Improvement of the measurement method of rock magnetic properties based on the scanning SQUID microscope with an in-situ magnetization/demagnetization field

Abstract

The magnetic properties of geological samples provide significant information about the Earth, which can reflect the evolution of the continent and the oceans [1], [2]. A scanning SQUID microscope with an in-situ magnetization/ demagnetization field (Fig 1) had been developed by us [3], which can obtain not only the distribution of the magnetic field but also the remanent curves of a wafer sample at each point. In this paper, we developed an improved method to measure the remanent curves of rock magnetic properties. The remanent curves at each point can characterize the magnetic properties of the samples. But selecting all the points on the sample for measurement would consume a lot of time; randomly selecting some points to measure would lose important information. For the above problems, we proposed a measurement method to choose the characteristic point. At first, we mapped the distribution of magnetic field of a wafer sample which need to be measured, and the extreme points in the distribution of magnetic field were obtained by the contour map of magnetic field. Because the extreme value of the magnetic field generally appears above the position of the magnetic particles, and the field value in the vicinity of the magnetic particles is gradually reduced, we selected the extreme points in each area as the points to measure the remanent curves which are marked with red solid circle in the Fig. 2. Then the sample moving platform could move the sample to place the point to be measured below the probe. Based on the magnetization/ demagnetization field the remanent curves were obtained. Using the method, the magnetic properties of geological samples could be rapidly measured. Relative to the random selection and all selection, this method of selecting points to be measured based on the distribution of magnetic field can save measuring time without losing feature information. This work was supported by the National Science & Technology Support Program of the Ministry of Science and Technology of China (2015BAI01B07).