吉林大学学报(信息科学版)

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基于 Shearlet-AIC 算法的微地震初至拾取

巩佳琦,吴 宁   

  1. 吉林大学 通信工程学院,长春 130012
  • 收稿日期:2017-04-17 出版日期:2018-05-24 发布日期:2018-07-25
  • 作者简介:巩佳琦(1991— ) ,女,吉林省吉林市人,吉林大学硕士研究生,主要从事微弱信号检测和实时处理研究,(Tel) 86-18059866800(E-mail) 18059866800@ 163. com; 吴宁(1982— ) ,女,长春人,吉林大学副教授,主要从事数字信号处理和微弱信号检测研究,(Tel) 86-15543096625(E-mail) ning1337@ gmail. com。
  • 基金资助:
    国家自然科学基金资助项目( 41574096)

Research on Microseismic First Arrival Picking Method Based on Shearlet-AIC

GONG Jiaqi,WU Ning   

  1. College of Communication Engineering,Jilin University,Changchun 130012,China
  • Received:2017-04-17 Online:2018-05-24 Published:2018-07-25

摘要: 初至拾取是微地震数据处理的基本步骤及重要环节,在低信噪比情况下,传统的初至拾取方法性能不佳,无法满足实际需求。为此,提出一种新算法,该算法将时域 微 地 震 数 据 映 射 到 Shearlet 域,利 用 AIC(Akaike Information Criterion) 模型对 Shearlet 域各尺度层的数据实现初步识别,最小 AIC 值作为初至时刻。通过 大量实验验证 Shearlet-AIC 算法在低至 - 13 dB 信噪比下自动拾取的准确性,证实该算法优于传统初至拾取算法,解决了传统初至拾取算法在低信噪比时难以有效拾取微地震初至的难题。

关键词: 微地震, 初至拾取, 自动拾取, Shearlet 变换, AIC 信息准则

Abstract: Time picking is a crucial step in microseismic data processing,the picking results have great influence on hypocenter location. Especially when the SNR ( Signal-To-Noise Ratio) is low,it is difficult to obtain arrival times accurately with conventional approaches. We propose a new time picking approach based on the AIC( Akaike Information Criterion) and Shearlet transform named the Shearlet-AIC to solve the problems mentioned above. We divide microseismic data into several scales according to different statistical characteristic between signals and noise to obtain the feature of different frequency domain. We can acquire the minimum values that represent the picking results of frequency domain by using the Shearlet-AIC. To verify the reliability of the method,we experiment it on both synthetic and field datasets. The results show that our method can precisely pick the arrival times even when the SNR of data is as low as -13dB and the accuracy is superior to some existing method.

Key words: arrival time picking, automatic picking, akaike information criterion ( AIC), microseismicity, Shearlet transform

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