- Open Access
A method for locating rockfall impacts using signals recorded by a microseismic network
© The Author(s). 2017
- Received: 18 October 2017
- Accepted: 6 December 2017
- Published: 20 December 2017
Rockfall events are one of the most dangerous phenomena that often cause several damages both to people and facilities. During recent years, the scientific community focused the attention at evaluating the effectiveness of seismological methods in monitoring these phenomena. In this work, we present a quick and practical method to locate the rebounds of some man-induced boulders falls from a landslides crown located in the Northern Apennines (Central Italy). The reconstruction of the trajectories was obtained by means of back analysis performed through a Matlab code that takes into account both the DEM (Digital Elevation Model) of the ground, the geotechnical-geophysical characteristics of the slope and the arrival times of the seismic signals generated by the rock impacts on the ground.
The localization results have been compared with GPS coordinates of the points and videos footage acquired during the simulations, in order to assess the reliability of the method. In most cases, the retrieved impact points match with the real trajectories, showing a high reliability. Furthermore, four different cases have been identified as a function of the geomechanical, geophysical and morphological conditions. Due to the latter ones, in some case it was necessary to assume different values for the propagation velocity of the elastic waves in the ground, here assumed to be isotropic and homogeneous.
This work aims at evaluating the effectiveness of a quick and practical method to locate rockfall events using a small-aperture seismic network. The obtained results indicate that the technique can provide quantitative information about the area most prone to impact of detached blocks. The method still presents some uncertainty, but reducing some of the approximations (e.g. by better constraining the velocity model), it could lead to prompt and more accurate results, easily applicable to hazard estimates.
- Seismic monitoring
- Seismic network
- Rockfall simulation
- Traveltime function
Landslides are frequent and widespread geomorphological phenomena that often cause huge damages. Italy is one of the country more prone to landslides (Classified European Landslide Susceptibility Map, Günther et al. 2014). One of the greatest risk factor is the occurrence of boulders detachments from unstable rock slopes, potentially dangerous for people and goods. During last years news often highlighted the spread of these potentially lethal events (10/5/2010 Val Trebbia, Piacenza; 9/8/2011 Trentino). Therefore, it is necessary to gain further insights on these phenomena, with the aim to better comprehend the behavior of a rock falling down from a slope, to subsequently identify the areas where such phenomena could occur.
In particular, the identification of areas where a rockfall might happen, allows for the implementation of stabilization or protective measures before the occurrence of catastrophic events (Baillifard et al. 2003). The analysis of rockfall trajectories in fact, is essential to calibrate, design and distribute mitigation measures. Moreover, this turns into significant saving of money, according to Shuster and Leighton (1988) who estimated that if appropriate strategies are adopted, at least the losses caused by instability phenomena can be reduced by more than 90%.
In addition to the most common application of seismology (Allen, 1978; Jongmans and Garambois, 2007) as prospecting, sliding surface identification (Bruno & Martillier, 2000; McCann & Forster, 1990) and microzonation (Bour et al., 1998; Scott et al., 2006), seismic and microseismic networks have already been applied to study rockslide (Helmstetter and Garambois, 2010; Deparis et al. 2008; Dammeier et al. 2011), showing that seismic signals can provide interesting information on rockfall events (Norris, 1994; Dammeier et al., 2016; Hibert et al., 2014; Lacroix and Helmstetter, 2011). Indeed, crack propagation generate micro-seismic events (Manconi et al. 2016).
Furthermore, seismic measurements could be suitable for this purpose since they are non-invasive methods and are relatively inexpensive (Vilajosana et al. 2008).Within this framework, numerous researchers focused on the localization of rockfall events, with the aim to implement the proposed technique as a part of an early-warning system, able to quickly identify the most active portions of an unstable slope.
For this target, different techniques have been proposed in literature. In most cases, the localization of the impact points has been done using the polarization analysis of the seismic signal (Vilajosana et al. 2008; Levy et al. 2011); others (Bottelin et al. 2014; Lacroix and Helmstetter 2011) proposed the triangulation technique, or the beamforming method.
This study aims to localize rock impacts on the ground using the arrival times extracted from the traces with manual picking. This method is rarely used due to its complexity and the result loss of time (Dammeier et al. 2011; Colombero et al. 2016; Chen and Holland, 2016), especially when traces are affected by ambient noise. On the other hand, where a large number of data are not involved, the manual procedure can be a good compromise since it bars false misrepresents and allow some considerations concerning seismic noise and signal amplitude. Once the manual picking procedure has been carried out, the proposed method results to be very quick and practical, allowing localization without the need to study waveform, frequency content or amplitude, that can be required in other methods (e.g. the polarization analysis).
The work shows the accuracy of the method applied in our case study comparing the obtained results to the real blocks trajectories filmed during a field campaign when some man-induced rockfalls were carried on.
To reach the targets, we localized blocks impacts on the ground recorded as a single transient by the seismic network. The first step consisted in the manual picking of the arrival times, the second one focused on the localization of each detected transient to reconstruct the whole trajectory from the throw point to the arrival one (both measured also by GPS technique).
In some studies artificial rockfall events have been filmed in order to evaluate some parameters as velocity and energy (Berger et al. 2002; Vilajosana et al. 2008). In particular, a work similar to the one we are reporting, was carried on by Bottelin et al. (2014) during an event on the French Alps. In that case a rockslide was artificially triggered by blasting an unstable rock mass and a seismic network, recording in continuous, was settled. During the experiment, some cameras filmed the boulders and videos have been used to estimate the rockfalls velocities but not as a tool to verify the localization process reliability.
The slope, oriented approximately along the SE-NW direction (with a dip of about 30–38°) is mainly made of Maiolica Formation which consists of well stratified micritic limestone (from 10 cm to 1 m) with intercalation of thin clay layers. The rockslide is in the upper part of the quarry and has a rough trapezoidal shape. The upper boundary is associated to a big tension crack up to one meter wide and 100 m long, the lower one matches with the sliding surface that transversally cut the slope, and the western lateral boundary consists of a persistent fractures system derived from the coalescence of several joint families. The slide generally showed slow movements after heavy rainfalls (Ponziani et al. 2011, Intrieri et al. 2012), and due to the intense fracturing, it is particularly subject to rockfall phenomena.
Two major events, happened in 2004 and in 2005, and respectively involved a few tens of m3 and 2500 m3 (Graziani et al. 2009), getting the attention of the scientific community. Since then many studies have been carried out on the area by Alta Scuola di Perugia and the Department of Earth Sciences of the University of Florence trying to identify some parameters able to forecast the landslide movements.
The rockslide (Cruden and Varnes, 1996) has been instrumented starting from summer 2007 with a network equipped with 13 wire extensometers, 1 thermometer, 1 rain gauge and 3 cameras (Intrieri et al. 2012) that continuously monitored the slope. A seismic network was installed on December 2012 (Lotti et al. 2015; Amorese et al. 2015) to supplement the monitoring system. Moreover a WSN system was installed in 2013 composed of 15 wireless nodes, where one of these acts as network coordinator (NC), 3 clinometers (tiltmeters), 4 wire extensometers, 2 bar extensometers, and 4 soil hygrometers in the framework of a National Research Project (PRIN 2009) in cooperation by University of Florence, University of Bologna and Politecnico di Torino (Giorgetti et al. 2016). A ground based radar interferometry campaign was also conducted as part of the project (Barla & Antolini 2016, Antolini et al. 2016).
For the above mentioned purposes, the Department of Earth Sciences of the University of Florence tested the application of a microseismic network. The network, equipped with four stations, has been installed in collaboration with the Parsec Foundation (former Prato Ricerche). The stations acquired data in continuous mode from December 2012 to July 2013.
Data were recorded in miniSEED format (Halbert et al. 1988) and subsequently converted in SAC (Seismological Analysis Code, Goldstein et al., 2003; Goldstein & Snoke, 2005) format for processing operations.
To calibrate and verify the performance of the applied techniques, we carried out two days of field campaign (25/06/2013 and 04/07/2013), during which we triggered some man-induced rockfalls hurling about 95 blocks from different points of the slope (some of them are reported as dots in Fig. 2).
During the entire experiment, each throw was filmed by four high resolution cameras (two Canon EOS 600D, one Canon 660D and a Nikon D700, yellow cubes, Fig. 2), to compare the impact locations as retrieved from analysis of the seismic traces with the effective trajectories of the falling blocks.
We also measured the coordinates of start and final (when possible) blocks’ impact points with a dynamic GPS. Points coordinates were tracked on a geo-referenced Digital Terrain Model (DTM). The DTM has been obtained from a point cloud acquired by a laser scanning survey carried out in July 2007.
The localization of the boulders’ impact points was attained using an algorithm based on the non-linear inversion of seismic waves arrival times.
The solution is assumed to be associated with the grid node at which P(X0) takes a maximum.
Values of P(X0) are displayed as a colored surface superimposed on the gridded topographic surface; for each separate inversion, the grid node at which P(X0) takes its maximum represent the most likely epicentral location (Saccorotti et al., 1998). When the energy of single consecutive rock impacts is high enough to generate well-distinguishable seismic pulses having clear onsets, then the location procedure is iterated to track, through the subsequent locations, the trajectories of the falling blocks.
Seismic traces and manual picking procedure
For the analysis that follow a MATLAB code was used. The analysis was carried out for the vertical component (SHZ) since it showed the best signal to noise ratio.
For both simulation days, traces recorded by TOR 3 resulted to be more difficult to analyze and pick because of the higher distance from the impact points (sources), that caused the signal attenuation. Moreover, TOR3 shows a lower signal-to-noise-ratio that can be explained considering that this station is located in the upper part of the quarry, surrounded by trees that, especially during windy days, induce vibrations to the ground concealing transient signals.
The described procedure was applied to achieve the localization of subsequent impacts recorded during 57 rockfall simulations extracted from a database of 95 throws. Some of the throws have been discarded since the falls failed (the blocks immediately stopped) or the energy involved was too low and did not produce an event large enough to be detected by all stations.
Arrival times at TOR4 station obtained by manual picking. Event occurred on 25th June 2013 at 12:06 UTC
12 06 51.485
12 06 52.924
12 06 54.983
12 06 56.454
As expected, the results of the boulders fall simulations were not homogenous: the launch position, and the eventual breakage of the blocks determined differences in the trajectories and the kinematic of the failed mass. This is clearly visible in the localization results.
In particular, three different cases have been identified:
CASE1: Clear rebounds
Figure 8 illustrates the results of two experiments held on 25th June 2013 at 12:21 and at 12:12 (UTC), for which respectively 4 and 5 rebounds have been identified in the seismic traces. In both cases, the subsequent impacts were located within a limited area and could allow the identification of the zones most prone to rockfall impacts. (Fig. 8a, b).
CASE 2: Blocks hurled next to TOR2 station
CASE 3: Slides on a bedding plane
In this paper an algorithm based on the non-linear inversion of seismic waves arrival times recorded by a microseismic network (equipped with 4 stations) to localize the impact points of some boulders artificially thrown along a slope prone to rockfalls has been proposed. Results showed that the methodology is generally reliable and able to retrace the path travelled by the fallen blocks, since the calculated trajectories matched with real ones, unless some errors due to the assumptions. Nevertheless, the applicability of the method to each impact strictly depends on the area of impacts: when the approximation used are respected (bi-dimensional model and homogeneous medium with an isotropic propagation velocity, ‘CASE 1’), the identified source point is correctly located, otherwise, if the impact point is located on the outcropping bedrock (near TOR2, ‘CASE 2’) a modification of the medium velocity value is required in order to obtain a good match between the real and the retraced position.
The accuracy of the methodology is strictly connected to the positioning of the seismic network: the closer the impacts to the instruments, the higher the amplitude of the signals and consequently the manual picking will be feasible at all the stations.
As far as the blocks size is concerned, no influence on method efficiency has been found, but it’s worth to take into account that size of the same order of magnitude have been considered in this work.
The location procedure is intrinsically affected by uncertainties associated with different causes. These are: 1) measuring errors, due to difficulties in detecting the first arrival arising from low SNR or emergent traces; 2) intrinsic instrumental limitations, given by the step sampling of the digital signal (0,005 s); 3) theoretical approximations, due to the assumption of a constant propagation velocity (assumed equal to 2000 m/s); and 4) inaccuracies in the ray-tracing procedure, due to the fact that ray trajectories have been calculated as straight lines connecting the nodes of the gridded topographic surface and individual stations. Even under the homogeneous medium approximation, such trajectory cannot be realistic in case of rough topographic surfaces.
The proposed technique provides interesting information about the area that is most prone to impact of the detached blocks, and can represent a useful tool for mapping those areas that need to be protected by defense works (protection works). It is worth noting that the degree of uncertainty could be further reduced by minimizing certain approximations inherent to the method (as example, by using a reliable velocity model).
The work described in this paper was funded by the Italian Ministry of Instruction, University and Research (MIUR) in the framework of the National Research Project PRIN 2009 titled “Integration of monitoring and numerical modeling techniques for early warning of large rockslides”. The project was carried out by the Department of Earth Sciences of the University of Florence (National coordinator and responsible for the Research Unit: Prof. Nicola Casagli), the Department of Electrical and Information Engineering “Guglielmo Marconi” of the Università degli Studi di Bologna (responsible for the Research Unit: Prof. Andrea Giorgetti) and the Department of Structural, Building and Geotechnical Engineering of Politecnico di Torino (responsible for the Research Unit: Prof. Marco Barla).
Availability of data and materials
TG carried out data collection, conducted analysis and drafted the manuscript. AL AG MB FA helped in draft the manuscript. LL MN FG contributed to the fieldwork and were responsible for the digital modelling. GG and GS gave technical support and conceptual advice and contribute to the preparation of the manuscript. AF and LM handled network maintenance during the whole test period. NC supervised the project. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
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