Reclassification of Microseismic Events through Hypocenter Location: Case Study on an Unstable Rock Face in Northern Italy

Abstract

Passive seismic methods are increasingly used in monitoring unstable rock slopes that are likely to cause rockfalls. Event classification is a basic step in microseismic monitoring. However, the classification of events generated by the propagation of fractures and rockfalls is still uncertain due to their similar features in the time and frequency domains. Hypocenter localization might be a powerful tool to distinguish events generated by fracture propagation from those caused by rockfalls. In this study, a classification procedure based on hypocenter location was validated using a selected subset of high-quality data recorded by a five-geophone network installed on a steep rock slope in Northern Italy. Considering the complexity and heterogeneity of the rock mass, a 3D velocity model that was derived from a tomographic experiment was used. We performed the localization using the equal differential time method. The location results fairly fit our expectations on suspected rockfall events because most signals were located near the rock face. However, only 4 out of 20 suspected fracture events were unquestionably confirmed as fractures being located inside the rock mass and far enough from the rock face. Further improvements in location accuracy are still necessary to distinguish suspected fracture events located close to the rock face from rockfalls. This study demonstrates that hypocenter location is a promising method to improve the final classification of microseismic events.

UAV Tilt Photography Control for Numerical Simulation of High and Steep Rock Slopes

“…RocFall software operation has three parts: point operation, bounce operation, and slide operation [17]. Te operation of points determines whether the parameters involved in the simulation are correct, establishes the initial conditions for all the bounce operation and slide operation, and starts the bounce operation.…”

A Feasibility Study on Microseismic Monitoring of Rock Burst in Traffic Tunnels

“…In this process, the microseismic monitoring system inferred the location of the rupture after processing the signals accordingly. Microseismic signals have similar mechanisms of source and signal characteristics as natural seismic signals, and therefore the localization methods are mostly cited from seismology [86]. In essence, it is a common inversion process in geophysics; that is, after analyzing the first arrival time of an event and the corresponding coordinate position, the corresponding spatial position is inversed The direct shear wave is easily disturbed by the subsequent wave in the tail of the longitudinal wave, so the longitudinal wave signal is mainly used to locate the source.…”

Classification of Microseismic Signals Using Machine Learning

“…It involves the use of acoustic-electric sensors to monitor the generation of elastic waves during the rock mass failure process, thereby obtaining spatiotemporal strength information related to microseismic events and facilitating the prediction of mining-induced dynamic disasters [5][6][7][8]. Before predicting mining-induced dynamic disasters, a series of preprocessing steps are required for microseismic signals: microseismic signal classification [9][10][11][12][13], microseismic signal denoising [14,15], initial arrival time picking [16,17], and seismic source localization [18][19][20]. Microseismic signal classification represents the first step in the preprocessing of microseismic signals and is a crucial component to ensure the effectiveness of subsequent procedures.…”