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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >An Unsupervised Learning Method for Estimating Zero-Crossing-Time
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An Unsupervised Learning Method for Estimating Zero-Crossing-Time

机译：一种估计零交叉时间的无监督学习方法

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摘要

It is an effective way in seismic imaging to make full use of lateral seismic response to break through the limitation of vertical resolution and to improve the accuracy of interpretation. On zero-crossing-time (ZCT) amplitude slices, there is a clearer imprint of underground beds than on non-ZCT slices, providing an important foundation for characterizing interbedded thin beds. However, picking ZCTs is time-consuming with significant manual efforts. In the assumption of horizontally layered media with lateral invariance, we deduce the variation of the cluster number on ZCT and non-ZCT slices with the number of thin beds. Furthermore, based on the statistical analysis on all cluster numbers of a 3-D seismic data set, ZCT, and non-ZCT slices are distinguished according to the difference of the cluster number. As a result, all ZCTs are picked automatically. Considering the influence of noise, the method provides the estimated values and the estimated intervals for all ZCTs to improve reliability. No label is required with this unsupervised learning method. The feasibility and practicability of the proposed method have been verified with numerical and real data experiments.
机译：它是抗震成像的有效方法，以充分利用横向地震反应来突破垂直分辨率的限制，提高解释的准确性。在零交叉时间（ZCT）幅度切片上，地下床上的印记比在非ZCT切片上，为表征嵌入式薄床提供了一个重要的基础。然而，采摘ZCTS是耗时的，具有重要的手动努力。在横向不变性的水平分层介质的假设中，我们在ZCT和非ZCT切片上推断磁簇数与薄床的数量。此外，基于对3-D地震数据集的所有簇数，ZCT和非ZCT切片的统计分析根据簇数的差异而区分。结果，所有ZCT都会自动挑选。考虑到噪声的影响，该方法提供了所有ZCT的估计值和估计间隔，以提高可靠性。这种无监督的学习方法不需要标签。通过数值和实际数据实验验证了所提出的方法的可行性和实用性。

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