Karstified zone interpretation using deep learning algorithms: Convolutional neural networks applications and model interpretability with explainable AI. (February 2023)

• Record Type: Journal Article
• Authors: Jacinto, Marcos V.G.; Doria Neto, Adrião D.; de Castro, David L.; Bezerra, Francisco H.R.
• Is Part Of: Computers & geosciences. Volume 171(2023)
• Publication Date: 2023-02
• Subjects: Ground penetrating radar -- Karstified zones -- Deep learning -- Explainable AI; Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2022.105281
• Languages: English
• ISSNs: 0098-3004

Abstract

Abstract: Ground penetrating radar (GPR) is a non-invasive geophysical method that can be used to assist in mapping karstified zones in outcrop analogous for the characterization and understanding of carbonate oil reservoirs. Supported by GPR data, it is possible to understand the behavior of karstification processes in carbonate rocks and thus expand the knowledge to the reservoir level. In addition, Machine Learning (ML) and Deep Learning (DL) algorithms have allowed the application of computer vision techniques to identify geological structures and facies based on geophysical data, but mostly focused on seismic data, while GPR based applications still need to be explored. In this context, this study seeks to apply deep learning models in synthetic as well as real GPR data and attributes (similarity, energy, instantaneous frequency, instantaneous phase and Hilbert trace/similarity) based on convolutional neural networks using the U-Net architecture capable of assisting in the mapping of karstified zones imaged through GPR surveys. Moreover, explainable artificial intelligence (XAI) techniques using SHapley Additive exPlanation (SHAP) values are applied to improve the interpretability and explainability of the generated models. These techniques were employed in order to assess the rules found by the models, the modeling quality and the presence of biases in the model. The SHAP values show that the energy attribute was the feature that provided more information in both synthetic and real models. Instantaneous frequency and similarity were the second and third most important features in the synthetic model, while the other attributes presented less relevant contributions. Finally, we generated a model capable of aiding in mapping karstified zones using GPR data and attributes, promoting the understanding of complex models and allowing greater cooperation between experts in the geosciences and results generated through deep learning techniques. Highlights: Using Ground Penetration Radar in carbonates improves understanding of karstified zones. Convolutional Neural Networks can speed up the interpretation of GPR images of karstified carbonates. Shapley Explanation Values can be used to improve the explainability and reliability of AI-based interpretations of GPR data.