MagPySV: a Python package for processing and denoising geomagnetic observatory data

Cox, Grace; Brown, Will; Billingham, Laurence; Holme, Richard. 2017 MagPySV: a Python package for processing and denoising geomagnetic observatory data. [Lecture] In: Good Hope for Earth Sciences Joint IAPSO-IAMAS-IAGA Assembly, Cape Town, South Africa, 27 Aug 2017 - 1 Sept 2017. (Unpublished)

Before downloading, please read NORA policies.

Preview

Text
iaga_magpysv.pdf
Download (1MB) | Preview

Official URL: http://www.iapso-iamas-iaga2017.com/

Abstract/Summary

MagPySV: a python package for processing and denoising geomagnetic observatory data Measurements obtained at ground-based geomagnetic observatories are crucial to our understanding of secular variation (SV) of the geomagnetic field and permit investigations of Earth’s deep interior. Observatory monthly means are widely used for this purpose, but are highly sensitive to measurement errors due to their high temporal resolution and also suffer from significant external magnetic field contamination. These noise sources often obscure fine-scale details required for studying rapid observed features, such as geomagnetic jerks. Many current processing methods rely on piecemeal closed-source codes or are performed by hand on an ad hoc basis. This requires much time, effort and detailed knowledge of observatory data processing, and hampers efforts to reproduce the datasets underlying many published results. The aim of this work is to provide a consistent means of generating high resolution SV time series, with baseline jumps, outliers and external contamination removed. We present MagPySV, a python package designed to process and denoise observatory hourly means distributed by the World Data Centre for Geomagnetism at the British Geological Survey, Edinburgh. This package allows the user to obtain the dataset in WDC format from BGS servers and keep it up-to-date by replacing preliminary data with the most definitive version available. It produces time series of the X, Y and Z components of the field and SV at the desired frequency (typically monthly means), and applies corrections for all documented baseline jumps. Optionally, the user may exclude data using the Ap index, which removes effects from documented geomagnetic storms. Robust statistics are used to identify and remove outliers. The software extends the denoising methods of Wardinski & Holme (2011, GJI) and Brown et al (2013, PEPI), which use the covariance matrix of the residual between the observed SV and that predicted by a global field model to create and remove a proxy for external field signal from the data. This extension creates a single covariance matrix for all observatories of interest combined and applies the external field correction to all locations simultaneously. Finally, we present a denoised sequence of data produced by MagPySV, and discuss its application to geomagnetic jerks.

Item Type:	Publication - Conference Item (Lecture)
NORA Subject Terms:	Earth Sciences
Date made live:	10 Jan 2018 11:33 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/518911

Item Type:

Publication - Conference Item (Lecture)

NORA Subject Terms:

Earth Sciences

Date made live:

10 Jan 2018 11:33 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/518911