Snow avalanche energy estimation from seismic signal analysis

A method to determine the dissipated seismic energy into the ground by a down going avalanche is presented. Evaluation of the seismic energy is useful for avalanche size classification, model validation, and for characterization and better understanding of the avalanche evolution as it propagates downhill along the changing slope. The method was applied to two different type avalanches that were released artificially on 2004/02/28 and 2005/04/15 at Ryggfonn (Norway) avalanche experimental site, operated by the Norwegian Geotechnical Institute (NGI). The analysed seismic data were recorded by the University of Barcelona seismic instruments consisting of two three-component wide-range seismometers located respectively, in the middle and on the side of the avalanche path. The energy determination requires a priori seismic characterization of the site and the knowledge of the avalanche front speed. In this paper a seismic characterization (surface wave phase velocity and amplitude attenuation factor) of the Ryggfonn site is presented. This characterization will serve for subsequent studies. We attribute the main source of seismic signals for the studied events to basal friction and ploughing occurring at the avalanche front and related to the changing slope in the propagation path, which causes high seismic energy dissipation. A comparative study of the evolution of the dissipated seismic energy with the energy generated by a simple sliding block model of constant mass was performed. The observed differences highlight the importance of ploughing and basal friction and the specific characteristics of the avalanches, such as their length and type. The difference between the calculated total dissipated seismic energy for the two similar size avalanches reflects their different flow type. As expected, the dry/mixed event dissipates a smaller amount of energy (not, vert, similar 1.2 MJ) than the dry/dense event (not, vert, similar 2.8 MJ)

The Power of Models: Modeling Power Consumption for IoT devices

Low-energy technologies in the Internet of Things (IoTs) era are still unable to provide the reliability needed by the industrial world, particularly in terms of the wireless operation that pervasive deployments demand. While the industrial wireless performance has achieved an acceptable degree in communications, it is no easy task to determine an efficient energy-dimensioning of the device in order to meet the application requirements. This is especially true in the face of the uncertainty inherent in energy harvesting. Thus, it is of utmost importance to model and dimension the energy consumption of the IoT applications at the pre-deployment or pre-production stages, especially when considering critical factors, such as reduced cost, life-time, and available energy. This paper presents a comprehensive model for the power consumption of wireless sensor nodes. The model takes a system-level perspective to account for all energy expenditures: communications, acquisition and processing. Furthermore, it is based only on parameters that can empirically be quantified once the platform (i.e., technology) and the application (i.e., operating conditions) are defined. This results in a new framework for studying and analyzing the energy life-cycles in applications, and it is suitable for determining in advance the specific weight of application parameters, as well as for understanding the tolerance margins and tradeoffs in the system

Caracterización de avalanchas de nieve con métodos sismológicos.

Snow avalanches associated risk in mountain areas can be very high. The knowledge of these phenomena is very important in order to mitigate that risk. The avalanche research group at the University of Barcelona has been working with the seismic signals of snow avalanches since 1994 at different sites all around Europe. The availability of more than 50 seismic records of snow avalanches made possible the identification of the specific characteristics of the seismic signals generated by avalanches. Classical seismological techniques frequently used to study earthquakes have been imported to perform the seismic signal characterization. However, the recent studies of the group have been focused in determining the physical parameters that can help in the description of the phenomena. In this case, seismological techniques have been used, in addition of the characterization of the seismic signals to obtain information of the avalanche itself. In this paper an overview of the most successful results in this field are presented

Snow avalanche speed determination using seismic methods

We present a new method to determine the average propagation speed of avalanches using seismic techniques. Avalanche propagation speeds can reach 70 m/s and more, depending on a wide range of factors, such as the characteristics of the avalanche track (e.g. topography) and the snowpack properties (e.g. density). Since the damage produced by the avalanche depends primarily on the size and on the speed of the avalanche, the knowledge of the latter is therefore crucial for estimating avalanche induced hazard in inhabited mountain areas. However, our knowledge of this basic physical parameter is limited by the difficulty of conducting various measurements in the harsh winter weather conditions that often accompany this natural phenomenon. The method of avalanche speed determination presented in this paper is based on cross-correlation and time-frequency analysis techniques. The data used in this study come from the Ryggfonn (Norway) avalanche experimental site operated by the Norwegian Geotechnical Institute (NGI), and recorded by an array of 6 geophones buried along the main avalanche path during the 2003-2004 and 2004-2005 winter seasons. Specifically, we examine the speeds of 11 different events, characterized by size and snow type. The results obtained are compared with independent speed estimates from CW-radar and pressure plate measurements. As a result of these comparisons our method was validated and has proved to be successful and robust in all cases. We detected a systematic behaviour in the speed evolution among different types of avalanches. Specifically, we found that whereas dry/mixed type flow events display a complex type of speed evolution in the study area with a gradual acceleration and an abrupt deceleration, the speed of the wet snow avalanches decreases with distance in an approximately linear fashion. This generalization holds for different size events. In terms of time duration and maximum speed of the studied events, dry/mixed type avalanches lasted between 8 to 18 s and reached speeds up to 50 m/s, whereas the duration of wet avalanches ranged between 50 and 80 s and their maximum speeds were 10 m/

GPS studies of active deformation in the Pyrenees

The Pyrenees mountain belt, which separates the Iberian Peninsula from the rest of the European continent, is part of the AlpineHimalayan orogenic belt, formed as a result of a collision between the African and Eurasian Plates. Although the instrumental seismicity in the Pyrenees is moderate, in the past centuries a number of destructive earthquakes have occurred, which could indicate continuing tectonic activity of the area. We analyse GPS observations spanning 3.5 yr from 35 continuous stations in the Pyrenees region and find significant on-going extension perpendicular to the range at 2.5 ± 0.5 nstrain yr1, with the possibility of higher strain rates concentrated in the westernmost part of the range. This finding is in agreement with the predominantly normal faulting focal mechanisms of earthquakes that occur in the area and suggests a recurrence time for magnitude 6.5 earthquakes of 22002500 yr

Lean sensing: exploiting contextual information for most energy-efficient sensing

Cyber-physical technologies enable event-driven applications, which monitor in real-time the occurrence of certain inherently stochastic incidents. Those technologies are being widely deployed in cities around the world and one of their critical aspects is energy consumption, as they are mostly battery powered. The most representative examples of such applications today is smart parking. Since parking sensors are devoted to detect parking events in almost-real time, strategies like data aggregation are not well suited to optimize energy consumption. Furthermore, data compression is pointless, as events are essentially binary entities. Therefore, this paper introduces the concept of Lean Sensing, which enables the relaxation of sensing accuracy at the benefit of improved operational costs. To this end, this paper departs from the concept of instantaneous randomness and it explores the correlation structure that emerges from it in complex systems. Then, it examines the use of this system-wide aggregated contextual information to optimize power consumption, thus going in the opposite way; from the system-level representation to individual device power consumption. The discussed techniques include customizing the data acquisition to temporal correlations (i.e, to adapt sensor behavior to the expected activity) and inferring the system-state from incomplete information based on spatial correlations. These techniques are applied to real-world smart-parking application deployments, aiming to evaluate the impact that a number of system-level optimization strategies have on devices power consumption

Lean sensing: exploiting contextual information for most energy-efficient sensing

Cyber-physical technologies enable event-driven applications, which monitor in real-time the occurrence of certain inherently stochastic incidents. Those technologies are being widely deployed in cities around the world and one of their critical aspects is energy consumption, as they are mostly battery powered. The most representative examples of such applications today is smart parking. Since parking sensors are devoted to detect parking events in almost-real time, strategies like data aggregation are not well suited to optimize energy consumption. Furthermore, data compression is pointless, as events are essentially binary entities. Therefore, this paper introduces the concept of Lean Sensing, which enables the relaxation of sensing accuracy at the benefit of improved operational costs. To this end, this paper departs from the concept of instantaneous randomness and it explores the correlation structure that emerges from it in complex systems. Then, it examines the use of this system-wide aggregated contextual information to optimize power consumption, thus going in the opposite way; from the system-level representation to individual device power consumption. The discussed techniques include customizing the data acquisition to temporal correlations (i.e, to adapt sensor behavior to the expected activity) and inferring the system-state from incomplete information based on spatial correlations. These techniques are applied to real-world smart-parking application deployments, aiming to evaluate the impact that a number of system-level optimization strategies have on devices power consumption

GIS-based debris flow source and runout susceptibility assessment from DEM data - a case study in NW Nicaragua

In October 1998, Hurricane Mitch triggered numerous landslides (mainly debris flows) in Honduras and Nicaragua, resulting in a high death toll and in considerable damage to property. The potential application of relatively simple and affordable spatial prediction models for landslide hazard mapping in developing countries was studied. Our attention was focused on a region in NW Nicaragua, one of the most severely hit places during the Mitch event. A landslide map was obtained at 1:10 000 scale in a Geographic Information System (GIS) environment from the interpretation of aerial photographs and detailed field work. In this map the terrain failure zones were distinguished from the areas within the reach of the mobilized materials. A Digital Elevation Model (DEM) with 20 m×20 m of pixel size was also employed in the study area. A comparative analysis of the terrain failures caused by Hurricane Mitch and a selection of 4 terrain factors extracted from the DEM which, contributed to the terrain instability, was carried out. Land propensity to failure was determined with the aid of a bivariate analysis and GIS tools in a terrain failure susceptibility map. In order to estimate the areas that could be affected by the path or deposition of the mobilized materials, we considered the fact that under intense rainfall events debris flows tend to travel long distances following the maximum slope and merging with the drainage network. Using the TauDEM extension for ArcGIS software we generated automatically flow lines following the maximum slope in the DEM starting from the areas prone to failure in the terrain failure susceptibility map. The areas crossed by the flow lines from each terrain failure susceptibility class correspond to the runout susceptibility classes represented in a runout susceptibility map. The study of terrain failure and runout susceptibility enabled us to obtain a spatial prediction for landslides, which could contribute to landslide risk mitigation

Optimal rate allocation in cluster-tree WSNs

In this paper, we propose a solution to the problem of guaranteed time slot allocation in cluster-tree WSNs. Our design uses the so-called Network Utility Maximization (NUM) approach as far as we aim to provide a fair distribution of the available resources. From the point of view of implementation, we extend here the authors' proposed Coupled Decompositions Method (CDM) in order to compute the NUM problem inside the cluster tree topology and we prove the optimality of this new extended version of the method. As a result, we obtain a distributed solution that reduces the total amount of signalling information in the network up to a factor of 500 with respect to the classical techniques, that is, primal and dual decomposition. This is possible because the CDM finds the optimal solution with a small number of iterations. Furthermore, when we compare our solution to the standard-proposed First Come First Serve (FCFS) policy, we realize that FCFS becomes pretty unfair as the traffic load in the network increases and thus, a fair allocation of resources can be considered whenever the price to pay in terms of signaling and computational complexity is controlled

GIS-based debris flow source and runout susceptibility assessment from DEM data - a case study in NW Nicaragua

In October 1998, Hurricane Mitch triggered numerous landslides (mainly debris flows) in Honduras and Nicaragua, resulting in a high death toll and in considerable damage to property. The potential application of relatively simple and affordable spatial prediction models for landslide hazard mapping in developing countries was studied. Our attention was focused on a region in NW Nicaragua, one of the most severely hit places during the Mitch event. A landslide map was obtained at 1:10 000 scale in a Geographic Information System (GIS) environment from the interpretation of aerial photographs and detailed field work. In this map the terrain failure zones were distinguished from the areas within the reach of the mobilized materials. A Digital Elevation Model (DEM) with 20 m×20 m of pixel size was also employed in the study area. A comparative analysis of the terrain failures caused by Hurricane Mitch and a selection of 4 terrain factors extracted from the DEM which, contributed to the terrain instability, was carried out. Land propensity to failure was determined with the aid of a bivariate analysis and GIS tools in a terrain failure susceptibility map. In order to estimate the areas that could be affected by the path or deposition of the mobilized materials, we considered the fact that under intense rainfall events debris flows tend to travel long distances following the maximum slope and merging with the drainage network. Using the TauDEM extension for ArcGIS software we generated automatically flow lines following the maximum slope in the DEM starting from the areas prone to failure in the terrain failure susceptibility map. The areas crossed by the flow lines from each terrain failure susceptibility class correspond to the runout susceptibility classes represented in a runout susceptibility map. The study of terrain failure and runout susceptibility enabled us to obtain a spatial prediction for landslides, which could contribute to landslide risk mitigation