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Comparative Study of Cross-System Microseismic Energy Calculation and Fusion Methods—A Case Study

Year: 2025

Type: article

Abstract: Microseismic monitoring technology serves as a vital tool for assessing the stability of coal and rock masses. The precision of energy calculations and the ability to integrate data across different systems have a direct impact on the effectiveness of early warning systems for hazards such as rockburst. This study utilized the 6306 working face of Shandong Energy Group’s Dongtan Coal Mine as its experimental site to address data inconsistencies caused by variations in sensor responses, localization algorithms, and energy calculation methods among microseismic monitoring systems. Two microseismic monitoring platforms, designated as System A and System B, were deployed to conduct a comparative and integrative study of cross-system energy calculations. The optimization of sensor layout facilitated a comprehensive analysis of the differences between the two systems in terms of P-wave arrival times, amplitude–frequency characteristics, and localization accuracy. Results indicated that System A achieved significantly lower localization errors, with an average of 49 m, compared to System B’s average of 70 m. Substantial differences were also found in waveform amplitude and dominant frequency, with a correlation coefficient of only 0.59 between the two systems. To bridge these disparities, an energy calculation method based on the displacement gauge function was developed. By fitting a localized gauge function R(Δ) and incorporating empirical coefficients, the energy calculation outputs of both systems were harmonized. Validation experiments demonstrated that the linear correlation coefficient of energy calculations between Systems A and B increased to 0.977 under the new method, confirming its effectiveness for data unification. This research provides critical theoretical and technical guidance for integrating microseismic data across systems and establishing unified early warning standards, thus advancing the monitoring and prediction of dynamic hazards in mining environments.

Source: Applied Sciences

DOI: 10.3390/app152111488