4.7 Article

Evaluation of GRACE/GRACE Follow-On Time-Variable Gravity Field Models for Earthquake Detection above Mw8.0s in Spectral Domain

期刊

REMOTE SENSING
卷 13, 期 16, 页码 -

出版社

MDPI
DOI: 10.3390/rs13163075

关键词

coseismic gravity change; degree variance; earthquake; GRACE; monthly time-variable field model

资金

  1. National Natural Science Foundation of China [42074017, 41804019, 41931074, 41974096]
  2. Fundamental Research Funds for the Central Universities [2652018027]
  3. Qian Xuesen Laboratory of Space Technology, CAST [GZZKFJJ2020006]
  4. Open Research Fund of Key Laboratory of Space Utilization, Chinese Academy of Sciences [LSU-KFJJ-201902]
  5. China Geological Survey [20191006]

向作者/读者索取更多资源

This study compares the errors of GRACE/GFO with coseismic gravity variations caused by large earthquakes, showing that the precision of GFO monthly models is higher but still faces challenges in accurately extracting coseismic signals.
This study compares the Gravity Recovery And Climate Experiment (GRACE)/GRACE Follow-On (GFO) errors with the coseismic gravity variations generated by earthquakes above Mw8.0s that occurred during April 2002 similar to June 2017 and evaluates the influence of monthly model errors on the coseismic signal detection. The results show that the precision of GFO monthly models is approximately 38% higher than that of the GRACE monthly model and all the detected earthquakes have signal-to-noise ratio (SNR) larger than 1.8. The study concludes that the precision of the time-variable gravity fields should be improved by at least one order in order to detect all the coseismic gravity signals of earthquakes with M >= 8.0. By comparing the spectral intensity distribution of the GFO stack errors and the 2019 Mw8.0 Peru earthquake, it is found that the precision of the current GFO monthly model meets the requirement to detect the coseismic signal of the earthquake. However, due to the limited time length of the observations and the interference of the hydrological signal, the coseismic signals are, in practice, difficult to extract currently.

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