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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 measurementsNormalization using a nine‐term matrix calculated from sensor response estimate reduces discrepancies between data from two magnetometersDeconvolution restores consistent and high‐resolution data from measurements of a sample on two magnetometers with distinct sensor responses [ABSTRACT FROM AUTHOR]

Pass-through superconducting rock magnetometers (SRM) offer rapid and high-precision remanence measurements for continuous samples that are essential for modern paleomagnetism studies. However, continuous SRM measurements are inevitably smoothed and distorted due to the convolution effect of SRM sensor response. Deconvolution is necessary to restore accurate magnetization from pass-through SRM data, and robust deconvolution requires reliable estimate of SRM sensor response as well as understanding of uncertainties associated with the SRM measurement system. In this paper, we use the SRM at Kochi Core Center (KCC), Japan, as an example to introduce new tool and procedure for accurate and efficient estimate of SRM sensor response. To quantify uncertainties associated with the SRM measurement due to track positioning errors and test their effects on deconvolution, we employed laser interferometry for precise monitoring of track positions both with and without placing a u-channel sample on the SRM tray. The acquired KCC SRM sensor response shows significant cross-term of Z-axis magnetization on the X-axis pick-up coil and full widths of ~46-54 mm at half-maximum response for the three pick-up coils, which are significantly narrower than those (~73-80 mm) for the liquid He-free SRM at Oregon State University. Laser interferometry measurements on the KCC SRM tracking system indicate positioning uncertainties of ~0.1-0.2 and ~0.5 mm for tracking with and without u-channel sample on the tray, respectively. Positioning errors appear to have reproducible components of up to ~0.5 mm possibly due to patterns or damages on tray surface or rope used for the tracking system. Deconvolution of 50,000 simulated measurement data with realistic error introduced based on the position uncertainties indicates that although the SRM tracking system has recognizable positioning uncertainties, they do not significantly debilitate the use of deconvolution to accurately restore high-resolution signal. The simulated 'excursion' event associated with a significant magnetization intensity drop was clearly recovered in the deconvolved measurements with a maximum error of ~3° in inclination. [ABSTRACT FROM AUTHOR]