This study proposes a method for determining earthquake focal depths by combining the sPn phase with the waveform cross-correlation technique,based on waveform data recorded by the Fujian Seismic Network from the 2024...This study proposes a method for determining earthquake focal depths by combining the sPn phase with the waveform cross-correlation technique,based on waveform data recorded by the Fujian Seismic Network from the 2024 M 7.3 Hualien offshore earthquake and the 2025 M 6.2 Tainan earthquake.The Pn phase onset was precisely aligned using waveform cross-correlation,and the arrival time difference(Δt)between the sPn and Pn phases was extracted via a sliding time-window correlation method.The focal depths were derived using a layered velocity model for the Taiwan region.Results show that the calculated focal depth for the Hualien earthquake is 23.1 km(Δt=6.9 s),with a relative error of 2.7%compared to the official result(22.5 km)from the Central Weather Administration of Taiwan.For the Tainan earthquake,the depth is 17.9 km(Δt=6.1 s),with a relative error of 13.3%.In this study,we show that a cross-correlation threshold of 0.8 and a bandpass filtering of 0.1–0.3 Hz are efficient to suppress noise and significantly improve depth accuracy for shallow earthquakes with depth<30 km.Compared to traditional travel-time location methods,this approach exhibits superior noise resistance and computational efficiency.Future work will focus on optimizing 3D velocity structures,integrating multiple phases,and applying deep learning techniques such as convolutional neural networks,aiming to improve the results in a more reliable and automatic way,and to provide efficient support on earthquake emergency response.展开更多
基金funded by the National Key Research and Development Program of China(Grant No.2024YFC3012804)。
摘要This study proposes a method for determining earthquake focal depths by combining the sPn phase with the waveform cross-correlation technique,based on waveform data recorded by the Fujian Seismic Network from the 2024 M 7.3 Hualien offshore earthquake and the 2025 M 6.2 Tainan earthquake.The Pn phase onset was precisely aligned using waveform cross-correlation,and the arrival time difference(Δt)between the sPn and Pn phases was extracted via a sliding time-window correlation method.The focal depths were derived using a layered velocity model for the Taiwan region.Results show that the calculated focal depth for the Hualien earthquake is 23.1 km(Δt=6.9 s),with a relative error of 2.7%compared to the official result(22.5 km)from the Central Weather Administration of Taiwan.For the Tainan earthquake,the depth is 17.9 km(Δt=6.1 s),with a relative error of 13.3%.In this study,we show that a cross-correlation threshold of 0.8 and a bandpass filtering of 0.1–0.3 Hz are efficient to suppress noise and significantly improve depth accuracy for shallow earthquakes with depth<30 km.Compared to traditional travel-time location methods,this approach exhibits superior noise resistance and computational efficiency.Future work will focus on optimizing 3D velocity structures,integrating multiple phases,and applying deep learning techniques such as convolutional neural networks,aiming to improve the results in a more reliable and automatic way,and to provide efficient support on earthquake emergency response.