scholarly journals Infrasound array criteria for automatic detection and front velocity estimation of snow avalanches: towards a real-time early-warning system

2015 ◽  
Vol 15 (11) ◽  
pp. 2545-2555 ◽  
Author(s):  
E. Marchetti ◽  
M. Ripepe ◽  
G. Ulivieri ◽  
A. Kogelnig
Keyword(s):  
Risk Management ◽  
Real Time ◽  
Doppler Radar ◽  
Warning System ◽  
Velocity Estimation ◽  
Apparent Velocity ◽  
Snow Avalanches ◽  
Small Aperture ◽  
Forecast Models ◽  
Back Azimuth

Abstract. Avalanche risk management is strongly related to the ability to identify and timely report the occurrence of snow avalanches. Infrasound has been applied to avalanche research and monitoring for the last 20 years but it never turned into an operational tool to identify clear signals related to avalanches. We present here a method based on the analysis of infrasound signals recorded by a small aperture array in Ischgl (Austria), which provides a significant improvement to overcome this limit. The method is based on array-derived wave parameters, such as back azimuth and apparent velocity. The method defines threshold criteria for automatic avalanche identification by considering avalanches as a moving source of infrasound. We validate the efficiency of the automatic infrasound detection with continuous observations with Doppler radar and we show how the velocity of a snow avalanche in any given path around the array can be efficiently derived. Our results indicate that a proper infrasound array analysis allows a robust, real-time, remote detection of snow avalanches that is able to provide the number and the time of occurrence of snow avalanches occurring all around the array, which represent key information for a proper validation of avalanche forecast models and risk management in a given area.

scholarly journals Infrasound array criteria for automatic detection and front velocity estimation of snow avalanches: towards a real-time early-warning system

2015 ◽  
Vol 3 (4) ◽  
pp. 2709-2737 ◽  
Author(s):  
E. Marchetti ◽  
M. Ripepe ◽  
G. Ulivieri ◽  
A. Kogelnig
Keyword(s):  
Risk Management ◽  
Real Time ◽  
Doppler Radar ◽  
Warning System ◽  
Velocity Estimation ◽  
Apparent Velocity ◽  
Snow Avalanches ◽  
Small Aperture ◽  
Back Azimuth ◽  
Threshold Criteria

Abstract. Avalanche risk management is strongly related to the ability to identify and timely report the occurrence of snow avalanches. Infrasound has been applied to avalanche research and monitoring for the last 20 years but it never turned into an operational tool for the ambiguity to identify clear signals related to avalanches. We present here a new method based on the analysis of infrasound signals recorded by a small aperture array in Ischgl (Austria), which overcome now this limit. The method is based on array derived wave parameters, such as back-azimuth and apparent velocity. The method defines threshold criteria for automatic avalanche identification considering avalanches as a moving source of infrasound. We validate efficiency of the automatic infrasound detection with continuous observations with Doppler Radar and we show how dynamics parameters such as the velocity of a snow avalanche in any given path around the array can be efficiently derived. Our results indicate that a proper infrasound array analysis allows a robust, real-time, remote detection of snow avalanches that could thus contribute significantly to avalanche forecast and risk management.

Detection and source parameterization of small-energy fireball events in Western Alps with ground-based infrasonic arrays

2021 ◽  
Author(s):  
Giacomo Belli ◽  
Emanuele Pace ◽  
Emanuele Marchetti
Keyword(s):  
Western Alps ◽  
Apparent Velocity ◽  
Array Analysis ◽  
Small Energy ◽  
Small Aperture ◽  
Energy Estimation ◽  
Trajectory Reconstruction ◽  
Tnt Equivalent ◽  
Back Azimuth ◽  
Aperture Arrays

Summary We present infrasound signals generated by four fireball events occurred in Western Alps between 2016 and 2019 and that were recorded by small aperture arrays at source-to receiver distances < 300 km. Signals consist in a series of short-lived infrasonic arrivals that are closely spaced in time. Each arrival is identified as a cluster of detections with constant wave parameters (back-azimuth and apparent velocity), that change however from cluster to cluster. These arrivals are likely generated by multiple infrasonic sources (fragmentations or hypersonic flow) along the entry trajectory. We developed a method, based on 2D ray-tracing and on the independent optically determined time of the event, to locate the source position of the multiple arrivals from a single infrasonic array data and to reconstruct the 3D trajectory of a meteoroid in the Earth's atmosphere. The trajectories derived from infrasound array analysis are in excellent agreement with trajectories reconstructed from eyewitnesses reports for the four fireballs. Results suggest that the trajectory reconstruction is possible for meteoroid entries located up to ∼300 km from the array, with an accuracy that depends on the source-to-receiver distance and on the signal-to-noise level. We also estimate the energy of the four fireballs using three different empirical laws, based both on period and amplitude of recorded infrasonic signals, and discuss their applicability for the energy estimation of small energy fireball events ($\le 1{\rm{kt\,\,TNT\,\,equivalent}}$).

Installation and Performance of the Albuquerque Seismological Laboratory Small-Aperture Posthole Array

2020 ◽  
Vol 91 (4) ◽  
pp. 2425-2437
Author(s):  
Robert E. Anthony ◽  
Adam T. Ringler ◽  
David C. Wilson ◽  
J. Zebulon Maharrey ◽  
Gary Gyure ◽  
...  
Keyword(s):  
Real Time ◽  
P Wave ◽  
Apparent Velocity ◽  
Fiber Optic Gyroscope ◽  
Time Data ◽  
Original Design ◽  
Small Aperture ◽  
Low Frequencies ◽  
Deep Earth Structure ◽  
And Performance

Abstract The Global Seismographic Network (GSN) has been used extensively by seismologists to characterize large earthquakes and image deep earth structure. Although the network’s original design goals have been met, the seismological community has suggested that the incorporation of small-aperture seismic arrays at select sites may improve performance of the network and enable new observations. As a pilot study for this concept, we have created a 500 m aperture, nine-element broadband seismic array around the GSN station ANMO (Albuquerque, New Mexico) at the U.S. Geological Survey Albuquerque Seismological Laboratory (ASL). The array was formed by supplementing the secondary borehole seismometer (90 m depth) at ANMO with eight additional 2.6 m posthole sites. Each station’s seismometer was oriented using a fiber optic gyroscope to within 2.0° of north. Data quality, particularly on the vertical components, is excellent with median power levels closely tracking the secondary sensor at ANMO at frequencies lower than 1 Hz. Horizontal component data are more variable at low frequencies (<0.02  Hz), with the type of installation and local geography appearing to strongly influence the amount of tilt-induced noise. Throughout the article, we pose several fundamental questions related to the variability and precision of seismic wavefield measurements that we seek to address with data from this array. In addition, we calculate the array response and show a few examples of using the array to obtain back azimuths of a local event and a continuous narrowband noise source. The apparent velocity of the event across the array is then used to infer the local P-wave velocity at the ASL. Near-real-time data collected from the array along with collocated meteorological, magnetic, and infrasound data are freely available in near-real time from the Incorporated Research Institutions for Seismology Data Management Center.

Building a Natural-Hazard-Resilient High-Quality Seismic Network: How WI Network Sustained Hurricanes Maria and Irma

2020 ◽  
Vol 92 (1) ◽  
pp. 77-84 ◽  
Author(s):  
Jean-Marie Saurel ◽  
Jordane Corbeau ◽  
Sébastien Deroussi ◽  
Tristan Didier ◽  
Arnaud Lemarchand ◽  
...  
Keyword(s):  
Real Time ◽  
Seismic Data ◽  
Warning System ◽  
Volcanic Eruptions ◽  
Seismic Network ◽  
Tsunami Warning ◽  
Coastal Hazards ◽  
Lesser Antilles ◽  
Research Centre ◽  
Small Aperture

Abstract Between 2008 and 2014, the Institut de Physique du Globe de Paris (IPGP) and the University of the West Indies, Seismic Research Centre (UWI-SRC) designed and built a regional seismic network across the Lesser Antilles. One of the goals of the network is to provide real-time seismic data to the tsunami warning centers in the framework of the Intergovernmental Coordination Group working toward the establishment of a tsunami and other coastal hazards early warning system (ICG-CARIBE-EWS) for the Caribbean and adjacent regions (McNamara et al., 2016). In an area prone to hurricanes, earthquakes, tsunamis, and volcanic eruptions, we chose different techniques and technologies to ensure that our cooperated network could survive and keep providing data in case of major natural hazards. The Nanometrics very small aperture terminal (VSAT) technology is at the heart of the system. It allows for duplicated data collection at the three observatories (Trinidad, Martinique, and Guadeloupe; Anglade et al., 2015). In 2017, the network design and implementation were put to the test with Saffir–Simpson category 5 hurricanes Irma and Maria that went, respectively, through the north and central portion of the Lesser Antilles, mainly impacting the sites operated by volcanological and seismological observatories of IPGP in Martinique (Observatoire Volcanologique et Sismologique de Martinique [OVSM]) and in Guadeloupe (Observatoire Volcanologique et Sismologique de Guadeloupe [OVSG]). Our concepts proved to be valid with a major data shortage of less than 12 hr and only two stations having sustained heavy damage. In this article, we review the strengths and weaknesses of the initial design and discuss various steps that can be taken to enhance the ability of our cooperated network to provide timely real-time seismic data to tsunami warning centers under any circumstances.

scholarly journals Three-Month Real-Time Dengue Forecast Models: An Early Warning System for Outbreak Alerts and Policy Decision Support in Singapore

2016 ◽  
Vol 124 (9) ◽  
pp. 1369-1375 ◽  
Author(s):  
Yuan Shi ◽  
Xu Liu ◽  
Suet-Yheng Kok ◽  
Jayanthi Rajarethinam ◽  
Shaohong Liang ◽  
...  
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Policy Decision ◽  
Forecast Models

scholarly journals Building ANN-Based Regional Multi-Step-Ahead Flood Inundation Forecast Models

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1283 ◽  
Author(s):  
Li-Chiu Chang ◽  
Mohd Amin ◽  
Shun-Nien Yang ◽  
Fi-John Chang
Keyword(s):  
Real Time ◽  
Warning System ◽  
Google Earth ◽  
Flood Inundation ◽  
Self Organizing Map ◽  
Storm Events ◽  
Inundation Maps ◽  
Forecast Models ◽  
Som Network ◽  
Precautionary Measures

A regional inundation early warning system is crucial to alleviating flood risks and reducing loss of life and property. This study aims to provide real-time multi-step-ahead forecasting of flood inundation maps during storm events for flood early warnings in inundation-prone regions. For decades, the Kemaman River Basin, located on the east coast of the West Malaysia Peninsular, has suffered from monsoon floods that have caused serious damage. The downstream region with an area of approximately 100 km2 located on the east side of this basin is selected as the study area. We explore and implement a hybrid ANN-based regional flood inundation forecast system in the study area. The system combines two popular artificial neural networks—the self-organizing map (SOM) and the recurrent nonlinear autoregressive with exogenous inputs (RNARX)—to sequentially produce regional flood inundation maps during storm events. The results show that: (1) the 4 × 4 SOM network can effectively cluster regional inundation depths; (2) RNARX networks can accurately forecast the long-term (3–12 h) regional average inundation depths; and (3) the hybrid models can produce adequate real-time regional flood inundation maps. The proposed ANN-based model was shown to very quickly carry out multi-step-ahead forecasting of area-wide inundation depths with sufficient lead time (up to 12 h) and can visualize the forecasted results on Google Earth using user devices to help decision makers and residents take precautionary measures against flooding.

Safety Monitoring and Warning System for Subway Construction Workers using Wearable Technology

2019 ◽  
Vol 13 ◽  
Author(s):  
Jun-hua Chen ◽  
Da-hu Wang ◽  
Cun-yuan Sun
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Early Warning ◽  
Warning System ◽  
Safety Monitoring ◽  
Wearable Technology ◽  
Construction Workers ◽  
Great Success ◽  
Subway Construction ◽  
Monitoring And Early Warning

Objective: This study focused on the application of wearable technology in the safety monitoring and early warning for subway construction workers. Methods: With the help of real-time video surveillance and RFID positioning which was applied in the construction has realized the real-time monitoring and early warning of on-site construction to a certain extent, but there are still some problems. Real-time video surveillance technology relies on monitoring equipment, while the location of the equipment is fixed, so it is difficult to meet the full coverage of the construction site. However, wearable technologies can solve this problem, they have outstanding performance in collecting workers’ information, especially physiological state data and positioning data. Meanwhile, wearable technology has no impact on work and is not subject to the inference of dynamic environment. Results and conclusion: The first time the system applied to subway construction was a great success. During the construction of the station, the number of occurrences of safety warnings was 43 times, but the number of occurrences of safety accidents was 0, which showed that the safety monitoring and early warning system played a significant role and worked out perfectly.

scholarly journals A Machine Learning-Based Seismic Data Compression and Interpretation Using a Novel Shifted-Matrix Decomposition Algorithm

2021 ◽  
Vol 11 (11) ◽  
pp. 4874
Author(s):  
Milan Brankovic ◽  
Eduardo Gildin ◽  
Richard L. Gibson ◽  
Mark E. Everett
Keyword(s):  
Real Time ◽  
Seismic Data ◽  
Matrix Decomposition ◽  
Original Data ◽  
Velocity Estimation ◽  
Singular Vectors ◽  
Well Stimulation ◽  
Marine Seismic ◽  
Value Decomposition ◽  
Seismic Data Compression

Seismic data provides integral information in geophysical exploration, for locating hydrocarbon rich areas as well as for fracture monitoring during well stimulation. Because of its high frequency acquisition rate and dense spatial sampling, distributed acoustic sensing (DAS) has seen increasing application in microseimic monitoring. Given large volumes of data to be analyzed in real-time and impractical memory and storage requirements, fast compression and accurate interpretation methods are necessary for real-time monitoring campaigns using DAS. In response to the developments in data acquisition, we have created shifted-matrix decomposition (SMD) to compress seismic data by storing it into pairs of singular vectors coupled with shift vectors. This is achieved by shifting the columns of a matrix of seismic data before applying singular value decomposition (SVD) to it to extract a pair of singular vectors. The purpose of SMD is data denoising as well as compression, as reconstructing seismic data from its compressed form creates a denoised version of the original data. By analyzing the data in its compressed form, we can also run signal detection and velocity estimation analysis. Therefore, the developed algorithm can simultaneously compress and denoise seismic data while also analyzing compressed data to estimate signal presence and wave velocities. To show its efficiency, we compare SMD to local SVD and structure-oriented SVD, which are similar SVD-based methods used only for denoising seismic data. While the development of SMD is motivated by the increasing use of DAS, SMD can be applied to any seismic data obtained from a large number of receivers. For example, here we present initial applications of SMD to readily available marine seismic data.

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