Tomographic Experiments for Defining the 3D Velocity Model of an Unstable Rock Slope to Support Microseismic Event Interpretation

Abstract

To monitor the stability of a mountain slope in northern Italy, microseismic monitoring technique has been used since 2013. Locating microseismic events is a basic step of this technique. We performed a seismic tomographic survey on the mountain surface above the rock face to obtain a reliable velocity distribution in the rock mass for the localization procedure. Seismic travel-time inversion showed high heterogeneity of the rock mass with strong contrast in velocity distribution. Low velocities were found at shallow depth on the top of the rock cliff and intermediate velocities were observed in the most critical area of the rock face corresponding to a partially detached pillar. Using the 3D velocity model obtained from inversion, localization tests were performed based on the Equal Differential Time (EDT) localization method. The results showed hypocenter misfits to be around 15 m for the five geophones of the microseismic network and the error was significantly decreased compared to the results produced by a constant velocity model. Although the localization errors are relatively large, the accuracy is sufficient to distinguish microseismic events occurring in the most critical zone of the monitored rock mass from microseismic events generated far away. Thus, the 3D velocity model will be used in future studies to improve the classification of the recorded events.

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

“…Two sensitivity tests using the checkerboard resolution test and restoring resolution test were performed to evaluate the resolution and stability of the inversion. According to the results obtained from these two sensitivity tests, the 3D velocity model based on the inversion can be a reliable representation of the velocity distribution within the rock mass with the best resolution of 6 × 6 m in the horizontal direction and 8 m in the vertical direction [35]. The main advantage of using this velocity model rather than a constant velocity, which is commonly used when no velocity information is available, is the expected improvement of the localization accuracy.…”

“…The layout of the geophones was also constrained by the limitation on the number of available channels, and the necessity of performing field installation with a reasonable effort and avoiding excessive risks. More information on data acquisition can be found in the literature [32,35].…”

“…Due to heterogeneities within the rock mass, weathering effects, uneven stress distribution, and fracture location and orientation, the seismic velocity distribution is quite complex and assuming a constant velocity model is quite unrealistic. Since seismic tomography is a helpful tool to estimate the velocity distribution, a tomographic survey was carefully designed and performed on the summit of the rock face [35,37].…”

“…Twenty-six hammer shots were performed at 12 source positions distributed over the summit of the rock mass, trying to extend as much as possible the rock volume explored by the tomographic survey, as shown in Figure 6a. In order to further expand the volume crossed by the seismic rays, the data measured in previous source tests with the hammer and seismic gun, including 20 shots performed at five different source positions [35], were also added to the dataset. The extent of the investigation area is 40 × 35 m in the horizontal direction and about 100 m in the vertical direction.…”

Multi-Classification of Complex Microseismic Waveforms Using Convolutional Neural Network: A Case Study in Tunnel Engineering

“…As a new real-time monitoring technology of rock mass stability, microseismic monitoring technology has been extensively applied in tunnel, mines, slopes, and other dynamic disaster early warning system projects [1][2][3][4][5][6][7][8][9]. This method can help effectively evaluate the current fracture status of surrounding rocks by analyzing the microseismic data recorded during monitoring, and then help further evaluate and predict the potential risk areas of rock masses.…”