Department of Geophysics, Stanford University, Stanford, CA, 94305, USA.
Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, 02138, USA.
Nat Commun. 2021 Mar 4;12(1):1432. doi: 10.1038/s41467-021-21670-x.
An immediate report of the source focal mechanism with full automation after a destructive earthquake is crucial for timely characterizing the faulting geometry, evaluating the stress perturbation, and assessing the aftershock patterns. Advanced technologies such as Artificial Intelligence (AI) has been introduced to solve various problems in real-time seismology, but the real-time source focal mechanism is still a challenge. Here we propose a novel deep learning method namely Focal Mechanism Network (FMNet) to address this problem. The FMNet trained with 787,320 synthetic samples successfully estimates the focal mechanisms of four 2019 Ridgecrest earthquakes with magnitude larger than Mw 5.4. The network learns the global waveform characteristics from theoretical data, thereby allowing the extensive applications of the proposed method to regions of potential seismic hazards with or without historical earthquake data. After receiving data, the network takes less than two hundred milliseconds for predicting the source focal mechanism reliably on a single CPU.
破坏性地震后立即自动报告震源机制，对于及时刻画断层几何、评估应力变化和评估余震模式至关重要。人工智能（AI）等先进技术已被引入到实时地震学中以解决各种问题，但实时震源机制仍然是一个挑战。在这里，我们提出了一种新的深度学习方法，即震源机制网络（FMNet）来解决这个问题。FMNet 利用 787320 个合成样本进行训练，成功估计了四次 2019 年里氏震级大于 5.4 的里奇克雷斯特地震的震源机制。该网络从理论数据中学习全局波形特征，从而使得所提出的方法可以广泛应用于有或没有历史地震数据的潜在地震危险区域。在接收到数据后，该网络在单个 CPU 上可靠地预测震源机制的时间不到 200 毫秒。
