A Deep Convolutional Network for Seismic Shot-Gather Image Quality Classification
Computer Vision and Pattern Recognition
2019-12-04 v1 Machine Learning
Image and Video Processing
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.
Cite
@article{arxiv.1912.01148,
title = {A Deep Convolutional Network for Seismic Shot-Gather Image Quality Classification},
author = {Eduardo Betine Bucker and Antonio José Grandson Busson and Ruy Luiz Milidiú and Sérgio Colcher and Bruno Pereira Dias and André Bulcão},
journal= {arXiv preprint arXiv:1912.01148},
year = {2019}
}