A Comparative Analysis of Machine Learning Models for First-break Arrival Picking

First-break (FB) picking is an important and necessary step in seismic data processing and there is a need to develop precise and accurate auto-picking solutions. Our investigation in this study includes eight machine learning models. We use 1195 raw traces to extract several features and train for accurate picking and monitoring the performance of each model using well-defined evaluation metrics. Careful investigation of the scores shows that a single metric alone is not sufficient to evaluate the arrival picking models in real-time. Correlation analysis of predicted probabilities and predicted classes of machine learning models confirm that the performance metrics that use predicted probabilities have higher score value than those that use predicted classes. Our study which incorporates comparisons of different machine learning models based on different performance metrics, training time, and feature importance indicates that the approach we developed in this study is helpful and provides an opportunity to determine the real-time suitability of different methodologies for automatic FB arrival picking with clear deep insight. Based on performance scores, we bench-marked the Extra Tree classifier as the most efficient model for FB arrival picking with accuracy and F1-score above 95%.

@article{Ayub2021,
  title     = {A Comparative Analysis of Machine Learning Models for First-break Arrival Picking},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {1},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Mohammed Ayub and SanLinn I. Kaka},
  doi       = {10.14569/IJACSA.2021.0120157},
  url       = {https://doi.org/10.14569/IJACSA.2021.0120157}
}