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Objectives: Earthquake monitoring can help us comprehend the formation of the earthquake, and it's also important for earthquake early warning. However, there are three main problems when using traditional seismometers to monitor crustal deformation. (1)The seismometer is seriously affected by low-frequency noise and baseline error, and the displacement result generated by its integration will diverge.(2) The seismometer has a limited measuring range. (3) The seismometer cannot observe the permanent crustal de‐ formation. Methods: To solve the above problems, we combined accelerometers and global navigation satellite system(GNSS) high-precision data-processing methods using embedded technology and developed an integrated GNSS strong seismograph SMAG2000 for real-time seismic monitoring. The SMAG2000 consists of the accelerometer, GNSS board, and embedded development board. The algorithms of real-time precise point positioning and data fusion are operated in the SMAG2000. We conducted the shaker table test to evaluate the performance of the SMAG2000.The shake table simulated different vibrations and out‐ put the reference displacement and acceleration. The experiment tested the performance of the SMAG2000 under the stationary state, sinusoidal vibration, and simulated seismic wave vibration, respectively. Results: Firstly, The standard deviations of the noise were 0.57 cm/s² and 0.84 cm for acceleration and dis‐ placement, respectively. Secondly, the standard deviation of the difference between the results and reference for the sine wave was better than 0.4 cm, and the correlation coefficient was better than 0.99. Finally, the standard deviation and correlation coefficient of seismic waves are 0.293 cm and 0.986 for strong earth‐ quakes. Those were 0.360 cm and 0.951 for weak earthquakes. Conclusions: The result implies that SMAG2000 can monitor seismic displacement and deformation in real-time mode. Moreover, the SMAG2000 uses domestic parts and has the advantages of low cost, small size, and low power consumption. The instrument has no range limit and can obtain permanent displacement. Thus, the SMAG2000 has great development potential and market prospects for seismic monitoring research. [ABSTRACT FROM AUTHOR]

Both seismograph and deformation stations recorded low frequency pulselike signals before the Wenchuan earthquake. In order to further analyze and judge the authenticity of such pulse signals, one earthquake pulselike records detection method based on deep learning is proposed. Take the Wenchuan earthquake as an example, firstly, the continuous waveform data of 9 days before the Wenchuan earthquake in 49 stations in Sichuan province are collected and are taken as training data set. The two dimensional time - spectrum of the waveform data are calculated by continuous wavelet transform method. Then, based on the deep autoencoder neural network and time-spectrum of the waveform data, the network model of pulselike records recognition is obtained and used for automatic identification of earthquake pulselike signals. Experimental results show that our deep autoencoder network model has good stability and the recognition percentage for new data is over 93%. Finally, the spatial distribution of pulselike signals in Sich-uan Province one month before the Wenchuan earthquake is preliminarily calculated, and a possible explanation is given from the perspective of slow slip motion of the faults. [ABSTRACT FROM AUTHOR]