Process and visualize ground-penetrating radar (GPR) data with signal processing, velocity analysis, and depth conversion. Use when Claude needs to: (1) Load GPR files (.DZT, .DT1, .GPR, .rd3), (2) Apply dewow, gain, and filters to radargrams, (3) Convert two-way travel time to depth, (4) Perform CMP/WARR velocity analysis, (5) Apply topographic corrections, (6) Export processed profiles as images or SEG-Y, (7) Batch process multiple GPR survey lines.
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Version1.0.0
LicenseMIT
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skill mdSKILL.md4,934 B
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SKILL.md
GPRPy - Ground Penetrating Radar Processing
Quick Reference
import gprpy.gprpy as gp
import matplotlib.pyplot as plt
# Load and display
data = gp.gprpyProfile()
data.importdata('profile.DZT')
data.showProfile()
plt.show()
# Access data
print(f"Traces: {data.data.shape[1]}")
print(f"Samples: {data.data.shape[0]}")
print(f"Time range: {data.twtt.max():.1f} ns")
Supported Formats
Format
Manufacturer
.DZT
GSSI
.DT1
Sensors & Software
.GPR
MALA
.rd3/.rad
MALA
.sgy
SEG-Y
Essential Operations
Basic Processing
data = gp.gprpyProfile()
data.importdata('profile.DZT')
data.dewow(window=10) # Remove low-frequency drift
data.remMeanTrace(ntraces=50) # Remove background ringing
data.tpowGain(power=1.5) # Time-power gain
data.agcGain(window=25) # Automatic gain control
data.showProfile()
Full commercial processing suite, advanced migration
Commercial license required
Custom scipy
Custom signal processing, research algorithms
Must build everything from scratch
Use gprpy when you need open-source GPR processing in Python, batch processing of survey lines, or integration with other Python geoscience tools.
Consider alternatives when you need forward modelling of GPR responses (use GPRMax), advanced migration or commercial-grade processing (use REFLEXW), or highly custom signal processing algorithms (use scipy directly).
Common Workflows
Process raw GPR profile for interpretation
Import raw data with gp.gprpyProfile() and importdata()
Apply dewow filter to remove low-frequency drift
Remove mean trace to eliminate background ringing
Apply time-power gain or AGC for depth equalization
Apply bandpass filter to remove noise
Determine velocity from CMP analysis or material tables
Convert time axis to depth with setVelocity()
Apply topographic correction if survey has elevation changes