DRRGlobal: Uncovering the weak phases from global seismograms using the damped rank-reduction method

IF 4.2 2区 地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computers & Geosciences Pub Date : 2024-08-23 DOI:10.1016/j.cageo.2024.105687
Wei Chen , Yangkang Chen
{"title":"DRRGlobal: Uncovering the weak phases from global seismograms using the damped rank-reduction method","authors":"Wei Chen ,&nbsp;Yangkang Chen","doi":"10.1016/j.cageo.2024.105687","DOIUrl":null,"url":null,"abstract":"<div><p>Some target seismic signals in the earthquake data can be very weak compared with interfering phases, and are thus difficult to detect, which further hinders the effective usage of these weak phases for subsequent high-resolution imaging of earth interiors. The strong ambient noise makes this situation even more troublesome since the weak signals can be mostly buried in the noise. Here, we present an open-source package for uncovering the weak phases from global seismograms. We adopt a two-step scheme to reconstruct and denoise array data. The first step is weighted average interpolation which puts the data into irregular grids. The second step adopts the weighted projection-onto-convex sets based on damped rank-reduction to further interpolate and denoise for the binned data. Taking the complexity of the weak signal into consideration, we adopt the automatic strategy to select an appropriate rank in different localized windows. We conduct several synthetic tests to carefully investigate the performance regarding effectiveness, robustness, and efficiency, and compare the algorithm with the frequency–wavenumber-domain projection onto convex sets method that is already used in the global seismology literature. Finally, the proposed framework is validated via a recorded array data set of the 1995 May 5 Philippines earthquake.</p></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"192 ","pages":"Article 105687"},"PeriodicalIF":4.2000,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Geosciences","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0098300424001705","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

Abstract

Some target seismic signals in the earthquake data can be very weak compared with interfering phases, and are thus difficult to detect, which further hinders the effective usage of these weak phases for subsequent high-resolution imaging of earth interiors. The strong ambient noise makes this situation even more troublesome since the weak signals can be mostly buried in the noise. Here, we present an open-source package for uncovering the weak phases from global seismograms. We adopt a two-step scheme to reconstruct and denoise array data. The first step is weighted average interpolation which puts the data into irregular grids. The second step adopts the weighted projection-onto-convex sets based on damped rank-reduction to further interpolate and denoise for the binned data. Taking the complexity of the weak signal into consideration, we adopt the automatic strategy to select an appropriate rank in different localized windows. We conduct several synthetic tests to carefully investigate the performance regarding effectiveness, robustness, and efficiency, and compare the algorithm with the frequency–wavenumber-domain projection onto convex sets method that is already used in the global seismology literature. Finally, the proposed framework is validated via a recorded array data set of the 1995 May 5 Philippines earthquake.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
DRRGlobal:使用阻尼秩还原法从全球地震图中发现弱相
与干扰相位相比,地震数据中的某些目标地震信号可能非常微弱,因此很难探测到,这进一步阻碍了有效利用这些微弱相位对地球内部进行后续高分辨率成像。强烈的环境噪声使这种情况变得更加麻烦,因为微弱的信号可能大部分被掩盖在噪声中。在此,我们提出了一个开源软件包,用于从全球地震图中发现弱相位。我们采用两步法重建和去噪阵列数据。第一步是加权平均插值,将数据放入不规则网格中。第二步采用基于阻尼秩还原的加权投影到凸集,进一步对二进制数据进行插值和去噪。考虑到微弱信号的复杂性,我们采用了自动策略,在不同的局部窗口中选择合适的秩。我们进行了多次合成测试,仔细研究了该算法在有效性、鲁棒性和效率方面的表现,并将该算法与全球地震学文献中已使用的凸集频域投影法进行了比较。最后,通过 1995 年 5 月 5 日菲律宾地震的记录阵列数据集对所提出的框架进行了验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Computers & Geosciences
Computers & Geosciences 地学-地球科学综合
CiteScore
9.30
自引率
6.80%
发文量
164
审稿时长
3.4 months
期刊介绍: Computers & Geosciences publishes high impact, original research at the interface between Computer Sciences and Geosciences. Publications should apply modern computer science paradigms, whether computational or informatics-based, to address problems in the geosciences.
期刊最新文献
Multimodal feature integration network for lithology identification from point cloud data A two-dimensional magnetotelluric deep learning inversion approach based on improved Dense Convolutional Network Removing atmospheric noise from InSAR interferograms in mountainous regions with a convolutional neural network Novel empirical curvelet denoising strategy for suppressing mixed noise of microseismic data Curvilinear lineament extraction: Bayesian optimization of Principal Component Wavelet Analysis and Hysteresis Thresholding
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1