SEISMIC BACKGROUND DISTURBANCE DUE TO GEOMAGNETIC IMPULSES

2017 ◽  
Keyword(s):  
Seismic Background

Retrieval of reflections from seismic background-noise measurements

2007 ◽  
Vol 34 (4) ◽  
Author(s):  
Deyan Draganov ◽  
Kees Wapenaar ◽  
Wim Mulder ◽  
Johannes Singer ◽  
Arie Verdel
Keyword(s):  
Background Noise ◽  
Noise Measurements ◽  
Seismic Background

High-frequency analysis of seismic background noise as a function of wind speed and shallow depth

1996 ◽  
Vol 86 (5) ◽  
pp. 1507-1515 ◽  
Author(s):  
Mitchell M. Withers ◽  
Richard C. Aster ◽  
Christopher J. Young ◽  
Eric P. Chael
Keyword(s):  
Wind Speed ◽  
High Frequency ◽  
Background Noise ◽  
Signal To Noise Ratio ◽  
Shallow Depth ◽  
High Frequency Signal ◽  
Wind Speeds ◽  
Noise Characteristics ◽  
Cultural Noise ◽  
Seismic Background

Abstract We used a deep (1500 m) cased borehole near the town of Datil in west-central New Mexico to study high-frequency (>1 Hz) seismic noise characteristics. The remote site had very low levels of cultural noise, but strong winds (winter and spring) made the site an excellent candidate to study the effects of wind noise on seismograms. Along with a three-component set of surface sensors (Teledyne Geotech GS-13), a vertical borehole seismometer (GS-28) was deployed at a variety of depths (5, 43, and 85 m) to investigate signal and noise variations. Wind speed was measured with an anemometer. Event-triggered and time-triggered data streams were recorded on a RefTek 72-02 data acquisition system located at the site. Our data show little cultural noise and a strong correlation between wind speed and seismic background noise. The minimum wind speed at which the seismic background noise appears to be influenced varies with depth: 3 m/sec at the surface, 3.5 m/sec at 43 m in depth, and 4 m/sec at 85 m in depth. For wind speed below 3 to 4 m/sec, we observe omni-directional background noise that is coherent at frequencies below 15 Hz. This coherence is destroyed when wind speeds exceed 3 to 4 m/sec. We use a test event (Md ∼ 1.6) and superimposed noise to investigate signal-to-noise ratio (SNR) improvement with sensor depth. For the low Q valley fill of the Datil borehole (DBH) site, we have found that SNR can be improved by as much as 20 to 40 dB between 23 and 55 Hz and 10 to 20 dB between 10 and 20 Hz, by deploying at a 43-m depth rather than at the surface. At the surface, there is little signal above noise in the 23- to 55-Hz frequency band for wind speeds greater than 8 m/sec. Thus, high-frequency signal information that is lost at the surface can be recorded by deploying at the relatively shallow depth of 40 m. Because we observe only minor further reductions in seismic background noise (SBN) at deeper depths, 40 m is likely to be a reasonable deployment depth for other high-frequency-monitoring sites in similar environmental and geologic conditions.

scholarly journals Characterization of seasonal glacial seismicity from a single-station on-ice record at Holtedahlfonna, Svalbard

2019 ◽  
Vol 60 (79) ◽  
pp. 23-36 ◽  
Author(s):  
Andreas Köhler ◽  
Valerie Maupin ◽  
Christopher Nuth ◽  
Ward van Pelt
Keyword(s):  
Seismic Velocity ◽  
Temporal Distribution ◽  
Velocity Model ◽  
Ice Dynamics ◽  
Near Surface ◽  
Waveform Modeling ◽  
Single Station ◽  
Glacier Runoff ◽  
Fast Flow ◽  
Seismic Background

ABSTRACTGlacial seismicity provides important insights into glacier dynamic processes. We study the temporal distribution of cryogenic seismic signals (icequakes) at Holtedahlfonna, Svalbard, between April and August 2016 using a single three-component sensor. We investigate sources of observed icequakes using polarization analysis and waveform modeling. Processes responsible for five icequake categories are suggested, incorporating observations of previous studies into our interpretation. We infer that the most dominant icequake type is generated by surface crevasse opening through hydrofracturing. Secondly, bursts of high-frequency signals are presumably caused by repeated near-surface crevassing due to high strain rates during glacier fast-flow episodes. Furthermore, signals related to resonance in water-filled cracks, fracturing or settling events in dry firn or snow before the melt season, and processes at the glacier bed are observed. Amplitude of seismic background noise is clearly related to glacier runoff. We process ambient seismic noise to invert horizontal-to-vertical spectral ratios for a sub-surface seismic velocity model used to model icequake signals. Our study shows that a single seismic sensor provides useful information about seasonal ice dynamics in case deployment of a network is not feasible.

scholarly journals Using the ETAS Model for Catalog Declustering and Seismic Background Assessment

2010 ◽  
Vol 167 (6-7) ◽  
pp. 819-830 ◽  
Author(s):  
R. Console ◽  
D. D. Jackson ◽  
Y. Y. Kagan
Keyword(s):  
Etas Model ◽  
Seismic Background

Seismic Interferometry Applied to Seismic Background-Noise Field Data

2006 ◽  
Author(s):  
Deyan Draganov ◽  
Kees Wapenaar ◽  
Wim Mulder ◽  
Johannes Singer ◽  
Arie Verdel
Keyword(s):  
Background Noise ◽  
Field Data ◽  
Seismic Interferometry ◽  
Noise Field ◽  
Seismic Background

scholarly journals The Finnish National Seismic Network: Toward Fully Automated Analysis of Low-Magnitude Seismic Events

2021 ◽  
Author(s):  
Toni Veikkolainen ◽  
Jari Kortström ◽  
Tommi Vuorinen ◽  
Ilmo Salmenperä ◽  
Tuija Luhta ◽  
...  
Keyword(s):  
Background Activity ◽  
Automated Analysis ◽  
Seismic Events ◽  
Crystalline Bedrock ◽  
Management Concepts ◽  
Challenges And Opportunities ◽  
Anthropogenic Seismicity ◽  
Seismic Networks ◽  
High Level ◽  
Seismic Background

Abstract We present an overview of the seismic networks, products, and services in Finland, northern Europe, and the challenges and opportunities associated with the unique combination of prevailing crystalline bedrock, low natural intraplate seismic background activity, and a high level of anthropogenic seismicity. We introduce national and local seismic networks, explain the databases, analysis tools, and data management concepts, outline the Finnish macroseismic service, and showcase data from the 2017 M 3.3 Liminka earthquake in Ostrobothnia, Finland.

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