Interpretação de zonas carstificadas usando redes neurais convolucionais e interpretabilidade através de Explainable AI
Ground penetrating radar (GPR) is a geophysical tool that can be used to assist in mapping karstified zones in analogs for the characterization and understanding of carbonate reservoirs. With the aid of GPR, it is possible to understand the behavior of the karstification processes in carbonates and,...
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| Formato: | Dissertação |
| Idioma: | pt_BR |
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Universidade Federal do Rio Grande do Norte
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| Endereço do item: | https://repositorio.ufrn.br/handle/123456789/46833 |
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| Resumo: | Ground penetrating radar (GPR) is a geophysical tool that can be used to assist in mapping
karstified zones in analogs for the characterization and understanding of carbonate reservoirs.
With the aid of GPR, it is possible to understand the behavior of the karstification processes in
carbonates and, thus, expand the knowledge to the reservoir level, as well as make parallels
with analogues in the Brazilian pre-salt. 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, as obtained through the
GPR tool, but mainly with based on seismic. In this context, and using as data eight GPR
sections, and five attributes generated from them (energy, similarity, instantaneous phase,
instantaneous frequency, and the ratio between the Hilbert trace and the similarity), this study
seeks to apply DL models based on convolutional neural networks using the U-Net
architecture. 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.
Moreover, distinct settings with regard to background SHAP values were tested and compared
to assess how they influence model explainability. As demonstrated in the results, the U-Net
architecture developed was able to perform image segmentation through GPR data and,
consequently, map and differentiate karstified from non-karstified zones. Furthermore, the
SHAP values show that the energy attribute was the feature that provided more information in
the modeling and consequently provided a greater weight in the model rules while the other
features presented less relevant contributions. Furthermore, the type of sampling used to define
the reference values for the SHAP values resulted in different interpretations of the
contributions of the features. Finally, the present work generated models capable of assisting
in the mapping of karstified zones, further supported by a technique capable of promoting the
understanding of complex models and allowing greater cooperation between experts in
geosciences and the results generated through ML and DL techniques. |
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