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= Adjusted geomagnetic data—Theoretical basis and validation =
{"@context": "https://schema.org", "@type": "CreativeWork", "additionalType": "USGS Numbered Series", "name": "Adjusted geomagnetic data\u2014Theoretical basis and validation", "identifier": [{"@type": "PropertyValue", "propertyID": "USGS Publications Warehouse IndexID", "value": "ofr20201053", "url": "https://pubs.usgs.gov/publication/ofr20201053"}, {"@type": "PropertyValue", "propertyID": "USGS Publications Warehouse Internal ID", "value": 70211586}, {"@type": "PropertyValue", "propertyID": "DOI", "value": "10.3133/ofr20201053", "url": "https://doi.org/10.3133/ofr20201053"}], "inLanguage": "en", "isPartOf": [{"@type": "CreativeWorkSeries", "name": "Open-File Report"}], "datePublished": "2020", "dateModified": "2020-08-04", "abstract": "Adjusted geomagnetic data are magnetometer measurements with provisional correction factors applied such that vector quantities are oriented in a local Cartesian frame in which the X axis points north, the Y axis points east, and the Z axis points down. These correction factors are determined from so-called absolute measurements, which are \u201cground truth\u201d observations made in the field using specialized magnetometers and survey equipment that are (nearly) colocated with the automated and continuously running magnetic measurement instrumentation. Correction factors can be substantial, up to hundreds of nanoTeslas, depending on the geologic and geomagnetic characteristics of the observatory site. They also tend to evolve over time because of instrument response instability and changing site characteristics. Historically, correction factors were determined offline, up to 1 year or more post-measurement, and applied to raw measurements to produce \u201cDefinitive\u201d data for scientific analysis. Growing demand for corrected real-time geomagnetic data to better support space weather operations motivated development of an \u201cAdjusted\u201d geomagnetic data product. Modern computational tools, and some notable practical concerns, dictated a transition to affine transformations in lieu of more traditional baseline corrections, as well as a calibration parameter estimation algorithm that is more robust and statistically optimal, and therefore better suited for automated and unsupervised execution. A theoretical basis for this algorithm is presented, along with a demonstration and validation based on a comparison of results obtained with traditional techniques. Discrepancies between Definitive corrected data and near real-time Adjusted data obtained using affine transformations are minimal, generally much less than 5 nanoTeslas per vector component, and less than 1 nanoTesla for the total field magnitude, which satisfies International Real-Time Magnetic Observatory Network (INTERMAGNET) standards.", "description": "iv, 19 p.", "publisher": {"@type": "Organization", "name": "U.S. Geological Survey"}, "author": [{"@type": "Person", "name": "Rigler, E. Joshua erigler@usgs.gov", "givenName": "E. Joshua", "familyName": "Rigler", "email": "erigler@usgs.gov", "identifier": {"@type": "PropertyValue", "propertyID": "ORCID", "value": "0000-0003-4850-3953", "url": "https://orcid.org/0000-0003-4850-3953"}, "affiliation": [{"@type": "Organization", "name": "Geologic Hazards Science Center", "url": "https://www.usgs.gov/centers/geologic-hazards-science-center"}]}, {"@type": "Person", "name": "Claycomb, Abram E. aclaycomb@usgs.gov", "givenName": "Abram E.", "familyName": "Claycomb", "email": "aclaycomb@usgs.gov", "identifier": {"@type": "PropertyValue", "propertyID": "ORCID", "value": "0000-0002-2908-2586", "url": "https://orcid.org/0000-0002-2908-2586"}, "affiliation": [{"@type": "Organization", "name": "Geologic Hazards Science Center", "url": "https://www.usgs.gov/centers/geologic-hazards-science-center"}]}], "funder": [{"@type": "Organization", "name": "Geologic Hazards Science Center", "url": "https://www.usgs.gov/centers/geologic-hazards-science-center"}]}
Adjusted geomagnetic data are magnetometer measurements with provisional correction factors applied such that vector quantities are oriented in a local Cartesian frame in which the X axis points north, the Y axis points east, and the Z axis points down. These correction factors are determined from so-called absolute measurements, which are “ground truth” observations made in the field using specialized magnetometers and survey equipment that are (nearly) colocated with the automated and continuously running magnetic measurement instrumentation. Correction factors can be substantial, up to hundreds of nanoTeslas, depending on the geologic and geomagnetic characteristics of the observatory site. They also tend to evolve over time because of instrument response instability and changing site characteristics. Historically, correction factors were determined offline, up to 1 year or more post-measurement, and applied to raw measurements to produce “Definitive” data for scientific analysis. Growing demand for corrected real-time geomagnetic data to better support space weather operations motivated development of an “Adjusted” geomagnetic data product. Modern computational tools, and some notable practical concerns, dictated a transition to affine transformations in lieu of more traditional baseline corrections, as well as a calibration parameter estimation algorithm that is more robust and statistically optimal, and therefore better suited for automated and unsupervised execution. A theoretical basis for this algorithm is presented, along with a demonstration and validation based on a comparison of results obtained with traditional techniques. Discrepancies between Definitive corrected data and near real-time Adjusted data obtained using affine transformations are minimal, generally much less than 5 nanoTeslas per vector component, and less than 1 nanoTesla for the total field magnitude, which satisfies International Real-Time Magnetic Observatory Network (INTERMAGNET) standards.
 
== Table of Contents ==
* Abstract
* Motivation
* Traditional Baseline Adjustments
* Affine Transformations
* Estimating Affine Transformation
* Adaptive Affine Matrices
* Adjusting Data
* Summary and Conclusions
* Acknowledgments
* References Cited