A Novel Method of Detecting Underground Nuclear Explosion at Specific Site Based on Seismic Waveform Fingerprint
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摘要: 核爆炸监测是禁核试核查的关键技术.为监测全球可能发生的核试验，全面禁止核试验条约规定了一套严格的核查机制.其中国际监测系统（International Monitoring System，IMS）的波形数据实时传输至国际数据中心（International Data Centre，IDC）进行处理和分析，分别在大约1 h、4 h和6 h给出三个不同阶段的自动处理结果.对于特定地区的核爆监测，直接依赖IDC结果存在响应滞后和误检率高的问题.本文提出了一种基于地震波形指纹的快速检测方法Seisprint.该方法借鉴音频指纹识别思想，将历史核爆波形作为模板，利用滑动窗口与特征提取将连续波形压缩为多个二进制指纹，通过快速相似性匹配与聚类实现核爆事件自动检测并实时报警.采用朝鲜周边两个IMS地震台站和我国东北地区4个地震台站记录的朝鲜6次地下核试验以及历史天然地震事件数据对Seisprint进行测试.Seisprint生成的指纹可以有效区分核爆与非核爆信号，且具有较强的抗噪性；可在数分钟内完成多个地震台站24小时连续波形数据的处理，实现核爆事件的快速准确检出.结果表明，Seisprint可提高特定地区核爆事件监测的时效性和准确性.Abstract: Nuclear explosion monitoring is a critical technology for nuclear test ban verification. In order to monitor potential nuclear tests worldwide, the Comprehensive Nuclear-Test-Ban Treaty (CTBT) establishes a rigorous verification regime. Within this framework, waveform data from the International Monitoring System (IMS) are transmitted in real time to the International Data Centre (IDC) for processing and analyzing. The IDC delivers automated processing results at three stages: approximately 1 h, 4 h and 6 h after data acquisition. For region-specific nuclear explosion monitoring, direct reliance on IDC results faces challenges of delayed response and high false detection rates. To address these limitations, we propose Seisprint, a rapid detection method based on seismic waveform fingerprints. Inspired by audio fingerprinting techniques, Seisprint utilizes historical nuclear explosion waveforms as templates. Continuous seismic waveforms are compressed into multiple binary fingerprints through sliding-window feature extraction. Automated nuclear event detection and real-time alerts are achieved via rapid similarity matching and clustering. The method was tested using data from two IMS seismic stations near North Korea and four seismic stations in Northeast China, covering six historical underground nuclear tests and natural seismic events in North Korea. The fingerprints generated by Seisprint effectively distinguish nuclear explosions from non-nuclear signals and demonstrate strong robustness against noise. The method processes an entire day of continuous data from multiple seismic stations within just a few minutes, enabling rapid detection of underground nuclear explosion events. Results confirm that Seisprint promote the timeliness and accuracy of underground nuclear explosion detection in specific area.
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图 1 2018-2024年间朝鲜核试验场周边天然地震事件震级分布
Fig. 1. Magnitude distribution of earthquakes around the North Korean nuclear test site from 2018 to 2024
图 2 朝鲜核试验场周边台站位置与事件分布
Fig. 2. Station locations and event distribution around the North Korean nuclear test site
图 4 MDJ台站记录的6次朝鲜地下核爆事件的波形（20 Hz）
Fig. 4. Waveforms of six North Korean underground nuclear test events recorded at MDJ station
图 5 指纹提取步骤
图a、c、e、g、i为KS31台站记录到的2021年6月13日发生在目标区域的一次天然地震事件，图b、d、f、h、j为KS31台站记录到的朝鲜第二次地下核爆事件；其波形采样率均为100 Hz
Fig. 5. Fingerprint extraction steps
图 8 不同类型的指纹与模板指纹的相似度分布
Fig. 8. Similarity distribution between different types of fingerprints and the template fingerprint
图 9 模板波形（a）与加噪后SNR=2 dB的波形（b）
图中显示为1~2 Hz滤波后的波形，红色虚线为P波到时
Fig. 9. Original seismic waveform (a) and noise-added waveform with SNR=2 dB (b)
图 10 KDN台站指纹精度与基线相似度分布（SNR=2 dB）
Fig. 10. Distribution of fingerprint accuracy and baseline similarity at station KDN (SNR=2 dB)
图 12 检测事件的多台站波形
图a~c为报警事件，图d~f为其余候选事件
Fig. 12. Multi-station waveforms of detected events
表 1 朝鲜2006-2017年6次核试验（用NKT1-NKT6表示）以及NKT6后发生的3次余震事件（用PEV1-PEV3表示）参数
Table 1. Parameters of the six nuclear test events in North Korea from 2006 to 2017 (denoted as NKT1-NKT6) and three subsequent aftershock events following NKT6 (denoted as PEV1-PEV3)
朝鲜地区事件 日期 UTC时间 纬度 经度 震级(mb) NKT1 2006/10/09 01:35:28 41.29° 129.09° 4.1 NKT2 2009/05/25 00:54:43 41.31° 129.04° 4.5 NKT3 2013/02/12 02:57:51 41.30° 129.06° 4.9 NKT4 2016/01/06 01:30:01 41.30° 129.05° 4.8 NKT5 2016/09/09 00:30:01 41.30° 129.05° 5.1 NKT6 2017/09/03 03:30:01 41.32° 129.03° 6.1 PEV1 2017/09/03 03:38:32 41.30° 129.07° 4.1 PEV2 2017/09/23 08:29:16 41.23° 129.35° 3.4 PEV3 2017/10/12 16:41:08 41.33° 129.17° 2.9 
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表 2 Seisprint算法关键参数
Table 2. Key parameters of the Seisprint algorithm
Seisprint参数名称 参数值 时频谱图窗口长度(s) 6 时频谱图窗口间隔(s) 0.2 采样率(Hz) 20 频谱图像长度(采样点数) 64 频谱图像间隔(采样点数) 5 频谱图像宽度（采样点数） 32 稀疏度参数K 800 指纹长度 4 096 哈希签名长度$ p $ 400 哈希表个数$ l $ 100 相似度阈值$ v $ 2 多台站关联阈值 4/6
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表 3 检测事件多台站互相关检测结果
Table 3. Cross-correlation detection results of events at multiple stations
事件时间 MDJ CN2 KS31 USA0B YNB KDN 平均互相关系数 判定结果 2013/02/12/02:58:56 0.72 0.66 0.49 0.63 0.68 0.62 0.63 真实核爆 2016/09/09/00:30:46 0.91 0.76 0.67 0.25 0.90 0.67 0.69 真实核爆 2016/09/09/11:56:38 0.20 0.20 0.22 0.20 0.18 0.32 0.22 误判 2016/09/15/19:54:32 0.22 0.32 0.25 0.23 0.28 0.33 0.27 误判 2016/09/19/11:37:57 0.15 0.21 0.26 0.12 0.17 0.26 0.195 误判 2017/09/03/03:30:43 0.63 0.58 0.62 0.57 0.62 0.61 0.605 真实核爆
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表 4 三种方法的事件检测结果
Table 4. Event detection results of the three methods
检测方法 日期 时间 时间误差(s) Seisprint 2013‒02‒12 02:57:56 +5 2016‒09‒09 00:30:46 +45 2017‒09‒03 03:30:43 +42 NET-VISA 2013‒02‒12 02:57:50 ‒1 2016‒09‒09 00:30:04 +3 2016‒09‒09 00:30:12 / 2017‒09‒03 03:30:01 0 2017‒09‒03 03:30:05 / 波形互相关 2013‒02‒12 23:58:34 / 2013‒02‒12 02:58:00 +9 2016‒09‒09 00:30:11 +10 2017‒09‒03 03:30:09 +8 2017‒09‒03 23:58:15 /
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