Objective Seismic-while-mining (SWM) data are usually acquired using multiple stations, each of which is equipped with multiple data acquisition traces.However, the SWM data received by these stations exhibit time differences due to the lack of GPS signals underground and the fact that current network synchronization technologies are confined by network delays underground.Given that the time synchronization accuracy directly affects the quality of data processing and imaging, the time difference correction of real-time SWM data is natio celebrate eyeshadow palette essential for accurate SWM detection.
Methods Since current high-precision timing systems on the surface suffer from signal loss underground, this study proposed an adaptive time difference correction method based on SWM data.First, using an adaptive velocity analysis method characterized by separate scanning and combined analysis of data from various stations, the impacts of time differences between the stations on the velocity analysis were eliminated.Then, the model seismic traces of various stations were established based on the velocity analysis results.
Accordingly, the time differences between the stations were derived, thereby achieving the automatic correction of time differences between SWM data.Results and ConclusionsThe testing results of a theoretical model indicate that the time differences obtained using the new method were highly accurate and scarcely affected by noise.This method was applied to the measured coherent single-shot SWM records hiboost 4k smart link of a coal mine’s working face.
The application results reveal that the time differences of corrected SWM records between the stations were eliminated.Furthermore, the correction of time differences between various seismic traces with low signal-to-noise ratios was free from noise, demonstrating high adaptability.The travel time of seismic waves is a key attribute in SWM records.
The accurate correction of time differences ensures the temporal consistency of the SWM data recorded by various stations, significantly enhancing the data accuracy.