A Deep Convolutional Network for Seismic Shot-Gather Image Quality Classification
This work addresses seismogram quality assurance for geophysics, but it is incremental as it applies an existing deep learning method to a new domain-specific dataset.
The paper tackled the problem of seismic shot-gather image quality classification by introducing a manually labeled dataset of 6,613 examples and training a CNN classifier, achieving an F1-score of 93.56% on the test set.
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.