Add WDMAM dataset to load_earth_magnetic_anomaly

[willschlitzer]

This adds the wdmam as a data_source to load_earth_magnetic_anomaly to load the World Digital Magnetic Anomaly Map dataset.

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[seisman]

Similar to the comment https://github.com/GenericMappingTools/pygmt/pull/2240/files#r1047080605, the name load_earth_wdmam is not clear, but load_earth_world_digital_magnetic_anomaly_map is too long

[willschlitzer]

I figure that if it's the name for the GMT dataset, it shouldn't be too difficult to use that for the PyGMT function to download it. I think I would feel more concerned if it was commonly used data set like load_earth_relief, but I believe that users looking for the WDMAM dataset will understand the meaning behind load_earth_wdmam.

[willschlitzer]

@GenericMappingTools/pygmt-maintainers Any ideas on a good function name?

[yvonnefroehlich]

Hm, currently not really. The name load_earth_world_digital_magnetic_anomaly_map is quite long. However, the shortcut wdmam is not or only for "experienced" users understandable. load_earth_magnetic_anomaly is used for EMAG2 Global Earth Magnetic Anomaly Model, in which model was skipped. Do we need both earth and world? So maybe load_word_digital_magnetic_anomaly? This is not really short, but at least a bit more in the length range of load_earth_vertical_gravity_gradient.

[willschlitzer]

I think load_world_digital_magnetic_anomaly could be easily confused with load_earth_magnetic_anomaly.

[seisman]

I know very little about these two datasets, but from the papers (https://doi.org/10.1186/s40623-016-0404-6, https://doi.org/10.1029/2009GC002471):

Note that EMAG2 represents magnetic anomalies at 4 km altitude above the geoid in 2–arc min resolution, while the WDMAM grid was specified at 5 km altitude in 3–arc min resolution.

Conversely, the Earth Magnetic Anomaly Map EMAG-2 (Maus et al. 2009) interpolates between sparse marine magnetic data using directional gridding and extrapolation based on the age map of the seafloor (Müller et al. 2008), but the result displays artificially elongated features due to the improper interpolation of anomalies unrelated to seafloor spreading (associated, for instance, to seamounts).

As I understand them, both EMAG-2 and WDMAM are magnetic anomaly models, but the details diff. So, just like load_earth_relief, perhaps we should add the data_source parameter (valid values are emag2 and wdmam) to the load_earth_magnetic_anomaly function.

[willschlitzer]

That's a good idea.

[willschlitzer]

@GenericMappingTools/pygmt-maintainers I'm going to be away until the 26th and only on phone/website, so please feel free to push any necessary changes to this PR.

[willschlitzer]

I added wdmam= to load_earth_magnetic_anomaly and moved the WDMAM tests to test_datasets_earth_magnetic_anomaly.py.

[weiji14]

For consistency with load_earth_relief, would it be better to use a single data_source parameter with 3 options (emag, emag4km, wdmam)? Names to be decided.

[seisman]

I prefer to the name emag2 instead, because emag2 is the official name of the dataset (https://doi.org/10.7289/V5H70CVX), and emag4km can be renamed to emag2_4km instead.

[weiji14]

/format

[weiji14]

Looks good, thanks @willschlitzer!