Analysis of H-Component of Geomagnetic Variation in Indonesian Region

Nia Syafitri, Angga Yolanda Putra, Erlansyah, Muzirwan, Hadi Rasidi, Singgih Anggi Purnama, Helmi Suryaputra, La Ode Muhammad Musafar Kilowasid, Kuncoro Wisnu, Iskandar Bakri, Lambang Nurdiansah, F. Nuraeni, Cahyo Purnomo, S. Rasimeng
{"title":"Analysis of H-Component of Geomagnetic Variation in Indonesian Region","authors":"Nia Syafitri, Angga Yolanda Putra, Erlansyah, Muzirwan, Hadi Rasidi, Singgih Anggi Purnama, Helmi Suryaputra, La Ode Muhammad Musafar Kilowasid, Kuncoro Wisnu, Iskandar Bakri, Lambang Nurdiansah, F. Nuraeni, Cahyo Purnomo, S. Rasimeng","doi":"10.1145/3575882.3575948","DOIUrl":null,"url":null,"abstract":"This paper analyzed geomagnetic variation of H-component from several geomagnetic stations in Indonesia, that are Kototabang (KTB), Pontianak (PTN), Tanjungsari (TJS), Pare-pare (PRP), Manado (MND), Kupang (KPG), and Pamengpeuk (PMK) during 2014. The study aims to analyze and obtain data set that are suitable for PTN geomagnetic data so that they can fill in the data gaps of PTN. We performed data analysis using standard deviation for PTN and other stations. We found that KPG, MND, PMK, and PRP have smaller deviation standard about 10 nT at geomagnetically quiet conditions. Meanwhile, in disturbed conditions, the maximum standard deviation is 17 nT. Substitution of data for geomagnetic quiet conditions should be done with caution and requires further analysis. The substitution of data for disturbed conditions can be done safely because the maximum standard deviation is smaller than the range of disturbance conversion into K index.","PeriodicalId":367340,"journal":{"name":"Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3575882.3575948","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

This paper analyzed geomagnetic variation of H-component from several geomagnetic stations in Indonesia, that are Kototabang (KTB), Pontianak (PTN), Tanjungsari (TJS), Pare-pare (PRP), Manado (MND), Kupang (KPG), and Pamengpeuk (PMK) during 2014. The study aims to analyze and obtain data set that are suitable for PTN geomagnetic data so that they can fill in the data gaps of PTN. We performed data analysis using standard deviation for PTN and other stations. We found that KPG, MND, PMK, and PRP have smaller deviation standard about 10 nT at geomagnetically quiet conditions. Meanwhile, in disturbed conditions, the maximum standard deviation is 17 nT. Substitution of data for geomagnetic quiet conditions should be done with caution and requires further analysis. The substitution of data for disturbed conditions can be done safely because the maximum standard deviation is smaller than the range of disturbance conversion into K index.
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
印度尼西亚地区地磁变化h分量分析
本文分析了2014年印尼Kototabang (KTB)、Pontianak (PTN)、Tanjungsari (TJS)、Pare-pare (PRP)、mando (MND)、Kupang (KPG)和Pamengpeuk (PMK)几个地磁站h分量的地磁变化。本研究旨在分析并获得适合PTN地磁数据的数据集,以填补PTN的数据空白。我们对PTN和其他台站的数据进行了标准差分析。我们发现KPG, MND, PMK和PRP在地磁安静条件下的偏差标准较小,约为10 nT。同时,在扰动条件下,最大标准差为17nt。将数据替换为地磁安静条件时应谨慎,需要进一步分析。由于最大标准差小于扰动转换为K指数的范围,因此可以安全地对扰动条件的数据进行替换。
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
自引率
0.00%
发文量
0
期刊最新文献
Modelling the climate factors affecting forest fire in Sumatra using Random Forest and Artificial Neural Network Parallel Programming in Finite Difference Method to Solve Turing's Model of Spot Pattern Identification of Hoya Plant using Convolutional Neural Network (CNN) and Transfer Learning Android-based Forest Fire Danger Rating Information System for Early Prevention of Forest / Land fires Leak Detection using Non-Intrusive Ultrasonic Water Flowmeter Sensor in Water Distribution Networks
×
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