Advancement in scanning magnetic microscopy utilizing high-sensitivity room-temperature TMR sensors for geological applications

Advancement in scanning magnetic microscopy utilizing high-sensitivity room-temperature TMR sensors for geological applications | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Advancement in scanning magnetic microscopy utilizing high-sensitivity room-temperature TMR sensors for geological applications Hirokuni Oda, Seiji Kumagai, Kosuke Fujiwara, Hitoshi Matsuzaki, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4948283/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Dec, 2024 Read the published version in Earth, Planets and Space → Version 1 posted 5 You are reading this latest preprint version Abstract Scanning magnetic microscopes enable high-sensitivity mapping of magnetic fields in thin geological sections, facilitating submillimeter-to-submicrometer scale studies of paleomagnetism and rock magnetism. Magnetic fields of geological samples have been mapped using various sensors, including Hall-effect devices, magneto-impedance devices, superconducting quantum interference devices (SQUIDs), quantum diamond devices, and tunnel magnetoresistance (TMR) devices. This study proposes magnetic microscopy using high-sensitivity room-temperature TMR sensors developed for magnetocardiography. The goal was to create high-performance magnetic microscopes that do not require laborious techniques, such as cryogenic technology. An XYZ stage developed for a scanning SQUID microscope (SSM) was used to demonstrate and evaluate magnetic microscopy with TMR sensors. The original TMR sensors developed for biomagnetic sensing composed of serially connected TMR elements with a total length of 3 mm were shortened to 1 mm (Sensor #1) and 0.4 mm length (Sensor #2). Background measurements at 50 Hz show magnetic field sensitivities better than 200 nT/√Hz and 600 nT/√Hz at 1 Hz for Sensor #1 and Sensor #2, respectively. By averaging 10 points of the original 50 Hz sampling, magnetic field sensitivities are better than 30 nT/√Hz and 90 nT/√Hz at 1 Hz for Sensor #1 and Sensor #2, respectively. To demonstrate TMR sensors as magnetic microscopes, a vertically magnetized Hawaii basalt thin section was measured and compared with a SQUID-acquired magnetic field map. Magnetic scanning images obtained with TMR sensors on a 0.1 mm grid were compared with those of scanning SQUID microscope (SSM) after adjusting the lift-off by upward continuation and integrated along the length of the sensors. The results demonstrated that magnetic images for 1 mm-long (0.4 mm-long) sensors aligned along the y-axis and x-axis are consistent with those after upward continuation to 0.3 mm (0.25 mm) and 0.4 mm (0.25 mm) and convolution by 1×10 (1×4) and 10×1 (4×1) matrix, respectively. Overall, the high-sensitivity TMR sensors exhibited promising performance. Further improvements can be made by optimizing the sensors, preamplifiers, and measurement systems for magnetic microscopy to achieve an optimum target resolution. tunnel magneto-resistive sensor scanning magnetic microscopy TMR microscope SQUID microscope geological thin section basalt upward continuation noise level Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Scanning magnetic microscopy is an essential methodology for mapping the magnetic fields of room-temperature samples with high spatial resolution and sensitivity to unravel a broad range of problems in science, engineering, and medicine through the imaging of magnetization and current source distributions. Scanning magnetic microscopy for geological applications has been developed for approximately two decades, providing information on submillimeter-to-submicrometer-scale magnetizations to interpret paleomagnetic and rock magnetic phenomena occurring at macroscopic scales. Examples of geological applications include studies on meteorites (Weiss et al. 2000 ; Fu et al. 2014 ), volcanic rocks (Weiss et al. 2007b ), sub-millimeter-scale magnetostratigraphy of marine ferromanganese crusts (e.g. Oda et al. 2011 ; Noguchi et al. 2017 ), and magnetic moment measurements of single zircon crystals for paleointensity estimates (e.g. Fu et al. 2017; Tarduno et al. 2020 ). Until now, various kinds of magnetic sensors have been used for scanning magnetic microscopy, including Hall-effect device (e.g. Kletetschka et al. 2013 ), magneto-impedance (MI) device (Uehara and Nakamura, 2007 ), superconducting quantum interference device (SQUID) (Fong et al. 2005 ; Weiss et al. 2007a , b ; Oda et al. 2011 ; Kawai et al. 2016 ; Oda et al. 2016 ), quantum diamond device (e.g. Fu et al. 2014 ; Fu et al. 2017; Volk et al. 2022 ), and tunnel magneto-resistance (TMR) device (e.g., Lima et al. 2014 ; Church and McEnroe, 2018 ; Pastore et al. 2019 ). The latter three devices are sensitive and promising based on quantum physics, with the SQUID being considered the most sensitive. The noise level of the magnetic field measured with a SQUID-based magnetometer can be approximately 10 fT or less at 1 Hz with a 45 mm diameter superconducting pick-up coil (Storm et al. 2017 ). Although the SQUID sensitivity is being reduced as the size of a pick-up coil reduces, the magnetic field noise level is 1.1 pT/ Hz at 1 Hz for a SQUID microscope with a 200 µm × 200 µm pickup loop at Geological Survey of Japan (GSJ), National Institute of Advanced Industrial Science and Technology (AIST) (Kawai et al. 2016 ). The major disadvantage of SQUID sensors is their requirement for cryogens or cryogenic technology, which are expensive and laborious. Additionally, the room-temperature SQUID microscope has a limited lift-off (sensor-to-sample distance), typically more than 80‒100 µm (e.g., Lima et al. 2014 ). On the other hand, a quantum diamond device has requirements of laser light and microwave sources for resonance detection and has a limitation in that it typically operates by applying a biased magnetic field of ~ 1 mT (e.g., Volk et al. 2022 ). Tunnel magnetoresistance (TMR) devices are promising, especially for magnetic microscopy, because of their relatively large field dynamic range (pico-Tesla to milli-Tesla), broad frequency response, small active area, simplicity of measurement, and moderate cost. Electrons tunnel across a thin insulating layer sandwiched between two ferromagnetic metal layers when a bias voltage is applied across magnetic tunnel junctions (MTJ). The magnetic moment of one of the ferromagnetic layers was pinned, whereas that of the other was free to move with an applied magnetic field. The relative orientation of the magnetization of the two ferromagnetic layers controls the tunneling current, hence the resistance of the device is a function of the applied magnetic field. The advantage of TMR sensors over giant magnetoresistance (GMR) sensors or Hall-effect sensors is a slight bias current, which is suitable for magnetic microscopy as the sensors could be placed at a short distance of ~ 10µm for magnetic imaging, and the magnetic field created by a current should be minimized (e.g., Lima et al. 2014 ). In general, the upper bound of the magnetic field range is limited well below the Earth’s magnetic field strength for a magnetic microscope using a SQUID sensor (for example, the SSM at GSJ can measure magnetic fields within ± ~ 7000 nT; Oda et al. 2016 ). To overcome this limitation and cover the dynamic ranges required, some studies have conducted magnetic microscopy using both SQUID and TMR sensors (e.g., ter Maart et al. 2024). To make this more straightforward and practical, it is possible to extend the lower bound of the dynamic range of a magnetic microscope based on TMR sensors to the subnanometer region. Recently, the sensitivity of TMR sensors with CoFeB/MgO/ CoFeB-MTJs has been improved, which has allowed the detection of weak biomagnetic fields, such as cardiac magnetic fields (magnetocardiography: MCG) and brain magnetic fields (magnetoencephalography: MEG) (Oogane et al. 2021 ). In this study, magnetic microscopy using high-sensitivity room-temperature TMR sensors developed for MCG and MEG is demonstrated. The goal is to develop high-performance magnetic microscopes that do not require laborious technologies and are free from biased magnetic fields, limiting their applications in magnetization measurements. 2. Methods 2.1. Experimental setup as a TMR magnetic microscope An XYZ stage and controller developed for a scanning SQUID microscope (Oda et al. 2016 ) were used to demonstrate and evaluate magnetic microscopy with TMR sensors. The TMR sensors were placed in a two-layered magnetic shield for SSM above the sample holder (Fig. 1 a). A thin geological section was placed on a sample holder, and the vertically upward magnetic field component was scanned in the X- and Y-axes, approximately 0.3 mm above the sample (Fig. 1 b and 1 c). Scanning was conducted using the software SQUIDMagScan (Oda et al. 2016 ), which enables data acquisition along the measurement lines in the + Y direction with incremental movement in the + X direction. Two TMR sensors developed for MCG, and MEG was used for the measurements. The original 3 mm-long TMR sensor was housed in an aluminum body (Fig. 1 a), and the magnetic field component along longitudinal direction of the aluminum body was detected. Multiple TMR elements were serially connected along the length of the 3 mm long TMR sensor. This allows the TMR sensor to achieve a high magnetic field detectivity in combination with a flux concentrator (Oogane et al. 2021 ). The flux concentrator is removed to achieve a high spatial resolution suitable for magnetic microscopy. Additionally, the length of the TMR sensors was reduced to 1 mm (Sensor #1) and 0.4 mm (Sensor #2) for high spatial resolution by reducing the number of serially connected TMR elements. For evaluation, either of the two TMR sensors was connected to the analog voltage input of the ADC equipped with the XYZ stage controller through a DC-preamplifier prepared for this study. A precision DC power supply (Agilent Technologies, model E3620A) was used to provide ± 10 V to the DC preamplifier. 2.2 Calibration Calibration of the two TMR sensors was performed using a line current produced by a precision current source (Lake Shore Cryotronics Model 121), according to Oda et al. ( 2016 ). A straight aluminum wire was placed on a glass plate covered with a thin film (Fig. 1 b). Calibration measurements were made at 0.1 mm intervals along three lines on the Y-axis, with each line separated by 10 mm along the X-axis (Figures S1 and S2). A line current of 3 mA was used, and the measured magnetic field data were fitted to a theoretical curve for an infinite line current. The length of the TMR sensor was aligned parallel to the line current to minimize discrepancies from the theoretical curves. The calibration factors were calculated as 290 and 315 nT/V for Sensors #1 and #2, respectively. The lift-off values of the TMR sensors were estimated to be 0.265 and 0.187 mm for Sensors #1 and #2, respectively. 2.3 Blank measurements and evaluation of noise The sampling frequency was 50 Hz, using SQUIDMagScan and an XYZ-stage controller (Oda et al. 2016 ). As an initial evaluation, blank measurements were conducted using the TMR sensors without moving the XYZ stage. For each sensor, continuous measurements were performed for more than 13 h at a frequency of 50 Hz. Figure 2 a shows the signals in the magnetic field unit for the first 60 s for Sensor #1 (blue lines) and Sensor #2 (red lines). To reduce the noise, measurements with 10 points averaging for more than 22 h at a nominal sampling frequency of 5 Hz were performed. Each dataset was divided into multiple segments, and a periodogram was produced by stacking the spectral densities after the application of the Hanning window using Igor Pro . 2.4 A geological sample, measurements, and data processing A Hawaiian basalt thin section measured using a scanning SQUID microscope by Oda et al. ( 2016 ) was used. An optical image was taken using a flatbed optical color scanner (EPSON GT-X980) with a pixel size of 4 µm × 4 µm (Fig. 3 a). Anhysteretic remanent magnetization (ARM) in a DC magnetic field of 50 µT and an AC magnetic field of 80 mT was acquired in the downward direction perpendicular to the thin section, which is the same as the SSM measurements by Oda et al. ( 2016 ). Magnetic field scanning was conducted on 0.1 mm grids in rectangular areas. Two TMR sensors (Sensors #1 and #2) were used for the magnetic scans in two configurations: one with the length direction along the y-axis and the other along the x-axis. The XYZ stage was moved along the Y- and X-axes at a speed of 50 mm/min. The data were acquired in 0.3 secs. After each step, the stepping motor is stopped. Drift correction was applied to the raw magnetic field data following Oda et al. ( 2016 ). The lower and upper margins of the sample were measured and considered to have zero magnetic fields. Figure 3 e and Fig. 3 b are the magnetic images before and after drift correction, respectively. The starting (lower margin) and ending (upper margin) areas of each line scan along the + Y-direction were registered for drift correction, averaged, and used for linear drift correction. The improvement in the drift-corrected magnetic image (Fig. 3 b) over the raw magnetic image (Fig. 3 e) is noticeable. The effect of the median filter on magnetic images is also demonstrated. A median filter is a nonlinear digital filtering technique often used to remove noise from an image or signal (e.g., Huang et al. 1979 ). An example of a magnetic image obtained after the application of a 3×3 median filter is shown in Fig. 3 f. The median filter was effective at removing spike noise. The magnetic image after the application of the median filter exhibited smoother features (Fig. 3 f) than that without the median filter (Fig. 3 b). Although the visibility of the magnetic image was improved, the median filter degraded its resolution of a magnetic image. Distortions in magnetic images may have unexpected effects on the results of upward continuation. Thus, median filters were not applied to the magnetic images in this study. 2.5 Upward continuation of magnetic images An SSM image of the basaltic thin section with an ARM was obtained previously (see Fig. 14c of Oda et al. 2016 ). The SSM images were used to compare with the magnetic images obtained by the TMR sensors. To adjust for the differences in lift-offs between the images of the TMR sensors and the SSM, an upward continuation filter was applied to the SSM magnetic image. Upward continuation is the process of transforming potential field data (magnetic and gravity) from a flat plane towards a higher plane (e.g., Blakely, 2016). An upward continuation filter is usually conducted in a 2-D frequency space using an FFT. In this study, the grdfft command of Generic Mapping Tools (GMT) was used (Wessel et al. 2019 ). The upward-continuing magnetic images were used as inputs for the convolution filter, as described in the following subsection. 2.6 Convolution of magnetic images The magnetic field-sensing regions of the TMR sensors had widths of 0.1 µm and lengths of 1 mm (Sensor #1) or 0.4 mm (Sensor #2). On the other hand, the sensing region of the SQUID sensor used for SSM is about 0.2 mm×0.2 mm since the size of the pickup coil is 0.2 mm×0.2 mm (Kawai et al. 2016 ). The measurements using the TMR and SQUID sensors were conducted on a 0.1 mm grid. The magnetic field was measured by the SQUID sensor with a lift-off of 216 µm, which is assumed as the actual vertical component of the magnetic field at 0.216 mm elevation on 0.1 mm grids. A convolution operation was conducted to simulate the integration effect of serially connected TMR elements. Integration of the magnetic field with a 1 mm-long sensor along the y-axis (and no integration along the x-axis) was conducted by convolution with the matrix [0.5 1 1 1 1 1 1 1 1 1 0.5] T followed by normalization. The integration of the magnetic field with a 1 mm-long sensor along the x-axis (and no integration along the y-axis) was conducted by convolution with the matrix [0.5 1 1 1 1 1 1 1 1 1 0.5]. Similarly, integration of the magnetic field with a 0.4 mm-long sensor along the y-axis and x-axis was conducted by convolution with matrices [0.5 1 1 1 0.5] T and [0.5 1 1 1 0.5], respectively. Each magnetic image obtained by one of the two TMR sensors was compared and evaluated with the magnetic image of the SSM after upward continuation to a proper elevation and convolution along either the y- or x-axis. 3. Results 3.1. Noise characteristics Figure 2 shows the background magnetic field measurements. The blue and red lines in Fig. 2 a represent the magnetic field signals converted using the calibration constants. The dotted lines are raw data obtained by 50 Hz sampling, and the solid lines are 10 points average. Maximum variations in one minute, which is comparable to a typical time for a single line scan in the + Y direction, were within ± 35 nT both for Sensor #1 and Sensor #2. The short-term fluctuations of a few seconds were smaller for Sensor #1 than for Sensor #2. Because the noise measured with the SSM in the same magnetic shield was approximately 50 pT (Oda et al. 2016 ), the fluctuations recognized in the TMR sensor measurements could originate from the TMR sensors or the DC-preamplifier. The power spectral density (PSD) of the background measurements (Fig. 2 c) exhibited patterns typical of 1/f noise for both TMR sensors, with and without 10 10-point average. The PSD of the raw data for Sensor #1 (broken blue line) is much smaller than that of Sensor #2 (broken red line). PSD is ~ 200 nT/√Hz@1 Hz for Sensor #1, whereas that for Sensor #2 is ~ 600 nT/√Hz@1 Hz. PSD after 10 points average for Sensor #1 (solid blue line) and Sensor #2 (solid red line) are ~ 30 nT/√Hz@1 Hz and ~ 90 nT/√Hz@1 Hz, respectively. The PSD was reduced by a factor of approximately seven after 10 points averaging. The PSD increased with decreasing sensor length by a factor of approximately three. This is approximately comparable to the ratio of sensor lengths (1 mm/0.4 mm = 2.5) of the TMR sensors. 3.2. Magnetic images of TMR sensors and comparison with SSM Figure 3 shows the magnetic field images of Sensor #1 and an optical image. To better visualize the variability in the image, the color scale is set from − 100 nT (blue) to + 100 nT (red). Figure 3 b shows the magnetic image after drift correction for the measurements of 10 points average with the length of the sensor oriented parallel to the y-axis. The noise in the image outside the basalt sample is much smaller than the full-color scale (± 100nT). Stretching distortion of the image along the y-axis was observed, reflecting the anisotropic shape of the sensing region of the TMR sensor. Figure 3 c and 3 d show the magnetic images measured with the sensor length oriented parallel to the x-axis after drift correction with no and 10 points average, respectively. In both images, a stretching distortion of the image along the x-axis can be recognized. Improvements in the magnetic image with ten 10-point average (Fig. 3 d) over the image with no average (Fig. 3 c) were not clearly identified in the background noise. To visualize the effects of sensor elevation (lift-offs) and integration along the length direction on the magnetic image, upward continuations, and convolutions were applied to the basalt magnetic image measured using the SSM. The SSM magnetic images taken 0.216 mm above the thin section (Fig. 4 b) were upward continued 0.034 mm, 0.084 mm, 0.134 mm, and 0.184 mm (to total distances of 0.25 mm, 0.3 mm, 0.35 mm, and 0.4 mm with 0.05 mm increments), respectively (Figs. 4 c‒f). Spatial resolution and magnetic field intensity field were reduced by increasing the virtual distance between the measurement point and the thin section. The magnetic images of the TMR sensors (Figs. 5 a, d, e, g) were compared with the SSM images after the application of upward continuation and convolution (Figs. 5 b, c, f, h). The color scales for all the figures in Fig. 5 are set to ± 300 nT to visualize magnetic structures in the basalt sample better. The five upward-continued magnetic images created for total distances of 0.25 mm, 0.3 mm, 0.35 mm, and 0.4 mm were used as the base images for convolutions. For each magnetic image of one of the two TMR sensors, with the length oriented toward either the y-axis or x-axis, an optimum image was selected from the five magnetic images in terms of spatial resolution and magnetic field intensity. As a result, the magnetic images for Sensor #1 with the length direction oriented along the y-axis (x-axis) could be associated with the SSM magnetic image after upward continuation to 0.3 mm (0.4 mm). The magnetic images for Sensor #2 could be related to the SSM magnetic image after upward continuation to 0.25 mm, both for length directions oriented along the y- and x-axes. The overall consistency guarantees that the calibrations of the TMR sensors, magnetic field measurements, upward continuation, and convolutions, as well as the SQUID sensor calibration and measurement, are reliable. 4. Discussions 4.1. Noise level of TMR microscope systems and magnetic field sensitivity Lima et al. ( 2014 ) developed a magnetic microscope using a TMR sensor and reduced the noise level by introducing a custom-made preamplifier. To allow comparisons with Lima et al. ( 2014 ), RMS noises in the frequency band between 0.1 Hz and 10 Hz were estimated using the raw 50 Hz sampling data of Sensor #1 (1 mm-long TMR sensor) and Sensor #2 (0.4 mm-long TMR sensor) as 58.8 nT and 185 nT, respectively. RMS noises for 10 points average in the frequency band between 0.1 Hz and 2.5 Hz were estimated as 5.87 nT and 17.8 nT for Sensor #1 and Sensor #2, respectively. To allow a fair comparison of RMS noises for raw 50 Hz sampling with those after 10 points average, RMS noises in the frequency band with no average between 0.1 Hz and 2.5 Hz were also estimated, which gave RMS noise values of 58.4 nT and 184 nT, respectively. The discrepancy of the RMS noises with no average in the frequency bands between 0.1‒10 Hz and 0.1‒2.5 Hz is relatively small. The RMS noise level of a TMR microscope used by Lima et al. ( 2014 ) was 150 nT for frequency bands between 0.1 Hz and 10 Hz. This is comparable to the RMS noise of 185 nT for Sensor #2 with no average. The RMS noise of 58.8 nT for Sensor #1, with no averaging, was approximately one-third of that of Lima et al. ( 2014 ). After a 10-point average, the RMS noise levels were reduced to 5.87 nT and 17.8 nT for Sensor #1 and Sensor #2, respectively. These values were one order of magnitude smaller than the RMS noise level reported by Lima et al. ( 2014 ). 4.2. Detectivity of magnetic stripes for magnetostratigraphy Church and McEnroe ( 2018 ) reported a noise floor of 250 nT RMS in the region well outside the sample boundaries using a TMR sensor with an active area of 0.9 mm along with the implementation of the electronics described by Lima et al. ( 2014 ). Their TMR sensor was comparable to the 1 mm-long TMR sensor (Sensor #1) in terms of the length of the active sensing area. In this case, the RMS in the region well outside the sample boundaries was 11 nT for the raw measurements, as shown in Fig. 3 c (Sensor #1; 1 mm-long TMR sensor). The reduction in the noise level may be partly due to the magnetic shielding utilized for magnetic microscopy in this study. The improvements in the noise floor compared with the system by Church and McEnroe ( 2018 ) are ~ 25 folds, which is promising for the measurements of magnetic field stripes recorded in geological thin sections originating from the geomagnetic reversal boundaries of weakly magnetized marine ferromanganese crusts and sediments. The typical magnetic anomaly patterns for the scanning measurements above thin sections of marine ferromanganese crusts exhibit peak-to-peak variations of ± ~ 5 nT for thin sections with 200 µm thickness measured with a lift-off of 170 µm after application of upward continuation to 370 µm (Oda et al. 2011 ). This is comparable to the estimated lift-offs conducted in this study for the measurements with the TMR sensors (0.25‒0.4 mm) shown in section 3.4. Considering the situation mentioned above, this study’s practical goal is to achieve magnetic field sensitivity of less than 5 nT, aiming to realize sub-nT magnetic field resolution with a spatial resolution of around 50‒200 µm. This is particularly desirable for magnetostratigraphic dating for imaging ultrafine-scale magnetic stripes as records of geomagnetic reversals preserved in ferromanganese crusts (e.g., Oda et al. 2011 ; Noguchi et al. 2017 ). It is also helpful in mapping sub-millimeter-scale magnetization regions of thin sections to restore ultra-high-resolution magnetic field variations recorded in sediments. Further improvements can be made by optimizing the TMR sensor, pre-amplifier, measurement system, and software to perform high-performance magnetic imaging of geological samples to elucidate fundamental issues in geosciences. 4.3. Magnetic moment sensitivity Another important aspect of magnetic microscopy in paleomagnetism and rock magnetism is magnetic moment sensitivity. Commercially available superconducting rock magnetometers (e.g., 2G Enterprises Model 755), which measure net moment without mapping fields, have sensitivities around 1 × 10 –14 ‒4 × 10 –12 Am 2 (e.g., Lima et al. 2014 ; Kato et al. 2024 ). A sensitivity one order of magnitude higher than that of SQUID magnetometers with an opening of 42 mm diameter (2G Enterprises Model 755) was achieved using an ultrasensitive 3-component DC SQUID magnetometer with a sample hole 6.3 mm in diameter (2G Enterprises) (Tarduno et al. 2015 ). On the other hand, the SQUID microscope has a magnetic moment sensitivity of 10 –15 ‒10 –14 Am 2 at 100 µm from the sample (Lima et al. 2014 ; Oda et al. 2016 ). The following is a helpful formula provided by Lima et al. ( 2014 ) for calculating the magnetic dipole moment sensitivity: m min = 4×10 7 h 3 B noise (1) where h is the minimum lift-off achievable with the instrument, and B noise is the measured RMS value of the equivalent magnetic field noise. Lima et al. ( 2014 ) estimated the magnetic moment sensitivity of their TMR microscope system as ~ 10 –14 Am 2 . Here, the magnetic moment sensitivity according to their formula using 11 nT as B noise is calculated, which was estimated in the previous sub-section for a 1 mm-long TMR sensor (Sensor #1) with no average after the application of drift correction. If 300 µm is chosen as h , which is comparable to the experiments conducted in this study, 1.1 ×10 –11 Am 2 is obtained. By choosing 100 µm as h , which is similar to the minimum lift-offs for SSM, 4.0 ×10 –13 Am 2 is obtained. By selecting 30 µm or 10 µm as h , which is achievable with the adjustable z-axis linear stage, 1.1 ×10 –14 Am 2 or 4.0 ×10 –16 Am 2 is attained. The final magnetic dipole moment sensitivity for a lift-off of 10 µm (4.0 ×10 –16 Am 2 ) is much smaller than that achievable by SQUID magnetometers and SSM. It is also smaller than the magnetic dipole moment sensitivity value of 10 –14 Am 2 reported by Lima et al. ( 2014 ) for their TMR microscope with the minimum lift-off of 7 µm. 5. Conclusions Scanning magnetic microscopy utilizing high-sensitivity room-temperature TMR sensors developed for biomedical applications such as magnetocardiography and magnetoencephalography was demonstrated and evaluated. The background noise measurements were made on the two TMR sensors (Sensor #1 with 1 mm-long and Sensor #2 with 0.4 mm-long) with a sampling frequency of 50 Hz, which demonstrate magnetic field detectivities better than 200 nT/√Hz and 600 nT/√Hz at 1 Hz, respectively. By taking 10 points average, magnetic field sensitivities are better than 30 nT/√Hz and 90 nT/√Hz at 1 Hz, respectively. To demonstrate the performance of the TMR sensors as a scanning magnetic microscope, a thin section of a Hawaii basalt sample was measured on a 0.1 mm grid and compared with the SSM image after adjusting the lift-offs by upward continuation and convolution of the magnetic image along the length of the sensors to simulate the integration effect. After upward continuation and convolution, the magnetic images for 1 mm-long (0.4 mm-long) TMR sensors aligned in the y-axis and x-axis are quite consistent with those after upward continuation to 0.3 mm (0.25 mm) and 0.4 mm (0.25 mm) and convolution, respectively. The RMS of the background measurements in the region well outside the sample boundaries was 11 nT for the raw measurements after drift corrections. The magnetic stripes measured using an SSM on thin sections of marine ferromanganese crusts, which have a thickness of 0.2 mm, exhibit peak-to-peak variations of ± ~ 5 nT at a distance of 370 µm (Oda et al. 2011 ). The achievement of magnetic field sensitivity of less than five nT with a spatial resolution around 50‒200 µm is desirable, which is within the possible range at the same time. Further improvements can be made by optimizing the sensors, preamplifiers, measurement systems, and post-processing software for the magnetic imaging of ferromanganese crusts and other geological materials that record geomagnetic field reversal boundaries to realize magnetic microscopy suitable for submillimeter-scale magnetostratigraphy practically. Abbreviations AIST National Institute of Advanced Industrial Science and Technology ARM anhysteretic remanent magnetization GMR giant magnetoresistance GSJ Geological Survey of Japan MTJ magnetic tunnel junction SQUID superconducting quantum interference device SSM scanning SQUID microscope TMR tunnel magneto-resistance Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The raw and processed experimental data are available upon request from H. Oda ( [email protected] ). Competing interests The authors declare that they have no competing interests. Funding This project was funded by the Japan Society for the Promotion of Science KAKENHI grant 21H04523 to HO. HO was also supported by an FY2023 grant from GSJ for solving social issues and strengthening industrial competitiveness. HO, MO, and HK were supported by an FY2024 Matching Research Support Project grant between Tohoku University and AIST. MO is also supported by the SIP 3 rd project. Authors' contributions HO, SK, KF, HM, MO, and HK designed the experiments. SK, KF, and HM prepared the TMR sensors. HW designed and manufactured the DC-preamplifier. HO, SK, KF, HM, HW, NF, and AT conducted the experiments. HO conducted, and NF helped with the data analyses. HO prepared figures and wrote the initial version of the manuscript. 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IEEE Trans Appl Supercond 26:1600905. https://doi.org/10.1109/TASC.2016.2536751 Kletetschka G, Schnabl P, Šifnerová K, Tasáryová Z, Manda Š, Pruner P (2013) Magnetic scanning and interpretation of paleomagnetic data from Prague Synform’s volcanics. Studia Geophys Geod 57:103–117. https://doi.org/10.1007/s11200-012-0723-4 Lima EA, Bruno AC, Carvalho HR, Weiss BP (2014) Scanning magnetic tunnel junction microscope for high-resolution imaging of remanent magnetization fields. Meas Sci Technol 25:105401. https://doi.org/10.1088/0957-0233/25/10/105401 Lima EA, Weiss BP (2016) Ultra-high sensitivity moment magnetometry of geological samples using magnetic microscopy. Geochem Geophys Geosyst 17:3754–3774. https://doi.org/10.1002/2016GC006487 Noguchi A, Oda H, Yamamoto Y, Usui A, Sato M, Kawai J (2017) Scanning SQUID microscopy of a ferromanganese crust from the northwestern Pacific: Submillimeter scale magnetostratigraphy as a new tool for age determination and mapping of environmental magnetic parameters. Geophys Res Lett 44:5360–5367. https://doi.org/10.1002/2017GL073201 Storm J-H, Hömmen P, Drung D, Körber R (2017) An ultra-sensitive and wideband magnetometer based on a superconducting quantum interference device. Appl Phys Lett 110:072603. https://doi.org/10.1063/1.4976823 Oogane M, Fujiwara K, Kanno A, Nakano T, Wagatsuma H, Arimoto T, Mizukami S, Kumagai S, Matsuzaki H, Nakasato N, Ando Y (2021) Sub-pT magnetic field detection by tunnel magneto-resistive sensors. Appl Phys Express 14:123002. https://doi.org/10.35848/1882-0786/ac3809 Oda H, Usui A, Miyagi I, Joshima M, Weiss BP, Schantz C, Fong LE, McBride KK, Harder R, Baudenbacher FJ (2011) Ultra-fine scale magnetostratigraphy of marine ferromanganese crust with the SQUID microscope. Geology 39:227–230. https://doi.org/10.1130/G31610.1 Oda H, Kawai J, Miyamoto M, Miyagi I, Sato M, Noguchi A, Yamamoto Y, Fujihira J, Natsuhara N, Aramaki Y, Masuda T, Xuan C (2016) Scanning SQUID microscope system for geological samples: system integration and initial evaluation. Earth Planet Space 68:179. https://doi.org/10.1186/s40623-016-0549-3 Oda H, Kawai J, Usui A, Yamamoto Y, Noguchi A, Miyagi I, Miyamoto M, Fujihira J, Sato M (2020) Development of scanning SQUID microscope system and its applications on geological samples: A case study on marine ferromanganese crust. J Phys Conf Ser 1590:012037. https://doi.org/10.1088/1742-6596/1590/1/012037 Pastore Z, Church NS, McEnroe SA (2019) Multistep parametric inversion of scanning magnetic microscopy data for modeling magnetization of multidomain magnetite. Geochem Geophys Geosyst 20:5334–5351. https://doi.org/10.1029/2019GC008542 Tarduno JA, Cottrell RD, Davis WJ, Nimmo F, Bono RK (2015) A Hadean to Paleoarchean geodynamo recorded by single zircon crystals. Science 349:521–524. https://doi.org/10.1126/science.aaa9114 Tarduno JA, Cottrell RD, Bono RK, Oda H, Davis WJ, Fayek M, van’t Erve O, Nimmo F, Huang W, Thern ER, Fearn S, Mitra G, Smirnov AV, Blackman EG (2020) Paleomagnetism indicates that primary magnetite in zircon records a strong Hadean geodynamo. Proc Natl Acad Sci 117:2309–2318. https://doi.org/10.1073/pnas.1916553117 ter Maat GW, Church NS, Oda H, Pastore Z, McEnroe SA (2024) Geomagnetism and Electromagnetism Characterization and imaging magnetic minerals from ultramafic roots of a LIP: implication for deep crustal magnetic sources. Geophys J Int 236:1577–1595. https://doi.org/10.1093/gji/ggad479 Uehara M, Nakamura N (2007) Scanning magnetic microscope system utilizing a magneto-impedance sensor. for a nondestructive diagnostic tool of geological samples Rev Sci Instrum 78: 043708. https://doi.org/10.1063/1.2722402 Volk MWR, Fu RR, Trubko R, Kehayias P, Glenn DR, Lima EA (2022) QDMlab: A MATLAB toolbox for analyzing quantum diamond microscope (QDM) magnetic field maps. Comput Geosci 167:105198. https://doi.org/10.1016/j.cageo.2022.105198 Weiss BP, Lima EA, Fong LE, Baudenbacher FJ (2007a) Paleomagnetic analysis using SQUID microscopy. J Geophys Res 112:B09105. https://doi.org/10.1029/2007JB004940 Weiss BP, Lima EA, Fong LE, Baudenbacher FJ (2007b) Paleointensity of the Earth’s magnetic field using SQUID microscopy. Earth Planet Sci Lett 264:61–71. https://doi.org/10.1016/j.epsl.2007.08.038 Weiss BP, Kirschvink JL, Baudenbacher FJ, Vali H, Peters NT, Macdonald FA, Wikswo JP Jr (2000) A low temperature transfer of ALH84001 from Mars to Earth. Science 290:791–795. https://doi.org/10.1126/science.290.5492.791 Wessel P, Luis JF, Uieda L, Scharroo R, Wobbe F, Smith WHF, Tian D (2019) The Generic Mapping Tools Version 6. Geochem Geophys Geosyst 20:5556–5564. https://doi.org/10.1029/2019GC008515 Supplementary Files TMRGraphicalAbstract.png Cite Share Download PDF Status: Published Journal Publication published 23 Dec, 2024 Read the published version in Earth, Planets and Space → Version 1 posted Editorial decision: Minor Revision 15 Nov, 2024 Reviewers agreed at journal 07 Oct, 2024 Reviewers invited by journal 25 Aug, 2024 Editor assigned by journal 21 Aug, 2024 First submitted to journal 20 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4948283","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":344812820,"identity":"056f8169-94b9-4167-8da6-c71d7df1a43e","order_by":0,"name":"Hirokuni Oda","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIie3QsWoCMRzH8V84uCx/95TzISKCVSjkVXRxuk1wKkU4OJfUWR/j8AUKgRsrbg4Ol8XZUihCoTRyjp6nm0i+Uwj5QP5/wOe7w2QAsAlAxzP2EKd7upKwuSNUS1CSktPlx2XPvGHtIt02wZPs++W1qxT4ukBzW0l6CW+3snRHoHwUxbkYaNBIgnbVHzNh+GRTQ2oTyygORd/hoZvI1BOIuP3b/RPqOpKVpBOxVDANnteRoDX/NMdZxr33mZvFUCD7l2ZZ5czqsXF7Spabw8+b4tOpLb509cbOFJDEQH/cQsAL4HAb8fl8vofuHyiYSl6eFta2AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-7142-9208","institution":"Geological Survey of Japan, AIST","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Hirokuni","middleName":"","lastName":"Oda","suffix":""},{"id":344812821,"identity":"a5030522-a007-4a88-bfd2-15069de35739","order_by":1,"name":"Seiji Kumagai","email":"","orcid":"","institution":"Spin Sensing Factory Corp.","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Seiji","middleName":"","lastName":"Kumagai","suffix":""},{"id":344812822,"identity":"ee8cebe0-44dc-4d8e-85a0-647ee3d7a7c7","order_by":2,"name":"Kosuke Fujiwara","email":"","orcid":"","institution":"Spin Sensing Factory Corp.","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kosuke","middleName":"","lastName":"Fujiwara","suffix":""},{"id":344812823,"identity":"f0133fe7-1a47-4b30-a84a-4de71b65a3f0","order_by":3,"name":"Hitoshi Matsuzaki","email":"","orcid":"","institution":"Spin Sensing Factory Corp.","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hitoshi","middleName":"","lastName":"Matsuzaki","suffix":""},{"id":344812824,"identity":"a77ac451-4feb-4444-af15-4fb400b6a83a","order_by":4,"name":"Hiroshi Wagatsuma","email":"","orcid":"","institution":"Spin Sensing Factory Corp.","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hiroshi","middleName":"","lastName":"Wagatsuma","suffix":""},{"id":344812825,"identity":"42f026a7-06dd-4fba-8425-72b28c7c263e","order_by":5,"name":"Mikihiko Oogane","email":"","orcid":"","institution":"Tohoku University: Tohoku Daigaku","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mikihiko","middleName":"","lastName":"Oogane","suffix":""},{"id":344812826,"identity":"273e8af0-7355-49ea-ab9e-b386633d9318","order_by":6,"name":"Hitoshi Kubota","email":"","orcid":"","institution":"AIST: Kokuritsu Kenkyu Kaihatsu Hojin Sangyo Gijutsu Sogo Kenkyujo","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hitoshi","middleName":"","lastName":"Kubota","suffix":""},{"id":344812827,"identity":"af1c65d7-48ed-4ec8-a2a1-fbb74de7151a","order_by":7,"name":"Naoto Fukuyo","email":"","orcid":"","institution":"AIST: Kokuritsu Kenkyu Kaihatsu Hojin Sangyo Gijutsu Sogo Kenkyujo","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Naoto","middleName":"","lastName":"Fukuyo","suffix":""},{"id":344812828,"identity":"7632d4a8-a7c5-4061-b55a-39b1b2c07aac","order_by":8,"name":"Akihiro Tanimoto","email":"","orcid":"","institution":"Ibaraki University: Ibaraki Daigaku","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Akihiro","middleName":"","lastName":"Tanimoto","suffix":""}],"badges":[],"createdAt":"2024-08-21 03:28:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4948283/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4948283/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40623-024-02118-0","type":"published","date":"2024-12-23T15:57:29+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66766806,"identity":"3fccc37f-472d-4ce4-bc16-ad0309387704","added_by":"auto","created_at":"2024-10-16 09:31:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":6490508,"visible":true,"origin":"","legend":"\u003cp\u003eTMR sensors and magnetic scanning measurements. (a) A TMR sensor (black) is fixed to a plastic holder. Length direction of the TMR sensor is aligned parallel to y-axis. (b) Set-up for calibration measurements. Length direction of the TMR sensor is parallel to x-axis. A wire extends along x-axis. (c) Set-up for a measurement with a basalt thin section.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4948283/v1/3c6bfa400ec68584121c8444.png"},{"id":66766796,"identity":"316b1c81-db57-4aea-8637-6aa63e0e06bc","added_by":"auto","created_at":"2024-10-16 09:31:29","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":605340,"visible":true,"origin":"","legend":"\u003cp\u003eBackground magnetic field and the spectrum of TMR sensors. (a) Magnetic field for TMR sensors with 1mm-length (blue) and 0.4mm-length (red), respectively. Zero levels for red curves are offset for better visibility. (b) Power spectral density of magnetic field for TMR sensors with 1mm-length (blue) and 0.4mm-length (red), respectively. In each figure, broken and solid lines are measurements with and without 10 points average, respectively.\u003c/p\u003e","description":"","filename":"F1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4948283/v1/40616e059a2cab00ab1025c5.jpg"},{"id":66766805,"identity":"acf996f2-ecba-4994-8bc0-859a670754b5","added_by":"auto","created_at":"2024-10-16 09:31:29","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":795297,"visible":true,"origin":"","legend":"\u003cp\u003eMagnetic images of a basalt thin section using TMR sensors. (a) An image obtained by an optical scanner. (b) Magnetic image using a 1mm-long TMR sensor with the length direction parallel to y-axis. Each grid point is 10-point average of measurements with a sampling rate of 50 Hz. (c) (d) Magnetic image using a 1mm-length TMR sensor with the length direction parallel to x-axis. Each grid is a single measurement (c) and 10 points average (d). (e) Raw magnetic image for the image (b). (f) magnetic image after application of 3×3 median filter on image (b).\u003c/p\u003e","description":"","filename":"F2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4948283/v1/cc131ec47a2776f2228421a1.jpg"},{"id":66766809,"identity":"92ed6bfa-514f-402c-8330-812913610968","added_by":"auto","created_at":"2024-10-16 09:31:29","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":615205,"visible":true,"origin":"","legend":"\u003cp\u003eMagnetic images of a basalt thin section using SSM. (a) An optical image. (b) SSM magnetic image with a lift-off of 216μm. Magnetic images with an upward continuation of (c) 34μm, (d) 84μm, (e) 134μm, and (f) 184μm, corresponding to total distances of 250μm, 300μm, 350μm, and 400μm, respectively.\u003c/p\u003e","description":"","filename":"F3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4948283/v1/41b9c4f3dd3f2f5696ef2c95.jpg"},{"id":66768232,"identity":"1bf64717-cf80-4f80-9655-18f64ca64c03","added_by":"auto","created_at":"2024-10-16 09:39:29","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":772281,"visible":true,"origin":"","legend":"\u003cp\u003eMagnetic images of a basalt thin section using TMR sensors compared with those using SSM after upward continuation and convolution. (a) Magnetic image using a 1mm-length TMR sensor with the length direction parallel to y-axis. (b) SSM magnetic image after adjusting the lift-off to 300μm by upward continuation, then convolved with matrix [0.5 1 1 1 1 1 1 1 1 1 0.5]\u003csup\u003eT\u003c/sup\u003e. (c) Magnetic image using a 1mm-length TMR sensor with the length direction parallel to x-axis. (d) Magnetic image using a SQUID microscope after adjusting the lift-off to 400μm by upward continuation, then convolved with matrix [0.5 1 1 1 1 1 1 1 1 1 0.5]. (e) Magnetic image using a 0.4mm- long TMR sensor with the length direction parallel to y-axis. (f) SSM magnetic image after adjusting the lift-off to 250μm by upward continuation, then convolved with matrix [0.5 1 1 1 0.5]\u003csup\u003e T\u003c/sup\u003e. (g) Magnetic image using a 0.4mm- long TMR sensor with the length direction parallel to x-axis. (h) SSM magnetic image after adjusting the lift-off to 250μm by upward continuation, then convolved with matrix [0.5 1 1 1 0.5].\u003c/p\u003e","description":"","filename":"F4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4948283/v1/2ee2b704a94c492bae7882bb.jpg"},{"id":72640623,"identity":"4a1b02b0-dd2c-47d1-b0f6-309115b18a2c","added_by":"auto","created_at":"2024-12-30 16:07:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10988478,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4948283/v1/88f37020-aee9-494a-8f9b-2f266168cdfd.pdf"},{"id":66766807,"identity":"79582536-6f60-4fac-abf1-c7a45e2f6af5","added_by":"auto","created_at":"2024-10-16 09:31:29","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":1823261,"visible":true,"origin":"","legend":"","description":"","filename":"TMRGraphicalAbstract.png","url":"https://assets-eu.researchsquare.com/files/rs-4948283/v1/6f7558af60a0b774d2cd4f59.png"}],"financialInterests":"","formattedTitle":"Advancement in scanning magnetic microscopy utilizing high-sensitivity room-temperature TMR sensors for geological applications","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eScanning magnetic microscopy is an essential methodology for mapping the magnetic fields of room-temperature samples with high spatial resolution and sensitivity to unravel a broad range of problems in science, engineering, and medicine through the imaging of magnetization and current source distributions. Scanning magnetic microscopy for geological applications has been developed for approximately two decades, providing information on submillimeter-to-submicrometer-scale magnetizations to interpret paleomagnetic and rock magnetic phenomena occurring at macroscopic scales. Examples of geological applications include studies on meteorites (Weiss et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Fu et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), volcanic rocks (Weiss et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007b\u003c/span\u003e), sub-millimeter-scale magnetostratigraphy of marine ferromanganese crusts (e.g. Oda et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Noguchi et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and magnetic moment measurements of single zircon crystals for paleointensity estimates (e.g. Fu et al. 2017; Tarduno et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUntil now, various kinds of magnetic sensors have been used for scanning magnetic microscopy, including Hall-effect device (e.g. Kletetschka et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), magneto-impedance (MI) device (Uehara and Nakamura, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), superconducting quantum interference device (SQUID) (Fong et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Weiss et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007a\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003eb\u003c/span\u003e; Oda et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Kawai et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Oda et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), quantum diamond device (e.g. Fu et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Fu et al. 2017; Volk et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and tunnel magneto-resistance (TMR) device (e.g., Lima et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Church and McEnroe, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pastore et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The latter three devices are sensitive and promising based on quantum physics, with the SQUID being considered the most sensitive. The noise level of the magnetic field measured with a SQUID-based magnetometer can be approximately 10 fT or less at 1 Hz with a 45 mm diameter superconducting pick-up coil (Storm et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Although the SQUID sensitivity is being reduced as the size of a pick-up coil reduces, the magnetic field noise level is 1.1 pT/ Hz at 1 Hz for a SQUID microscope with a 200 \u0026micro;m \u0026times; 200 \u0026micro;m pickup loop at Geological Survey of Japan (GSJ), National Institute of Advanced Industrial Science and Technology (AIST) (Kawai et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The major disadvantage of SQUID sensors is their requirement for cryogens or cryogenic technology, which are expensive and laborious. Additionally, the room-temperature SQUID microscope has a limited lift-off (sensor-to-sample distance), typically more than 80‒100 \u0026micro;m (e.g., Lima et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). On the other hand, a quantum diamond device has requirements of laser light and microwave sources for resonance detection and has a limitation in that it typically operates by applying a biased magnetic field of ~\u0026thinsp;1 mT (e.g., Volk et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTunnel magnetoresistance (TMR) devices are promising, especially for magnetic microscopy, because of their relatively large field dynamic range (pico-Tesla to milli-Tesla), broad frequency response, small active area, simplicity of measurement, and moderate cost. Electrons tunnel across a thin insulating layer sandwiched between two ferromagnetic metal layers when a bias voltage is applied across magnetic tunnel junctions (MTJ). The magnetic moment of one of the ferromagnetic layers was pinned, whereas that of the other was free to move with an applied magnetic field. The relative orientation of the magnetization of the two ferromagnetic layers controls the tunneling current, hence the resistance of the device is a function of the applied magnetic field. The advantage of TMR sensors over giant magnetoresistance (GMR) sensors or Hall-effect sensors is a slight bias current, which is suitable for magnetic microscopy as the sensors could be placed at a short distance of ~\u0026thinsp;10\u0026micro;m for magnetic imaging, and the magnetic field created by a current should be minimized (e.g., Lima et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In general, the upper bound of the magnetic field range is limited well below the Earth\u0026rsquo;s magnetic field strength for a magnetic microscope using a SQUID sensor (for example, the SSM at GSJ can measure magnetic fields within \u0026plusmn;\u0026thinsp;~\u0026thinsp;7000 nT; Oda et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). To overcome this limitation and cover the dynamic ranges required, some studies have conducted magnetic microscopy using both SQUID and TMR sensors (e.g., ter Maart et al. 2024). To make this more straightforward and practical, it is possible to extend the lower bound of the dynamic range of a magnetic microscope based on TMR sensors to the subnanometer region.\u003c/p\u003e \u003cp\u003eRecently, the sensitivity of TMR sensors with CoFeB/MgO/ CoFeB-MTJs has been improved, which has allowed the detection of weak biomagnetic fields, such as cardiac magnetic fields (magnetocardiography: MCG) and brain magnetic fields (magnetoencephalography: MEG) (Oogane et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this study, magnetic microscopy using high-sensitivity room-temperature TMR sensors developed for MCG and MEG is demonstrated. The goal is to develop high-performance magnetic microscopes that do not require laborious technologies and are free from biased magnetic fields, limiting their applications in magnetization measurements.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Experimental setup as a TMR magnetic microscope\u003c/h2\u003e \u003cp\u003eAn XYZ stage and controller developed for a scanning SQUID microscope (Oda et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) were used to demonstrate and evaluate magnetic microscopy with TMR sensors. The TMR sensors were placed in a two-layered magnetic shield for SSM above the sample holder (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). A thin geological section was placed on a sample holder, and the vertically upward magnetic field component was scanned in the X- and Y-axes, approximately 0.3 mm above the sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Scanning was conducted using the software \u003cem\u003eSQUIDMagScan\u003c/em\u003e (Oda et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), which enables data acquisition along the measurement lines in the +\u0026thinsp;Y direction with incremental movement in the +\u0026thinsp;X direction.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTwo TMR sensors developed for MCG, and MEG was used for the measurements. The original 3 mm-long TMR sensor was housed in an aluminum body (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), and the magnetic field component along longitudinal direction of the aluminum body was detected. Multiple TMR elements were serially connected along the length of the 3 mm long TMR sensor. This allows the TMR sensor to achieve a high magnetic field detectivity in combination with a flux concentrator (Oogane et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The flux concentrator is removed to achieve a high spatial resolution suitable for magnetic microscopy. Additionally, the length of the TMR sensors was reduced to 1 mm (Sensor #1) and 0.4 mm (Sensor #2) for high spatial resolution by reducing the number of serially connected TMR elements. For evaluation, either of the two TMR sensors was connected to the analog voltage input of the ADC equipped with the XYZ stage controller through a DC-preamplifier prepared for this study. A precision DC power supply (Agilent Technologies, model E3620A) was used to provide\u0026thinsp;\u0026plusmn;\u0026thinsp;10 V to the DC preamplifier.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Calibration\u003c/h2\u003e \u003cp\u003eCalibration of the two TMR sensors was performed using a line current produced by a precision current source (Lake Shore Cryotronics Model 121), according to Oda et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). A straight aluminum wire was placed on a glass plate covered with a thin film (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Calibration measurements were made at 0.1 mm intervals along three lines on the Y-axis, with each line separated by 10 mm along the X-axis (Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2). A line current of 3 mA was used, and the measured magnetic field data were fitted to a theoretical curve for an infinite line current. The length of the TMR sensor was aligned parallel to the line current to minimize discrepancies from the theoretical curves. The calibration factors were calculated as 290 and 315 nT/V for Sensors #1 and #2, respectively. The lift-off values of the TMR sensors were estimated to be 0.265 and 0.187 mm for Sensors #1 and #2, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Blank measurements and evaluation of noise\u003c/h2\u003e \u003cp\u003eThe sampling frequency was 50 Hz, using \u003cem\u003eSQUIDMagScan\u003c/em\u003e and an XYZ-stage controller (Oda et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). As an initial evaluation, blank measurements were conducted using the TMR sensors without moving the XYZ stage. For each sensor, continuous measurements were performed for more than 13 h at a frequency of 50 Hz. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea shows the signals in the magnetic field unit for the first 60 s for Sensor #1 (blue lines) and Sensor #2 (red lines). To reduce the noise, measurements with 10 points averaging for more than 22 h at a nominal sampling frequency of 5 Hz were performed. Each dataset was divided into multiple segments, and a periodogram was produced by stacking the spectral densities after the application of the Hanning window using \u003cem\u003eIgor Pro\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 A geological sample, measurements, and data processing\u003c/h2\u003e \u003cp\u003eA Hawaiian basalt thin section measured using a scanning SQUID microscope by Oda et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) was used. An optical image was taken using a flatbed optical color scanner (EPSON GT-X980) with a pixel size of 4 \u0026micro;m \u0026times; 4 \u0026micro;m (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Anhysteretic remanent magnetization (ARM) in a DC magnetic field of 50 \u0026micro;T and an AC magnetic field of 80 mT was acquired in the downward direction perpendicular to the thin section, which is the same as the SSM measurements by Oda et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Magnetic field scanning was conducted on 0.1 mm grids in rectangular areas. Two TMR sensors (Sensors #1 and #2) were used for the magnetic scans in two configurations: one with the length direction along the y-axis and the other along the x-axis. The XYZ stage was moved along the Y- and X-axes at a speed of 50 mm/min. The data were acquired in 0.3 secs. After each step, the stepping motor is stopped.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDrift correction was applied to the raw magnetic field data following Oda et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The lower and upper margins of the sample were measured and considered to have zero magnetic fields. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb are the magnetic images before and after drift correction, respectively. The starting (lower margin) and ending (upper margin) areas of each line scan along the +\u0026thinsp;Y-direction were registered for drift correction, averaged, and used for linear drift correction. The improvement in the drift-corrected magnetic image (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb) over the raw magnetic image (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee) is noticeable.\u003c/p\u003e \u003cp\u003eThe effect of the median filter on magnetic images is also demonstrated. A median filter is a nonlinear digital filtering technique often used to remove noise from an image or signal (e.g., Huang et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). An example of a magnetic image obtained after the application of a 3\u0026times;3 median filter is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef. The median filter was effective at removing spike noise. The magnetic image after the application of the median filter exhibited smoother features (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef) than that without the median filter (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Although the visibility of the magnetic image was improved, the median filter degraded its resolution of a magnetic image. Distortions in magnetic images may have unexpected effects on the results of upward continuation. Thus, median filters were not applied to the magnetic images in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Upward continuation of magnetic images\u003c/h2\u003e \u003cp\u003eAn SSM image of the basaltic thin section with an ARM was obtained previously (see Fig.\u0026nbsp;14c of Oda et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The SSM images were used to compare with the magnetic images obtained by the TMR sensors. To adjust for the differences in lift-offs between the images of the TMR sensors and the SSM, an upward continuation filter was applied to the SSM magnetic image. Upward continuation is the process of transforming potential field data (magnetic and gravity) from a flat plane towards a higher plane (e.g., Blakely, 2016). An upward continuation filter is usually conducted in a 2-D frequency space using an FFT. In this study, the \u003cem\u003egrdfft\u003c/em\u003e command of Generic Mapping Tools (GMT) was used (Wessel et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The upward-continuing magnetic images were used as inputs for the convolution filter, as described in the following subsection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Convolution of magnetic images\u003c/h2\u003e \u003cp\u003eThe magnetic field-sensing regions of the TMR sensors had widths of 0.1 \u0026micro;m and lengths of 1 mm (Sensor #1) or 0.4 mm (Sensor #2). On the other hand, the sensing region of the SQUID sensor used for SSM is about 0.2 mm\u0026times;0.2 mm since the size of the pickup coil is 0.2 mm\u0026times;0.2 mm (Kawai et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The measurements using the TMR and SQUID sensors were conducted on a 0.1 mm grid. The magnetic field was measured by the SQUID sensor with a lift-off of 216 \u0026micro;m, which is assumed as the actual vertical component of the magnetic field at 0.216 mm elevation on 0.1 mm grids. A convolution operation was conducted to simulate the integration effect of serially connected TMR elements. Integration of the magnetic field with a 1 mm-long sensor along the y-axis (and no integration along the x-axis) was conducted by convolution with the matrix [0.5 1 1 1 1 1 1 1 1 1 0.5]\u003csup\u003eT\u003c/sup\u003e followed by normalization. The integration of the magnetic field with a 1 mm-long sensor along the x-axis (and no integration along the y-axis) was conducted by convolution with the matrix [0.5 1 1 1 1 1 1 1 1 1 0.5]. Similarly, integration of the magnetic field with a 0.4 mm-long sensor along the y-axis and x-axis was conducted by convolution with matrices [0.5 1 1 1 0.5] \u003csup\u003eT\u003c/sup\u003e and [0.5 1 1 1 0.5], respectively. Each magnetic image obtained by one of the two TMR sensors was compared and evaluated with the magnetic image of the SSM after upward continuation to a proper elevation and convolution along either the y- or x-axis.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Noise characteristics\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the background magnetic field measurements. The blue and red lines in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea represent the magnetic field signals converted using the calibration constants. The dotted lines are raw data obtained by 50 Hz sampling, and the solid lines are 10 points average. Maximum variations in one minute, which is comparable to a typical time for a single line scan in the +\u0026thinsp;Y direction, were within \u0026plusmn;\u0026thinsp;35 nT both for Sensor #1 and Sensor #2. The short-term fluctuations of a few seconds were smaller for Sensor #1 than for Sensor #2. Because the noise measured with the SSM in the same magnetic shield was approximately 50 pT (Oda et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), the fluctuations recognized in the TMR sensor measurements could originate from the TMR sensors or the DC-preamplifier.\u003c/p\u003e \u003cp\u003eThe power spectral density (PSD) of the background measurements (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec) exhibited patterns typical of 1/f noise for both TMR sensors, with and without 10 10-point average. The PSD of the raw data for Sensor #1 (broken blue line) is much smaller than that of Sensor #2 (broken red line). PSD is ~\u0026thinsp;200 nT/\u0026radic;Hz@1 Hz for Sensor #1, whereas that for Sensor #2 is ~\u0026thinsp;600 nT/\u0026radic;Hz@1 Hz. PSD after 10 points average for Sensor #1 (solid blue line) and Sensor #2 (solid red line) are ~\u0026thinsp;30 nT/\u0026radic;Hz@1 Hz and ~\u0026thinsp;90 nT/\u0026radic;Hz@1 Hz, respectively. The PSD was reduced by a factor of approximately seven after 10 points averaging. The PSD increased with decreasing sensor length by a factor of approximately three. This is approximately comparable to the ratio of sensor lengths (1 mm/0.4 mm\u0026thinsp;=\u0026thinsp;2.5) of the TMR sensors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Magnetic images of TMR sensors and comparison with SSM\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the magnetic field images of Sensor #1 and an optical image. To better visualize the variability in the image, the color scale is set from \u0026minus;\u0026thinsp;100 nT (blue) to +\u0026thinsp;100 nT (red). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb shows the magnetic image after drift correction for the measurements of 10 points average with the length of the sensor oriented parallel to the y-axis. The noise in the image outside the basalt sample is much smaller than the full-color scale (\u0026plusmn;\u0026thinsp;100nT). Stretching distortion of the image along the y-axis was observed, reflecting the anisotropic shape of the sensing region of the TMR sensor.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed show the magnetic images measured with the sensor length oriented parallel to the x-axis after drift correction with no and 10 points average, respectively. In both images, a stretching distortion of the image along the x-axis can be recognized. Improvements in the magnetic image with ten 10-point average (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed) over the image with no average (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec) were not clearly identified in the background noise.\u003c/p\u003e \u003cp\u003eTo visualize the effects of sensor elevation (lift-offs) and integration along the length direction on the magnetic image, upward continuations, and convolutions were applied to the basalt magnetic image measured using the SSM. The SSM magnetic images taken 0.216 mm above the thin section (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb) were upward continued 0.034 mm, 0.084 mm, 0.134 mm, and 0.184 mm (to total distances of 0.25 mm, 0.3 mm, 0.35 mm, and 0.4 mm with 0.05 mm increments), respectively (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec‒f). Spatial resolution and magnetic field intensity field were reduced by increasing the virtual distance between the measurement point and the thin section.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe magnetic images of the TMR sensors (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, d, e, g) were compared with the SSM images after the application of upward continuation and convolution (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, c, f, h). The color scales for all the figures in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e are set to \u0026plusmn;\u0026thinsp;300 nT to visualize magnetic structures in the basalt sample better. The five upward-continued magnetic images created for total distances of 0.25 mm, 0.3 mm, 0.35 mm, and 0.4 mm were used as the base images for convolutions. For each magnetic image of one of the two TMR sensors, with the length oriented toward either the y-axis or x-axis, an optimum image was selected from the five magnetic images in terms of spatial resolution and magnetic field intensity. As a result, the magnetic images for Sensor #1 with the length direction oriented along the y-axis (x-axis) could be associated with the SSM magnetic image after upward continuation to 0.3 mm (0.4 mm). The magnetic images for Sensor #2 could be related to the SSM magnetic image after upward continuation to 0.25 mm, both for length directions oriented along the y- and x-axes. The overall consistency guarantees that the calibrations of the TMR sensors, magnetic field measurements, upward continuation, and convolutions, as well as the SQUID sensor calibration and measurement, are reliable.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussions","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Noise level of TMR microscope systems and magnetic field sensitivity\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eLima et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) developed a magnetic microscope using a TMR sensor and reduced the noise level by introducing a custom-made preamplifier. To allow comparisons with Lima et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), RMS noises in the frequency band between 0.1 Hz and 10 Hz were estimated using the raw 50 Hz sampling data of Sensor #1 (1 mm-long TMR sensor) and Sensor #2 (0.4 mm-long TMR sensor) as 58.8 nT and 185 nT, respectively. RMS noises for 10 points average in the frequency band between 0.1 Hz and 2.5 Hz were estimated as 5.87 nT and 17.8 nT for Sensor #1 and Sensor #2, respectively. To allow a fair comparison of RMS noises for raw 50 Hz sampling with those after 10 points average, RMS noises in the frequency band with no average between 0.1 Hz and 2.5 Hz were also estimated, which gave RMS noise values of 58.4 nT and 184 nT, respectively. The discrepancy of the RMS noises with no average in the frequency bands between 0.1‒10 Hz and 0.1‒2.5 Hz is relatively small.\u003c/p\u003e \u003cp\u003eThe RMS noise level of a TMR microscope used by Lima et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) was 150 nT for frequency bands between 0.1 Hz and 10 Hz. This is comparable to the RMS noise of 185 nT for Sensor #2 with no average. The RMS noise of 58.8 nT for Sensor #1, with no averaging, was approximately one-third of that of Lima et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). After a 10-point average, the RMS noise levels were reduced to 5.87 nT and 17.8 nT for Sensor #1 and Sensor #2, respectively. These values were one order of magnitude smaller than the RMS noise level reported by Lima et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Detectivity of magnetic stripes for magnetostratigraphy\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eChurch and McEnroe (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) reported a noise floor of 250 nT RMS in the region well outside the sample boundaries using a TMR sensor with an active area of 0.9 mm along with the implementation of the electronics described by Lima et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Their TMR sensor was comparable to the 1 mm-long TMR sensor (Sensor #1) in terms of the length of the active sensing area. In this case, the RMS in the region well outside the sample boundaries was 11 nT for the raw measurements, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec (Sensor #1; 1 mm-long TMR sensor). The reduction in the noise level may be partly due to the magnetic shielding utilized for magnetic microscopy in this study. The improvements in the noise floor compared with the system by Church and McEnroe (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) are ~\u0026thinsp;25 folds, which is promising for the measurements of magnetic field stripes recorded in geological thin sections originating from the geomagnetic reversal boundaries of weakly magnetized marine ferromanganese crusts and sediments.\u003c/p\u003e \u003cp\u003eThe typical magnetic anomaly patterns for the scanning measurements above thin sections of marine ferromanganese crusts exhibit peak-to-peak variations of \u0026plusmn;\u0026thinsp;~\u0026thinsp;5 nT for thin sections with 200 \u0026micro;m thickness measured with a lift-off of 170 \u0026micro;m after application of upward continuation to 370 \u0026micro;m (Oda et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This is comparable to the estimated lift-offs conducted in this study for the measurements with the TMR sensors (0.25‒0.4 mm) shown in section 3.4. Considering the situation mentioned above, this study\u0026rsquo;s practical goal is to achieve magnetic field sensitivity of less than 5 nT, aiming to realize sub-nT magnetic field resolution with a spatial resolution of around 50‒200 \u0026micro;m. This is particularly desirable for magnetostratigraphic dating for imaging ultrafine-scale magnetic stripes as records of geomagnetic reversals preserved in ferromanganese crusts (e.g., Oda et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Noguchi et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). It is also helpful in mapping sub-millimeter-scale magnetization regions of thin sections to restore ultra-high-resolution magnetic field variations recorded in sediments. Further improvements can be made by optimizing the TMR sensor, pre-amplifier, measurement system, and software to perform high-performance magnetic imaging of geological samples to elucidate fundamental issues in geosciences.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Magnetic moment sensitivity\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAnother important aspect of magnetic microscopy in paleomagnetism and rock magnetism is magnetic moment sensitivity. Commercially available superconducting rock magnetometers (e.g., 2G Enterprises Model 755), which measure net moment without mapping fields, have sensitivities around 1 \u0026times; 10\u003csup\u003e\u0026ndash;14\u003c/sup\u003e‒4 \u0026times; 10\u003csup\u003e\u0026ndash;12\u003c/sup\u003e Am\u003csup\u003e2\u003c/sup\u003e (e.g., Lima et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kato et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A sensitivity one order of magnitude higher than that of SQUID magnetometers with an opening of 42 mm diameter (2G Enterprises Model 755) was achieved using an ultrasensitive 3-component DC SQUID magnetometer with a sample hole 6.3 mm in diameter (2G Enterprises) (Tarduno et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). On the other hand, the SQUID microscope has a magnetic moment sensitivity of 10\u003csup\u003e\u0026ndash;15\u003c/sup\u003e‒10\u003csup\u003e\u0026ndash;14\u003c/sup\u003e Am\u003csup\u003e2\u003c/sup\u003e at 100 \u0026micro;m from the sample (Lima et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Oda et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe following is a helpful formula provided by Lima et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) for calculating the magnetic dipole moment sensitivity:\u003c/p\u003e \u003cp\u003e \u003cem\u003em\u003c/em\u003e \u003csub\u003emin\u003c/sub\u003e \u003cem\u003e= 4\u0026times;10\u003c/em\u003e\u003csup\u003e\u003cem\u003e7\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eB\u003c/em\u003e\u003csub\u003enoise\u003c/sub\u003e (1)\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eh\u003c/em\u003e is the minimum lift-off achievable with the instrument, and \u003cem\u003eB\u003c/em\u003e\u003csub\u003enoise\u003c/sub\u003e is the measured RMS value of the equivalent magnetic field noise. Lima et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) estimated the magnetic moment sensitivity of their TMR microscope system as ~\u0026thinsp;10\u003csup\u003e\u0026ndash;14\u003c/sup\u003e Am\u003csup\u003e2\u003c/sup\u003e. Here, the magnetic moment sensitivity according to their formula using 11 nT as \u003cem\u003eB\u003c/em\u003e\u003csub\u003enoise\u003c/sub\u003e is calculated, which was estimated in the previous sub-section for a 1 mm-long TMR sensor (Sensor #1) with no average after the application of drift correction. If 300 \u0026micro;m is chosen as \u003cem\u003eh\u003c/em\u003e, which is comparable to the experiments conducted in this study, 1.1 \u0026times;10\u003csup\u003e\u0026ndash;11\u003c/sup\u003e Am\u003csup\u003e2\u003c/sup\u003e is obtained. By choosing 100 \u0026micro;m as \u003cem\u003eh\u003c/em\u003e, which is similar to the minimum lift-offs for SSM, 4.0 \u0026times;10\u003csup\u003e\u0026ndash;13\u003c/sup\u003e Am\u003csup\u003e2\u003c/sup\u003e is obtained. By selecting 30 \u0026micro;m or 10 \u0026micro;m as \u003cem\u003eh\u003c/em\u003e, which is achievable with the adjustable z-axis linear stage, 1.1 \u0026times;10\u003csup\u003e\u0026ndash;14\u003c/sup\u003e Am\u003csup\u003e2\u003c/sup\u003e or 4.0 \u0026times;10\u003csup\u003e\u0026ndash;16\u003c/sup\u003e Am\u003csup\u003e2\u003c/sup\u003e is attained. The final magnetic dipole moment sensitivity for a lift-off of 10 \u0026micro;m (4.0 \u0026times;10\u003csup\u003e\u0026ndash;16\u003c/sup\u003e Am\u003csup\u003e2\u003c/sup\u003e) is much smaller than that achievable by SQUID magnetometers and SSM. It is also smaller than the magnetic dipole moment sensitivity value of 10\u003csup\u003e\u0026ndash;14\u003c/sup\u003e Am\u003csup\u003e2\u003c/sup\u003e reported by Lima et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) for their TMR microscope with the minimum lift-off of 7 \u0026micro;m.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eScanning magnetic microscopy utilizing high-sensitivity room-temperature TMR sensors developed for biomedical applications such as magnetocardiography and magnetoencephalography was demonstrated and evaluated. The background noise measurements were made on the two TMR sensors (Sensor #1 with 1 mm-long and Sensor #2 with 0.4 mm-long) with a sampling frequency of 50 Hz, which demonstrate magnetic field detectivities better than 200 nT/\u0026radic;Hz and 600 nT/\u0026radic;Hz at 1 Hz, respectively. By taking 10 points average, magnetic field sensitivities are better than 30 nT/\u0026radic;Hz and 90 nT/\u0026radic;Hz at 1 Hz, respectively.\u003c/p\u003e \u003cp\u003eTo demonstrate the performance of the TMR sensors as a scanning magnetic microscope, a thin section of a Hawaii basalt sample was measured on a 0.1 mm grid and compared with the SSM image after adjusting the lift-offs by upward continuation and convolution of the magnetic image along the length of the sensors to simulate the integration effect. After upward continuation and convolution, the magnetic images for 1 mm-long (0.4 mm-long) TMR sensors aligned in the y-axis and x-axis are quite consistent with those after upward continuation to 0.3 mm (0.25 mm) and 0.4 mm (0.25 mm) and convolution, respectively.\u003c/p\u003e \u003cp\u003eThe RMS of the background measurements in the region well outside the sample boundaries was 11 nT for the raw measurements after drift corrections. The magnetic stripes measured using an SSM on thin sections of marine ferromanganese crusts, which have a thickness of 0.2 mm, exhibit peak-to-peak variations of \u0026plusmn;\u0026thinsp;~\u0026thinsp;5 nT at a distance of 370 \u0026micro;m (Oda et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The achievement of magnetic field sensitivity of less than five nT with a spatial resolution around 50‒200 \u0026micro;m is desirable, which is within the possible range at the same time. Further improvements can be made by optimizing the sensors, preamplifiers, measurement systems, and post-processing software for the magnetic imaging of ferromanganese crusts and other geological materials that record geomagnetic field reversal boundaries to realize magnetic microscopy suitable for submillimeter-scale magnetostratigraphy practically.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAIST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Institute of Advanced Industrial Science and Technology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eARM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eanhysteretic remanent magnetization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egiant magnetoresistance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGSJ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeological Survey of Japan\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMTJ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emagnetic tunnel junction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSQUID\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esuperconducting quantum interference device\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSSM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003escanning SQUID microscope\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etunnel magneto-resistance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw and processed experimental data are available upon request from H. Oda ([email protected]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was funded by\u0026nbsp;the Japan Society for the Promotion of Science\u0026nbsp;KAKENHI grant\u0026nbsp;21H04523 to HO. HO was also supported by\u0026nbsp;an\u0026nbsp;FY2023 grant from GSJ for solving social issues and strengthening industrial competitiveness. HO, MO, and HK\u0026nbsp;were supported by\u0026nbsp;an\u0026nbsp;FY2024 Matching Research Support Project grant between Tohoku University and AIST. MO is also supported by the SIP 3\u003csup\u003erd\u003c/sup\u003e project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHO, SK, KF, HM, MO, and HK designed the experiments. SK, KF, and HM prepared the TMR sensors. HW designed and manufactured the DC-preamplifier. HO, SK, KF, HM, HW, NF, and AT conducted the experiments. HO conducted, and NF helped\u0026nbsp;with the data analyses. HO\u0026nbsp;prepared figures and wrote\u0026nbsp;the\u0026nbsp;initial version of the manuscript. All\u0026nbsp;the authors have read and approved the final version of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Yuhji Yamamoto for providing\u0026nbsp;thin basalt\u0026nbsp;sections for\u0026nbsp;analysis and\u0026nbsp;Hideki Yoshikawa for manufacturing the holders\u0026nbsp;of the TMR sensors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBlakely RJ (1996) Potential Theory in Gravity and Magnetic Applications. 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Geochem Geophys Geosyst 20:5556\u0026ndash;5564. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2019GC008515\u003c/span\u003e\u003cspan address=\"10.1029/2019GC008515\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"earth-planets-and-space","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"epsp","sideBox":"Learn more about [Earth, Planets and Space](http://earth-planets-space.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/epsp/default.aspx","title":"Earth, Planets and Space","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"tunnel magneto-resistive sensor, scanning magnetic microscopy, TMR microscope, SQUID microscope, geological thin section, basalt, upward continuation, noise level","lastPublishedDoi":"10.21203/rs.3.rs-4948283/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4948283/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eScanning magnetic microscopes enable high-sensitivity mapping of magnetic fields in thin geological sections, facilitating submillimeter-to-submicrometer scale studies of paleomagnetism and rock magnetism. Magnetic fields of geological samples have been mapped using various sensors, including Hall-effect devices, magneto-impedance devices, superconducting quantum interference devices (SQUIDs), quantum diamond devices, and tunnel magnetoresistance (TMR) devices. This study proposes magnetic microscopy using high-sensitivity room-temperature TMR sensors developed for magnetocardiography. The goal was to create high-performance magnetic microscopes that do not require laborious techniques, such as cryogenic technology. An XYZ stage developed for a scanning SQUID microscope (SSM) was used to demonstrate and evaluate magnetic microscopy with TMR sensors. The original TMR sensors developed for biomagnetic sensing composed of serially connected TMR elements with a total length of 3 mm were shortened to 1 mm (Sensor #1) and 0.4 mm length (Sensor #2). Background measurements at 50 Hz show magnetic field sensitivities better than 200 nT/\u0026radic;Hz and 600 nT/\u0026radic;Hz at 1 Hz for Sensor #1 and Sensor #2, respectively. By averaging 10 points of the original 50 Hz sampling, magnetic field sensitivities are better than 30 nT/\u0026radic;Hz and 90 nT/\u0026radic;Hz at 1 Hz for Sensor #1 and Sensor #2, respectively. To demonstrate TMR sensors as magnetic microscopes, a vertically magnetized Hawaii basalt thin section was measured and compared with a SQUID-acquired magnetic field map. Magnetic scanning images obtained with TMR sensors on a 0.1 mm grid were compared with those of scanning SQUID microscope (SSM) after adjusting the lift-off by upward continuation and integrated along the length of the sensors. The results demonstrated that magnetic images for 1 mm-long (0.4 mm-long) sensors aligned along the y-axis and x-axis are consistent with those after upward continuation to 0.3 mm (0.25 mm) and 0.4 mm (0.25 mm) and convolution by 1\u0026times;10 (1\u0026times;4) and 10\u0026times;1 (4\u0026times;1) matrix, respectively. Overall, the high-sensitivity TMR sensors exhibited promising performance. Further improvements can be made by optimizing the sensors, preamplifiers, and measurement systems for magnetic microscopy to achieve an optimum target resolution.\u003c/p\u003e","manuscriptTitle":"Advancement in scanning magnetic microscopy utilizing high-sensitivity room-temperature TMR sensors for geological applications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-16 09:31:24","doi":"10.21203/rs.3.rs-4948283/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor Revision","date":"2024-11-15T14:01:34+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-10-07T14:07:06+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-25T14:05:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-22T02:49:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Earth, Planets and Space","date":"2024-08-20T23:28:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"earth-planets-and-space","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"epsp","sideBox":"Learn more about [Earth, Planets and Space](http://earth-planets-space.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/epsp/default.aspx","title":"Earth, Planets and Space","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"777546aa-efd6-41c5-9455-d436d775b8e8","owner":[],"postedDate":"October 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-30T16:01:23+00:00","versionOfRecord":{"articleIdentity":"rs-4948283","link":"https://doi.org/10.1186/s40623-024-02118-0","journal":{"identity":"earth-planets-and-space","isVorOnly":false,"title":"Earth, Planets and Space"},"publishedOn":"2024-12-23 15:57:29","publishedOnDateReadable":"December 23rd, 2024"},"versionCreatedAt":"2024-10-16 09:31:24","video":"","vorDoi":"10.1186/s40623-024-02118-0","vorDoiUrl":"https://doi.org/10.1186/s40623-024-02118-0","workflowStages":[]},"version":"v1","identity":"rs-4948283","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4948283","identity":"rs-4948283","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}