A Deep Convolutional Network for Seismic Shot-Gather Image Quality Classification. Betine, E., Busson, A., Milidiú, R., Colcher, S., Dias, B., & Bulcão, A. December, 2019. abstract bibtex Deep Learning-based models such as Convolutional Neural Networks, have led to significant advancements in several areas of computing applications. Seismogram quality assurance is a relevant Geophysics task, since in the early stages of seismic processing, we are required to identify and fix noisy sail lines. In this work, we introduce a real-world seismogram quality classification dataset based on 6,613 examples, manually labeled by human experts as good, bad or ugly, according to their noise intensity. This dataset is used to train a CNN classifier for seismic shot-gathers quality prediction. In our empirical evaluation, we observe an F1-score of 93.56% in the test set.
@misc{betine_deep_2019,
title = {A {Deep} {Convolutional} {Network} for {Seismic} {Shot}-{Gather} {Image} {Quality} {Classification}},
abstract = {Deep Learning-based models such as Convolutional Neural Networks, have led to significant advancements in several areas of computing applications. Seismogram quality assurance is a relevant Geophysics task, since in the early stages of seismic processing, we are required to identify and fix noisy sail lines. In this work, we introduce a real-world seismogram quality classification dataset based on 6,613 examples, manually labeled by human experts as good, bad or ugly, according to their noise intensity. This dataset is used to train a CNN classifier for seismic shot-gathers quality prediction. In our empirical evaluation, we observe an F1-score of 93.56\% in the test set.},
author = {Betine, Eduardo and Busson, Antonio and Milidiú, Ruy and Colcher, Sérgio and Dias, Bruno and Bulcão, André},
month = dec,
year = {2019}
}
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