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Sensor response estimate and cross calibration of paleomagnetic measurements on passthrough superconducting rock magnetometers

Sensor response estimate and cross calibration of paleomagnetic measurements on passthrough superconducting rock magnetometers
Sensor response estimate and cross calibration of paleomagnetic measurements on passthrough superconducting rock magnetometers
Pass‐through superconducting rock magnetometers (SRMs) enable rapid and precise remanence measurement of continuous samples and are essential for paleomagnetic studies. Due to convolution effect of the SRM sensor response, pass‐through measurements need to be deconvolved to restore accurate and high‐resolution signal. A key step toward successful deconvolution is a reliable estimate of the SRM sensor response. Here, we present new tool URESPONSE for accurate SRM sensor response estimate based on measurements of a well‐calibrated magnetic point source. URESONSE allows sensor response to be estimated for continuous samples with different cross‐section geometry. We estimate sensor responses for an old liquid helium‐cooled SRM (SRM‐old) and a new liquid helium‐free SRM (SRM‐new) at the University of Southampton and compare remanence measurement of a u‐channel on both SRMs before and after deconvolution. For each SRM, sensor response estimates based on data collected using different magnetic point source samples and/or measurement procedures generally yield small differences (std. <~1%), while sensor response estimates for continuous samples with different cross‐section geometry often show larger differences (std. up to ~2%). Compared with SRM‐old, SRM‐new has smaller cross‐axis responses, less negative zones, and significantly broader main axis responses. We demonstrate that normalization of data using a nine‐element “effective length” matrix calculated from sensor response estimate is necessary to minimize differences in measurements on two SRMs. Deconvolution of measurements on two SRMs using accurate sensor response estimates yields highly consistent and high‐resolution results, while deconvolution using inaccurate sensor response data can lead to significant differences especially for data from SRM‐old that has large cross‐axis responses.
Xuan, Chuang
3f3cad12-b17b-46ae-957a-b362def5b837
Oda, Hirokuni
cc28ac8c-fe68-4f59-a5d5-8910ffa8a7cd
Xuan, Chuang
3f3cad12-b17b-46ae-957a-b362def5b837
Oda, Hirokuni
cc28ac8c-fe68-4f59-a5d5-8910ffa8a7cd

Xuan, Chuang and Oda, Hirokuni (2019) Sensor response estimate and cross calibration of paleomagnetic measurements on passthrough superconducting rock magnetometers. G3: Geochemistry, Geophysics, Geosystems. (doi:10.1029/2019GC008597).

Record type: Article

Abstract

Pass‐through superconducting rock magnetometers (SRMs) enable rapid and precise remanence measurement of continuous samples and are essential for paleomagnetic studies. Due to convolution effect of the SRM sensor response, pass‐through measurements need to be deconvolved to restore accurate and high‐resolution signal. A key step toward successful deconvolution is a reliable estimate of the SRM sensor response. Here, we present new tool URESPONSE for accurate SRM sensor response estimate based on measurements of a well‐calibrated magnetic point source. URESONSE allows sensor response to be estimated for continuous samples with different cross‐section geometry. We estimate sensor responses for an old liquid helium‐cooled SRM (SRM‐old) and a new liquid helium‐free SRM (SRM‐new) at the University of Southampton and compare remanence measurement of a u‐channel on both SRMs before and after deconvolution. For each SRM, sensor response estimates based on data collected using different magnetic point source samples and/or measurement procedures generally yield small differences (std. <~1%), while sensor response estimates for continuous samples with different cross‐section geometry often show larger differences (std. up to ~2%). Compared with SRM‐old, SRM‐new has smaller cross‐axis responses, less negative zones, and significantly broader main axis responses. We demonstrate that normalization of data using a nine‐element “effective length” matrix calculated from sensor response estimate is necessary to minimize differences in measurements on two SRMs. Deconvolution of measurements on two SRMs using accurate sensor response estimates yields highly consistent and high‐resolution results, while deconvolution using inaccurate sensor response data can lead to significant differences especially for data from SRM‐old that has large cross‐axis responses.

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More information

Accepted/In Press date: 14 October 2019
e-pub ahead of print date: 5 November 2019

Identifiers

Local EPrints ID: 435150
URI: http://eprints.soton.ac.uk/id/eprint/435150
PURE UUID: b20c188f-5654-4f09-988e-bda91cb70ce1
ORCID for Chuang Xuan: ORCID iD orcid.org/0000-0003-4043-3073

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Date deposited: 23 Oct 2019 16:30
Last modified: 18 Feb 2021 17:22

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