We developed an algorithm to automatically detect and track reflections within GPR data sets, based on their reflection strength. The procedure divides each GPR trace into a series of energy packets, each containing reflected, diffracted, interfering, or noise-related signals. Close packets in nearby traces are then connected, using the local reflection dips, to create a web covering the entire GPR profile. Through this web, the algorithm tracks, and marks as horizons, the main reflections, while redundant horizons and possible false positives are automatically removed. The procedure can be applied with just minimal signal processing, and requires only limited input from the interpreter. The algorithm was able to accurately track all the recorded reflections within GPR data sets acquired on coastal sand dunes, and to filter out deeper false positives.

Automated reflection strength tracking for improved stratigraphic interpretation of GPR data sets

Dossi, Matteo;Forte, Emanuele
;
Pipan, Michele
2022-01-01

Abstract

We developed an algorithm to automatically detect and track reflections within GPR data sets, based on their reflection strength. The procedure divides each GPR trace into a series of energy packets, each containing reflected, diffracted, interfering, or noise-related signals. Close packets in nearby traces are then connected, using the local reflection dips, to create a web covering the entire GPR profile. Through this web, the algorithm tracks, and marks as horizons, the main reflections, while redundant horizons and possible false positives are automatically removed. The procedure can be applied with just minimal signal processing, and requires only limited input from the interpreter. The algorithm was able to accurately track all the recorded reflections within GPR data sets acquired on coastal sand dunes, and to filter out deeper false positives.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/3039019
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