Uncertainty Quantification for Magnetic Field Maps and Models

Accurate mapping and modeling of the Earth's complex and dynamic magnetic field is an on-going challenge. These maps and models are used in a wide range of applications from geologic studies to advanced navigation systems. Data that support these maps and models come from a wide range of sources ranging from vintage aeromagnetic surveys to modern satellite sensors. Complete, high resolution maps of the Earth's magnetic field are built through the compilation and combination of long wavelength field models to short wavelength trackline or survey measurements.

Uncertainty quantification for these maps and models presents a significant challenge and has not been addressed comprehensively, particularly for full-field maps and models. Following on approaches developed for error estimation of topographic-bathymetric grids (Amante, 2018), we are designing an approach for application to magnetic field data in a variety of global regions with different magnetic sources and available data distributions. For example, we are examining data from variably oriented marine tracklines over continental shelf and oceanic crust as well as from regularly spaced airborne surveys over geologically complex continental crust.

Initial error models generally assume Gaussian error characteristics, particularly for combination of errors from various components of model construction and data compilation. However, it is known, for example, that the magnetic minerals in the Earth's crust (an important source for magnetic anomalies with wavelengths ranging from 10's of km to 10's of meters) are not normally distributed so that the magnetic variations arising from these sources will generally not have a purely Gaussian distribution.

We will present examples of magnetic compilations and associated point by point uncertainty quantification based on both real and synthetic data. We look forward to lively discussion with experts in the field of data assimilation and uncertainty quantification.

Amante, C.J., 2018. Estimating Coastal Digital Elevation Model Uncertainty. Journal of Coastal Research , 34(6), 1382 - 1397. Coconut Creek (Florida), ISSN 0749-0208. 10.2112/JCOASTRES-D-17-00211.1