Seismic velocity changes concentrated at the shallow structure as inferred from correlation analyses of ambient noise during volcano deformation at Izu-Oshima, Japan

2017 ◽  
Vol 122 (8) ◽  
pp. 6721-6736 ◽  
Author(s):  
Tomoya Takano ◽  
Takeshi Nishimura ◽  
Hisashi Nakahara
Keyword(s):  
Ambient Noise ◽  
Seismic Velocity ◽  
Volcano Deformation ◽  
Correlation Analyses ◽  
Seismic Velocity Changes ◽  
Velocity Changes ◽  
Shallow Structure

scholarly journals Seismic velocity changes caused by the Earth tide: Ambient noise correlation analyses of small-array data

2014 ◽  
Vol 41 (17) ◽  
pp. 6131-6136 ◽  
Author(s):  
Tomoya Takano ◽  
Takeshi Nishimura ◽  
Hisashi Nakahara ◽  
Yusaku Ohta ◽  
Sachiko Tanaka
Keyword(s):  
Ambient Noise ◽  
Seismic Velocity ◽  
Earth Tide ◽  
Noise Correlation ◽  
Array Data ◽  
The Earth ◽  
Correlation Analyses ◽  
Seismic Velocity Changes ◽  
Small Array ◽  
Velocity Changes

Spatial Evaluation of Pore Pressure Variations Related to Rainfall from Seismic Velocity Changes

2021 ◽  
Author(s):  
Rezkia Dewi Andajani ◽  
Takeshi Tsuji ◽  
Roel Snieder ◽  
Tatsunori Ikeda
Keyword(s):  
Pore Pressure ◽  
Negative Correlation ◽  
Ambient Noise ◽  
Seismic Velocity ◽  
Diffusion Mechanism ◽  
Velocity Change ◽  
Diffusion Parameters ◽  
The Status ◽  
Seismic Velocity Changes ◽  
Velocity Changes

<p>Crustal pore pressure, which could trigger seismicity and volcanic activity, varies with fluid invasion. Various studies have discussed the potential of using seismic velocity changes from ambient noise to evaluate pore pressure conditions, especially due to rainfall perturbations. Although the influence of rainfall on seismic velocity changes has been reported, consideration of the spatial influence on rainfall towards seismic velocity and its mechanism have not been well understood. We investigated the mechanism of rainfall-induced pore pressure diffusion in southwestern Japan, using seismic velocity change (Vs) inferred from ambient noise. We modeled pore pressure changes from rainfall data based on a diffusion mechanism at the locations where infiltration is indicated. By calculating the correlation between Vs changes and the modeled pore pressure with various hydraulic diffusion parameters, the optimum hydraulic diffusion parameter was obtained. We estimated the diffusion parameters with the highest negative correlation between pore pressure and Vs change because a negative correlation indicates pore pressure increase due to diffusion induced by groundwater load. Furthermore, the spatial variation of the hydraulic diffusivity infers the heterogeneity of the rocks in different locations. This finding suggests that the response of pore pressure induced by rainfall percolation depends on location.  We show that seismic velocity monitoring can be used to evaluate the status of pore pressure at different locations, which is useful for fluid injection, CO<sub>2</sub> wellbore storage, and geothermal development.</p>

Can land subsidence be monitored with ambient seismic auto-correlations?

2021 ◽  
Author(s):  
Laura Ermert ◽  
Marine Denolle ◽  
Enrique Cabral Cano ◽  
Estelle Chaussard ◽  
Dario Solano Rojas
Keyword(s):  
Ambient Noise ◽  
Seismic Velocity ◽  
Urban Infrastructure ◽  
Temporal Correlations ◽  
Long Term Effect ◽  
Seismic Velocity Changes ◽  
Subsurface Layers ◽  
Velocity Changes ◽  
Seasonal Oscillations

<p>Mexico City has been undergoing rapid subsidence for more than 100 years due to groundwater extraction. During the 2010s, rates surpassing 30 centimeters/year were observed by satellite geodetic measurements. Not only does this subsidence pose grave challenges for buildings, urban infrastructure, and water management, but it also changes the seismic response of the affected subsurface layers and thereby alters the seismic hazard in the metropolis that has seen devastating site effects both in the 1985 Michoacan and 2017 Puebla earthquake. We use data and numerical modeling of ambient noise auto-correlations to gain a better insight into the subsidence process through ambient noise techniques.</p><p>We establish a baseline for the long-term and seasonal variations of seismic velocity near the basin from long-term recordings of the Geoscope station UNM, located at the Universidad Nacional Autónoma de México in the geotechnical hill zone. We further study temporary recordings from the MASE array (MASE (2007): Meso America Subduction Experiment. Caltech. Dataset. doi:10.7909/C3RN35SP) to see how subsidence and other factors may influence seismic velocity in the geotechnical hill, transition, and lake zones.</p><p>We find that seasonal oscillations and a strong, rapid velocity drop coincident with the 2017 Puebla earthquake overlay a multi-year increasing trend in seismic velocity. We cautiously interpret the multi-year increase as a long-term effect of subsidence. We further study the temporal correlations of seismic velocity changes with other environmental time series like precipitation, and model auto-correlations in order to improve our understanding of their composition and sensitivity.</p>

Effect of snowfalls on relative seismic velocity changes measured by ambient noise correlation

2021 ◽  
Author(s):  
Antoine Guillemot ◽  
Alec Van Herwijnen ◽  
Laurent Baillet ◽  
Eric Larose
Keyword(s):  
Snow Cover ◽  
Solid Earth ◽  
Ambient Noise ◽  
Seismic Velocity ◽  
Coda Wave ◽  
Noise Correlation ◽  
Geophysical Research ◽  
Seismic Velocity Changes ◽  
Velocity Changes ◽  
Seismic Measurements

<p>Seismic noise correlation is a broadly used method to monitor the subsurface, in order to detect physical processes into the surveyed medium such changes in rigidity, fluid injection or cracking <sup>(1)</sup>. The influence of several environmental variables on measured seismic observables were studied, such as temperature, groundwater level fluctuations, and freeze-thawing cycles <sup>(2)</sup>. In mountainous, cold temperate and polar sites, the presence of a snowcover can also affect relative seismic velocity changes (dV/V), but this relation is relatively poorly documented and ambiguous <sup>(3)(4)</sup>. In this study, we analyzed raw seismic recordings from a snowy flat field site located above Davos (Switzerland), during one entire winter season (from December 2018 to June 2019). Our goal was to better understand the effect of snowfall and snowmelt events on dV/V measurements through both seismic and meteorological instrumentation.</p><p>We identified three snowfall events with a substantial response of dV/V measurements (drops of several percent between 15 and 25 Hz), suggesting a detectable change in elastic properties of the medium due to the additional fresh snow.</p><p>To better interpret the measurements, we used a physical model to compute frequency dependent changes in the Rayleigh wave velocity computed before and after the events. Elastic parameters of the ground subsurface were obtained from a seismic refraction survey, whereas snow cover properties were obtained from the snow cover model SNOWPACK. The decrease in dV/V due to a snowfall were well reproduced, with the same order of magnitude than observed values, confirming the importance of the effect of fresh and dry snow on seismic measurements.</p><p>We also observed a decrease in dV/V with snowmelt periods, but we were not able to reproduce those changes with our model. Overall, our results highlight the effect of the snowcover on seismic measurements, but more work is needed to accurately model this response, in particular for the presence of liquid water in the snowcover.</p><p> </p><p><strong>References</strong></p><ul><li>(1) Larose, E., Carrière, S., Voisin, C., Bottelin, P., Baillet, L., Guéguen, P., Walter, F., et al. (2015) Environmental seismology: What can we learn on earth surface processes with ambient noise? Journal of Applied Geophysics, <strong>116</strong>, 62–74. doi:10.1016/j.jappgeo.2015.02.001</li> <li>(2) Le Breton, M., Larose, É., Baillet, L., Bontemps, N. & Guillemot, A. (2020) Landslide Monitoring Using Seismic Ambient Noise Interferometry: Challenges and Applications. Earth-Science Reviews</li> <li>(3) Hotovec‐Ellis, A.J., Gomberg, J., Vidale, J.E. & Creager, K.C. (2014) A continuous record of intereruption velocity change at Mount St. Helens from coda wave interferometry. Journal of Geophysical Research: Solid Earth, <strong>119</strong>, 2199–2214. doi:10.1002/2013JB010742</li> <li>(4) Wang, Q.-Y., Brenguier, F., Campillo, M., Lecointre, A., Takeda, T. & Aoki, Y. (2017) Seasonal Crustal Seismic Velocity Changes Throughout Japan. Journal of Geophysical Research: Solid Earth, <strong>122</strong>, 7987–8002. doi:10.1002/2017JB014307</li> </ul>

scholarly journals Spatial and Temporal Influence of Sea Level on Inland Stress based on Seismic Velocity Monitoring

2021 ◽  
Author(s):  
Rezkia Dewi Andajani ◽  
Takeshi Tsuji ◽  
Roel Snieder ◽  
Tatsunori Ikeda
Keyword(s):  
Sea Level ◽  
Ambient Noise ◽  
Seismic Velocity ◽  
Seismic Station ◽  
Local Stress ◽  
Seismic Velocity Changes ◽  
Ocean Loading ◽  
Temporal Influence ◽  
Velocity Changes ◽  
The Impact

Abstract Earth’s crust responds to perturbations from various environmental factors. To evaluate this response, seismic velocity changes offer an indirect diagnostic, especially where velocity can be monitored on an ongoing basis from ambient seismic noise. Investigating the connection between the seismic velocity changes and external perturbations could be useful for characterizing dynamic activities in the crust. The seismic velocity is known to be sensitive to variations in meteorological signals such as temperature, snow, and precipitation as well as changes in sea level. Among these perturbations, the impact of variations in sea level on velocity changes inferred from seismic interferometry of ambient noise is not well known. This study investigates the influence of the ocean in a 3-year record of ambient noise seismic velocity monitoring in the Chugoku and Shikoku regions of southwest Japan. First, we applied a bandpass filter to determine the optimal period band for discriminating among different influences on seismic velocity. Then, we applied a regression analysis between the proximity of seismic station pairs to the coast and the ocean influence, as indicated by the correlation of sea level to seismic velocity changes between pairs of stations. Our study suggests that for periods between 0.0036 to 0.01 cycle/day (100–274 days), the ocean’s influence on seismic velocity decreases with increasing distance of station pairs from the coast. The increasing sea level deforms the ocean floor, affecting the stress in the adjacent coast. The stress change induced by the ocean loading may extend at least dozens of kilometers from the coast. The correlation between sea level and inland seismic velocity changes are negative or positive. Although it is difficult to clearly interpret the correlation based on simple model, they could depend on the in situ local stress, orientation of dominant crack, and hydraulic conductivity. Our study shows that seismic monitoring may be useful for evaluating the perturbation in the crust associated with an external load.

scholarly journals Seismic velocity changes at White Island volcano, New Zealand, using ten years of ambient noise interferometry

2021 ◽  
Author(s):  
◽  
Alexander Yates
Keyword(s):  
Ambient Noise ◽  
Seismic Velocity ◽  
Environmental Influence ◽  
Seismic Stations ◽  
Noise Interferometry ◽  
Stress Changes ◽  
Volcanic Processes ◽  
Seismic Velocity Changes ◽  
Velocity Changes ◽  
White Island

<p>Seismic velocity changes at volcanoes carry information about stresses present within hydrothermal and magmatic systems. In this thesis, temporal velocity changes are measured at White Island volcano using ambient noise interferometry between 2007–2017. This period contains multiple well-documented eruptions starting in 2012, following an inactive period that extends back over a decade. Three primary objectives are identified: (1) investigate what seismic velocity changes can tell us about dynamic changes beneath the volcano, (2) investigate non-volcanic sources and their possible influence on interpretations, and (3) consider the potential for real-time monitoring using ambient-noise. These objectives extend beyond White Island volcano, with implications for ambient noise monitoring of volcanoes globally.  Two different approaches are used to measure velocity changes at White Island. The first involves cross-correlating noise recorded by pairs of seismic stations. Velocity changes are sought by averaging changes recorded across ten station-pairs that consist of an onshore station and a station on the volcano. The second approach involves cross-correlating the different components of individual seismic stations. This represents a less traditional approach to monitoring volcanoes, but is well-suited to White Island which has one permanent station active throughout eruptive activity. Single seismic stations located onshore are also processed to investigate background regional changes.  Two periods of long-term velocity increases are detected at the volcano. The first occurs during a highly active period in 2012–2013 and the second occurs in the months preceding an explosive eruption in April 2016. Comparison with velocities recorded by onshore stations suggest a meteorological source for these changes is unlikely. Velocity increases are therefore interpreted to reflect cracks closing under increased pressures beneath the volcano. Similarly, a rapid decline in the velocity within 2–3 months of the April 2016 eruption is interpreted to reflect depressurization of the system.  In addition to volcanic sources, we also find clear evidence of non-volcanic processes influencing velocity changes at the volcano. Two clear co-seismic velocity decreases of approximately 0.05–0.1% are associated with a Mw 5.2 earthquake in 2008 — within 10 km of the volcano — and the Mw 7.1 East Cape earthquake in 2016. The East Cape earthquake — located 200 km away from the volcano — produces significant velocity decreases over a large region, as detected by stations onshore and on White Island. This likely reflects dynamic stress changes as a result of passing seismic waves, with an eruption two weeks later interpreted here to have been triggered by this event. Finally, we identify similarities between annual variations recorded by onshore stations and changes at the volcano, suggesting an environmental influence. Velocity changes at White Island therefore represent a complex interaction of volcanic and non-volcanic processes, highlighting the need for improved understanding of external sources of change to accurately detect short-term eruptive precursors.</p>

Total Electron Content and Seismic Ambient Noise analysis prior to significant earthquakes

2020 ◽  
Author(s):  
Sabyasachi Sahu ◽  
Kajaljyoti Borah ◽  
Prashant Kumar Champati ray
Keyword(s):  
Total Electron Content ◽  
Ambient Noise ◽  
Seismic Velocity ◽  
Noise Analysis ◽  
Global Navigation Satellite Systems ◽  
Electron Content ◽  
Total Electron ◽  
Seismic Ambient Noise ◽  
Seismic Velocity Changes ◽  
Velocity Changes

&lt;p&gt;The seismo-ionospheric interaction study with respect to earthquake events using Total Electron Content (TEC) data derived from Global Navigation Satellite Systems (GNSS) receivers can be used to detect pre-earthquake ionospheric anomalies. This is primarily because ionospheric anomaly variation has been emerged as one of the most promising precursors. In recent times, many studies have reported pre-seismic ionospheric anomalies of TEC prior to major earthquakes. However, the results are not uniform and therefore, considerable amount of data processing and validation is required before this can be used in operational mode. &amp;#160;To ensure the seismogenic cause of TEC variation, geomagnetic and solar-activities are also compared with TEC values prior to the earthquakes and our analysis has proved that TEC anomalies can be used as earthquake precursors. Several global events and Himalayan earthquakes have been studied and results are very encouraging for developing a methodology that can qualify for detection of early sign of earthquakes. It may be far from early warning system (EWS) with information on magnitude, location and time, but it is a significant achievement in the field of earthquake geology where no methodology exists on forewarning of seismic events.&lt;/p&gt;&lt;p&gt;&amp;#160;Seismic velocity changes computed by applying modern techniques in seismic interferometry reveals that considerably large earthquakes can trigger a decline in seismic velocity prior to the mainshock. Cross-correlation of diffuse wave fields, including ambient seismic noise can provide the Green&amp;#8217;s function between pair of receivers recording seismic activity. Using the known properties of the seismic ambient noise, recorded over a large period of time, seismic velocity changes before the earthquake has been observed which can act as a potential precursor. Decrease in the seismic velocity few days before the main event suggest that co-seismic damage begins to occur even before the mainshock, which could be a result of foreshocks. The main shock records the lowest relative seismic velocity change. The potential use of the ambient noise as an earthquake precursor can be concluded after rigorous analysis.&lt;/p&gt;

scholarly journals Temporal Seismic Velocity Changes During the 2020 Rapid Inflation at Mt. Thorbjorn-Svartsengi, Iceland, Using Seismic Ambient Noise

2021 ◽  
Author(s):  
YESIM CUBUK SABUNCU ◽  
Kristín Jónsdóttir ◽  
Corentin Caudron ◽  
Thomas Lecocq ◽  
Michelle Parks ◽  
...  
Keyword(s):  
Ambient Noise ◽  
Seismic Velocity ◽  
Seismic Ambient Noise ◽  
Seismic Velocity Changes ◽  
Velocity Changes

scholarly journals Temporal Seismic Velocity Changes During the 2020 Rapid Inflation at Mt. Thorbjorn-Svartsengi, Iceland, Using Seismic Ambient Noise

2021 ◽  
Author(s):  
YESIM CUBUK SABUNCU ◽  
Kristín Jónsdóttir ◽  
Corentin Caudron ◽  
Thomas Lecocq ◽  
Michelle Parks ◽  
...  
Keyword(s):  
Ambient Noise ◽  
Seismic Velocity ◽  
Seismic Ambient Noise ◽  
Seismic Velocity Changes ◽  
Velocity Changes

scholarly journals Temporal Seismic Velocity Changes During the 2020 Rapid Inflation at Mt. Þorbjörn‐Svartsengi, Iceland, Using Seismic Ambient Noise

2021 ◽  
Author(s):  
Yesim Cubuk‐Sabuncu ◽  
Kristín Jónsdóttir ◽  
Corentin Caudron ◽  
Thomas Lecocq ◽  
Michelle Maree Parks ◽  
...  
Keyword(s):  
Ambient Noise ◽  
Seismic Velocity ◽  
Seismic Ambient Noise ◽  
Seismic Velocity Changes ◽  
Velocity Changes
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