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.

翻译：对于真实的、大型的和复杂的科学数据集,深度学习方法对设计来说可能非常困难。在这项工作中,我们展示了对精密和高效规模的深层学习分类器的全面搜索,以从通过分布式声学遥感(DAS)获得的地震数据中确定可用的能源。在培训期间,我们只使用贴有标签的图像子集,但能够找到能够准确普及到未知信号模式的合适模型。我们显示,如果使用16倍以上的GPUs,我们就可以将50 000个图像数据集的培训速度提高两个以上级。