Identificação de falhas geológicas em sísmicas usando Redes Neurais Convolucionais

Abstract

Approaches using machine learning are being used to support activities in Geoscience. Among the possible applications, some are aimed at interpreting seismic data in tasks such as identifying features or identifying faults. In particular, this work assists the seismic interpretation and can bring gains by reducing manual work and the time spent studying the geological area. This dissertation describes how a tool capable of selecting points representing geometric sequences in seismic and discontinuities in these sequences can be developed. Thus, in this work, a study of types of deep neural networks in seismic geological data was done. From these works, we have the identification of 2D faults or fractures. Experiments with deep neural network training in seismic were also carried out to serve as the basis for the proposed work. With this study and these experiments, a new network architecture of the encoder-decoder type was proposed and evaluated, making image segmentation identify faults. This architecture is based on DNFS, StNet, and FaultNet networks. The work also generated contributions in producing and annotating a dataset with annotated seismic fault data available for access and used in experiments. Our future steps include fostering solutions to identify faults or critically stressed fractures according to the tension field.