The University of Southampton
University of Southampton Institutional Repository

Deconvolution of continuous paleomagnetic data from pass-through magnetometer: A new algorithm to restore geomagnetic and environmental information based on realistic optimization

Deconvolution of continuous paleomagnetic data from pass-through magnetometer: A new algorithm to restore geomagnetic and environmental information based on realistic optimization
Deconvolution of continuous paleomagnetic data from pass-through magnetometer: A new algorithm to restore geomagnetic and environmental information based on realistic optimization
The development of pass-through superconducting rock magnetometers (SRM) has greatly promoted collection of paleomagnetic data from continuous long-core samples. The output of pass-through measurement is smoothed and distorted due to convolution of magnetization with the magnetometer sensor response. Although several studies could restore high-resolution paleomagnetic signal through deconvolution of pass-through measurement, difficulties in accurately measuring the magnetometer sensor response have hindered the application of deconvolution. We acquired reliable sensor response of an SRM at the Oregon State University based on repeated measurements of a precisely fabricated magnetic point source. In addition, we present an improved deconvolution algorithm based on Akaike's Bayesian Information Criterion (ABIC) minimization, incorporating new parameters to account for errors in sample measurement position and length. The new algorithm was tested using synthetic data constructed by convolving “true” paleomagnetic signal containing an “excursion” with the sensor response. Realistic noise was added to the synthetic measurement using Monte Carlo method based on measurement noise distribution acquired from 200 repeated measurements of a u-channel sample. Deconvolution of 1000 synthetic measurements with realistic noise closely resembles the “true” magnetization, and successfully restored fine-scale magnetization variations including the “excursion”. Our analyses show that inaccuracy in sample measurement position and length significantly affects deconvolution estimation, and can be resolved using the new deconvolution algorithm. Optimized deconvolution of 20 repeated measurements of a u-channel sample yielded highly consistent deconvolution results and estimates of error in sample measurement position and length, demonstrating the reliability of the new deconvolution algorithm for real pass-through measurements.
superconducting rock magnetometer, deconvolution, sensor response, u-channel sample, ABIC minimization, measurement noise
1525-2027
3907-3924
Oda, Hirokuni
cc28ac8c-fe68-4f59-a5d5-8910ffa8a7cd
Xuan, Chuang
3f3cad12-b17b-46ae-957a-b362def5b837
Oda, Hirokuni
cc28ac8c-fe68-4f59-a5d5-8910ffa8a7cd
Xuan, Chuang
3f3cad12-b17b-46ae-957a-b362def5b837

Oda, Hirokuni and Xuan, Chuang (2014) Deconvolution of continuous paleomagnetic data from pass-through magnetometer: A new algorithm to restore geomagnetic and environmental information based on realistic optimization. Geochemistry, Geophysics, Geosystems, 15 (10), 3907-3924. (doi:10.1002/2014GC005513).

Record type: Article

Abstract

The development of pass-through superconducting rock magnetometers (SRM) has greatly promoted collection of paleomagnetic data from continuous long-core samples. The output of pass-through measurement is smoothed and distorted due to convolution of magnetization with the magnetometer sensor response. Although several studies could restore high-resolution paleomagnetic signal through deconvolution of pass-through measurement, difficulties in accurately measuring the magnetometer sensor response have hindered the application of deconvolution. We acquired reliable sensor response of an SRM at the Oregon State University based on repeated measurements of a precisely fabricated magnetic point source. In addition, we present an improved deconvolution algorithm based on Akaike's Bayesian Information Criterion (ABIC) minimization, incorporating new parameters to account for errors in sample measurement position and length. The new algorithm was tested using synthetic data constructed by convolving “true” paleomagnetic signal containing an “excursion” with the sensor response. Realistic noise was added to the synthetic measurement using Monte Carlo method based on measurement noise distribution acquired from 200 repeated measurements of a u-channel sample. Deconvolution of 1000 synthetic measurements with realistic noise closely resembles the “true” magnetization, and successfully restored fine-scale magnetization variations including the “excursion”. Our analyses show that inaccuracy in sample measurement position and length significantly affects deconvolution estimation, and can be resolved using the new deconvolution algorithm. Optimized deconvolution of 20 repeated measurements of a u-channel sample yielded highly consistent deconvolution results and estimates of error in sample measurement position and length, demonstrating the reliability of the new deconvolution algorithm for real pass-through measurements.

This record has no associated files available for download.

More information

e-pub ahead of print date: 21 October 2014
Published date: October 2014
Keywords: superconducting rock magnetometer, deconvolution, sensor response, u-channel sample, ABIC minimization, measurement noise
Organisations: Paleooceanography & Palaeoclimate

Identifiers

Local EPrints ID: 370340
URI: http://eprints.soton.ac.uk/id/eprint/370340
ISSN: 1525-2027
PURE UUID: b3a2a640-64fe-48ae-b8b4-0040b8d31443
ORCID for Chuang Xuan: ORCID iD orcid.org/0000-0003-4043-3073

Catalogue record

Date deposited: 22 Oct 2014 09:35
Last modified: 15 Mar 2024 03:48

Export record

Altmetrics

Contributors

Author: Hirokuni Oda
Author: Chuang Xuan ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×