Quality assessment of resistivity monitoring data of coal mines

Resistivity monitoring of coal mines is an important technical means of preventing water hazards on the roof and floor of a mining face. However, it is difficult to guarantee high-quality resistivity data monitored and collected due to the complex and extremely strong electromagnetic interference in the monitoring environment, as well as the limitations of the maximum emission current for the purpose of safe coal mining. To select and utilize highly reliable data from the massive data monitored daily, this study compared and analyzed the advantages and disadvantages of commonly used methods for quality assessment of data obtained using the DC resistivity method. Aiming at the characteristics of automatic, intelligent, full-waveform, and uninterrupted data acquisition in the resistivity monitoring of coal mines, this study investigated the time- and frequency-domain characteristics of the full-waveform resistivity data. Focusing on the transmitted current, noise of original data, and data stability, this study proposed a quantitative method for data quality assessment using the principles of statistics and designed four assessment parameters, namely the intensity and stability of the transmitted current, the temporal and spatial stability of received signals, and the signal-to-noise ratio of the original data. Accordingly, this study established a quality assessment process for resistivity monitoring data and finally applied the quality assessment process to the monitoring data collected underground. As indicated by the application results, the process proposed in this study has multiple advantages, such as strong pertinence, rich means, and the comprehensive reflection of noise interference, and are suitable for the automatic, intelligent, and full-waveform uninterruptible data acquisition of underground resistivity monitoring. Therefore, the quality assessment process proposed in this study can overcome the insufficient quality control of the resistivity monitoring data of coal mines under current technical conditions.