Features of different types of seismic events in China’s Capital Region

Seismic records produced by different seismic sources vary.In this study,we compared the waveform records and time-frequency characteristics of tectonic earthquakes,artificial explosions,and mine collapses in China’s Capital Region.The results show that tectonic earthquakes are characterized by stronger S-wave energy than P-wave energy,obvious high-frequency components,and wide frequency bands of P and S waves.Artificial explosions are characterized by greater P-wave amplitude than S-wave amplitude and near-station surface wave development.Mine collapses are characterized by lower overall frequency,more obvious surface waves,and longer duration.We extracted quantitative discriminants based on the analysis of different event records,with 31 feature values in 7 categories(P/S maximum amplitude ratio,high/low frequency energy ratio,P/S spectral ratio,corner frequency,duration,the second-order moment of spectrum,and energy strongest point).A comparison of the ability of these feature values to recognize distinct events showed that the 6-17 Hz P/S spectral ratio was able to completely distinguish artificial explosions from the other two types of events.The S-wave corner frequency performed relatively well in identifying all three types of events,with an accuracy of over 90%.Additionally,a support vector machine was used to comprehensively distinguish multiple features,with an accuracy for all three types of events reaching up to 100%.

FOLMS-AMDCNet:an automatic recognition scheme for multiple-antenna OFDM systems

The existing recognition algorithms of space-time block code(STBC)for multi-antenna(MA)orthogonal frequencydivision multiplexing(OFDM)systems use feature extraction and hypothesis testing to identify the signal types in a complex communication environment.However,owing to the restrictions on the prior information and channel conditions,these existing algorithms cannot perform well under strong interference and noncooperative communication conditions.To overcome these defects,this study introduces deep learning into the STBCOFDM signal recognition field and proposes a recognition method based on the fourth-order lag moment spectrum(FOLMS)and attention-guided multi-scale dilated convolution network(AMDCNet).The fourth-order lag moment vectors of the received signals are calculated,and vectors are stitched to form two-dimensional FOLMS,which is used as the input of the deep learning-based model.Then,the multi-scale dilated convolution is used to extract the details of images at different scales,and a convolutional block attention module(CBAM)is introduced to construct the attention-guided multi-scale dilated convolution module(AMDCM)to make the network be more focused on the target area and obtian the multi-scale guided features.Finally,the concatenate fusion,residual block and fully-connected layers are applied to acquire the STBC-OFDM signal types.Simulation experiments show that the average recognition probability of the proposed method at−12 dB is higher than 98%.Compared with the existing algorithms,the recognition performance of the proposed method is significantly improved and has good adaptability to environments with strong disturbances.In addition,the proposed deep learning-based model can directly identify the pre-processed FOLMS samples without a priori information on channel and noise,which is more suitable for non-cooperative communication systems than the existing algorithms.