TLS- and inventory-based Magnitude – Frequency relationship for rockfall in Montserrat and Castellfollit de la Roca

Hazard scenarios are defined by a representative event of a certain magnitude, which corresponds to a frequency of occurrence or annual probability. In rockfall, scenario magnitude is identified by the total volume detached. Therefore, in diffuse hazard assessment it is crucial to fit this relationship magnitude/frequency, called McF, where cumulated frequency is quoted in spatial & temporal terms. Inventories are the classical source of data to deal with this objective. Last decade, TLS or digital photogrammetry monitoring came to offer a complementary approach. The samples obtained by the two methods have a specific coverage and each has its own lack of information that can be compensated together.Peer ReviewedPostprint (published version

¿Cuánto grande es 'grande' en los movimientos de ladera? Encuesta sobre la idea de magnitud y su comunicación

La idea de magnitud de un fenómeno de ladera parece muy evidente, pero sometida a análisis no resulta tan simple. Además, carecemos de una escala de referencia para su valoración cualitativa. Esta indefinición en la magnitud se traslada al concepto de peligrosidad, lo cual dificulta la comunicación, incluso entre técnicos, y más aun con públicos más amplios, quienes resultan imprescindibles aliados para la implementación efectiva de estrategias de mitigación del riesgo. Esta preocupación por la comunicación de la idea de peligrosidad ha orientado en todo momento la elaboración de la guía técnica para la elaboración de Estudios de Identificación de Riesgos Geológicos (EIRG) por parte del ICGC. Esta figura resulta una pieza clave para la consideración de los riesgos geológicos en el urbanismo en Cataluña y hacer efectivo el mandato legislativo en la materia. A raíz de estos trabajos se ha desarrollado una escala de magnitud que pretende ser de la máxima simplicidad y claridad para lograr una comunicación adecuada del riesgo. En esta comunicación pretendemos realizar un test de viabilidad de la escala mediante una encuesta participativa a las personas participantes al simposio.Postprint (published version

Multi-technique approach to rockfall monitoring in the Montserrat massif (Catalonia, NE Spain)

Montserrat Mountain is located near Barcelona in Catalonia, in the northeast of Spain, and its massif is formed by conglomerate interleaved by siltstone/sandstone with steep slopes very prone to rockfalls. The increasing number of visitors in the monastery area, reaching 2.4 million per year, has highlighted the risk derived from rockfalls for this building area and also for the terrestrial accesses, both roads and the rack railway. A risk mitigation plan has been launched, and its first phase during 2014-2016 has been focused largely on testing several monitoring techniques for their later implementation. The results of the pilot tests, performed as a development from previous sparse experiences and data, are presented together with the first insights obtained. These tests combine four monitoring techniques under different conditions of continuity in space and time domains, which are: displacement monitoring with Ground-based Synthetic Aperture Radar and characterization at slope scale, with an extremely non-uniform atmospheric phase screen due to the stepped topography and atmosphere stratification; Terrestrial Laser Scanner surveys quantifying the frequency of small or even previously unnoticed rockfalls, and monitoring rock block centimetre scale displacements; the monitoring of rock joints implemented through a wireless sensor network with an ad hoc design of ZigBee loggers developed by ICGC; and, finally, monitoring singular rock needles with Total Station.Peer ReviewedPostprint (author's final draft

Using mixed reality for the visualization and dissemination of complex 3D models in geosciences: application to the Montserrat massif (Spain)

In the last two decades, both the amount and quality of geoinformation in the geosciences field have improved substantially due to the increasingly more widespread use of techniques such as Laser Scanning (LiDAR), digital photogrammetry, unmanned aerial vehicles, geophysical reconnaissance (seismic, electrical, geomagnetic), and ground-penetrating radar (GPR), among others. Furthermore, the advances in computing, storage and visualization resources allow the acquisition of 3D terrain models (surface and underground) with unprecedented ease and versatility. However, despite these scientific and technical developments, it is still a common practice to simplify the 3D data in 2D static images, losing part of its communicative potential. The objective of this paper is to demonstrate the possibilities of extended reality (XR) for communication and sharing of 3D geoinformation in the field of geosciences. A brief review of the different variants within XR is followed by the presentation of the design and functionalities of headset-type mixed-reality (MR) devices, which allow the 3D models to be investigated collaboratively by several users in the office environment. The specific focus is on the functionalities of Microsoft’s HoloLens 2 untethered holographic head mounted display (HMD), and the ADA Platform App by Clirio, which is used to manage model viewing with the HMD. We demonstrate the capabilities of MR for the visualization and dissemination of complex 3D information in geosciences in data rich and self-directed immersive environment, through selected 3D models (most of them of the Montserrat massif). Finally, we highlight the educational possibilities of MR technology. Today MR has an incipient and reduced use; we hope that it will gain popularity as the barriers of entry become lower.This research was funded by MCIN/ AEI/10.13039/501100011033: PID2019-103974RB-I00 and by Interreg V-A, POCTEFA: EFA364/19.Peer ReviewedPostprint (published version

Rockfall Magnitude-Frequency Relationship Based on Multi-Source Data from Monitoring and Inventory

Quantitative hazard analysis of rockfalls is a fundamental tool for sustainable risk management, even more so in places where the preservation of natural heritage and people's safety must find the right balance. The first step consists in determining the magnitude-frequency relationship, which corresponds to the apparently simple question: how big and how often will a rockfall be detached from anywhere in the cliff? However, there is usually only scarce data on past activity from which to derive a quantitative answer. Methods are proposed to optimize the exploitation of multi-source inventories, introducing sampling extent as a main attribute for the analysis. This work explores the maximum possible synergy between data sources as different as traditional inventories of observed events and current remote sensing techniques. Both information sources may converge, providing complementary results in the magnitude-frequency relationship, taking advantage of each strength that overcomes the correspondent weakness. Results allow characterizing rockfall detachment hazardous conditions and reveal many of the underlying conditioning factors, which are analyzed in this paper. High variability of the hazard over time and space has been found, with strong dependencies on influential external factors. Therefore, it will be necessary to give the appropriate reading to the magnitude-frequency scenarios, depending on the application of risk management tools (e.g., hazard zoning, quantitative risk analysis, or actions that bring us closer to its forecast). In this sense, some criteria and proxies for hazard assessment are proposed in the paper

Using several monitoring techniques to measure the rock mass deformation in the Montserrat Massif

Montserrat Mountain is located near Barcelona in Catalonia, at the north-east corner of Spain, and its massif is formed by conglomerate interleaved by siltstone/sandstone with steep slopes very prone to rock falls. The increasing visitor's number in the monastery area, reaching 2.4 million per year, has pointed out the risk derived from rock falls for this building area and also for the terrestrial accesses, both roads and rack railway. A risk mitigation plan is currently been applied for 2014-2016 that contains monitoring testing and implementation as a key point. The preliminary results of the pilot tests carried out during 2014 are presented, also profiting from previous sparse experiences and data, and combining 4 monitoring techniques under different conditions of continuity in space and time domains, which are: displacement monitoring with Ground-based Synthetic Aperture Radar and characterization at slope scale, with an extremely non uniform atmospheric phase screen because of the stepped topography and atmosphere stratification; Terrestrial Laser Scanner surveys quantifying frequency for unnoticed activity of small rock falls, and monitoring rock block displacements over 1cm; monitoring of rock joints with a wireless net of sensors; and tentative surveying for singular rocky needles with Total Station

Machine Learning-Based Rockfalls Detection with 3D Point Clouds, Example in the Montserrat Massif (Spain)

Rock slope monitoring using 3D point cloud data allows the creation of rockfall inventories, provided that an efficient methodology is available to quantify the activity. However, monitoring with high temporal and spatial resolution entails the processing of a great volume of data, which can become a problem for the processing system. The standard methodology for monitoring includes the steps of data capture, point cloud alignment, the measure of differences, clustering differences, and identification of rockfalls. In this article, we propose a new methodology adapted from existing algorithms (multiscale model to model cloud comparison and density-based spatial clustering of applications with noise algorithm) and machine learning techniques to facilitate the identification of rockfalls from compared temporary 3D point clouds, possibly the step with most user interpretation. Point clouds are processed to generate 33 new features related to the rock cliff differences, predominant differences, or orientation for classification with 11 machine learning models, combined with 2 undersampling and 13 oversampling methods. The proposed methodology is divided into two software packages: point cloud monitoring and cluster classification. The prediction model applied in two study cases in the Montserrat conglomeratic massif (Barcelona, Spain) reveal that a reduction of 98% in the initial number of clusters is sufficient to identify the totality of rockfalls in the first case study. The second case study requires a 96% reduction to identify 90% of the rockfalls, suggesting that the homogeneity of the rockfall characteristics is a key factor for the correct prediction of the machine learning models

Multi-technique approach to rockfall monitoring in the Montserrat massif (Catalonia, NE Spain)

Montserrat Mountain is located near Barcelona in Catalonia, in the northeast of Spain, and its massif is formed by conglomerate interleaved by siltstone/sandstone with steep slopes very prone to rockfalls. The increasing number of visitors in the monastery area, reaching 2.4 million per year, has highlighted the risk derived from rockfalls for this building area and also for the terrestrial accesses, both roads and the rack railway. A risk mitigation plan has been launched, and its first phase during 2014-2016 has been focused largely on testing several monitoring techniques for their later implementation. The results of the pilot tests, performed as a development from previous sparse experiences and data, are presented together with the first insights obtained. These tests combine four monitoring techniques under different conditions of continuity in space and time domains, which are: displacement monitoring with Ground-based Synthetic Aperture Radar and characterization at slope scale, with an extremely non-uniform atmospheric phase screen due to the stepped topography and atmosphere stratification; Terrestrial Laser Scanner surveys quantifying the frequency of small or even previously unnoticed rockfalls, and monitoring rock block centimetre scale displacements; the monitoring of rock joints implemented through a wireless sensor network with an ad hoc design of ZigBee loggers developed by ICGC; and, finally, monitoring singular rock needles with Total Station

Los desprendimientos de rocas en la montaña de Montserrat. Un riesgo geológico relevante

Postprint (published version

Comparison of several geomatic techniques for rockfall monitoring

Rockfalls are slope instabilities very frequent and harmful in mountainous areas. They cause damage in infrastructures (roads and railways), buildings, vehicles and people.Several testswerecarried out to understand better these events. The field activities comprised real scale tests and the characterization of natural events in the N-E of Spain, mainly in the Pyrenees range. Moreover, in order to understand the behaviourof the blocks during the fall real scale tests were carried out. We dropped a total of 124 rock blocs under controlled conditions. Prior to the block release and during their propagation downslope, several geomatic techniques wereused to monitor the volumes, shapes and trajectories ofthe original blocks and their fragments (due to breakage); it is worth to highlight the videogrammetry to determinethe trajectoriesof the blocks. In order to survey the natural rock walls,source of the rockfalls, the so-called massive data capture by photogrammetry (both terrestrial and UAV-drone with image and video) and Terrestrial Laser Scanning(TLS) have been used, in this way the different techniques can be compared. Finally, for the monitoring of some rock cliffs, with recurrent rockfalls, the TLS was used, trying to catch some precursory displacements that may help in the risk management of the areas at the bottom. In our contribution, the aforementioned geomatic techniques (videogrammetry, photogrammetry –terrestrial or aerial --, and TLS) are combined and compared, highlighting the pros and cons of the different methods and their applications according to environmental conditions.Peer ReviewedPostprint (published version