Machine Learning for Improved Current Density Reconstruction from 2D Vector Magnetic Images

TL;DR

This paper introduces a deep learning approach to reconstruct electrical current densities from 2D magnetic field images, outperforming traditional methods especially in noisy or distant measurements, enabling faster and more sensitive analysis.

Contribution

The study presents a novel deep convolutional neural network for current density reconstruction from magnetic images, significantly improving accuracy over analytic methods in challenging conditions.

Findings

Outperforms analytic methods in noisy and high standoff data

Reduces data collection time by approximately 400 times

Enables detection of weaker and 3D current sources

Abstract

The reconstruction of electrical current densities from magnetic field measurements is an important technique with applications in materials science, circuit design, quality control, plasma physics, and biology. Analytic reconstruction methods exist for planar currents, but break down in the presence of high spatial frequency noise or large standoff distance, restricting the types of systems that can be studied. Here, we demonstrate the use of a deep convolutional neural network for current density reconstruction from two-dimensional (2D) images of vector magnetic fields acquired by a quantum diamond microscope (QDM) utilizing a surface layer of Nitrogen Vacancy (NV) centers in diamond. Trained network performance significantly exceeds analytic reconstruction for data with high noise or large standoff distances. This machine learning technique can perform quality inversions on lower SNR data, reducing the data collection time by a factor of about 400 and permitting reconstructions of weaker and three-dimensional current sources.