Purpose The purpose of this paper is to solve the problem that the location of the initiation point cannot be measured accurately in the shallow underground space, this paper proposes a method, which is based on fusion of multidimensional vibration sensor information, to locate single shallow underground sources. Design/methodology/approach First, in this paper, using the characteristics of low multipath interference and good P-wave polarization in the near field, the adaptive covariance matrix algorithm is used to extract the polarization angle information of the P-wave and the short term averaging/long term averaging algorithm is used to extract the first break travel time information. Second, a hybrid positioning model based on travel time and polarization angle is constructed. Third, the positioning model is taken as the particle update fitness function of quantum-behaved particle swarm optimization and calculation is performed in the hybrid positioning model. Finally, the experiment verification is carried out in the field. Findings The experimental results show that, with root mean square error, spherical error probable and fitness value as evaluation indicators, the positioning performance of this method is better than that without speed prediction. And the positioning accuracy of this method has been improved by nearly 30%, giving all of the three tests a positioning error within 0.5 m and a fitness less than 1. Originality/value This method provides a new idea for high-precision positioning of shallow underground single source. It has a certain engineering application value in the fields of directional demolition of engineering blasting, water inrush and burst mud prediction, fuze position measurement, underground initiation point positioning of ammunition, mine blasting monitoring and so on.
Source positioning based on energy time-inverse focus is a hot subject in the sphere of shallow underground source positioning. Due to the grave wave group aliasing and the complex, irregular geological structure typical of the shallow underground explosion, the reconstruction accuracy of the energy focus is low and thus the recognition of the focus is a difficult task, ultimately leading to a low accuracy of source positioning. To address the above problems, this paper proposes a method based on deep learning energy focus recognition, whereby the process of recognizing and positioning the energy focus in an energy field is made equivalent to the end-to-end feature extraction of the energy field-energy focus. The time-variant characteristics of explosive vibration signals are put to use in the construction of an adaptive time window. First, within the time window and by combining cross-correlation and autocorrelation operations, a 3D energy field image sequence in the time-space domain is produced by grouped energy synthesis; second, a densely connected 3DCNN network is built and, through multiple layer span layer splicing, a higher repetitive use is made of the focus point features in the energy field images; third, a spatial pyramid pooling network is used to extract multi-scale features from different focus areas, which helps achieve high-precision focus recognition. Finally, numerical simulations and field tests were conducted.The results demonstrated that compared with the quantum particle swarm optimization (QPSO)-based energy focus search method, the proposed one is more effectively in recognizing the coordinates of the focus in the energy field, thus allowing high-precision localization of shallow underground sources. This method is of some engineering application value in the field of underground source positioning.
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