Search

The detection technology of the microseismic source center of underground damage is mainly used for the location of the underground damage, positioning of explosion point of artillery shells in weapon testing ground. Aiming at the problem that clock error of microseismic acquisition sensor and the lack of high frequency information of acquisition data, this paper proposes a distributed microseismic source location for underground damage based on 3D two-stream network. The method uses spatial scanning to make the low-dimensional vibration wave information highly dimensional, transforms the seismic waveform data from the time domain into the energy domain, and establishes the correspondence between the source center and the high-dimensional energy distribution. Then, the energy domain features are extracted through the 3D-CNN network, and the LSTM network gradually optimizes the focal center region to obtain accurate focal center location. The results show that the source location of underground damage microseismic based on 3D dual-current network is obviously superior to the traditional traveltime-like source location method, and the proposed method has stronger data fitting ability and anti-interference ability in high SNR data. The results of static explosion test in small field show that the proposed method is superior to the traditional method in near field microseismic source location.

As supervised person re-identification (Re-Id) requires massive labeled pedestrian data and it is very difficult to collect sufficient labeled data in reality, unsupervised Re-Id approaches attract much more attention than the former. Existing unsupervised person Re-Id models learn global features of pedestrian from whole images or several constant patches. These models ignore the difference of each region in the whole pedestrian images for feature representation, such as occluded and pose invariant regions, and thus reduce the robustness of models for cross-view feature learning. To solve these issues, we propose an Unsupervised Region Attention Network (URAN) that can learn the cross-view region attention features from the cropped pedestrian images, fixed by region importance weights on images. The proposed URAN designs a Pedestrian Region Biased Enhance (PRBE) loss to produce high attention weights for most important regions in pedestrian images. Furthermore, the URAN employs a first neighbor relation grouping algorithm and a First Neighbor Relation Constraint (FNRC) loss to provide the training direction of the unsupervised region attention network, such that the region attention features are discriminant enough for unsupervised person Re-Id task. In experiments, we consider two popular datasets, Market1501 and DukeMTMC-reID, as evaluation of PRBE and FNRC loss, and their balance parameter to demonstrate the effectiveness and efficiency of the proposed URAN, and the experimental results show that the URAN provides better performance than the-state-of-the-arts (higher than existing methods at least 1.1%).

The technology used to locate the sources of shallow underground seismic activity is mainly used to assess underground damage, the positioning of bombs in ordnance test fields, and the positioning of charges in engineering blasting. Due to sensor acquisition clock errors and the lack of generalizability of vibration data, the current sourced location methods cannot easily meet the requirements of precise positioning and system reaction time in assessing near-field microseismic activity. Based on the seismic wave analysis technique and the deep reinforcement learning method, this paper proposes a dynamic model for locating shallow underground seismic sources. The model allows the generalization of low-dimensional vibration waves information to high dimensions through spatial scanning. The process of source detection is treated as a Markov process. The correspondence between the center of a source and a high-dimensional energy distribution is established, and training is then gradually performed using the deep reinforcement learning method. The optimization of the center of the source to accurately determine the position of the focal center is provisionally called the reinforcement learning source scanning algorithm (RL-SSA). In addition, a small site-based static explosion test shows that the positioning method can greatly improve the positioning accuracy (