论文标题
对真实地球物理数据的深入学习：用于分布式声感应研究的案例研究
Deep Learning on Real Geophysical Data: A Case Study for Distributed Acoustic Sensing Research
论文作者
论文摘要
真实，大，复杂的科学数据集的深度学习方法对于设计可能非常具有挑战性。在这项工作中，我们进行了完整的搜索，以进行精心调整且有效缩放的深度学习分类器，以从使用分布式声学传感（DAS）获取的地震数据中识别可用的能量。在训练过程中仅使用标记图像的子集，但我们能够确定可以准确推广到未知信号模式的合适模型。我们表明，通过使用GPU的16倍，我们可以在50,000图像数据集上将训练速度提高两个以上的数量级。
Deep Learning approaches for real, large, and complex scientific data sets can be very challenging to design. In this work, we present a complete search for a finely-tuned and efficiently scaled deep learning classifier to identify usable energy from seismic data acquired using Distributed Acoustic Sensing (DAS). While using only a subset of labeled images during training, we were able to identify suitable models that can be accurately generalized to unknown signal patterns. We show that by using 16 times more GPUs, we can increase the training speed by more than two orders of magnitude on a 50,000-image data set.
