Air Force Institute of Technology, Wright-Patterson AFB, OH 45433, USA.
Air Force Technical Applications Center, Patrick AFB, FL 32925, USA.
Sensors (Basel). 2019 Jan 31;19(3):597. doi: 10.3390/s19030597.
The detection of seismic events at regional and teleseismic distances is critical to Nuclear Treaty Monitoring. Traditionally, detecting regional and teleseismic events has required the use of an expensive multi-instrument seismic array; however in this work, we present DeepPick, a novel seismic detection algorithm capable of array-like detection performance from a single-trace. We achieve this performance through three novel steps: First, a high-fidelity dataset is constructed by pairing array-beam catalog arrival-times with single-trace waveforms from the reference instrument of the array. Second, an idealized characteristic function is created, with exponential peaks aligned to the cataloged arrival times. Third, a deep temporal convolutional neural network is employed to learn the complex non-linear filters required to transform the single-trace waveforms into corresponding idealized characteristic functions. The training data consists of all arrivals in the International Seismological Centre Database for seven seismic arrays over a five year window from 1 January 2010 to 1 January 2015, yielding a total training set of 608,362 detections. The test set consists of the same seven arrays over a one year window from 1 January 2015 to 1 January 2016. We report our results by training the algorithm on six of the arrays and testing it on the seventh, so as to demonstrate the generalization and transportability of the technique to new stations. Detection performance against this test set is outstanding, yielding significant improvements in recall over existing techniques. Fixing a type-I error rate of 0.001, the algorithm achieves an overall recall (true positive rate) of 56% against the 141,095 array-beam arrivals in the test set, yielding 78,802 correct detections. This is more than twice the 37,572 detections made by an STA/LTA detector over the same period, and represents a 35% improvement over the 58,515 detections made by a state-of-the-art kurtosis-based detector. Furthermore, DeepPick provides at least a 4 dB improvement in detector sensitivity across the board, and is more computationally efficient, with run-times an order of magnitude faster than either of the other techniques tested. These results demonstrate the potential of our algorithm to significantly enhance the effectiveness of the global treaty monitoring network.
地震事件在区域和远震距离的检测对核条约监测至关重要。传统上，检测区域和远震事件需要使用昂贵的多仪器地震阵列；然而，在这项工作中，我们提出了 DeepPick，这是一种新的地震检测算法，能够从单迹中实现类似阵列的检测性能。我们通过三个新步骤实现了这一性能：首先，通过将阵列波束目录到达时间与阵列参考仪器的单迹波形配对，构建一个高保真数据集。其次，创建一个理想的特征函数，其指数峰值与目录到达时间对齐。最后，采用深度时间卷积神经网络来学习将单迹波形转换为相应理想特征函数所需的复杂非线性滤波器。训练数据由 2010 年 1 月 1 日至 2015 年 1 月 1 日的五年期间国际地震中心数据库中的七个地震阵所有到达事件组成，总共训练了 608362 个检测结果。测试集由 2015 年 1 月 1 日至 2016 年 1 月 1 日的相同七个阵组成。我们通过在六个阵上训练算法并在第七个阵上进行测试来报告结果，以证明该技术对新台站的泛化和可传输性。该算法在测试集上的检测性能非常出色，与现有技术相比，召回率显著提高。在固定 0.001 的Ⅰ型错误率的情况下，该算法在测试集的 141095 个阵-波束到达中实现了 56%的整体召回率（真阳性率），产生了 78802 个正确检测结果。这比同一时期的 STA/LTA 检测器的 37572 个检测结果多了一倍以上，比基于峰度的最先进的检测器的 58515 个检测结果提高了 35%。此外，DeepPick 在整个范围内提供至少 4dB 的检测灵敏度提高，并且计算效率更高，运行时间比测试的其他两种技术快一个数量级。这些结果表明，我们的算法有潜力显著提高全球条约监测网络的有效性。
