the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 29 Sep 2025
A new magnetic anomaly map for Greenland based on a combination of equivalent source modeling and spherical harmonic expansion
Abstract. The Greenland Magnetic Map (GREENMAG) is a new compilation of magnetic anomaly data that covers the inland ice, ice-free coastal areas, and adjacent shelf regions of Greenland. GREENMAG is based on all accessible modern regional aeromagnetic surveys from Greenland and vintage datasets without GPS positioning in areas where modern data are lacking.
The magnetic anomaly map is generated by a combination of equivalent source (ES) modeling and spherical harmonic expansion. Hereby, the data points are used at their actual measurement location as input data for the inversion of the ES modeling. The equivalent sources are represented by magnetic dipoles that are arranged in three uniform grids with different source spacing and depths (coarsest spacing: 10 x 10 km; medium spacing: 2 x 2 km; finest spacing: 0.7 x 0.7 km). Regularization in the inversion for the different equivalent source grids are chosen such that the resulting resolution is adapted to the largely varying magnetic data coverage in Greenland. Since long wavelength components in aeromagnetic data are considered unreliable, they are replaced by the LCS-1 satellite model based on magnetic gradient measurements of the Swarm and CHAMP missions. For combination, the responses from the individual equivalent dipole sources are transferred to spherical harmonics and replaced for degree n=13-133 by the Gaussian coefficients of the LCS-1 model.
The final magnetic anomaly map is calculated from the combined model at a constant height of 2000 m.a.s.l. (WGS84) and with a grid spacing of 400 x 400 m.
The comparison between the GREENMAG and the earlier compilation from the Circum-Arctic Mapping Project (CAMP-M) highlights the enhanced level of detail now available across many regions of Greenland.
- Preprint
(52573 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 25 Nov 2025)
-
RC1: 'Comment on essd-2025-448', Rick Saltus, 07 Oct 2025
reply
-
EC1: 'Reply on RC1', Robert Jackisch, 09 Oct 2025
reply
The manuscript containing reviewer comments from Referee 1
-
EC1: 'Reply on RC1', Robert Jackisch, 09 Oct 2025
reply
Data sets
GREENMAG – Magnetic anomaly map of Greenland Björn Henning Heincke and Wolfgang Szwillus https://doi.org/10.22008/FK2/LQN5YJ
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 264 | 61 | 11 | 336 | 6 | 7 |
- HTML: 264
- PDF: 61
- XML: 11
- Total: 336
- BibTeX: 6
- EndNote: 7
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
This is an excellent presentation of a state of the art compilation of magnetic survey data to produce a comprehensive data grid. The authors provide clear discussion of the approach and rationale for their modeling decisions.
I have made a few comments and suggestions as shown in the attached annotated manuscript.
One recurring comment deals with the use of the term "error" when "uncertainty" is meant. Errors can be positive or negative whereas uncertainty indicates the probable range in which the true value is likely to occur. You can only know error if you know the truth for comparison.
The authors deal with uncertainty in a general probabilistic way - they mention the initial data uncertainty attributed to the original survey data (ranging from 5 to 50 nT as an average attribute per survey). This value is used for weighting of the data in the inversion for equivalent source values. They mention the overall goodness of fit for the equivalent source combined model relative to the original data. They also explain the method for solving in the inversion for the probable DC offset between the individual surveys. An addition source of uncertainty is the long wavelength uncertainty in the LCS-1 model incorporated in the final grid. I realize it is difficult to propagate uncertainty through all these steps, but I think it is worth some discussion and, ideally, the authors would produce some sort of overall uncertainty grid to accompany the data grid.
Another approach to validating the grid and uncertainty would be to identify trusted long survey lines that were not included in the model for comparison with values extracted from the grid.
However, even without additional discussion/assessment of grid uncertainty, this paper is excellent and worthy of publication. Similar scale data compilations to date have generally not included detailed uncertainty assessment. Similarly, many authors conflate the terms error and uncertainty and most readers can discern the difference.