Imaging the lithospheric structure beneath the South China Sea (SCS) not only provides crucial constraints on the tectonic history of southeastern Asia, but also provides an important sample for understanding the evolution of the oceanic lithosphere. However, due to the lack of seismic sampling, the lithospheric structures beneath the SCS are not well resolved. Here, with China National Seismic Network, we observe the “Lid signal” generated by earthquakes below the lithosphere, which propagates through the fast mantle Lid and acts as the first arrival. The Lid signal provides robust constraints on the structure of the lithosphere. Through modeling the Lid signals of events occurred at different depths, we find that the thickness of the lithosphere beneath the northern margin of the SCS is ∼65 km. Across the lithosphere‐asthenosphere boundary, the SH velocity decreases by 6% ∼ 8% within 60 km. Such a large velocity contrast may be due to the presence of melt in the asthenosphere. We also detect another velocity jump at a depth of ∼250 km in both SH and P wave data, which correlates with the depth of the X‐discontinuity. A comparison between the lithospheric structure beneath the SCS basin and other oceanic plates further suggests a hot asthenosphere beneath the SCS, which could be related to the existence of the Hainan Plume nearby.
The wide application of seismic dense arrays has facilitated the monitoring of the coseismic velocity disturbance of small and medium earthquakes. In this study, a repeating earthquake cluster near the 2018 Ms 4.5 Shimian earthquake was relocated based on 10 groups of repeating earthquakes that occurred from 2013 to 2019, which were recorded by the Xichang seismic array. A repeating pair was obtained by estimating the overlap of the rupture region. Coda-wave interferometry was carried out in different frequency bands using the moving window cross spectrum and wavelet-domain trace stretching (WTS) methods. Our results show that velocity change at each frequency point can be obtained with the WTS method, and thus its frequency resolution is higher. In addition, the velocity changes of coda waves strongly depend on the frequency in the Shimian area, varying from +0.10% in the high-frequency band (5–10 Hz) to −0.23% in the low-frequency band (0.5–2 Hz). In particular, XC04, which is the station that closest to the epicenter, shows the largest velocity change in the low-frequency band, but the velocity change gradually decreases as the distance from the epicenter increases. It has been suggested that the low-frequency components of the coda waves of repeating earthquakes are more sensitive to medium variation. Combined with the earthquake relocation in the Shimian area, it was found that the normalized depth sensitivity calculated based on scattered waves can retain >10% sensitivity in the source area of the Shimian earthquake. Furthermore, the negative velocity changes calculated from low-frequency coda waves are likely attributed to the Shimian earthquake. It is recommended that the average interstation spacing of seismic dense array should be <30 km to facilitate the monitoring of the coseismic changes of small and medium earthquakes.
An MS 6.4 earthquake occurred in Yangbi, Yunan Province, China, on 21 May 2021. The epicenter was on the blind branch fault in the west of the Weixi–Qiaohou–Weishan fault, but no surface rupture was obvious. In the present study, the continuous vertical component of waveforms that were recorded in six nearby permanent stations was collected and the noise cross-correlation and autocorrelation techniques were utilized to investigate velocity changes that were induced by the Yangbi Earthquake. Velocity changes based on the single-station autocorrelation method reveal mainly coseismic declines, and a maximum of .09% was recorded in the EYA station. Results from the cross-correlation technique show both positive and negative velocity changes, and these lasted for approximately 3 months. The volumetric strain that was generated by the Yangbi Earthquake at a depth of 5 km exhibits an obvious four-quadrant distribution. Station pairs in the dilatation region (e.g., EYA–HEQ) mainly display a decrease in velocity, whereas those in the contraction region (e.g., BAS–TUS, TUS–YUL, and LUS–TUS) show an increase in velocity. Based on the depth sensitivity of scattered waves, velocity changes that were obtained using the noise cross-correlation involve the highest weight coefficients near the related two stations. Regarding stations of one station pair in different stress loading regions, the static stress of the station that is nearest to the epicenter exerted a greater impact on the velocity change. The observed velocity changes are likely attributed to a combination of near-surface physical damage and static stress changes. The validation of clock errors with magnitudes of seconds that were obtained using the noise cross-correlation and effects of these errors on measured velocity changes are also discussed.
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