mlconverter
Convert chrons to ages using selected magnetic timescale
Synopsis
gmt mlconverter [ ML_data ] [ -A ] [ -G[s] ] [ -IFZid ] [ -Tc|g|o|s ] [ GMT_V_OPT ]
Note: No space is allowed between the option flag and the associated arguments.
Description
mlconverter is a module developed as part of the Global Seafloor Fabric and Magnetic Lineation Project [see GSFML for a full description of the project]. It reads a magnetic pick data file (or stdin) and converts chron text-strings to ages using a selected magnetic time scale. The input data must be OGR/GMT data files of the form distributed by the GSFML project.
Optional Arguments
- ML_data
A magnetic ML pick data OGR/GMT file. If not given then we read standard input.
- -A
Append the metadata to the output records as additional columns [Default only writes lon, lat, age records].
- -G[s]
Generate an extended OGR/GMT table by appending the crustal age. Append s to repair any lax chron nomenclature, if needed.
- -Tc|g|o|s
Select the magnetic time scale to use. Choose from c (Cande and Kent, 1995), g (Gee and Kent, 2007), o (Ogg, 2012), or s (Gradstein, 2004) [g].
- -V[level]
Select verbosity level [w]. (See full description) (See cookbook information).
- -Zacut/vcut/fcut/wcut
We will attempt to assign a single quality index Q that summarize how good we believe a model fit to be. This assignment relies of four threshold values that need to be determined empirically. Here, a_cut is the minimum peak-to-trough amplitude (in Eotvos) of a model for the crossing profile [25], v_cut is the minimum variance reduction offered by the model (in %) [50], f_cu is the minimum F statistic computed for the model [50], and w_cut is a typical FZ trough width (in km) [15]. Currently, the first three quantities are used to arrive at a 5-level quality index (0-1) for fitted models, as follows: (1) Very Good: Requires model parameters to exceed all three thresholds; (0.75) Good: Requires amplitude and variance reduction to exceed thresholds; (0.5) Fair: Requires the variance reduction only to exceed its threshold; (0.25) Poor: Requires the amplitude only to exceed its threshold; and (0) Bad: None of the criteria were met. We compute separate quality indices for the trough and blend models. For the empirical trough model we only have estimates or peak-to-trough amplitude, A, and trough width, W. Here, we form the ratio (A/a_cut) over (W/w_cut), take \(\tan^{-1}\) of this ratio and scale the result to yield the range 0-1 rounded to the nearest multiple of 0.25.
- -borecord[+b|l] (more …)
Select native binary format for table output.
- -donodata[+ccol] (more …)
Replace output columns that equal NaN with nodata.
- -icols[+l][+ddivisor][+sscale|d|k][+ooffset][,…][,t[word]] (more …)
Select input columns and transformations (0 is first column, t is trailing text, append word to read one word only).
- -ocols[+l][+ddivisor][+sscale|d|k][+ooffset][,…][,t[word]] (more …)
Select output columns and transformations (0 is first column, t is trailing text, append word to write one word only).
- -q[i|o][~]rows|limits[+ccol][+a|t|s] (more …)
Select input or output rows or data limit(s) [all].
- -^ or just -
Print a short message about the syntax of the command, then exit (Note: on Windows just use -).
- -+ or just +
Print an extensive usage (help) message, including the explanation of any module-specific option (but not the GMT common options), then exit.
- -? or no arguments
Print a complete usage (help) message, including the explanation of all options, then exit.
- --PAR=value
Temporarily override a GMT default setting; repeatable. See gmt.conf for parameters.
Examples
To convert chrons to ages using the Cande and Kent, 1995 timescale, and append the metadata at the end of the record, try:
gmt mlconverter -A -Tc ML_datafile.gmt > convertedfile.txt
See Also
gmt fzanalyzer, fzblender, fzinformer, fzmapper, fzmodeler, fzprofiler
References
Wessel, P., Matthews, K. J., Müller, R. D., Mazzoni, A., Whittaker, J. M., Myhill, R., Chandler, M. T., 2015, “Semiautomatic fracture zone tracking”, Geochem. Geophys. Geosyst., 16 (7), 2462–2472. https://doi.org/10.1002/2015GC005853.