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.

中文翻译：

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基于反卷积域互相关算法的随机巷道源信号处理

利用随机巷道源的先进地震探测技术是适应动态智能技术发展对隐蔽和灾难性地质构造探测的有利手段。然而，掘进机产生的复杂信号是连续和随机的，地震子波比常规震源子波更宽更长，并且这些复杂子波不能直接进行数据处理。针对这一问题，基于反褶积和互相关两种基本技术，提出反褶积域互相关算法对连续随机掘进信号进行处理，将这些信号转换为法向脉冲地震数据，并提取直接波和反射波以及其他信息的到达。第一的，基于理论公式的推导解释了该算法的可行性。然后，为了验证该算法对实际数据处理的适用性，对来自篡改源的随机信号进行了处理；连续随机信号成功转换为脉冲信号，提取的同相直达波清晰，连续性好。此外，将连续随机信号处理结果与有源源信号进行比较，发现一致性较好，验证了算法的有效性。最后，利用实际采集的掘进机源信号对该算法进行了应用分析。实现该算法后，随机掘进机信号成功转换为法向脉冲地震信号，提取直达波。根据直达波的到达时间，计算得到煤层纵波速度为1918 m/s，与实际速度一致。对试验结果的综合分析证实，反褶积域的互相关算法既可行又有效，处理后的地震信号可用于后续的处理和解释。