规划至关重要.
研究的目的:
- 提出一种不确定性表征方法,用于使用双支持向量回归 (TSVR) 来进行表面重建.
- 有效地整合多样化的建模数据,并利用高可靠性样本.
- 将井道数据纳入,以提高重建准确度和减少不确定性.
主要方法:
- 利用TSVR通过量子和有限约束生成不确定性间隔.
- 在预测点上使用不平等约束的纳入井路径点.
- 制定了不确定性表征作为两个较小规模的二进制编程问题,以减少计算时间.
主要成果:
- 在真实故障和合成数据集上验证了拟议的方法.
- 证明了各种数据源的有效集成.
- 展示了数据如何限制不确定性信封,部分减轻了重建不确定性.
结论:
- 提出的基于TSVR的方法准确地描述和可视化了表面重建的不确定性.
- 井道数据的整合显著限制了不确定性信封.
- 这种方法增强了地下建模和勘探中的风险分析和规划.
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