Graph Convolution Networks for Seismic Events Classification using raw waveform data from multiple stations

Author(s):  
Gwantae Kim ◽  
Bonhwa Ku ◽  
Jae-kwang Ahn ◽  
Hanseok Ko
Keyword(s):  
2020 ◽  
Author(s):  
Kathleen McKee ◽  
Diana Roman ◽  
David Fee ◽  
Gregory Waite ◽  
Maurizio Ripepe

<p>Very long period (VLP) seismic signals observed in volcanic environments are thought to be produced by magma and gas flow through conduits. Stromboli Volcano, Italy, typically produces hundreds of VLPs per day. These have been generally attributed to the flow of gas slugs through the shallow plumbing system and thus linked to the mechanism thought to drive Strombolian explosions. During a 6-day-long seismo-acoustic campaign in May 2018 (a period characterized by relatively low activity) we recorded 1900+ seismic events, the majority of which have significant energy in the VLP (2-100 s) band. We used a coincident STA/LTA trigger to identify seismic events in continuous waveform data and then used the PeakMatch algorithm (Rodgers et al., 2015) to identify seismic multiplets, with a focus on VLPs. To identify explosions, we applied the same coincident trigger to infrasound data, and manually identified gas jetting events using spectrograms and high-pass-filtered (20 Hz) waveforms. </p><p> </p><p>We identified ~250 explosions and ~600 jetting events. Seismic multiplet analysis identified two main families of repeating events. Family 1 (F1) has over 500 events and Family 2 (F2) has over 150 events based on a 0.7 correlation threshold. We find that F1 VLPs coincide in time with ~6% of explosions and ~0.8% of jetting events and F2 VLPs coincide in time with ~28% of explosions and ~2.7% of jetting events (we term these “silent VLPs”). These VLPs do not correspond with lava effusion (Marchetti and Ripepe, 2005; Ripepe et al., 2015). F2 have a higher dominant period (8-10 s) compared to F1 (3-4 s). The repeating VLPs are part of a broadband signal and the higher frequencies start after the VLP. VLP peak amplitudes are generally larger for F1 events. The dip of the VLP particle motion roughly locates the F1 and F2 VLP source centroids beneath the active crater and are stable throughout the dataset. Both VLP displacements show a small outward, large inward, and subsequent large outward motion from the crater. The lack of explosions relative to repeating VLPs does not support the slug model, where a slug rises through a conduit, generates a VLP through interactions with changes in conduit geometry, and then bursts at the lava free surface. Our observations support the plug model (Suckale et al., 2016). We suggest the “silent” VLPs are generated when the gas bubbles interact with and move into the semipermeable plug. Then the plug behaves as a mechanical filter for gas escape and allows for passive and explosive escape mechanisms.</p>


2016 ◽  
Vol 136 (5) ◽  
pp. 252-258 ◽  
Author(s):  
Chiharu Shimizu ◽  
Mitsuteru Sato ◽  
Yasuji Hongo ◽  
Fuminori Tsuchiya ◽  
Yukihiro Takahashi

2005 ◽  
Vol 2 (2) ◽  
pp. 25
Author(s):  
Noraliza Hamzah ◽  
Wan Nor Ainin Wan Abdullah ◽  
Pauziah Mohd Arsad

Power Quality disturbances problems have gained widespread interest worldwide due to the proliferation of power electronic load such as adjustable speed drives, computer, industrial drives, communication and medical equipments. This paper presents a technique based on wavelet and probabilistic neural network to detect and classify power quality disturbances, which are harmonic, voltage sag, swell and oscillatory transient. The power quality disturbances are obtained from the waveform data collected from premises, which include the UiTM Sarawak, Faculty of Science Computer in Shah Alam, Jati College, Menara UiTM, PP Seksyen 18 and Putra LRT. Reliable Power Meter is used for data monitoring and the data is further processed using the Microsoft Excel software. From the processed data, power quality disturbances are detected using the wavelet technique. After the disturbances being detected, it is then classified using the Probabilistic Neural Network. Sixty data has been chosen for the training of the Probabilistic Neural Network and ten data has been used for the testing of the neural network. The results are further interfaced using matlab script code.  Results from the research have been very promising which proved that the wavelet technique and Probabilistic Neural Network is capable to be used for power quality disturbances detection and classification.


1988 ◽  
Author(s):  
William N. Alexander ◽  
Raymond H. Kimmel ◽  
Lauren Malaspina

Author(s):  
Talbot C. Imlay

This chapter examines the post-war efforts of European socialists to reconstitute the Socialist International. Initial efforts to cooperate culminated in an international socialist conference in Berne in February 1919 at which socialists from the two wartime camps met for the first time. In the end, however, it would take four years to reconstitute the International with the creation of the Labour and Socialist International (LSI) in 1923. That it took so long to do so is a testimony to the impact of the Great War and to the Bolshevik revolution. Together, these two seismic events compelled socialists to reconsider the meaning and purpose of socialism. The search for answers sparked prolonged debates between and within the major parties, profoundly reconfiguring the pre-war world of European socialism. One prominent stake in this lengthy process, moreover, was the nature of socialist internationalism—both its content and its functioning.


Geosciences ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 16
Author(s):  
Christina Oikonomou ◽  
Haris Haralambous ◽  
Sergey Pulinets ◽  
Aakriti Khadka ◽  
Shukra R. Paudel ◽  
...  

The purpose of the present study is to investigate simultaneously pre-earthquake ionospheric and atmospheric disturbances by the application of different methodologies, with the ultimate aim to detect their possible link with the impending seismic event. Three large earthquakes in Mexico are selected (8.2 Mw, 7.1 Mw and 6.6 Mw during 8 and 19 September 2017 and 21 January 2016 respectively), while ionospheric variations during the entire year 2017 prior to 37 earthquakes are also examined. In particular, Total Electron Content (TEC) retrieved from Global Navigation Satellite System (GNSS) networks and Atmospheric Chemical Potential (ACP) variations extracted from an atmospheric model are analyzed by performing statistical and spectral analysis on TEC measurements with the aid of Global Ionospheric Maps (GIMs), Ionospheric Precursor Mask (IPM) methodology and time series and regional maps of ACP. It is found that both large and short scale ionospheric anomalies occurring from few hours to a few days prior to the seismic events may be linked to the forthcoming events and most of them are nearly concurrent with atmospheric anomalies happening during the same day. This analysis also highlights that even in low-latitude areas it is possible to discern pre-earthquake ionospheric disturbances possibly linked with the imminent seismic events.


Sign in / Sign up

Export Citation Format

Share Document