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人工智能论文:SALSI:盐丘检测的新地震属性(SalSi: A new seismic attribute for salt dome detection)

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hjrinfo 发表于 2019-1-11 11:20:16 | 显示全部楼层 |阅读模式
hjrinfo 2019-1-11 11:20:16 206 0 显示全部楼层
人工智能论文:SALSI:盐丘检测的新地震属性(SalSi: A new seismic attribute for salt dome detection)在本文中,我们提出了一个基于显着性的属性SalSi来检测地震体内的saltdome体。 SalSi基于显着性理论和人类视觉系统(HVS)的建模。在这项工作中,我们的目标是突出地震体的部分,这些部分受到人类解释者的高度关注,并且基于地震图像的显着特征,我们检测到盐丘。实验结果表明,SalSi对北海F3区块采集的地震数据集的有效性。主观上,我们使用了不同盐丘组合算法的基本事实和输出来验证SalSi的结果。为了客观评价结果,我们使用了接收器操作特征(ROC)曲线和曲线下的区域(AUC)来证明SalSi是地震解释的一个有前途和无效的属性。
In this paper, we propose a saliency-based attribute, SalSi, to detect saltdome bodies within seismic volumes.SalSi is based on the saliency theory andmodeling of the human vision system (HVS).In this work, we aim to highlightthe parts of the seismic volume that receive highest attention from the humaninterpreter, and based on the salient features of a seismic image, we detectthe salt domes.Experimental results show the effectiveness of SalSi on thereal seismic dataset acquired from the North Sea, F3 block.Subjectively, wehave used the ground truth and the output of different salt dome delineationalgorithms to validate the results of SalSi.For the objective evaluation ofresults, we have used the receiver operating characteristics (ROC) curves andarea under the curves (AUC) to demonstrate SalSi is a promising and aneffective attribute for seismic interpretation.人工智能论文:SALSI:盐丘检测的新地震属性(SalSi: A new seismic attribute for salt dome detection) uLL65in3v3KwRk9w.jpg
URL地址:https://arxiv.org/abs/1901.02937     ----pdf下载地址:https://arxiv.org/pdf/1901.02937    ----人工智能论文:SALSI:盐丘检测的新地震属性(SalSi: A new seismic attribute for salt dome detection)
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