论文标题
双向复发性神经网络，用于地震事件检测
Bidirectional recurrent neural networks for seismic event detection
论文作者
论文摘要
实时，准确的被动地震事件检测是一系列监测应用的关键安全措施，从储层稳定性到碳储存到火山震颤检测。最常见的检测程序仍然是长期平均水平（STA/LTA）触发器的短期平均水平，尽管它的常见陷阱需要大于一个的信噪比，并且对触发参数高度敏感。尽管已经提出了许多替代方案，但它们通常是针对特定的监视设置量身定制的，因此不能在全球范围内使用，或者它们在计算上太昂贵，因此不能实时运行。这项工作引入了一种深度学习方法，以替代STA/LTA触发器。双向，长期记忆，神经网络仅在合成痕迹上训练。对合成和现场数据进行了评估，神经网络方法在正确检测到的到达的数量以及减少错误检测到的事件的数量方面显着优于sta/lta触发。它的实时适用性已通过单个处理单元实时处理600个轨迹证明。
Real time, accurate passive seismic event detection is a critical safety measure across a range of monitoring applications from reservoir stability to carbon storage to volcanic tremor detection. The most common detection procedure remains the Short-Term-Average to Long-Term-Average (STA/LTA) trigger despite its common pitfalls of requiring a signal-to-noise ratio greater than one and being highly sensitive to the trigger parameters. Whilst numerous alternatives have been proposed, they often are tailored to a specific monitoring setting and therefore cannot be globally applied, or they are too computationally expensive therefore cannot be run real time. This work introduces a deep learning approach to event detection that is an alternative to the STA/LTA trigger. A bi-directional, long-short-term memory, neural network is trained solely on synthetic traces. Evaluated on synthetic and field data, the neural network approach significantly outperforms the STA/LTA trigger both on the number of correctly detected arrivals as well as on reducing the number of falsely detected events. Its real time applicability is proven with 600 traces processed in real time on a single processing unit.
