Analysis of anomalous environmental responses to the 5.5 earthquake in Pingyuan County, Dezhou in august 2023 based on GNSS
-
摘要: 为了探测2023年8月6日山东德州平原县5.5级地震引起的异常环境响应，本文基于震中东北方向约26 km的GNSS观测数据，提出滑动时变频率方法并引入四分位距(interquartile range，IQR)法对地震引发的异常环境响应进行分析. 结果显示，地震发生后N和E方向坐标速度时间序列在9~10 s处出现显著异常，垂向在16 s时出现轻微异常. 滑动时变频率方法能够在速度时间序列异常跳变发生前5~10 s探测到明显变化，显示了更高的灵敏度. 研究还发现，在地震发生前一天，电离层在震中上空出现了异常扰动，异常值达到4 TECU. 研究表明：GNSS技术能有效探测地震过程中的异常环境响应，为地震监测与预警提供了新的视角和工具.
-
关键词:
- 全球导航卫星系统(GNSS) /
- 滑动时变频率 /
- 地震异常 /
- 四分位距(IQR)法
Abstract: To investigate the abnormal environmental response caused by the magnitude 5.5 earthquake in Pingyuan County, Shandong Province, on August 6th, 2023, this study based on the Global Navigation Satellite System (GNSS) observation data located about 26 km northeast of the epicenter, proposes a moving time-varying frequency method and incorporates the interquartile range (IQR) method to analyze the abnormal environmental responses triggered by the earthquake. The results indicate significant anomalies in the N and E directional coordinate velocity time series at 9 to 10 seconds after the earthquake, with a minor anomaly in the U direction at 16 seconds. Moreover, the sliding time-varying frequency method detects noticeable changes 5 to 10 seconds before the abnormal jump in the velocity time series, demonstrating higher sensitivity. The study also discovered abnormal disturbances in the ionosphere above the epicenter the day before the earthquake, with the anomaly reaching 4 TECU. This research demonstrates the effectiveness of GNSS technology in detecting abnormal environmental responses during earthquakes, offering a new perspective and tools for earthquake monitoring and early warning. -
[1] 杨剑, 张宇, 胡良晨, 等. 2017年九寨沟M7.0地震前临震电离层异常研究[J]. 地震, 2022, 42(4): 100-110. [2] 蔡华, 李子申, 王敏, 等. 汶川Mw7.9与日本Mw9.0地震同震电离层扰动研究[J]. 武汉大学学报(信息科学版), 2013, 38(6): 716-719,724. [3] 张怀, 聂兆生, 刘刚, 等. 高频BDS/GPS融合PPP确定2021年玛多M_W7.4地震三要素[J]. 地震研究, 2023, 46(2): 216-225. [4] BESHR A, ZARZOURA F. Using artificial neural networks for GNSS observations analysis and displacement prediction of suspension highway bridge[J]. Innovative infrastructure solutions, 2021, 6(1): 1-15. DOI: 10.1007/s41062-021-00458-4 [5] TU R, ZHANG R, ZHANG P, et al. Integration of single-frequency GNSS and strong-motion observations for real-time earthquake monitoring[J]. Remote sensing, 2018, 10(6): 886. DOI: 10.3390/rs10060886 [6] WANG J, JIANG W, LI Z, et al. A new multi-scale sliding window LSTM framework (MSSW-LSTM): a case study for GNSS time-series prediction[J]. Remote sensing, 2021, 13(16): 3328. DOI: 10.3390/rs13163328 [7] 马玉, 祝芙英. 基于GPS TEC的尼泊尔M_W7.8地震同震电离层扰动研究[J]. 大地测量与地球动力学, 2020, 40(9): 957-961,969. [8] GAO Z, LI Y, SHAN X, et al. Earthquake magnitude estimation from high-rate GNSS data: a case study of the 2021 Mw 7.3 Maduo earthquake[J]. Remote sensing, 2021, 13(21): 4478. DOI: 10.3390/rs13214478 [9] 姚宜斌, 翟长治, 孔建, 等. 2015年尼泊尔地震的震前电离层异常探测[J]. 测绘学报, 2016, 45(4): 385-395. [10] 姜卫平, 许才军, 李志伟, 等. 利用空间观测技术研究青海玛多7.4级地震孕育发生变形时空特征[J]. 地球物理学报, 2022, 65(2): 495-508. [11] 张一彬, 钟慧鑫, 张亭, 等. 2019-05-26秘鲁北部地震前低纬电离层变化分析[J]. 大地测量与地球动力学, 2023, 43(2): 135-140. [12] 翟笃林, 祝芙英, 林剑, 等. 基于地基GPS-TEC的中国区域地震电离层效应研究[J]. 中国地震, 2020, 36(4): 857-871. [13] 唐龙, 郭博峰, 李哲. 利用日本GPS网探测2011年Tohoku海啸引发的电离层扰动[J]. 地球物理学报, 2017, 60(2): 507-513. [14] LARSON K, LAY T, YAMAZAKI Y, et al. Dynamic sea level variation from GNSS: 2020 Shumagin earthquake tsunami resonance and hurricane Laura[J]. Geophysical research letters, 2021, 48(4): e2020GL091378. DOI: 10.1029/2020GL091378 [15] XU P L, DU F, SHU Y M, et al. Regularized reconstruction of peak ground velocity and acceleration from very high-rate GNSS precise point positioning with applications to the 2013 Lushan Mw6.6 earthquake[J]. Journal of geodesy, 2021, 95(1): 1-22. DOI: 10.1007/s00190-020-01449-6 [16] SUN Y. GNSS brings us back on the ground from ionosphere[J]. Geoscience letters, 2019, 6(1): 1-9. DOI: 10.1186/s40562-019-0144-0 [17] KING L S, UNWIN M, RAWLINSON J, et al. Towards a topographically-accurate reflection point prediction algorithm for operational spaceborne GNSS reflectometry-development and verification[J]. Remote sensing, 2021, 13(5): 1031. DOI: 10.3390/rs13051031 [18] EH N. Elementary statistical methods[J]. Nature, 1934, 133(3349): 9. DOI: 10.1038/133009c0 -
下载:
点击查看大图
图(10)
计量
- 文章访问数: 65
- HTML全文浏览量: 24
- PDF下载量: 10
- 被引次数: 0
