scholarly journals Improvement of back-azimuth estimation in real-time by using a single station record

2012 ◽  
Vol 64 (3) ◽  
pp. 305-308 ◽  
Author(s):  
Shunta Noda ◽  
Shunroku Yamamoto ◽  
Shinji Sato ◽  
Naoyasu Iwata ◽  
Masahiro Korenaga ◽  
...  
Keyword(s):  
Real Time ◽  
Single Station ◽  
Back Azimuth ◽  
Station Record

Towards real-time monitoring with a seismic antenna at Merapi volcano

2020 ◽  
Author(s):  
Jean-Philippe Metaxian ◽  
Agus Budi Santoso ◽  
François Beauducel ◽  
Nabil Dahamna ◽  
Vadim Monteiller ◽  
...  
Keyword(s):  
Real Time ◽  
Frequency Band ◽  
Time Delays ◽  
Time Windows ◽  
Single Frequency ◽  
Acquisition Time ◽  
Density Currents ◽  
Slowness Vector ◽  
Back Azimuth ◽  
Real Time Application

<p>Seismic antennas are often used on volcanoes to analyse emergent signals as LP events or tremor.  In fact, they can be used for any kind of seismicity whether the signal is impulsive or emergent. In this work we are using a seismic antenna as an instrument for monitoring the continuous seismic signal, with the objective of a real-time application.</p><p>A seismic antenna composed of 5 broadband stations equipped with Guralp CMG-6TD stations was installed in November 2013 close to the summit of Merapi, on the site called Pasar Bubar. Sensors have a flat response characteristic from 30 s to the Nyquist frequency (50 Hz). This network has an aperture of 280 m. The shortest distance between sensors is 100 m.</p><p> </p><p>In the perspective of a real-time application, the main analysis, which consists of estimating the slowness vector, requires a shorter computation time than the data acquisition time. We thus focused on a signal processing technique based on the calculation of time delays on the vertical component only and in a single frequency band. Given a set of time delays and associated errors calculated between each couple of sensors in the frequency domain, the corresponding slowness vectors can be recovered by inversion. Slowness vectors are estimated for successive time-windows in the frequency band 0.5-3 Hz. Temporal series of back-azimuth and apparent slowness are deduced with respect to time.</p><p>The analysis strategy for monitoring is then the following: A weight function expressed as a function of the derivatives of the time delays is calculated for successive moving time-windows. This function was designed in order to identify areas of stability of the back-azimuth values as function of time. A PDF of the back-azimuth and apparent slowness is then estimated for time series of 1 hour. This gives information on the dominant activity by time unit.</p><p>We will show the results obtained with the analysis of several months of continuous signal which are including different types of events generated by the on-going eruptive activity of Merapi: 1) volcano-tectonic events, 2) Multi-Phase (MP) events related with magma ascent in the conduit, 3) low-frequency events, 4) Rock-falls and 5) Pyroclastic density currents.</p>

Implementation of array analysis on seismic signals from volcanoes in Iceland recorded on the small-aperture HEKSISZ array

2020 ◽  
Author(s):  
Tim Sonneman ◽  
Kristín Vogfjörd ◽  
Christopher Bean ◽  
Benedikt Halldórsson ◽  
Johannes Schweitzer
Keyword(s):  
Data Analysis ◽  
Real Time ◽  
Seismic Array ◽  
Raspberry Pi ◽  
Seismic Zone ◽  
Seismic Signals ◽  
Secondary Phases ◽  
Small Aperture ◽  
Array Data ◽  
Single Station

<p>We present preliminary results and progress updates of ongoing work at the Icelandic Meteorological Office carried out within the EUROVOLC work package on Volcano pre-eruptive unrest detection schemes. Our main goal is improved understanding of volcanic systems and fracture zones in South Iceland. This requires enhanced detection and mapping capabilities of seismic events from volcanoes in the Eastern Volcanic Zone (EVZ) and faults in the South Iceland Seismic Zone (SISZ), including continuous real-time analysis of seismic signals associated with magma movement in volcanoes and activity on faults in South Iceland. The chosen measures to achieve these tasks are the deployment of a seismic array at the intersection between the EVZ and the SISZ, the implementation of appropriate real-time array data processing and the investigation of spatiotemporal seismic source characteristics such as tracking of magma movements and intrusions from deep to shallow levels in the crust to image the volcanoes’ plumbing systems, shallow caldera seismicity, and earthquake rupture propagation and microseismicity on nearby tectonic faults. Through funding from an Icelandic infrastructure grant and cooperation between IMO and DIAS, the HEKSISZ small-aperture seismic array is being installed about 6 km south of Hekla. The array, which will consist of 12 stations (7 broadband seismometers and at least 5 additional Raspberry PI seismometers and some co-located accelerometers), builds upon experience gained from temporary array operations in the FUTUREVOLC project and will be the first permanent seismic array in Iceland. The array is surrounded by four different volcanic systems and a prominent fracture zone, providing an abundance of seismicity for analysis. The detection of volcanic and local earthquake events depends on signal coherency and the algorithms used. The signal coherency is mainly affected by array geometry and the site and noise conditions. To analyze the wavefield we will use algorithms such as beamforming, signal-to-noise triggers, FK analysis, and cross-correlation on both vertical and horizontal channels. The implementation is through free open-source software, based mainly on Python obspy and further extensions. While the array is still in the process of coming online, we use data from its existing central permanent network station, MJO to analyze signals from the volcanoes and faults in preparation for the future array data analysis. Relevant single-station observations are first arrival polarization and search for existence and timing of secondary phases, such as surface and Moho reflections from different distances and depths. These observed peculiarities will guide the focus of the array data analysis, specifically as one of the main interests is the depth determination of magma movements and intrusions below Hekla. The volcanic region may have strong lateral crustal heterogeneities, so if significant azimuthal deviations are estimated from the single-station analysis, correction parameters for the array will need to be constrained as well. To further test how a future array might perform in this location, we invert synthetic sources at various depths and distances and also use observed source arrays to search for additional phases from possible conversions and reflections and measure their phase velocities.</p>

scholarly journals Automatic seismic event recognition and later phase identification for broadband seismograms

1996 ◽  
Vol 86 (6) ◽  
pp. 1896-1909
Author(s):  
Cheng Tong ◽  
Brian L. N. Kennett
Keyword(s):  
Real Time ◽  
Seismic Event ◽  
Expert Knowledge ◽  
Epicentral Distance ◽  
Event Recognition ◽  
Phase Identification ◽  
Classification Problems ◽  
Real Time Analysis ◽  
Single Station ◽  
Broadband Seismograms

Abstract Knowledge of the patterns of frequently observed seismic phases associated with specific distances and depths have been well developed and applied by seismologists (see, e.g., Richter, 1958; Kulhánek, 1990). However, up till now, the expertise of recognizing seismic event patterns for teleseisms has not been translated into automatic processing procedure. A new approach is developed to automate this kind of heuristic human expertise in order to provide a means of improving preliminary event locations from a single site. An automatic interpretation system exploiting three-component broadband seismograms is used to recognize the pattern of seismic arrivals associated with the presence of a seismic event in real time accompanied by an identification of the individual phases. For a single station, such a real-time analysis can be used to provide a preliminary estimation of the location of the event. The inputs to the interpretation process are a set of features for detected phases produced by another real-time phase analyzer. The combinations of these features are investigated using a novel approach to the construction of an expert system. The automatic system exploits expert information to test likely assumptions about phase character and hence epicentral distance and depth. Some hypotheses about the nature of the event will be rejected as implausible, and for the remainder, an assessment is given of the likelihood of the interpretation based on the fit to the character of all available information. This event-recognition procedure provides an effective and feasible means of interprating events at all distances, and characterizing information between hundreds of different possible classes of patterns even when the observation is incomplete. The procedure is based on “assumption trees” and provides a useful tool for classification problems in which a number of factors have to be identified. The control set of expert knowledge used in testing hypotheses is maintained separately from the computational algorithm used in the assumption search; in consequence, the information base can be readily updated.

scholarly journals Research on the Particle Filter Single-Station Target Tracking Algorithm Based on Particle Number Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lieping Zhang ◽  
Jinghua Nie ◽  
Shenglan Zhang ◽  
Yanlin Yu ◽  
Yong Liang ◽  
...  
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Real Time ◽  
Particle Number ◽  
Tracking Algorithm ◽  
Tracking Accuracy ◽  
Time Performance ◽  
Single Station ◽  
Tracking Model ◽  
Number Of Particles

Given that the tracking accuracy and real-time performance of the particle filter (PF) target tracking algorithm are greatly affected by the number of sampled particles, a PF target tracking algorithm based on particle number optimization under the single-station environment was proposed in this study. First, a single-station target tracking model was established, and the corresponding PF algorithm was designed. Next, a tracking simulation experiment was carried out on the PF target tracking algorithm under different numbers of particles with the root mean square error (RMSE) and filtering time as the evaluation indexes. On this basis, the optimal number of particles, which could meet the accuracy and real-time performance requirements, was determined and taken as the number of particles of the proposed algorithm. The MATLAB simulation results revealed that compared with the unscented Kalman filter (UKF), the single-station PF target tracking algorithm based on particle number optimization not only was of high tracking accuracy but also could meet the real-time performance requirement.

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>

scholarly journals Denoising ambient seismic field correlation functions with convolutional autoencoders

2019 ◽  
Vol 220 (3) ◽  
pp. 1521-1535
Author(s):  
Loïc Viens ◽  
Chris Van Houtte
Keyword(s):  
Real Time ◽  
Correlation Functions ◽  
Cross Correlation ◽  
Seismic Velocity ◽  
Temporal Evolution ◽  
Seismic Interferometry ◽  
Filter Method ◽  
Near Surface ◽  
Single Station ◽  
Field Correlation

SUMMARY Seismic interferometry is an established method for monitoring the temporal evolution of the Earth’s physical properties. We introduce a new technique to improve the precision and temporal resolution of seismic monitoring studies based on deep learning. Our method uses a convolutional denoising autoencoder, called ConvDeNoise, to denoise ambient seismic field correlation functions. The technique can be applied to traditional two-station cross-correlation functions but this study focuses on single-station cross-correlation (SC) functions. SC functions are computed by cross correlating the different components of a single seismic station and can be used to monitor the temporal evolution of the Earth’s near surface. We train and apply our algorithm to SC functions computed with a time resolution of 20 min at seismic stations in the Tokyo metropolitan area, Japan. We show that the relative seismic velocity change [dv/v(t)] computed from SC functions denoised with ConvDeNoise has less variability than that calculated from raw SC functions. Compared to other denoising methods such as the SVD-based Wiener Filter method developed by Moreau et al., the dv/v results obtained after using our algorithm have similar precision. The advantage of our technique is that once the algorithm is trained, it can be apply to denoise near-real-time SC functions. The near-real-time aspect of our denoising algorithm may be useful for operational hazard forecasting models, for example when applying seismic interferometry at an active volcano.

Real‐Time Back Azimuth for Earthquake Early Warning

2015 ◽  
Vol 105 (4) ◽  
pp. 2274-2285 ◽  
Author(s):  
Andreas S. Eisermann ◽  
Alon Ziv ◽  
Gilles H. Wust‐Bloch
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Earthquake Early Warning ◽  
Back Azimuth
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