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
针对传统铁路电话专网数字化迁移中业务连续性与成本等方面的挑战,提出一种基于切片分组网(Slicing Packet Network,SPN)架构的平滑迁移技术方案。该技术方案融合双模并行割接、7号信令系统(Signaling System No.7,SS7)至铁路会话初始协...针对传统铁路电话专网数字化迁移中业务连续性与成本等方面的挑战,提出一种基于切片分组网(Slicing Packet Network,SPN)架构的平滑迁移技术方案。该技术方案融合双模并行割接、7号信令系统(Signaling System No.7,SS7)至铁路会话初始协议(Session Initiation Protocol for Railway,SIP-R)转换、切片服务质量(Quality of Service,QoS)保障及长短期记忆(Long Short-Term Memory,LSTM)异常监测机制,实现了业务无中断迁移。实验结果表明,所提技术方案的迁移成功率达98%,端到端时延降至80 ms,设备复用率超过70%,为铁路通信系统的数字化、智能化转型提供了可靠且可复制的技术路径。展开更多
为支撑5G-R(5th Generation Mobile Communication Technology for Railway)建设,需构建具备高隔离、高可靠、可管可控特性的新一代承载网络。文章对比分析了切片分组网(SPN)、IP无线接入网(IPRAN)与光传送网(OTN)等3种主流承载技术,并...为支撑5G-R(5th Generation Mobile Communication Technology for Railway)建设,需构建具备高隔离、高可靠、可管可控特性的新一代承载网络。文章对比分析了切片分组网(SPN)、IP无线接入网(IPRAN)与光传送网(OTN)等3种主流承载技术,并通过实验验证及性能测试,评估其在切片隔离性、传输时延、时间同步等方面的表现。研究表明,SPN技术深度融合时分复用与分组交换优势,支持从L1到L3的综合业务承载,具备硬隔离切片、超高精度同步、多业务融合承载等关键能力,能够有效满足铁路5G-R及既有通信业务的高安全、高可靠承载要求,具备良好推广前景。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.
摘要城域网作为移动回传网络、专线、人工智能(artificial intelligence,AI)智算中心、泛在算力连接等重要业务的基础设施,是全球传输技术领域的研究热点与产业竞争焦点。随着新业务从信息消费向产业应用拓展,在网络切片有望成为信息通信服务提供的新模式背景下,全面阐述了多维融合转发的核心理念、“以太网内生时分复用(time division multiplexing,TDM)”的核心转发机制、切片分组网(slicing packet network,SPN)的系统架构和技术体系。当前,SPN已实现规模应用部署,并形成系列国际标准,成为ITU-T继同步数字体系(synchronous digital hierarchy,SDH)、光传送网(optical transport network,OTN)之后的新一代传输网技术体系。
摘要针对传统铁路电话专网数字化迁移中业务连续性与成本等方面的挑战,提出一种基于切片分组网(Slicing Packet Network,SPN)架构的平滑迁移技术方案。该技术方案融合双模并行割接、7号信令系统(Signaling System No.7,SS7)至铁路会话初始协议(Session Initiation Protocol for Railway,SIP-R)转换、切片服务质量(Quality of Service,QoS)保障及长短期记忆(Long Short-Term Memory,LSTM)异常监测机制,实现了业务无中断迁移。实验结果表明,所提技术方案的迁移成功率达98%,端到端时延降至80 ms,设备复用率超过70%,为铁路通信系统的数字化、智能化转型提供了可靠且可复制的技术路径。
摘要为支撑5G-R(5th Generation Mobile Communication Technology for Railway)建设,需构建具备高隔离、高可靠、可管可控特性的新一代承载网络。文章对比分析了切片分组网(SPN)、IP无线接入网(IPRAN)与光传送网(OTN)等3种主流承载技术,并通过实验验证及性能测试,评估其在切片隔离性、传输时延、时间同步等方面的表现。研究表明,SPN技术深度融合时分复用与分组交换优势,支持从L1到L3的综合业务承载,具备硬隔离切片、超高精度同步、多业务融合承载等关键能力,能够有效满足铁路5G-R及既有通信业务的高安全、高可靠承载要求,具备良好推广前景。