论文标题
地震位置和图形神经网络的幅度估计
Earthquake Location and Magnitude Estimation with Graph Neural Networks
论文作者
论文摘要
我们通过监督的学习方法解决了地震位置的传统问题和幅度估计，在该方法中，我们训练图形神经网络直接从输入挑选数据中预测估计，每个输入允许具有不同站点和位置的独特地震网络。我们使用假定的旅行时间和振幅距离衰减模型的合成模拟训练该模型。该体系结构使用一个图表表示站点，另一个图表表示模型空间。该输入包括数据的理论预测，给定的模型参数以及图形定义链接在空间局部元素上的邻接矩阵。如我们所示，此组合表示形式上的图形卷积在推理，数据融合和异常抑制方面非常有效。我们将结果与传统方法进行比较，并观察到有利的表现。
We solve the traditional problems of earthquake location and magnitude estimation through a supervised learning approach, where we train a Graph Neural Network to predict estimates directly from input pick data, and each input allows a distinct seismic network with variable number of stations and positions. We train the model using synthetic simulations from assumed travel-time and amplitude-distance attenuation models. The architecture uses one graph to represent the station set, and another to represent the model space. The input includes theoretical predictions of data, given model parameters, and the adjacency matrices of the graphs defined link spatially local elements. As we show, graph convolutions on this combined representation are highly effective at inference, data fusion, and outlier suppression. We compare our results with traditional methods and observe favorable performance.
