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An Unsupervised Learning Method for Estimating Zero-Crossing-Time
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DOI：10.1109/LGRS.2019.2942166.png)
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Abstract
En 中文
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.
Keywords:
Unsupervised learning
Reservoirs
Statistical analysis
Manuals
Reliability
Training
Neural networks
Cluster analysis
seismic image interpretation
thin bed
unsupervised learning
zero-crossing-time (ZCT)
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