Sensor Response Estimate and Cross Calibration of Paleomagnetic Measurements on Pass‐Through 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...
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| description | 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. |
| doi_str_mv | 10.1029/2019GC008597 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_24P</sourceid><recordid>TN_cdi_proquest_journals_2330660536</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2330660536</sourcerecordid><originalsourceid>FETCH-LOGICAL-a4342-ce5bcf7cc3929a17de3bc264f1b43ea5563f642c38755688390ae023be6065223</originalsourceid><addsrcrecordid>eNp9kLFOwzAQhi0EEqWw8QCWWCk4duwkI4pKQGpF1ZY5ctxLm5LYwXaEusEb8Iw8CSllYGK6f_j-O92H0GVAbgJCk1tKgiRLCYl5Eh2hQcApH1FCo-M_-RSdObclJAg5jwfoYwHaGYvn4FqjHeCx81UjPWCpVzi1xjmcyroqrPSV0diUeCZrMI1ca_CVwlOQrrPQgPYO98BMOvf1_rncWNOtN3jRtWCV0atO-Uqv8dyoFzz9KZsGPFh3jk5KWTu4-J1D9Hw_XqYPo8lT9pjeTUYyZCEdKeCFKiOlWEITGUQrYIWiIiyDImQgOResFCFVLI76HMcsIRIIZQUIIjilbIiuDntba147cD7fms7q_mROGSNCEM5ET10fKLV_3UKZt7b3YXd5QPK95Pyv5B5nB_ytqmH3L5tnWTamlHDKvgHgo4Ao</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2330660536</pqid></control><display><type>article</type><title>Sensor Response Estimate and Cross Calibration of Paleomagnetic Measurements on Pass‐Through Superconducting Rock Magnetometers</title><source>Wiley Online Library Open Access</source><creator>Xuan, Chuang ; Oda, Hirokuni</creator><creatorcontrib>Xuan, Chuang ; Oda, Hirokuni</creatorcontrib><description>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.
Plain Language Summary
Pass‐through superconducting rock magnetometer is one of the most versatile tools for measuring magnetic signals preserved in rocks, sediments, and other materials. It allows long samples to be measured continuously at high speed and has greatly contributed to paleomagnetic and environmental magnetic studies. Data acquired on these magnetometers are smoothed and distorted because of the way the magnetometer's sensors respond to signal carried by the sample. To overcome these effects, we developed a software to estimate how the magnetometers' sensors respond to sample signal in 3‐D space. The software reads measurement data collected using a small volume sample with known stable magnetic signal and uses the data to calculate the magnetometer's response to samples with different shapes. The sensors of two different magnetometers appear to respond to signal carried by the same sample in distinct ways, and measurements of the same sample on the two magnetometers show significant differences. A simple correction using factors calculated by the software can largely reduce these differences. We also show that estimates of the magnetometer's sensor responses produced by the software can be used to restore detailed and consistent magnetic signals through inverse calculation.
Key Points
We present a software for accurate estimate of magnetometer sensor response needed for reliable deconvolution of paleomagnetic measurements
Normalization using a nine‐term matrix calculated from sensor response estimate reduces discrepancies between data from two magnetometers
Deconvolution restores consistent and high‐resolution data from measurements of a sample on two magnetometers with distinct sensor responses</description><identifier>ISSN: 1525-2027</identifier><identifier>EISSN: 1525-2027</identifier><identifier>DOI: 10.1029/2019GC008597</identifier><language>eng</language><publisher>Washington: John Wiley & Sons, Inc</publisher><subject>Convolution ; Data ; Deconvolution ; Estimates ; Helium ; magnetic point source ; Magnetic studies ; Magnetometers ; Measurement ; Palaeomagnetism ; Paleomagnetic studies ; Paleomagnetism ; Procedures ; Resolution ; Rocks ; sensor response estimate ; Sensors ; Software ; superconducting rock magnetometer ; u‐channel sample ; Water pollution</subject><ispartof>Geochemistry, geophysics, geosystems : G3, 2019-11, Vol.20 (11), p.4676-4692</ispartof><rights>2019. American Geophysical Union. All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a4342-ce5bcf7cc3929a17de3bc264f1b43ea5563f642c38755688390ae023be6065223</citedby><cites>FETCH-LOGICAL-a4342-ce5bcf7cc3929a17de3bc264f1b43ea5563f642c38755688390ae023be6065223</cites><orcidid>0000-0001-7142-9208 ; 0000-0003-4043-3073</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1029%2F2019GC008597$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1029%2F2019GC008597$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,11541,27901,27902,45550,45551,46027,46451</link.rule.ids><linktorsrc>$$Uhttps://onlinelibrary.wiley.com/doi/abs/10.1029%2F2019GC008597$$EView_record_in_Wiley-Blackwell$$FView_record_in_$$GWiley-Blackwell</linktorsrc></links><search><creatorcontrib>Xuan, Chuang</creatorcontrib><creatorcontrib>Oda, Hirokuni</creatorcontrib><title>Sensor Response Estimate and Cross Calibration of Paleomagnetic Measurements on Pass‐Through Superconducting Rock Magnetometers</title><title>Geochemistry, geophysics, geosystems : G3</title><description>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.
Plain Language Summary
Pass‐through superconducting rock magnetometer is one of the most versatile tools for measuring magnetic signals preserved in rocks, sediments, and other materials. It allows long samples to be measured continuously at high speed and has greatly contributed to paleomagnetic and environmental magnetic studies. Data acquired on these magnetometers are smoothed and distorted because of the way the magnetometer's sensors respond to signal carried by the sample. To overcome these effects, we developed a software to estimate how the magnetometers' sensors respond to sample signal in 3‐D space. The software reads measurement data collected using a small volume sample with known stable magnetic signal and uses the data to calculate the magnetometer's response to samples with different shapes. The sensors of two different magnetometers appear to respond to signal carried by the same sample in distinct ways, and measurements of the same sample on the two magnetometers show significant differences. A simple correction using factors calculated by the software can largely reduce these differences. We also show that estimates of the magnetometer's sensor responses produced by the software can be used to restore detailed and consistent magnetic signals through inverse calculation.
Key Points
We present a software for accurate estimate of magnetometer sensor response needed for reliable deconvolution of paleomagnetic measurements
Normalization using a nine‐term matrix calculated from sensor response estimate reduces discrepancies between data from two magnetometers
Deconvolution restores consistent and high‐resolution data from measurements of a sample on two magnetometers with distinct sensor responses</description><subject>Convolution</subject><subject>Data</subject><subject>Deconvolution</subject><subject>Estimates</subject><subject>Helium</subject><subject>magnetic point source</subject><subject>Magnetic studies</subject><subject>Magnetometers</subject><subject>Measurement</subject><subject>Palaeomagnetism</subject><subject>Paleomagnetic studies</subject><subject>Paleomagnetism</subject><subject>Procedures</subject><subject>Resolution</subject><subject>Rocks</subject><subject>sensor response estimate</subject><subject>Sensors</subject><subject>Software</subject><subject>superconducting rock magnetometer</subject><subject>u‐channel sample</subject><subject>Water pollution</subject><issn>1525-2027</issn><issn>1525-2027</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp9kLFOwzAQhi0EEqWw8QCWWCk4duwkI4pKQGpF1ZY5ctxLm5LYwXaEusEb8Iw8CSllYGK6f_j-O92H0GVAbgJCk1tKgiRLCYl5Eh2hQcApH1FCo-M_-RSdObclJAg5jwfoYwHaGYvn4FqjHeCx81UjPWCpVzi1xjmcyroqrPSV0diUeCZrMI1ca_CVwlOQrrPQgPYO98BMOvf1_rncWNOtN3jRtWCV0atO-Uqv8dyoFzz9KZsGPFh3jk5KWTu4-J1D9Hw_XqYPo8lT9pjeTUYyZCEdKeCFKiOlWEITGUQrYIWiIiyDImQgOResFCFVLI76HMcsIRIIZQUIIjilbIiuDntba147cD7fms7q_mROGSNCEM5ET10fKLV_3UKZt7b3YXd5QPK95Pyv5B5nB_ytqmH3L5tnWTamlHDKvgHgo4Ao</recordid><startdate>201911</startdate><enddate>201911</enddate><creator>Xuan, Chuang</creator><creator>Oda, Hirokuni</creator><general>John Wiley & Sons, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TG</scope><scope>7TN</scope><scope>F1W</scope><scope>H96</scope><scope>KL.</scope><scope>L.G</scope><orcidid>https://orcid.org/0000-0001-7142-9208</orcidid><orcidid>https://orcid.org/0000-0003-4043-3073</orcidid></search><sort><creationdate>201911</creationdate><title>Sensor Response Estimate and Cross Calibration of Paleomagnetic Measurements on Pass‐Through Superconducting Rock Magnetometers</title><author>Xuan, Chuang ; Oda, Hirokuni</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a4342-ce5bcf7cc3929a17de3bc264f1b43ea5563f642c38755688390ae023be6065223</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Convolution</topic><topic>Data</topic><topic>Deconvolution</topic><topic>Estimates</topic><topic>Helium</topic><topic>magnetic point source</topic><topic>Magnetic studies</topic><topic>Magnetometers</topic><topic>Measurement</topic><topic>Palaeomagnetism</topic><topic>Paleomagnetic studies</topic><topic>Paleomagnetism</topic><topic>Procedures</topic><topic>Resolution</topic><topic>Rocks</topic><topic>sensor response estimate</topic><topic>Sensors</topic><topic>Software</topic><topic>superconducting rock magnetometer</topic><topic>u‐channel sample</topic><topic>Water pollution</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xuan, Chuang</creatorcontrib><creatorcontrib>Oda, Hirokuni</creatorcontrib><collection>CrossRef</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Oceanic Abstracts</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><jtitle>Geochemistry, geophysics, geosystems : G3</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Xuan, Chuang</au><au>Oda, Hirokuni</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Sensor Response Estimate and Cross Calibration of Paleomagnetic Measurements on Pass‐Through Superconducting Rock Magnetometers</atitle><jtitle>Geochemistry, geophysics, geosystems : G3</jtitle><date>2019-11</date><risdate>2019</risdate><volume>20</volume><issue>11</issue><spage>4676</spage><epage>4692</epage><pages>4676-4692</pages><issn>1525-2027</issn><eissn>1525-2027</eissn><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.
Plain Language Summary
Pass‐through superconducting rock magnetometer is one of the most versatile tools for measuring magnetic signals preserved in rocks, sediments, and other materials. It allows long samples to be measured continuously at high speed and has greatly contributed to paleomagnetic and environmental magnetic studies. Data acquired on these magnetometers are smoothed and distorted because of the way the magnetometer's sensors respond to signal carried by the sample. To overcome these effects, we developed a software to estimate how the magnetometers' sensors respond to sample signal in 3‐D space. The software reads measurement data collected using a small volume sample with known stable magnetic signal and uses the data to calculate the magnetometer's response to samples with different shapes. The sensors of two different magnetometers appear to respond to signal carried by the same sample in distinct ways, and measurements of the same sample on the two magnetometers show significant differences. A simple correction using factors calculated by the software can largely reduce these differences. We also show that estimates of the magnetometer's sensor responses produced by the software can be used to restore detailed and consistent magnetic signals through inverse calculation.
Key Points
We present a software for accurate estimate of magnetometer sensor response needed for reliable deconvolution of paleomagnetic measurements
Normalization using a nine‐term matrix calculated from sensor response estimate reduces discrepancies between data from two magnetometers
Deconvolution restores consistent and high‐resolution data from measurements of a sample on two magnetometers with distinct sensor responses</abstract><cop>Washington</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1029/2019GC008597</doi><tpages>17</tpages><orcidid>https://orcid.org/0000-0001-7142-9208</orcidid><orcidid>https://orcid.org/0000-0003-4043-3073</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | Geochemistry, geophysics, geosystems : G3, 2019-11, Vol.20 (11), p.4676-4692 |
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| language | eng |
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| source | Wiley Online Library Open Access |
| subjects | Convolution Data Deconvolution Estimates Helium magnetic point source Magnetic studies Magnetometers Measurement Palaeomagnetism Paleomagnetic studies Paleomagnetism Procedures Resolution Rocks sensor response estimate Sensors Software superconducting rock magnetometer u‐channel sample Water pollution |
| title | Sensor Response Estimate and Cross Calibration of Paleomagnetic Measurements on Pass‐Through Superconducting Rock Magnetometers |
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