Rockburst time warning method with blasting cycle as the unit based on microseismic information time series: a case study

Rockburst warning includes prediction of the position, intensity, and timing of potential rockburst. Rockburst time warning refers to the prediction of the moment at or time period during which a rockburst may occur. Due to the complex rockburst mechanism and many influencing factors, several key difficult-to-solve scientific problems currently remain in rockburst time warning research. In this article, microseismic (MS) monitoring is performed, and blasting is implemented as an iconic event to study the warning method for rockburst with blasting cycle as the unit of time. Focusing on this research goal, a deep learning method is applied to establish an MS information prediction model (MSIPM) and a rockburst time warning model (RBTWM) based on a long short-term memory network (LSTM). The MSIPM predicts the MS information for subsequent blasting cycles through the MS information time series of historical blasting cycles. The RBTWM predicts the potential rockburst intensity and which blasting cycle a rockburst may occur through the MS information time series obtained by fusing MS information from historical and subsequent blasting cycles. The developed method is applied in a railway tunnel excavated with the drilling and blasting method. The warning results of the test set demonstrate that the rockburst warning accuracies for the first, second, and third subsequent blasting cycles are approximately 74.6%, 71.2%, and 63.1%, respectively. In addition, further application and verification are carried out in the construction of another new railway tunnel. The rockburst warning accuracy for the first subsequent blasting cycles is approximately 80.0%. The application results show that the MSIPM and RBTWM provide warnings regarding the immediate rockburst time in blasting cycle units. The combination of MS monitoring and artificial intelligence represents a new idea for rockburst time warning.