Processing of random roadway source signals based on a cross-correlation algorithm in the deconvolution domain

Abstract

Advanced seismic detection technology utilising random roadway sources is advantageous method adapting to the development of dynamic intelligent technologies for the detection of hidden and disastrous geological structures. However, roadheaders generate complex signals, that are continuous and random with seismic wavelets that are wider and longer than conventional source wavelets, and these complex wavelets cannot be directly subjected to data processing. To address this problem, based on two basic techniques, namely, deconvolution and cross-correlation, a cross-correlation algorithm in the deconvolution domain is proposed to process continuous random roadheader signals, convert those signals into normal-impulse seismic data, and extract the arrivals of direct and reflected waves and other information. First, the feasibility of the algorithm is explained based on a derivation of theoretical formulas. Then, to verify the applicability of the algorithm to the processing of actual data, a random signal from a tamping source is processed; the continuous random signals are successfully converted into impulse signals, and the extracted in-phase direct waves are clear with good continuity. Moreover, the continuous random signal processing results are compared with an active source signal, and good consistency is found, verifying the effectiveness of the algorithm. Finally, an application analysis of the algorithm is carried out using an actual acquired roadheader source signal. After implementing the algorithm, the random roadheader signal is successfully converted into a normal-impulse seismic signal, and the direct wave arrivals are extracted. Based on the arrival times of the direct waves, the longitudinal wave velocity of the coal seam is calculated as 1918 m/s, which is consistent with the actual velocity. A comprehensive analysis of the test results confirms that the cross-correlation algorithm in the deconvolution domain is both feasible and effective and that the processed seismic signals can be used for subsequent processing and interpretation.