Title: Earthquake Source Depth Determination using Single Station Waveforms and Deep Learning Show Chinese title

Title: 基于单站波形和深度学习的地震震源深度确定

Abstract: In areas with limited station coverage, earthquake depth constraints are much less accurate than their latitude and longitude. Traditional travel-time-based location methods struggle to constrain depths due to imperfect station distribution and the strong trade-off between source depth and origin time. Identifying depth phases at regional distances is usually hindered by strong wave scattering, which is particularly challenging for low-magnitude events. Deep learning algorithms, capable of extracting various features from seismic waveforms, including phase arrivals, phase amplitudes, as well as phase frequency, offer promising constraints to earthquake depths. In this work, we propose a novel depth feature extraction network (named VGGDepth), which directly maps seismic waveforms to earthquake depth using three-component waveforms. The network structure is adapted from VGG16 in computer vision. It is designed to take single-station three-component waveforms as inputs and produce depths as outputs, achieving a direct mapping from waveforms to depths. Two scenarios are considered in our model development: (1) training and testing solely on the same seismic station, and (2) generalizing by training and testing on different seismic stations within a particular region. We demonstrate the efficacy of our methodology using seismic data from the 2016-2017 Central Apennines, Italy earthquake sequence. Results demonstrate that earthquake depths can be estimated from single stations with uncertainties of hundreds of meters. These uncertainties are further reduced by averaging results from multiple stations. Our method shows strong potential for earthquake depth determination, particularly for events recorded by single or sparsely distributed stations, such as historically instrumented earthquakes. Show Chinese abstract

Abstract: 在站网覆盖有限的地区，地震深度约束的准确性远低于其纬度和经度。 基于旅行时间的传统定位方法由于台站分布不完善以及震源深度与发震时刻之间的强权衡关系，难以准确约束深度。 在区域距离上识别深度相位通常受到强烈波散射的阻碍，这对小震尤其具有挑战性。 能够从地震波形中提取各种特征（包括相位到时、相位振幅以及相位频率）的深度学习算法，为地震深度提供了有前景的约束。 在本工作中，我们提出了一种新的深度特征提取网络（命名为 VGGDepth），该网络使用三成分波形直接将地震波形映射到地震深度。 网络结构借鉴了计算机视觉中的 VGG16。 它被设计为以单台站三成分波形作为输入，并输出深度，实现了从波形到深度的直接映射。 在我们的模型开发中考虑了两种情况：(1) 仅在同一地震台站上进行训练和测试，以及 (2) 在特定区域内不同地震台站上进行训练和测试以实现泛化。 我们利用意大利中部阿彭尼诺山脉 2016-2017 年地震序列的地震数据证明了我们方法的有效性。 结果表明，可以从单个台站估计地震深度，不确定性为数百米。 通过多个台站结果的平均，这些不确定性进一步降低。 我们的方法在地震深度确定方面表现出强大的潜力，特别是对于由单个或稀疏分布台站记录的事件，如历史上有仪器记录的地震。