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With the increasing demand for infrastructure construction as the global economy progresses, the need for exploration and utilization of deep underground space becomes more crucial. Microseismic (MS) monitoring technology has been widely used in deep underground tunnel projects for safety monitoring in China in recent years. In this paper, four aspects of MS monitoring technology developments, i.e. distribution of projects, environment and system characteristic, purpose, and effect of MS monitoring in deep tunnel projects in China were analyzed and summarized. The results show that the technology was mainly applied in the west of China with a wide range of project types. The maximum buried depth of the tunnels monitored reached 2525 m. The tunnel construction method was mainly drilling and blasting method. The lithologies of the tunnels were mainly marble, granite and basalt. The monitoring purpose was for disaster warning and mechanism understanding. In addition, the future development of MS monitoring technology in deep tunnels in China is prospected. The results will be helpful for a rapid development of MS monitoring technology in deep tunnels in China.

A new recognition method of Support Vector Machines (SVMs) combined with wavelet-based feature extraction is proposed for identifying drugs hidden in human body. Preliminary data sets of eight kinds of samples are acquired by a home-built instrument using energy-dispersive X-ray diffraction (EDXRD) technology in a short detection time. Small sample size, poor signal-to-noise ratio (SNR) and high dimension of data make drugs identification a challenging problem. In this paper, the potential effective method solves the problem well. The spectral signal with poor SNR is obtained and processed with wavelet for feature extraction and then the wavelet coefficients are used as the inputs of SVMs. A multi-classifier of SVMs based on binary tree architecture (SVMs-BAT) is trained. The method of SVMs-BAT combined with wavelet-based feature extraction (WSVMs-BAT) is firstly compared with two methods: one is single SVMs-BAT which uses original data as inputs without preprocessing, the other is SVMs-BAT combined with feature extraction based on principal component analysis (PCA-SVMs-BAT). The high identification accuracy of WSVMs-BAT indicates that the method of feature extraction using wavelet can effectively represent the original data better. Then the recognition result of the proposed method is also compared with artificial neural network (ANN) and K-nearest neighbor (KNN) methods. Our findings show that the proposed method combined with EDXRD technology provides a good access to achieve the aim of automatic identification of illicit drugs.

In the analysis towards the energy dispersive X-ray diffraction spectra of drugs and explosives concealed by body packing, for solving the case of high dimension, small sample and poor signal-to-noise ratio, the method of feature extraction based on discrete cosine transform plus linear discriminant analysis (DCT plus LDA) is introduced. DCT is employed for dimensionality reduction and LDA is employed for feature extraction. The signal energy is concentrated into a few coefficients by DCT and LDA seeks directions efficient for discriminant. Principal component analysis (PCA) and DCT plus PCA were investigated to compare with the features extracted by DCT plus LDA. Then, in order to avoid the influence of classifiers, neural network (NN) and support vector machines (SVM) were introduced to classify the samples according to the features. It is shown that the recognition rates obtained by DCT plus LDA are more accurate (averaged recognition rate > 97.4%). This work has demonstrated that DCT plus LDA is effective in featured extraction for identification of drugs and explosives concealed by body packing.

In the analysis of energy dispersive X-ray diffraction (EDXRD) spectra of drug body packing, feature extraction is a great challenge. In this work, nonnegative matrix factorization (NMF) is proposed to identify drug body packing. NMF was applied to extract features from EDXRD spectra of samples in a set of drugs and other materials concealed in an anthropomorphic phantom. Compared with the features extracted by principal component analysis (PCA) and robust PCA, the features extracted by NMF are physically significant, and can be easily interpreted as diffraction peaks of samples. The features were classified by K-nearest neighbor and support vector machine. The results indicated that the recognition rate using NMF was ideal (above 98%) and insensitive to classifiers. This investigation has demonstrated that NMF is effective in feature extraction for the identification of drug body packing.

A new method based on Fourier transform and coherence function was used to identify pure liquid precursor chemicals in this paper. Feature scattering spectra of several kinds of pure liquid precursor chemicals were detected through energy-dispersive X-ray scattering (EDXRS). These spectra, each of which was unique to particular liquid in the same case, were processed with coherence function after truncation and wavelet denoising. A suitable frequency was proved useful in calculation of similarity for liquid identification. In this work, the method mentioned above showed to be useful and effective to identify the unknown liquid materials whose samples were saved in the database.