Marine earthquakes are extremely dangerous. The Qiongdongnan (QDN) segment of the Continental Slope Fault Zone (CSFZ) in the northern South China Sea poses seismic risks that may trigger submarine landslides and tsunamis. Here, using the curved-grid finite-difference method (CGFDM), we established a dynamic spontaneous rupture model for this region southeast of Hainan Island, China. We simulated the wave propagation and strong ground motion resulting from these earthquakes and produced seismic intensity distribution maps. The maximum magnitude achieved across all models was MW7.7. A left-lateral strike-slip fault with a dip angle of 59.5° was used in the simulations, and 26 hypocenters at various positions and depths were selected. We further investigated the seismic waves and strong ground motions generated by these events. The results indicated that the velocity structure had a significant influence on the maximum slip concentration on the fault. Additionally, under the considered initial stress conditions, the earthquake magnitudes varied with depth for certain hypocenters. We analyzed the potential risk of earthquake-induced landslides using the pseudostatic method and introduced the factor of safety (FOS). The results showed that the northern landslide area contained a large section where the FOS was less than one, indicating the increased likelihood of landslides. In addition, for hypocenters at a depth of 8 km, earthquake with the smallest magnitude can generate a significantly stronger event than one with hypocenters at depths of 10 and 12 km. Overall, this fault poses significant risk for a chain of earthquake-landslide-tsunami disasters. Our study can provide a reliable reference for the development of disaster warning systems in the region and further our understanding of seismicity along the QDN segment.
By effectively isolating noise, deep borehole seismic observations offer clear advantages related to signal identification and seismic source research. However, quantitative assessments of noise reduction and signal-to-noise ratio (SNR) improvements in km-scale boreholes are limited. In this study, a deep borehole seismic observation platform in Changde, China, was used to investigate noise reduction and SNR improvement. Two Chinese TDE-120VB very broadband seismometers were deployed at a depth of 1,972.7 m and synchronous observations were made by co-located surface instruments. The results indicate that the background noise power spectral density (PSD) of the borehole system was 20–40 dB lower than that of the surface instruments at 0.001–25 Hz, with decreases of up to 60 dB in certain sub-bands. This corresponds to SNR improvements of 2–4 orders of magnitude. For the first time in deep borehole seismology, spectral entropy was applied to downhole seismic data, and cumulative sum change-point detection within a mean-variance statistical framework was introduced into the entropy series, which enhanced automatic seismic signal identification and noise discrimination. Spectral entropy analysis of the 2025 Dingri earthquake indicates that the borehole station maintained a low-entropy state for ∼4.5 h, which is more than twice the length of that observed at the surface (∼2 h). The findings support the optimization of deep borehole observation systems and provide a valuable reference for underground observation network development and seismic source research.
The MS6.8 earthquake that struck Dingri County on January 7, 2025, has prompted extensive investigations into its post-seismic hazards, focal mechanism, and seismogenic structure. However, the absence of a systematic understanding the sedimentary framework, structural architecture, and other key geological characteristics of the Dingmu Co fault-bounded basin has hindered such efforts. Drawing on a 1:50,000-scale regional geological survey, we examined the sedimentary and structural features of the basin. We further integrated pre-earthquake audio-frequency magnetotelluric (AMT) and soil-radon survey data with optically stimulated luminescence dating of the fault fracture zones. Our results reveal that lacustrine deposits in the basin host abundant seismically induced soft-sediment deformation structures. The northern sector contains an east-west-trending half-graben, and the eastern sector is dominated by several north-south-trending strike-slip normal faults. AMT data have identified multiple concealed faults, and soil-radon measurements indicate heightened fault activity east of the central basin area. Shaped by the combined influence of the north-south-trending Dingmu Co strike-slip-normal fault system and east-west-trending Lhagoi Kangri Detachment System, the basin has undergone at least three episodes of intense tectonic activity since the Late Pleistocene, at ∼53.4, ∼37.1–32.2, and ∼9.2 ka BP, and is presently experiencing renewed deformation. Coseismic deformation associated with the January 7 event was concentrated at the intersections of north-south-, east-west-, northeast-, and northwest-trending faults. These results indicate the need to prioritize such intersection zones in future geological monitoring and disaster risk assessments. Overall, this study offers new insights for advancing seismotectonic research and understanding earthquake hazards in the Himalayan orogenic belt.
With the increase in production depth and pressure, microseismic monitoring and location technology has become particularly important for ensuring the safety of oil and gas production. Therefore, we established a comprehensive well and ground microseismic location network specifically designed to accurately locate microseismic events induced by shale gas extraction. The proposed model, JGF-LocNet, integrates convolutional neural networks (CNNs), transformers, long short-term memory (LSTM) networks, and graph convolutional networks (GCNs) to achieve high-precision event localization with real-time computational efficiency, which closely reproduces the QuakeMigrate reference catalog. The method is velocity-model-free at its inference, but inherits velocity-model assumptions through training labels generated from the catalog. Unlike traditional methods that rely on velocity models, our approach requires only the input waveform, enabling JGF-LocNet to complete seismic event predictions within 0.3 s, with a positioning error of less than 40 m. Blind tests on two unseen arrays confirmed a <10% increase in AE-P90 while maintaining real-time latency. With an adequate number of training samples, JGF-LocNet is expected to deliver rapid, high-precision positioning with minimal errors. We conducted comparative analyses of the computational efficiency and positioning accuracy of our method against those of traditional approaches and other deep learning models. Our results demonstrate that the proposed method most closely reproduces the QuakeMigrate reference catalog in real time while retaining subsecond latency. Given the increased frequency and risk of dynamic disasters in high-pressure, deep, and complex geological structures, the real-time monitoring and precise localization of induced microseismic events provided by our proposed method are critical for ensuring industrial safety.
Owing to Kazakhstan’s transition to new construction standards that align with Eurocode 8, detailed seismic zoning (DSZ) maps of the country are necessary to assess seismic hazards using modern methods. The seismic hazard in construction zones is determined using seismic zoning maps with varying levels of detail. The methodology applied to obtain the DSZ maps was consistent with that used to develop the first probabilistic general seismic zoning maps of Kazakhstan, which were included in the 2017 building regulations for seismic zones and conformed to the Eurocodes. This study met the requirements of the DSZ project and included new data and analytical tools. In addition, the seismic databases and source models were updated. The seismic hazard assessments performed in this study included seismic sources and attenuation modeling, ground motion calculations, and determining the probability of motion exceeding the peak ground acceleration within 50 a, which yielded DSZ maps for the Turkestan region of Kazakhstan. On maps showing a 10% probability of exceedance within 50 a, the obtained values ranged from 0.020 g to 0.370 g, whereas for a 2% probability of exceedance in 50 a, the values ranged from 0.052 g to 0.720 g. The maximum predicted ground acceleration values were located in the southeastern part of Turkestan region within a high-seismicity zone containing active faults. The maps produced in this study facilitate assessments of the probability of destructive earthquakes in the region, thereby enabling the development of risk-reduction and mitigation strategies. The maps also serve as a basis for updating previous building codes.
