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Underground mines can experience certain levels of microseismic activity as a consequence of stress perturbations and rock damages due to mining excavations. The understanding of the complex interaction between state of stress modifications and the generation of induced seismicity is a fundamental purpose in order to control the rate of seismicity and guarantee mine workers safety. In this context, microseismic monitoring in active mines has become a worldwide consolidated tool for observing and analyzing mining-induced seismicity. The performance of seismic networks, in terms of events detectability and location accuracy, is, thus, one of the basic concerns. However, a good performance of seismic networks is often hard to achieve, especially in the complex environment of active mines characterized by very low magnitude seismic events and where network geometries are limited by available galleries and mine production plans. In this work we present a simple methodology for the evaluation of microseismic array performances (EMAP). For a given network geometry in a monitored volume, EMAP determines: (i) the minimum events magnitude that can be detected and located and (ii) the distribution of predicted location errors for a specific magnitude. Given a velocity model and the attenuation relationship, for several virtual seismic sources regularly located within the monitored volume, EMAP estimates synthetic amplitudes, wave arrival times and polarization angles at each seismic probe. Based on the background noise at stations, signal-to-noise ratios (S/N) are computed per each couple source-receiver. The magnitude of completeness is, then, determined comparing the S/N ratios with imposed threshold for detection and location, respectively. In addition, for the estimation of expected location errors, initial synthetic values of arrival times and polarization directions are perturbed with errors, which follow a Gaussian distribution. Performing several simulations per each grid point, location errors are then given by the average value of the difference between the synthetic locations and the ones determined after input data alteration. We applied EMAP algorithm to the underground seismic network of the deep metal mine of Garpenberg (Sweden), which is equipped with both one-component and three-component seismometers, over a monitored area of 64×106 m3. Considering an empirical attenuation law for amplitudes, an homogeneous velocity model and determining the noise level at stations from real records, we tested the algorithm for different geometries of the monitoring network. Detectability performances were evaluated in the local magnitude range 0 ≤ ML ≤ -4.5, while location errors were determined for a ML of -2. The detectability performances are in agreement with magnitudes of real microseismic events normally recorded and located in the site. Results showed smaller location errors in the central area of the seismic network, which is around 1200 meters below ground surface, where source points are more constrained. Geometrical effects in detection and location performances are observed and caused by the heterogeneous locations of the stations due to exploitation constrains that prevent a complete optimization of the network. As expected, performances are improved, both in terms of events detectability and location accuracy, for network configurations when increasing the number of seismic stations. These tests showed that EMAP works properly not only for the performance evaluation of existing arrays, but also for defining efficient network geometries that may significantly reduce initial location errors in complex sites. Besides the application to local underground arrays our methodology can be also suitable for regional earthquake monitoring networks.

INERIS and BOLIDEN are developing at Garpenberg mine (Sweden) new methodologies to monitor and to assess both quasi-static stress changes and ruptures in a seismic-prone area subject to deep mass-mining production. To achieve monitoring, a local mine seismic network has been deployed in the beginning of 2015 in the Lappberget area between 1100 and 1250 meter depth, in addition to 3D stress monitoring cells. Such network has been designed to fit with the sublevel stoping method and with the paste fill production/distribution system used by Boliden. Geophysical and geotechnical data are acquired continuously and near-to real time transferred to INERIS data centre through e.cenaris e.infrastructure for automated data processing and database management, along with mining datasets. In addition to continuous monitoring, a fine-grid 3D numerical model of the mine has been defined, in which the complex 3D shapes of the orebody and a major weakness unit are taken into account. It is submitted to successive step-by-step (exploited + backfilled stopes) simulations to assess the stress variations following a correct stress path, the plastic state, the safety factor, the strain and displacement fields, both elastic and plastic (shear and volumetric strain) energies in the mine in response to its development as stresses are monitored for quantitative comparison. Until November 2015, up to 20.000 m3 of ore were extracted within the monitored area, highlighting numerous stress shifts ranging from 0.1 MPa to 15 MPa with the stress cells. Methodology and data presented here are the preliminary results of the first year of monitoring. The study focuses on 3 main stress shifts recorded by the stress cells at level 1157, which are associated with the recorded seismicity, and then compared to the numerical model. Indeed significant immediate and differed stress shifts associated with induced microseismic events are recorded over a several-week period following the first blasts. In addition, modeling reveals that the presence of weak horizon (talc) influences the dynamic response of the mine by inducing creep and plasticity phenomena, which would explain the results obtained by geotechnical measurements and microseismicity.

Landslides are one of the major natural threats to human lives, settlements and infrastructure, causing enormous human suffering and property losses. Understanding the mechanisms influencing landslide dynamics is limited by the lack of dense and multi-parameter data. Nowadays landslide monitoring systems are more diffused and offer a temporary strategy to ensure public safety prior to definitive remediation or vulnerability-reducing works. They also provide accurate observation data enabling to improve the expertise and decision making. Since 2009, INERIS has been running a multi-parameter observations system placed along the West border of the very active zone of Séchilienne rockslide, where large cracks regularly open. This near real-time system is based on an integrated technological platform and combines in-depth microseismic, hydrogeological and geotechnical monitoring as well as meteorological and three-dimensional surface displacement measurements. The main time series obtained from this system includes the microseismic activity per day, the cumulative displacement and the daily rainfall. All these data are made available through INERIS cloud monitoring platform “e-cenaris” created. Indeed rainfall is one of the triggering factors of this landslide, but it is difficult to look for a relation between hydrology and kinematics in such complex geological conditions. The correlations between microseismic surges, acceleration phenomena of the surface and intense rainfalls have been visually and statistically analysed. However, it is not clear whether to define accurate qualitative similarities between such variables. Other studies are ongoing; in spite of data gaps and short-term monitoring, which complicate the study of this unstable slope.

Nowadays, underground mining industry has developed high-technology mass mining methods to optimise the productivity at deep levels. Such massive extraction induces high-level stress redistribution generating seismic events around the mining works, threatening safety and economics. For this reason mining irregular deep ore bodies calls for steadily enhanced scientific practises and technologies to guarantee the mine environment to be safer and stable for the miners and the infrastructures. INERIS, within the framework of the FP7 European project I2Mine and in partnership with the Swedish mining company Boliden, has developed new methodologies in order to monitor both quasi-static stress changes and ruptures in a seismic prone area. To this purpose, a unique local permanent microseismic and stress monitoring network has been installed into the deep-working Garpenberg mine situated to the north of Uppsala (Sweden). In this mine, ore is extracted using sublevel stoping with paste fill production/distribution system and long-hole drilling method. This monitoring network has been deployed between about 1100 and 1250 meter depth. It consists in six 1- component and five 3-component microseismic probes (14-Hz geophones) deployed in the Lappberget area, in addition to three 3D stress monitoring cells that focus on a very local exploited area. Objective is three-fold: to quantify accurately quasi-static stress changes and freshly-induced stress gradients with drift development in the orebody, to study quantitatively those stress changes versus induced detected and located microseismic ruptures, and possibly to identify quasi-static stress transfer from those seismic ruptures. Geophysical and geotechnical data are acquired continuously and automatically transferred to INERIS datacenter through the web. They are made available on a secured web cloud monitoring infrastructure called e.cenaris and completed with mine data. Such interface enables the visualisation of the monitoring data coming from the mine in quasi-real time and facilitates information exchanges and decision making for experts and stakeholders. On the basis of these data acquisition and sharing, preliminary analysis has been started to highlight whether stress variations and seismic sources behaviour might be directly bound with mine working evolution and could improve the knowledge on the equilibrium states inside the mine. Knowing such parameters indeed will be a potential solution to understand better the response of deep mining activities to the exploitation solicitations and to develop, if possible, methods to prevent from major hazards such as rock bursts and other ground failure phenomena.

We aim at improving our knowledge regarding slow-moving landslide evolution processes by means of passive seismic methods. Seismic arrays have been installed on two landslides (Super-Sauze, France and Valoria, Italy). We detect, locate and characterise three main types of seismic signals. One type corresponds to the regional earthquakes and is also external to the dynamics of the landslides. The two others are located into clusters close to fractured or scarp zones. The first type is interpreted as rock falls or brittle material propagating along the slide, while the second type is interpreted as fracture or shearing phenomena. We evaluate that displacements and precipitations might be correlated with seismic signals variations. Landslides are likely to move and to mechanically change in space and time, which implies a continuous monitoring of the seismic equipment and prevents from long-term acquisition., Nous souhaitons connaître les processus qui contrôlent les glissements de terrain lents à l’aide de méthodes sismiques passives. Nous installons des dispositifs d’écoute sismique sur les sites de Super-Sauze (France) et de Valoria (Italie). Nous détectons, localisons et caractérisons trois types principaux de signaux sismiques. Un type est associé aux séismes régionaux donc externe à la dynamique des glissements. Les deux autres sont localisés dans des foyers de fissures ou des ruptures de pente. Nous suggérons que le premier type est associé à des écroulements depuis l’escarpement et à du transport en surface, tandis que le second type est associé à des fractures et des cisaillements. On montre qu’il existe des corrélations entre ces signaux, les déplacements en surface et les précipitations. Les glissements de terrain sont des volumes variables dans le temps et dans l’espace, ce qui impose une maintenance régulière des équipements et complexifie l’acquisition de données permanentes.