RETREAT - a REal-time TREmor Analysis Tool

2020 ◽  
Author(s):  
Patrick Smith ◽  
Chris Bean
Keyword(s):  
Real Time ◽  
Volcanic Tremor ◽  
Seismic Array ◽  
Analysis Tool ◽  
Time Data ◽  
Array Analysis ◽  
Array Data ◽  
Real Time Data ◽  
Back Azimuth ◽  
Tremor Analysis

<p>The EUROVOLC project aims to promote an integrated and harmonised European volcanological community, and one of its main themes focuses on understanding sub-surface processes. Early identification of magma moving towards the surface is very important for the mitigation of risks from volcanic hazards, and joint research activities within the project aim to develop and improve volcano pre-eruptive detection schemes. Volcanic tremor is a sustained seismic signal associated with eruptions and is often linked to movement of magmatic fluids in the subsurface. However, it can occur pre-, syn- and post-eruption, and signals with similar spectral content can also be generated by several other processes (e.g. flooding, rockfalls). Hence one of the best ways of distinguishing between the processes underlying tremor generation is through tracking the evolution of its spatial location. Due to its continuous nature tremor cannot be located using classical seismological methods and so its source must be determined using alternatives such as seismic array analysis.</p><p>This work presents RETREAT, a REal-time TREmor Analysis Tool developed under EUROVOLC, that uses seismic array data and array processing techniques to detect, quantify and locate volcanic tremor signals. It is an open-source python-based tool that utilizes existing routines from the open-source <em>obspy</em> framework to carry out analysis of seismic array data in real-time. The tool performs f-k (frequency-wavenumber) analysis using beamforming to calculate the back azimuth and slowness in overlapping time windows, which can be used to detect and track the location of volcanic tremor sources.</p><p>A graphical and web-based interface has been developed which allows adjustment of highly configurable input parameters. These include options for setting the data source, pre-processing, timing and update options as well as the parameters for the seismic array analysis which must be carefully selected and tuned for the specified array. Once configured the tool fetches waveform data in real time and updates its output accordingly, returning plots of the array processing results (slowness and back azimuth values) as well as plots of the seismic waveform, envelope and spectrogram. The tool has been tested on real-time data using the <em>obspy</em> FDSN (International Federation of Digital Seismograph Networks) client to fetch data from the IRIS datacenter, using example array data from the small aperture SPITS seismic array in Spitsbergen, Svalbard. A 'replay’ mode using existing archive (non real-time) data has also been implemented and tested on array data from the 2014 eruption at Holuhraun and Bardarbunga volcano in Iceland, collected as part of the FUTUREVOLC project. The RETREAT tool is now ready for testing and implementation in a volcano monitoring setting at observatories. It will also be made freely available to download as a EUROVOLC community tool.</p>

A REal-time TREmor Analysis Tool for seismic and infrasonic arrays

2021 ◽  
Author(s):  
Patrick Smith ◽  
Chris Bean
Keyword(s):  
Real Time ◽  
Volcanic Tremor ◽  
Seismic Array ◽  
Surface Processes ◽  
Analysis Tool ◽  
Array Analysis ◽  
Small Aperture ◽  
Waveform Data ◽  
Array Data ◽  
Research Activities

<p>The EUROVOLC project aims to promote an integrated and harmonised European volcanological community, with one of its main themes focusing on understanding sub-surface processes. Early identification of magma moving towards the surface is very important for the mitigation of risks from volcanic hazards, and joint research activities within the project aim to develop and improve schemes for detecting pre-eruptive unrest. Volcanic tremor is a sustained seismic signal that is often associated with such volcanic unrest, and has been linked to the movement of magmatic fluids in the subsurface. However, signals with similar spectral content can be generated by other surface processes such as flooding, rockfalls or lahars. Hence, one of the best ways of distinguishing between different possible mechanisms for generating tremor is by tracking the location of its source, which is also important for mitigating volcanic risk. Due to its emergent nature, tremor cannot be located using travel-time based methods, and therefore alternatives such as amplitude-based techniques or array analysis must be used. Dense, small-aperture arrays are particularly suited for analyzing volcanic tremor, yet costs associated with installation and maintenance have meant few long-term or permanent seismic arrays in use for routine monitoring.</p><p>Given the potential for wider usage of arrays, this work presents a freely available python-based software tool, developed as part of the EUROVOLC project, that uses array data and array processing techniques to analyze and locate volcanic tremor signals. RETREAT utilizes existing routines from the open-source ObsPy framework to carry out analysis of array data in real-time and performs either f-k (frequency-wavenumber) analysis, or alternatively Least-Squares beamforming, to calculate the backazimuth and slowness in overlapping time windows, which can help track the location of volcanic tremor sources. A graphical, or web-based, interface is used to configure a set of input parameters, before fetching chunks of waveform data and performing the array analysis. On each update the tool returns several plots, including timeseries of the backazimuth and slowness, a polar representation of the relative power and a map of the array with the dominant backazimuth overlaid.</p><p>The tool has been tested using real-time seismic data from the small-aperture SPITS array in Spitsbergen, as well as on data from a small aperture seismic array deployed during the 2014 eruption of Bárðarbunga volcano, Iceland. Although designed specifically for seismic array data (with a particular focus on volcanic tremor), RETREAT can also be used with infrasound sensors and has been successfully tested on infrasonic array data of explosive activity recorded at Mt. Etna, Italy, in 2019.</p><p>Although RETREAT has been designed for deployment as part of volcano monitoring systems and provides the ability to track tremor sources in real-time, it also has the capability to analyse existing datasets for testing, comparison and research purposes. However, RETREAT is primarily intended for use in real-time monitoring settings and it is hoped that it will facilitate wider use of arrays in tracking volcanic tremor or infrasonic sources in real-time, thereby enhancing monitoring capabilities.</p>

399-P: Nurse-Driven Intervention Using Real-Time Data to Improve Hypoglycemia Rates in a Respiratory Care Unit

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 399-P
Author(s):  
ANN MARIE HASSE ◽  
RIFKA SCHULMAN ◽  
TORI CALDER
Keyword(s):  
Real Time ◽  
Respiratory Care ◽  
Time Data ◽  
Real Time Data

MHA releases largest real‐time data from MH help‐seekers

2021 ◽  
Vol 31 (6) ◽  
pp. 7-7
Author(s):  
Valerie A. Canady
Keyword(s):  
Real Time ◽  
Time Data ◽  
Real Time Data

Why Ecological Momentary Assessment Surveys Go Incomplete?: When It Happens and How It Impacts Data

2020 ◽  
Author(s):  
Yu-Hsiang Wu ◽  
Jingjing Xu ◽  
Elizabeth Stangl ◽  
Shareka Pentony ◽  
Dhruv Vyas ◽  
...  
Keyword(s):  
Real Time ◽  
Self Report ◽  
Data Logging ◽  
Time Data ◽  
Real Time Data ◽  
Report Data ◽  
Self Report Data ◽  
Ecological Momentary ◽  
The Impact ◽  
Momentary Assessment

Abstract Background Ecological momentary assessment (EMA) often requires respondents to complete surveys in the moment to report real-time experiences. Because EMA may seem disruptive or intrusive, respondents may not complete surveys as directed in certain circumstances. Purpose This article aims to determine the effect of environmental characteristics on the likelihood of instances where respondents do not complete EMA surveys (referred to as survey incompletion), and to estimate the impact of survey incompletion on EMA self-report data. Research Design An observational study. Study Sample Ten adults hearing aid (HA) users. Data Collection and Analysis Experienced, bilateral HA users were recruited and fit with study HAs. The study HAs were equipped with real-time data loggers, an algorithm that logged the data generated by HAs (e.g., overall sound level, environment classification, and feature status including microphone mode and amount of gain reduction). The study HAs were also connected via Bluetooth to a smartphone app, which collected the real-time data logging data as well as presented the participants with EMA surveys about their listening environments and experiences. The participants were sent out to wear the HAs and complete surveys for 1 week. Real-time data logging was triggered when participants completed surveys and when participants ignored or snoozed surveys. Data logging data were used to estimate the effect of environmental characteristics on the likelihood of survey incompletion, and to predict participants' responses to survey questions in the instances of survey incompletion. Results Across the 10 participants, 715 surveys were completed and survey incompletion occurred 228 times. Mixed effects logistic regression models indicated that survey incompletion was more likely to happen in the environments that were less quiet and contained more speech, noise, and machine sounds, and in the environments wherein directional microphones and noise reduction algorithms were enabled. The results of survey response prediction further indicated that the participants could have reported more challenging environments and more listening difficulty in the instances of survey incompletion. However, the difference in the distribution of survey responses between the observed responses and the combined observed and predicted responses was small. Conclusion The present study indicates that EMA survey incompletion occurs systematically. Although survey incompletion could bias EMA self-report data, the impact is likely to be small.

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