基于条件模型的油藏深度检索

M. B. Nunes, A. Poz, E. Alcântara, M. Curtarelli
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引用次数: 0

摘要

水深是绘制海图的重要尺度。准确提供水深信息的方法既昂贵又费时。因此,从70年代末开始,多光谱传感器开始用经验模型进行估算。在文献中没有使用深度间隔分割的经验模型进行研究,因此,我们评估了分割和单一水深模型的准确性。结果表明,在0 ~ 15 m范围内,单模型反演的RMSE为3.57 m,偏差约为-0.83 m;而分割模型的RMSE为2.29 m,偏差为0.41 m。在多光谱传感器海图更新中,采用分块模型比单一模型具有更好的更新效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Depth Retrieval From A Reservoir Using A Conditional-Based Model
Water depth is an important measure for nautical charts. Accurate methods to provide water depth information are expensive and time costing. For this reason, since late 70’s, it started to be estimate by multispectral sensors with empirical models. In the literature there is no investigation using empirical models partitioned in depth intervals, for this reason, we evaluated the accuracy of partitioned and single bathymetric models. The results have shown that to retrieve depth in from 0 to 15 m the single model provided an RMSE of 3.57 m, with a bias of about -0.83 m; while the RMSE for the partitioned model was 2.29 m with a bias of 0.41 m. For updating nautical charts using multispectral sensors it was concluded that the partitioned model can provide a better result than using a single model.
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