Abstract
Earth structural heterogeneities have a remarkable role in the petroleum economy for both exploration and production projects. Automatic detection of detailed structural heterogeneities is challenging when considering modern machine learning techniques like deep neural networks. Typically, these techniques can be an excellent tool for assisted interpretation of such heterogeneities, but it heavily depends on the amount of data to be trained. We propose an efficient and cost-effective architecture for detecting seismic structural heterogeneities using Convolutional Neural Networks (CNNs) combined with Attention layers. The attention mechanism reduces costs and enhances accuracy, even in cases with relatively noisy data. Our model has half the parameters compared to the state-of-the-art, and it outperforms previous methods in terms of Intersection over Union (IoU) by 0.6% and precision by 0.4%. By leveraging synthetic data, we apply transfer learning to train and fine-tune the model, addressing the challenge of limited annotated data availability.
Abstract (translated)
地球结构异质性在石油经济中具有显著的作用，无论是勘探还是生产项目。当考虑现代机器学习技术如深度神经网络时，自动检测详细结构异质性是非常具有挑战性的。通常，这些技术可以作为辅助解释这种异质性的工具，但它们对训练数据的数量非常依赖。我们提出了使用卷积神经网络（CNN）与注意力层相结合来检测地震结构异质性的高效且成本效益型架构。注意力机制可以降低成本并提高准确度，即使在相对嘈杂的数据中也是如此。与最先进的模型相比，我们的模型具有半数量的参数，并且在交叉 over Union（IoU）方面比前人方法提高了0.6%的准确度，而在精度方面提高了0.4%。通过利用合成数据，我们应用迁移学习来训练和微调模型，解决了缺乏充分注释数据可用性的挑战。
URL
https://arxiv.org/abs/2404.10170
