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

Abstract

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

UDECON: deconvolution optimization software for restoring high-resolution records from pass-through paleomagnetic measurements

“…Cross terms among the SRM's three orthogonal pickup coils, and possible rotation of x-y pickup coils around the z-axis relative to the plane of measurement tray may even lead to directional and intensity artifacts in the resulting measurement (e.g., Parker and Gee 2002;Roberts 2006;Jackson et al 2010;Oda and Xuan 2014). Deconvolution is necessary to overcome the convolution effect and obtain more accurate and significantly higher-resolution data comparable to that measured using back-to-back discrete samples (see Guyodo et al 2002).…”

“…Various algorithms have been presented for deconvolution of pass-through paleomagnetic data acquired on either whole-core or u-channel SRMs (e.g., Dodson et al 1974;Constable and Parker 1991;Oda and Shibuya 1994;1996;Jackson et al 2010). Oda and Xuan (2014) recently developed an improved deconvolution algorithm based on the Akaike's Bayesian Information Criterion (ABIC) minimization method introduced by Shibuya (1994, 1996). The new deconvolution algorithm optimizes ABIC not only for smoothness of the signal but also for realistic error in sample length and shift in measurement position introduced during sampling and measurement in the laboratory.…”

“…The new deconvolution algorithm optimizes ABIC not only for smoothness of the signal but also for realistic error in sample length and shift in measurement position introduced during sampling and measurement in the laboratory. Reliability and accuracy of the algorithm have been demonstrated using synthetic pass-through data with realistic measurement noises (see Oda and Xuan 2014).…”

“…The lack of easy-to-use software that can quickly implement the deconvolution schemes makes it difficult to deconvolve large amount of pass-through data on regular bases. In this paper, we present standalone graphical software UDECON designed to directly read pass-through measurement data file and allow fast and reliable deconvolution optimization using the algorithm developed by Oda and Xuan (2014). It should be noted that deconvolution optimization in UDECON is based on the following assumptions (see Shibuya and Michikawa 2000;Jackson et al 2010;Oda and Xuan 2014): (1) sample magnetization varies only as a function of depth; (2) smoothness of the magnetization variations is similar throughout the sample; and (3) measurement errors follow Gaussian distribution.…”

“…In this paper, we present standalone graphical software UDECON designed to directly read pass-through measurement data file and allow fast and reliable deconvolution optimization using the algorithm developed by Oda and Xuan (2014). It should be noted that deconvolution optimization in UDECON is based on the following assumptions (see Shibuya and Michikawa 2000;Jackson et al 2010;Oda and Xuan 2014): (1) sample magnetization varies only as a function of depth; (2) smoothness of the magnetization variations is similar throughout the sample; and (3) measurement errors follow Gaussian distribution. In the following sections, we first discuss necessary preparations for the pass-through measurement and SRM sensor response data prior to the use of UDECON and then provide detailed description of features available in the software as well as instructions to help accomplish successful deconvolution.…”