Rock mass damaging investigation through the analysis of microseismic monitoring data collected on rock masses

2021 ◽  
Author(s):  
Danilo D'Angiò ◽  
Luca Lenti ◽  
Salvatore Martino
Keyword(s):  
Rock Mass ◽  
Damping Ratio ◽  
Inelastic Strain ◽  
Microseismic Monitoring ◽  
Monitoring Data ◽  
Rock Properties ◽  
Analysis Approach ◽  
Monitoring Networks ◽  
Rock Masses ◽  
Main Research

<p>Rock mass damaging investigation is a main research topic in the ambit of rock fall hazard assessment. Roads and railways interruptions, as well as damages of buildings, are among the main inconveniences due to the detachment of unstable sectors of highly jointed rock masses. The contribution of rock mass creep together with natural and anthropic forcings leads to the accumulation of inelastic strain within the rock mass and to the formation of new joints or to the extension and movement of the pre-existing ones. The associated stress release produces tiny vibratory signals (known as microseismic emissions) that can be detected by on-site installed microseismic monitoring networks. Monthly and annual microseismic monitoring data can provide information on seismicity increase over certain periods and on the deterioration of rock properties as the elastic moduli. However, other seismic attributes may support the comprehension of rock mass damaging processes. In particular, the analysis over time of the damping ratio associated with the microseismic emissions could indicate transient and permanent changes within the micro-joint network. This analysis approach has been already conducted on a three-month long microseismic dataset collected at the Acuto field-lab, which is hosted in an abandoned quarry and is mainly exposed to environmental forcings (rainfalls and thermal cycles); moreover, to account also for anthropic vibrations, preliminary studies were carried out on a rock mass located in proximity of a railway. As a further perspective, the investigation of multi-year seismic dataset acquired on unstable rock masses will allow to better inspect the reliability of this analysis approach for rock mass damaging assessment.</p>

scholarly journals Rock mass diagnostics based on microseismic monitoring data at Sheregesh deposit

2017 ◽  
Vol 53 ◽  
pp. 012006
Author(s):  
TV Lobanova ◽  
GL Lindin ◽  
OL Trofimova ◽  
SK Shultaev ◽  
VV Prib
Keyword(s):  
Rock Mass ◽  
Microseismic Monitoring ◽  
Monitoring Data

Analysis of Unstable Rock-Mass Stability Based on Limit Equilibrium Method and Strength Reduction Method

2016 ◽  
Vol 858 ◽  
pp. 73-80
Author(s):  
Ying Kong ◽  
Hua Peng Shi ◽  
Hong Ming Yu
Keyword(s):  
Rock Mass ◽  
Failure Mode ◽  
Posterior Margin ◽  
Reduction Method ◽  
Limit Equilibrium ◽  
Limit Equilibrium Method ◽  
Strength Reduction ◽  
Strength Reduction Method ◽  
Rock Masses ◽  
Unstable Rock

With the slope unstable rock masses of a stope in Longsi mine, Jiaozuo City, China as the target, we computed and analyzed the stability of unstable rock masses using a limit equilibrium method (LEM) and a discrete element strength reduction method (SRM). Results show that the unstable rock masses are currently stable. Under the external actions of natural weathering, rainfall and earthquake, unstable rock mass 1 was manifested as a shear slip failure mode, and its stability was controlled jointly by bedding-plane and posterior-margin steep inclined joints. In comparison, unstable rock mass 2 was manifested as a tensile-crack toppling failure mode, and its stability was controlled by the perforation of posterior-margin joints. From the results of the 2 methods we find the safety factor determined from SRM is larger, but not significantly, than that from LEM, and SRM can simulate the progressive failure process of unstable rock masses. SRM also provides information about forces and deformation (e.g. stress-strain, and displacement) and more efficiently visualizes the parts at the slope that are susceptible to instability, suggesting SRM can be used as a supplementation of LEM.

Implementation of InfraRed Thermographic surveys in complex coastal areas: the case study of Polignano a Mare (southern Italy)

2021 ◽  
Author(s):  
Lidia Loiotine ◽  
Marco La Salandra ◽  
Gioacchino Francesco Andriani ◽  
Eliana Apicella ◽  
Michel Jaboyedoff ◽  
...  
Keyword(s):  
Rock Mass ◽  
Structure From Motion ◽  
Southern Italy ◽  
Thermal Imager ◽  
Rock Masses ◽  
Field Surveys ◽  
Thermal Images ◽  
Thermal Anomalies ◽  
Rock Face

<p><em>InfraRed Thermography</em> (IRT) spread quickly during the second half of the 20<sup>th</sup> century in the military, industrial and medical fields. This technique is at present widely used in the building sector to detect structural defects and energy losses. Being a non-destructive diagnostic technique, IRT was also introduced in the Earth Sciences, especially in the volcanology and environmental fields, yet its application for geostructural surveys is of recent development. Indeed, the acquisition of thermal images on rock masses could be an efficient tool for identifying fractures and voids, thus detecting signs of potential failures.<br>Further tests of thermal cameras on rock masses could help to evaluate the applicability, advantages and limits of the IRT technology for characterizing rock masses in different geological settings.<br>We present some results of IRT surveys carried out in the coastal area of Polignano a Mare (southern Italy), and their correlation with other remote sensing techniques (i.e. <em>Terrestrial Laser Scanning</em> and <em>Structure from Motion</em>). The case study (<em>Lama Monachile</em>) is represented by a 20 m-high cliff made up of Plio-Pleistocene calcarenites overlying Cretaceous limestones. Conjugate fracture systems, karst features, folds and faults, were detected in the rock mass during field surveys. In addition, dense vegetation and anthropogenic elements, which at places modified the natural setting of the rock mass, represent relevant disturbances for the characterization of the rock mass. In this context, IRT surveys were added to the other techniques, aimed at detecting the major discontinuities and fractured zones, based on potential thermal anomalies. <br>IRT surveys were carried out in December 2020 on the east side of the rock mass at <em>Lama Monachile</em> site. Thermal images were acquired every 20 minutes for 24 hours by means of a FLIR T-660 thermal imager mounted on a fixed tripod. Ambient air temperature and relative humidity were measured during the acquisition with a pocketsize thermo-hydrometer. A reflective paper was placed at the base of the cliff to measure the reflected apparent temperature. In addition, three thermocouple sensors were fixed to the different lithologic units of the rock face. These parameters, together with the distance between the FLIR T-660 and the rock face, were used in order to calibrate the thermal imager and correct the apparent temperatures recorded by the device, during the post-processing phase. Successively, vertical profiles showing the temperature of the rock face over time were extracted from the thermograms. Thermal anomalies were correlated with stratigraphic and Geological Strength Index profiles, obtained by means of field surveys and Structure from Motion techniques. The presence of fracture and voids in the rock mass was also investigated.</p>

Proposed evaluation methods for vibration serviceability of long span cable bridges

2021 ◽  
Author(s):  
E.-S. Hwang ◽  
M. T. Hwang ◽  
D. Y. Kim ◽  
K. J. Park
Keyword(s):  
Heavy Traffic ◽  
Damping Ratio ◽  
Flexible Structures ◽  
Evaluation Methods ◽  
Monitoring Data ◽  
Long Span ◽  
Vibration Serviceability ◽  
Long Term Monitoring ◽  
Term Monitoring

<p>Vibration serviceability becomes more important considerations in design and maintenance, especially for slender and flexible structures such as long span cable bridges. In this study, various evaluation methods for vibration serviceability for long span cable bridges are proposed. These methods are based on short and long-term monitoring data such as accelerations and displacements of bridges. Proposed methods include (1) method of evaluating vibration amplitude based on Reiher-Meister curves, (2) method of evaluating variations in natural frequencies and damping ratio,</p><p>(3) method of weighted rms(root-mean-square) acceleration based on ISO 2631-1, and (4) probabilistic analysis using long-term monitoring data. These methods are applied to example cable bridge and cases of normal traffic, heavy traffic, windy condition and sudden abnormal vibration are considered. The results of this study are expected to be implemented to real bridge monitoring system for real-time and periodic evaluation of vibration serviceability.</p>

scholarly journals Analysis of Shear Characteristics of Deep, Anchored Rock Mass under Creep Fatigue Loading

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xiaofeng Li ◽  
Zhixiang Yin
Keyword(s):  
Rock Mass ◽  
Rock Strength ◽  
Low Frequency ◽  
Fatigue Loading ◽  
Load Level ◽  
Rock Masses ◽  
Sandstone Rock ◽  
Fatigue Model ◽  
Creep Fatigue ◽  
Marble Rock

To study the influence of earthquakes and engineering disturbances on the deformation of deeply buried rock masses, shear tests were carried out on anchored sandstone rock masses, anchored marble rock masses, and anchored granite rock masses under creep fatigue loading, and a new creep fatigue model was established to characterize the deformation characteristics of anchored rock masses under creep fatigue loading. The creep fatigue curves of different lithologies clearly show three stages: creep attenuation, steady-state creep, and accelerated creep. Fatigue loading can increase the creep of anchored specimens, and the lower the rock strength is, the higher the creep variable under fatigue loading is. However, for the same rock strength, with the increase in load level, the creep variable produced by creep fatigue load presents a linear downward trend. Considering the changes in the mechanical properties of the anchored rock mass under creep fatigue loading, the creep fatigue model of anchored rock masses is established by introducing a function of the fatigue shear modulus, and the accuracy and applicability of the model are verified by laboratory creep fatigue test data. The model provides a theoretical basis for the study of anchored rock mass support under low-frequency earthquakes or blasting loads.

scholarly journals GroningenNet: Deep Learning for Low-Magnitude Earthquake Detection on a Multi-Level Sensor Network

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8080
Author(s):  
Ahmed Shaheen ◽  
Umair bin Waheed ◽  
Michael Fehler ◽  
Lubos Sokol ◽  
Sherif Hanafy
Keyword(s):  
Deep Learning ◽  
Template Matching ◽  
Carbon Capture And Storage ◽  
Microseismic Monitoring ◽  
Seismic Events ◽  
Learning Approaches ◽  
Monitoring Networks ◽  
Level Sensor ◽  
Multi Level ◽  
Low Magnitude

Automatic detection of low-magnitude earthquakes has become an increasingly important research topic in recent years due to a sharp increase in induced seismicity around the globe. The detection of low-magnitude seismic events is essential for microseismic monitoring of hydraulic fracturing, carbon capture and storage, and geothermal operations for hazard detection and mitigation. Moreover, the detection of micro-earthquakes is crucial to understanding the underlying mechanisms of larger earthquakes. Various algorithms, including deep learning methods, have been proposed over the years to detect such low-magnitude events. However, there is still a need for improving the robustness of these methods in discriminating between local sources of noise and weak seismic events. In this study, we propose a convolutional neural network (CNN) to detect seismic events from shallow borehole stations in Groningen, the Netherlands. We train a CNN model to detect low-magnitude earthquakes, harnessing the multi-level sensor configuration of the G-network in Groningen. Each G-network station consists of four geophones at depths of 50, 100, 150, and 200 m. Unlike prior deep learning approaches that use 3-component seismic records only at a single sensor level, we use records from the entire borehole as one training example. This allows us to train the CNN model using moveout patterns of the energy traveling across the borehole sensors to discriminate between events originating in the subsurface and local noise arriving from the surface. We compare the prediction accuracy of our trained CNN model to that of the STA/LTA and template matching algorithms on a two-month continuous record. We demonstrate that the CNN model shows significantly better performance than STA/LTA and template matching in detecting new events missing from the catalog and minimizing false detections. Moreover, we find that using the moveout feature allows us to effectively train our CNN model using only a fraction of the data that would be needed otherwise, saving plenty of manual labor in preparing training labels. The proposed approach can be easily applied to other microseismic monitoring networks with multi-level sensors.

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