Python tool to read and plot Geophysical Survey Systems Incorporated (GSSI) radar data in DZT format
Copyleft 🄯 2017-2019
readgssi is a tool intended for use as an open-source reader and preprocessing module for subsurface data collected with Geophysical Survey Systems Incorporated (GSSI) ground-penetrating georadar (GPR) devices. It has the capability to read DZT and DZG files with the same pre-extension name and plot the data contained in those files.
readgssi is also currently able to translate most DZT files to CSV and will be able to translate to other output formats including HDF5 (see future). Matlab code donated by Gabe Lewis, Dartmouth College Department of Earth Sciences. Python adaptation written with permission by Ian Nesbitt, University of Maine School of Earth and Climate Sciences.
The file read parameters are based on GSSI's DZT file description, similar to the ones available on pages 55-57 of the SIR-3000 manual. File structure is, unfortunately, prone to change at any time, and although I've been able to test with files from several systems, I have not encountered every iteration of file header yet. If you run into trouble, please create a github issue.
Questions, feature requests, and bugs: please open a github issue. Kindly provide the error output, describe what you are attempting to do, and attach the DZT/DZG file(s) causing you trouble.
Strongly recommended to install via anaconda:
Once you have anaconda running, installing requirements is pretty easy.
conda config --add channels conda-forge conda create -n readgssi python==3.7 pandas h5py pytz obspy conda activate readgssi pip install readgssi
That should allow you to run the commands below.
installing from source:
If you choose to install a specific commit rather than the latest working release of this software, you may download this package, unzip to your home folder, open a command line, then install in the following way:
pip install ~/readgssi
To display the help text:
$ readgssi -h usage: readgssi -i input.DZT [OPTIONS] optional flags: OPTION | ARGUMENT | FUNCTIONALITY -o, --output | file: /dir/f.ext | specify an output file -f, --format | string, eg. "csv" | specify output format (csv is the only working format currently) -p, --plot | +integer or "auto" | plot will be x inches high (dpi=150), or "auto". default: 10 -x, --xscale | string, eg. "dist" | readgssi will attempt to convert the x-axis to distance, time, or traces based on header values -z, --zscale | string, eg. "time" | readgssi will attempt to convert the x-axis to depth, time, or samples based on header values -n, --noshow | | suppress matplotlib popup window and simply save a figure (useful for multiple file processing) -c, --colormap | string, eg. "Greys" | specify the colormap (https://matplotlib.org/users/colormaps.html#grayscale-conversion) -g, --gain | positive (+)integer | gain value (higher=greater contrast, default: 1) -r, --bgr | | horizontal background removal algorithm (useful to remove ringing) -R, --reverse | | reverse (flip radargram horizontally) -w, --dewow | | trinomial dewow algorithm -t, --bandpass | +int-+int (MHz) | butterworth bandpass filter (positive integer range in megahertz; ex. 100-145) -b, --colorbar | | add a colorbar to the radar figure -a, --antfreq | positive integer | specify antenna frequency (read automatically if not given) -s, --stack | +integer or "auto" | specify trace stacking value or "auto" to autostack to ~2.5:1 x:y axis ratio -N, --normalize | | reads a .DZG NMEA data if it exists; otherwise tries to read a csv file with lat, lon, and time fields to distance normalize with -d, --spm | positive float | specify the samples per meter (spm) manually. overrides header value. -m, --histogram | | produce a histogram of data values -E, --epsr | float > 1.0 | user-defined epsilon sub r (sometimes referred to as "dielectric"; ignores value in DZT header) -Z, --zero | positive integer | skip this many samples from the top of the trace downward (useful for removing transceiver delay) naming scheme for exports: CHARACTERS | MEANING c0 | Profile from channel 0 (can range from 0 - 3) Dn | Distance normalization Tz233 | Time zero at 233 samples S8 | Stacked 8 times Rv | Profile read in reverse (flipped horizontally) Bgr | Background removal filter Dw | Dewow filter Bp100-145 | 2-corner bandpass filter applied from 100 to 145 MHz G30 | 30x contrast gain
From a unix command line:
readgssi -i DZT__001.DZT
Simply specifying an input DZT file like in the above command (
-i file) will display a host of data about the file including:
- name of GSSI control unit
- antenna model
- antenna frequency
- samples per trace
- bits per sample
- traces per second
- L1 dielectric as entered during survey
- sampling depth
- speed of light at given dielectric
- number of traces
- number of seconds
readgssi -i DZT__001.DZT -o test.csv -f CSV
Translates radar data array to CSV format, if that's your cup of tea. One might use this to export to Matlab. One CSV will be written per channel. The script will rename the output to 'test_100MHz.csv' automatically. No header information is included in the CSV.
readgssi -i DZT__001.DZT -s 8 -w -r -o test.csv -f CSV
Applies 8x stacking, dewow, and background removal filters before exporting to CSV.
example 1A: without gain
readgssi -i DZT__001.DZT -o -p 5 -s auto -m
The above command will cause
readgssi to save and show a plot named "TEST__001c0Tz233S6G1.png" with a y-size of 5 inches at 150 dpi (
-p 5) and the autostacking algorithm will stack the x-axis to some multiple of times shorter than the original data array for optimal viewing on a monitor, approximately 2.5*y (
-s auto). The plot will be rendered in the
Greys color scheme. The
-m flag will draw a histogram for each data channel.
example 1B: with gain
readgssi -i DZT__001.DZT -o 1b.png -p 5 -s auto -g 50 -m -r
This will cause
readgssi to create a plot from the same file, but matplotlib will save the plot as "1b.png" (
-o 1b.png). The script will plot the y-axis size (
-p 5) and automatically stack the x-axis to (
-s auto). The script will plot the data with a gain value of 50 (
-g 50), which will increase the plot contrast by a factor of 50. Next
readgssi will run the background removal (
-r) filter. Finally, the
-m flag will draw a histogram for each data channel. Note how the histogram changes when filters are applied.
example 1C: the right gain settings can be slightly different depending on your colormap
readgssi -i DZT__001.DZT -o 1c.png -p 5 -s auto -r -g 20 -c seismic
Here, a horizontal background removal is applied, but gain is turned down (
-g 20). The script uses matplotlib's "seismic" colormap (
-c seismic) which is specifically designed for this type of waterfall array plotting. Even without gain, you will often be able to easily see very slight signal perturbations. Given its use of red, however, it is not terribly colorblind-friendly for either of the two most common types of human colorblindness, which is why it is not used as the default colormap.
example 2A: no background removal
readgssi -i DZT__002.DZT -o 2a.png -p 5 -s 5 -n
Sometimes, files will look "washed out" due to a skew relative to the mean of the data. This is easily correctable. Here
readgssi will create a plot of size 5 and stack 5x (
-p 5 -s 5). Matplotlib will use the default "Greys" colormap and save a PNG of the figure, but the script will suppress the matplotlib window (using the
-n flag, useful for processing an entire directory full of DZTs at once).
example 2B: horizontal mean BGR algorithm applied
readgssi -i DZT__002.DZT -o 2b.png -p 5 -s 5 -n -r
The flag to get rid of the skew (or any horizontally uniform noise) is
-r. The script does the same thing as above, except
-r applies horizontal mean background removal to the profile. Note the difference in ringing artifacts and skew between examples 2a and 2b.
example 3A: (without) distance normalization
The default behavior of
readgssi is to plot the X-axis in survey time units (seconds). This can be changed using the
-x flag. To display in distance units, you must either have GPS information in DZG format, or specify the number of radar traces per meter using the
-d 24 -x meters will change the traces per meter value in the header to 24.0 and display the profile with distance in meters along the X-axis.
Files with GPS information are handled in a slightly different way. First,
readgssi will read a DZG file to create an array of distance information associated with marks in the DZT. (NOTE: If your project was recorded without DZG files but you have per-line GPS mark information in GPX format, please look at gpx2dzg for a method of creating a DZG file for each survey line) After reading the DZG, the program will expand or contract the GPR array based on the speed over ground between GPS points. It will then modify the traces per meter value from the header and display the profile with distance on the X-axis.
Here a file is processed and displayed without distance normalization:
readgssi -i DZT__003.DZT -o 3a.png -p 10 -s 5 -r -g 50
example 3B: distance normalization using a DZG file
To use DZG GPS information to distance normalize the profile and display in meters traveled, use the
-x meters flags.
readgssi will normalize the file in chunks to reduce memory usage. Here is the same file with distance normalization applied:
readgssi -i DZT__003.DZT -o 3b.png -p 10 -s 5 -r -g 50 -N -x meters
Ian M. Nesbitt, François-Xavier Simon, Thomas Paulin, 2018. readgssi - an open-source tool to read and plot GSSI ground-penetrating radar data. doi:10.5281/zenodo.1439119
- color bar shows up too large on some plots (matplotlib bug)
- short lines have axis limits that end up tall and narrow (plot sizing information should be calculated differently)
- explicit documentation
- automatic script testing for smoother dev
- create a class for surveyline objects, similar to
- GPS transcription from CSV with fields like
mark name, lon, lat, elev, time
- Use GPS altitude to adjust z position across profile
- GUI-based geologic/dielectric layer picking
- layer velocity calculation (using minimum of clustered hyperbola tail angle measurements, or manual input)
- velocity-based depth adjustments
- ability to incorporate ground truth measurements
- velocity gradient/angle of incidence-based array migration
- translation to common geophysical formats (HDF5, SEGY, etc.)
- integration with
vistafor 3D visualization of location-aware arrays
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