.png){kind=icon}
返回
Automatic microseismic events detection using morphological multiscale top-hat transformation
.png){kind=icon}
OA
AI
DOI：10.1016/j.petsci.2022.08.005.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The occurrence of microseismic is not random but is related to the physical properties of the under-ground medium. Due to the low intensity and the influence of noise, microseismic eventually lead to poor signal-to-noise ratio. We proposed a method for automatic detection of microseismic events by adoption of multiscale top-hat transformation. The method is based on the difference between the signal and noise in the multiscale top-hat transform section and achieves the detection on a specific section. The microseismic data are decomposed into different scales by multiscale morphology top-hat trans-formation firstly. Then the potential microseismic events could be detected by picking up the peak value in the multiscale top-hat section, and the characteristic profile obtains the start point with a specific threshold value. Finally, the synthetic data experiences demonstrate the advantages of this method under strong and weak noisy conditions, and the filed data example also shows its reliability and adaptability.(c) 2022 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keyword:
Microseismic events detection
Multiscale morphology
Top -hat transformation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.1
论文数:
2.1K
被引数:
7.0K
机构
引用论文
分享收藏Dynamic mechanical characterization of biodegradable composites of hydroxyapatite and polylactides
PREAI
分享收藏SegNet-based first-break picking via seismic waveform classification directly from shot gathers with sparsely distributed traces.png)
PETROLEUM SCIENCEOAAI
PETROLEUM SCIENCEIF6.1
OAAI分享收藏
查看更多内容