The South China Sea (SCS) located at the intersection of the three intercontinental plates of Eurasia, IndiaAustralia, and the Pacific Oceanis, is a typical marginal sea basin formed by the seafloor spreading under the tectonic background of plate convergence. Many crustal-scale studies indicate that the SCS basin has undergone asymmetric spreading, multi-phase ridge jumps, and intense post-spreading volcanic activity. Due to the lack of seismic data in the oceanic basin of the SCS, it remainsunclear about the scale and basin control of the Zhongnan fault, the magma source depth of the SCS basin, and the transport channel after the cessation of seafloor spreading. Phase velocity derived from ambient noise surface wave tomography may provide useful information to shed light on the mechanisms of the aforementioned problems. From October 2019 to July 2020, a 3D Ocean Bottom Seismometers (OBS) passive seismic observation experiment was carried out by the Second Institute of Oceanography, Ministry of Natural Resources (SIOMNR) in a broad area of the SCS. Based on the seismic ambient noise data recorded by 16 OBSs in the SCS basin, we inverted the phase velocity images over a period range of 10–20 s using ambient noise surface wave tomography. Our results indicate that the Zhongnan fault zone is a lithospheric-scalefault, which played a regulating role in the last oceanic ridge transition of the SCS basin from the East Subbasin to the Southwest Subbasin. In addition, the low-velocity body in the north flank of the Southwest Subbasin extends from the post-spreading seamounts on the ocean crust to the uppermost mantle (i.e., about 10–30 km), which indicates an oblique magma migration during the postspreading volcanism.
Abstract The Gakkel Ridge in the Eurasian Basin has the slowest seafloor spreading worldwide. The western Gakkel Ridge (3°W–85°E; 14–11 mm/a) alternate between magmatic and sparsely magmatic zones, while the eastern Gakkel Ridge (85–126°E; 11–6 mm/a) appears to be dominated by magmatic zones despite ultraslow spreading. Little is known about the seafloor spreading conditions in the past along the entire ridge. Here, we exploit the residual bathymetry and basement roughness to assess the crustal accretion process of the Gakkel Ridge over time using 23 published regional multichannel seismic reflection profiles. Full seafloor spreading rates were faster (20–24 mm/a) up to ∼45 Ma, and residual bathymetry for the older crust is deeper than the world average in the entire Eurasian Basin. There is a sharp transition to 300–400 m shallower residual bathymetry for seafloor <45 Ma in the eastern Eurasian Basin. The crustal roughness versus spreading rate of the western Eurasian Basin is on the global trend, while that of the eastern is significantly below. Both low roughness and shallow residual bathymetry of the eastern Eurasian Basin is close to that of oceanic crust for spreading rates above 30 mm/a, demonstrating increased magmatic production of the eastern Gakkel Ridge since ∼45 Ma. A recent mantle tomography model predicts partial melting in the upper mantle based on the low Vs anomaly underneath. The sedimentary pattern toward the Lomonosov Ridge indicates that this hot mantle anomaly started to cause dynamic uplift of the area at ∼45 Ma.
In order to meet the requirements of mobile marine seismometers to observe and record seismic signals, a study of fast and accurate seismic signal recognition was carried out. This paper introduces the use of the wavelet analysis method for seismic signal processing and recognition, and compares and analyzes the abilities of different wavelet basis functions to detect the seismic signal. By denoising and reconstructing the signal, the distribution law of the wavelet coefficients of seismic signal at different scales was obtained. On this basis, this paper proposes an identification model of seismic signals based on wavelet analysis and thereby solves the conflict between high speed and high accuracy of seismic signal recognition methods. In this study, the simulation was carried out in the Matlab2020b environment, and the feasibility of wavelet recognition algorithm was proven by applying this algorithm to the seismic signal database for experimental verification.
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