Analysis of geomagnetic observatory data and detection of geomagnetic jerks with the MOSFiT software package

IF 1.8 4区地球科学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Geoscientific Instrumentation Methods and Data Systems Pub Date : 2023-12-18 DOI:10.5194/gi-12-271-2023

Marcos Vinicius da Silva, Katia J. Pinheiro, Achim Ohlert, Jürgen Matzka

{"title":"Analysis of geomagnetic observatory data and detection of geomagnetic jerks with the MOSFiT software package","authors":"Marcos Vinicius da Silva, Katia J. Pinheiro, Achim Ohlert, Jürgen Matzka","doi":"10.5194/gi-12-271-2023","DOIUrl":null,"url":null,"abstract":"Abstract. MOSFiT (Magnetic Observatories and Stations Filtering Tool) is a Python package to visualize and filter data from magnetic observatories and magnetometer stations. The purpose of MOSFiT is to automatically isolate and analyze the secular variation (SV) information measured by geomagnetic observatory data. External field contributions may be reduced by selecting data according to local time and geomagnetic indices and by subtracting the magnetospheric field predictions of the CHAOS-7 model. MOSFiT calculates the SV by annual differences of monthly means, and geomagnetic jerk occurrence time and amplitude are automatically calculated by fitting two straight-line segments in a user-defined time interval of the SV time series. Here, we present the new Python package, validate it against independent results from previous publications and show its application. In particular, we quantify the RMS misfit between SV derived from processing schemes and the SV predicted by CHAOS-7. Analyzing the International Real-time Magnetic Observatory Network (INTERMAGNET) quasi-definitive data with MOSFiT allows for a timely investigation of SV, such as the detection of recent geomagnetic jerks. It can also be used for data selection for, e.g., external field studies or quality control of geomagnetic observatory data.","PeriodicalId":48742,"journal":{"name":"Geoscientific Instrumentation Methods and Data Systems","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geoscientific Instrumentation Methods and Data Systems","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.5194/gi-12-271-2023","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 0

Abstract

Abstract. MOSFiT (Magnetic Observatories and Stations Filtering Tool) is a Python package to visualize and filter data from magnetic observatories and magnetometer stations. The purpose of MOSFiT is to automatically isolate and analyze the secular variation (SV) information measured by geomagnetic observatory data. External field contributions may be reduced by selecting data according to local time and geomagnetic indices and by subtracting the magnetospheric field predictions of the CHAOS-7 model. MOSFiT calculates the SV by annual differences of monthly means, and geomagnetic jerk occurrence time and amplitude are automatically calculated by fitting two straight-line segments in a user-defined time interval of the SV time series. Here, we present the new Python package, validate it against independent results from previous publications and show its application. In particular, we quantify the RMS misfit between SV derived from processing schemes and the SV predicted by CHAOS-7. Analyzing the International Real-time Magnetic Observatory Network (INTERMAGNET) quasi-definitive data with MOSFiT allows for a timely investigation of SV, such as the detection of recent geomagnetic jerks. It can also be used for data selection for, e.g., external field studies or quality control of geomagnetic observatory data.

查看原文本刊更多论文

利用 MOSFiT 软件包分析地磁观测站数据和探测地磁抖动

摘要MOSFiT（磁性观测站和磁强计站过滤工具）是一个 Python 软件包，用于可视化和过滤磁性观测站和磁强计站的数据。MOSFiT 的目的是自动分离和分析地磁观测站数据测得的时序变化（SV）信息。可以根据当地时间和地磁指数选择数据，并减去 CHAOS-7 模型的磁层场预测值，从而减少外部磁场的贡献。MOSFiT 通过月平均值的年差计算 SV，并通过在 SV 时间序列的用户定义时间间隔内拟合两条直线段自动计算地磁跃变发生时间和振幅。在此，我们介绍了新的 Python 软件包，根据以前发表的独立结果对其进行了验证，并展示了其应用。特别是，我们量化了由处理方案得出的 SV 与 CHAOS-7 预测的 SV 之间的均方根误差。利用 MOSFiT 分析国际实时磁观测网络（INTERMAGNET）的准定义数据可以及时调查 SV，例如检测最近的地磁突变。它还可用于外部实地研究或地磁观测站数据质量控制等方面的数据选择。

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分钟内获得全文求助全文

来源期刊

Geoscientific Instrumentation Methods and Data Systems GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES

CiteScore

3.70

自引率

0.00%

发文量

审稿时长

37 weeks

期刊介绍： Geoscientific Instrumentation, Methods and Data Systems (GI) is an open-access interdisciplinary electronic journal for swift publication of original articles and short communications in the area of geoscientific instruments. It covers three main areas: (i) atmospheric and geospace sciences, (ii) earth science, and (iii) ocean science. A unique feature of the journal is the emphasis on synergy between science and technology that facilitates advances in GI. These advances include but are not limited to the following: concepts, design, and description of instrumentation and data systems; retrieval techniques of scientific products from measurements; calibration and data quality assessment; uncertainty in measurements; newly developed and planned research platforms and community instrumentation capabilities; major national and international field campaigns and observational research programs; new observational strategies to address societal needs in areas such as monitoring climate change and preventing natural disasters; networking of instruments for enhancing high temporal and spatial resolution of observations. GI has an innovative two-stage publication process involving the scientific discussion forum Geoscientific Instrumentation, Methods and Data Systems Discussions (GID), which has been designed to do the following: foster scientific discussion; maximize the effectiveness and transparency of scientific quality assurance; enable rapid publication; make scientific publications freely accessible.