  Magnetic surveys are conventionally performed by scanning a domain with a
portable scalar magnetic sensor. Unfortunately, scalar magnetometers are
expensive, power consuming and bulky. In many applications, calibrated vector
magnetometers can be used to perform magnetic surveys. In recent years
algorithms based on artificial intelligence (AI) achieve state-of-the-art
results in many modern applications. In this work we investigate an AI
algorithm for the classical scalar calibration of magnetometers. A simple, low
cost method for performing a magnetic survey is presented. The method utilizes
a low power consumption sensor with an AI calibration procedure that improves
the common calibration methods and suggests an alternative to the conventional
technology and algorithms. The setup of the survey system is optimized for
quick deployment in-situ right before performing the magnetic survey. We
present a calibration method based on a procedure of rotating the sensor in the
natural earth magnetic field for an optimal time period. This technique can
deal with a constant field offset and non-orthogonality issues and does not
require any external reference. The calibration is done by finding an estimator
that yields the calibration parameters and produces the best geometric fit to
the sensor readings. A comprehensive model considering the physical,
algorithmic and hardware properties of the magnetometer of the survey system is
presented. The geometric ellipsoid fitting approach is parametrically tested.
The calibration procedure reduced the root-mean-squared noise from the order of
104 nT to less than 10 nT with variance lower than 1 nT in a complete 360
degrees rotation in the natural earth magnetic field.
