In the present study, we propose a new approach for determining earthquake hypocentral parameters. This approach integrates computed theoretical seismograms and deep machine learning. The theoretical seismograms are generated through a realistic three-dimensional Earth model, and are then used to create spatial images of seismic wave propagation at the Earth’s surface. These snapshots are subsequently utilized as a training data set for a convolutional neural network. Neural networks for determining hypocentral parameters such as the epicenter, depth, occurrence time, and magnitude are established using the temporal evolution of the snapshots. These networks are applied to seismograms from the seismic observation network in the Hakone volcanic region in Japan to demonstrate the suitability of the proposed approach for locating earthquakes. We demonstrate that the determination accuracy of hypocentral parameters can be improved by including theoretical seismograms for different earthquake locations and sizes, in the learning data set for the deep machine learning. Using the proposed method, the hypocentral parameters are automatically determined within seconds after detecting an event. This method can potentially serve in monitoring earthquake activity in active volcanic areas such as the Hakone region.

在本研究中，我们提出了一种确定地震震源参数的新方法。这种方法集成了计算理论地震图和深度机器学习。理论地震图是通过真实的三维地球模型生成的，然后用于创建地球表面地震波传播的空间图像。这些快照随后被用作卷积神经网络的训练数据集。使用快照的时间演化建立用于确定震中参数（例如震中、深度、发生时间和震级）的神经网络。这些网络应用于日本箱根火山区地震观测网络的地震图，以证明所提出的地震定位方法的适用性。我们证明，通过在深度机器学习的学习数据集中包含不同地震位置和大小的理论地震图，可以提高震源参数的确定精度。使用所提出的方法，在检测到事件后的几秒钟内自动确定震源参数。这种方法有可能用于监测箱根地区等活跃火山区的地震活动。