The onsite early warning techniques that issue earthquake alerts based on the seismic response of single stations have proven to be quite successful in detecting damage. The magnitude of an earthquake and the epicentral distance are vital parameters for accessing the intensity of destruction at an observation point during an earthquake. This article presents a novel earthquake early warning (EEW) system model designed to predict epicentral distance. The proposed model synergizes the strengths of deep learning and shallow machine learning (ML) techniques, offering a new perspective on seismic event prediction. Specifically, the study introduces an architecture that uses a long short-term memory (LSTM) network with two attention mechanisms to extract high-level features from the initial 3 s of the primary waveform. These attention mechanisms are built for time- and feature-based dependencies. Global site-related features such as shear-wave velocities at various depths and station coordinates are also incorporated, enhancing the model’s predictive capacity. Following this, the scalable ML algorithm XGBoost is applied. The fusion of deep and shallow learning methods applied in the onsite prediction of epicentral distance makes this a significant contribution to the early detection of epicenter distance, azimuth, and focal depth. The next step is the single-station magnitude detection from the predicted epicenter distance, azimuth, focal depth, and other magnitude-dependent parameters. The study shows remarkable results for magnitude detection. The findings indicate the potential for improved prediction of earthquake parameters, contributing toward the ongoing goal of enhancing EEW systems and reducing the destructive impacts of seismic events.

中文翻译：

---

地震震中距离和震级现场估计的深度关注模型


事实证明，根据单站地震响应发出地震警报的现场预警技术在发现损害方面非常成功。地震震级和震中距是获取地震期间观测点破坏强度的重要参数。本文提出了一种新颖的地震早期预警（EEW）系统模型，旨在预测震中距离。所提出的模型结合了深度学习和浅层机器学习（ML）技术的优势，为地震事件预测提供了新的视角。具体来说，该研究引入了一种架构，该架构使用具有两种注意力机制的长短期记忆 (LSTM) 网络，从主波形的最初 3 秒中提取高级特征。这些注意力机制是为基于时间和特征的依赖性而构建的。还纳入了与全球站点相关的特征，例如不同深度的剪切波速度和台站坐标，从而增强了模型的预测能力。接下来，应用可扩展的 ML 算法 XGBoost。深度和浅层学习方法的融合应用于震中距现场预测，为震中距、方位角和震源深度的早期探测做出了重大贡献。下一步是根据预测的震中距离、方位角、震源深度和其他与震级相关的参数进行单站震级检测。该研究显示了震级检测的显着结果。研究结果表明改进地震参数预测的潜力，有助于实现增强EEW系统和减少地震事件破坏性影响的持续目标。