Yu ShiCheng, Miao JiaQing, Qin FeiLong
School of Big Data and Artificial Intelligence, Chengdu Technological University, Chengdu, 611730, China.
Basic Teaching Department, The Engineering & Technical College of Chengdu University of Technology, Leshan, 614000, China.
Sci Rep. 2024 Aug 23;14(1):19612. doi: 10.1038/s41598-024-70109-y.
Surface reconstruction plays a pivotal role in various fields, including reverse engineering, and oil and gas exploration. However, errors in available data and insufficient surface morphology information often introduce uncertainty into the reconstruction. It is crucial to accurately characterize and visualize the uncertainty in surface reconstruction for risk analysis and planning further data collection. To this end, this paper proposes an uncertainty characterization method based on twin support vector regression. First, various modeling data are effectively integrated and the information contained in the high-confidence sample is efficiently utilized through the uncertainty interval generated by quantiles and upper/lower bound constraints. Second, well-path points are incorporated by imposing inequality constraints on the corresponding prediction points. Finally, in order to reduce computation time, the problem of uncertainty characterization is formulated as two smaller-scale quadratic programming. The results obtained from a real fault dataset and a synthetic dataset validate the effectiveness of the proposed method. When well data are available, the generated uncertainty envelopes are constrained by well data, which can partially mitigate reconstruction uncertainties.
曲面重建在包括逆向工程以及石油和天然气勘探等各个领域中都起着关键作用。然而，现有数据中的误差和表面形态信息不足常常给重建带来不确定性。准确地表征和可视化曲面重建中的不确定性对于风险分析和规划进一步的数据采集至关重要。为此，本文提出了一种基于孪生支持向量回归的不确定性表征方法。首先，通过分位数生成的不确定性区间和上下界约束，有效地整合各种建模数据并高效利用高置信度样本中包含的信息。其次，通过对相应预测点施加不等式约束来纳入井眼轨迹点。最后，为了减少计算时间，将不确定性表征问题表述为两个较小规模的二次规划问题。从真实断层数据集和合成数据集获得的结果验证了所提方法的有效性。当有井数据可用时，生成的不确定性包络受井数据约束，这可以部分减轻重建的不确定性。
