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Underground space in Xuzhou City, North China, has become a favorable site for continuous geomagnetic observation to reveal geomagnetic-field variations and to monitor seismic activity due to its distinct advantage of an "ultra-quiet" and "ultra-clean" environment with low electromagnetic interference. In order to evaluate the underground electromagnetic environment at the Longdong coal mine area (LCMA) in Xuzhou City and establish an underground geomagnetic observatory, we conducted a pilot experiment of continuous geomagnetic observations synchronously underground and on the ground during May 2023. In this experiment, two close-space fluxgate magnetometers were deployed in the underground roadway of the LCMA to observe the variations of geomagnetic three components simultaneously, and a proton magnetometer was installed on its ground to observe the variation of geomagnetic total field. Furthermore, we analyzed the features of the wavelet spectrum of these observed data, variation curves of the geomagnetic field, and coupling of the geomagnetic-field variations between underground and ground. In addition, we investigated the characteristics of different frequency bands and horizontal gradient of the underground geomagnetic variation. Our achievements are as follows: 1) the underground geomagnetic environment has the advantage of being stable and clean with a low level of background noise and thus is suitable for high-precision, long-term, and continuous observation of the geomagnetic field; 2) the variation of the geomagnetic field underground is well coupled with that on the ground, with minor differences primarily attributed to an induced magnetic field generated in the surrounding rocks by the external magnetic-field variation; 3) underground geomagnetic observation can clearly record rich time-varying information of the geomagnetic field including calm variation, magnetic storms, geomagnetic pulsations and so on. Moreover, the geomagnetic gradient field contains higher-resolution time-varying information than the conventional geomagnetic field. Our pilot experiment suggests favorable underground sites with low background noise at the LCMA and provides valuable references for understanding the spatiotemporal variation characteristics of geomagnetic field. Based on the findings of our pilot experiment, an underground geomagnetic observatory and calibration station will be established at the LCMA, enabling high-precision, continuous geomagnetic and electromagnetic observation experiments in the future. These efforts will provide crucial constraints for the study of the variation of the geomagnetic-field variation, as well as seismic activity monitoring and disaster early warning related to the Tan-Lu fault zone. [ABSTRACT FROM AUTHOR]

3-D shear wave velocity and density models are important for understanding the structures, material composition, tectonic deformations and dynamical mechanisms of the Earth's crust. Such models are usually based on surface wave tomography and gravity inversion, which provide high resolution in the vertical and horizontal directions, respectively. The joint inversion of surface wave and gravity methods can promote the mutual constraints and complementary advantages of seismic and gravity information, improving the imaging resolution and reducing the uncertainty in the individual methods. However, the traditional joint inversion methods directly construct only the shear wave velocity model, excluding density models. We present a joint inversion method of surface wave and gravity data that simultaneously constructs both crustal shear wave velocity and density models. Unlike the previous studies, we reconfigure the seismic kernels of surface wave tomography to preserve the seismic kernel of density. Moreover, the gravity kernel of density is combined with the seismic kernels to establish an objective function of simultaneous joint inversion. Consequently, the imaging resolution of density structure is improved. Our method is validated on the northeastern Tibetan plateau. The inversion results show that the Dingxi, Jiuzhaigou and Jishishan earthquakes occurred in the high–low-anomaly transition zones of shear wave velocity or density, implying that they were induced by accumulation of strain energy in the upper crust of the northeastern Tibetan plateau when the surrounding harder blocks extruded during the tectonic deformation process. [ABSTRACT FROM AUTHOR]

Both crustal velocity and density models provide important constraints for understanding crustal tectonics, composition and magmatic system. However, conventional gravity inversion methods for density models are troubled by poor depth-resolution and non-uniqueness. We apply a joint inversion method to full-waveform ambient noise and gravity data for simultaneously deriving high-resolution 3-D crustal S -wave velocity (Vs) and density models. By constraints of seismic and gravity data, our joint inversion could significantly improve the resolution of density models and reduce the uncertainty on the inversion results. Our method is suitable for any areas especially with low seismicity and can be extended to basin-scale. We apply the method on the seismic and gravity data around the Jingpohu (JPH) volcanic area in Northeast China to obtain high-resolution 3-D crustal Vs and density models, which are subsequently used to build the crustal lithological model. Our models demonstrate that the JPH volcanic group is located near the junction of the Mudanjiang–Yilan and Dunhua–Mishan fault zones and was tectonically controlled by them. A steep crustal-scale mafic intrusion with high Vs and density is present beneath the JPH volcanic group, and we interpret it as the same product as the JPH volcanic basaltic rocks outcropped on the surface by the mantle-sourced magmatic upwelling. Furthermore, none significant molten magma reservoirs with low Vs and density are observed currently within the crust of the JPH volcanic group. Thus, our results support that the JPH volcanic group is a single volcanic genesis with a mantle-only magma source. [ABSTRACT FROM AUTHOR]

Aiming at the problem of explosion caused by leakage of flammable and explosive gas cylinder, a new type of safety protection device for gas bottle cabinet is designed to solve this problem. The design of this new type of gas bottle cabinet safety protection device is to use GSM voice, short message control technology, sensor technology, wifi technology, plc control technology and so on, to monitor the gas in the gas bottle cabinet in real time. Once the gas in the cylinder leaks and the concentration of the leakage gas reaches the set alarm concentration, the alarm will occur, and the exhaust fan will start to discharge the leakage gas and send an alarm message to the pre-set mobile phone at the same time.

Seizure prediction of epileptic preictal period through electroencephalogram (EEG) signals is important for clinical epilepsy diagnosis. However, recent deep learning-based methods commonly employ intra-subject training strategy and need sufficient data, which are laborious and time-consuming for a practical system and pose a great challenge for seizure predicting. Besides, multi-domain characterizations, including spatio-temporal-spectral dependencies in an epileptic brain are generally neglected or not considered simultaneously in current approaches, and this insufficiency commonly leads to suboptimal seizure prediction performance. To tackle the above issues, in this paper, we propose Contrastive Learning for Epileptic seizure Prediction (CLEP) using a Spatio-Temporal-Spectral Network (STS-Net). Specifically, the CLEP learns intrinsic epileptic EEG patterns across subjects by contrastive learning. The STS-Net extracts multi-scale temporal and spectral representations under different rhythms from raw EEG signals. Then, a novel triple attention layer (TAL) is employed to construct inter-dimensional interaction among multi-domain features. Moreover, a spatio dynamic graph convolution network (sdGCN) is proposed to dynamically model the spatial relationships between electrodes and aggregate spatial information. The proposed CLEP-STS-Net achieves a sensitivity of 96.7% and a false prediction rate of 0.072/h on the CHB-MIT scalp EEG database. We also validate the proposed method on clinical intracranial EEG (iEEG) database from our Xuanwu Hospital of Capital Medical University, and the predicting system yielded a sensitivity of 95%, a false prediction rate of 0.087/h. The experimental results outperform the state-of-the-art studies which validate the efficacy of our method. Our code is available at https://github.com/LianghuiGuo/CLEP-STS-Net. [ABSTRACT FROM AUTHOR]