scholarly journals Crustal seismic velocity responds to a magmatic intrusion and seasonal loading in Iceland’s Northern Volcanic Zone

2019 ◽  
Vol 5 (11) ◽  
pp. eaax6642 ◽  
Author(s):  
C. Donaldson ◽  
T. Winder ◽  
C. Caudron ◽  
R. S. White
Keyword(s):  
Atmospheric Pressure ◽  
Seismic Velocity ◽  
Volumetric Strain ◽  
Volcanic Zone ◽  
Noise Interferometry ◽  
Crustal Stresses ◽  
Single Station ◽  
Seismic Velocity Changes ◽  
Velocity Changes ◽  
Snow Thickness

Seismic noise interferometry is an exciting technique for studying volcanoes, providing a continuous measurement of seismic velocity changes (dv/v), which are sensitive to magmatic processes that affect the surrounding crust. However, understanding the exact mechanisms causing changes in dv/v is often difficult. We present dv/v measurements over 10 years in central Iceland, measured using single-station cross-component correlation functions from 51 instruments across a range of frequency bands. We observe a linear correlation between changes in dv/v and volumetric strain at stations in regions of both compression and dilatation associated with the 2014 Bárðarbunga-Holuhraun dike intrusion. Furthermore, a clear seasonal cycle in dv/v is modeled as resulting from elastic and poroelastic responses to changing snow thickness, atmospheric pressure, and groundwater level. This study comprehensively explains variations in dv/v arising from diverse crustal stresses and highlights the importance of deformation modeling when interpreting dv/v, with implications for volcano and environmental monitoring worldwide.

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>

Revealing seasonal crustal seismic velocity variations in Taiwan with single-station cross-component analysis 

2021 ◽  
Author(s):  
Kuan-Fu Feng ◽  
Hsin-Hua Huang ◽  
Ya-Ju Hsu ◽  
Yih-Min Wu
Keyword(s):  
Pore Pressure ◽  
Seasonal Variations ◽  
Seismic Velocity ◽  
Pressure Change ◽  
Temporal Variations ◽  
Air Pressure ◽  
Weather Data ◽  
Single Station ◽  
Seismic Velocity Changes ◽  
Velocity Changes

&lt;p&gt;Ambient noise interferometry is a promising technique for studying crustal behaviors, providing continuous measurements of seismic velocity changes (dv/v) in relation to physical processes in the crust over time. In addition to the tectonic-driven dv/v changes, dv/v is also known to be affected by environmental factors through rainfall-induced pore-pressure changes, air pressure loading changes, thermoelastic effects, and so forth. In this study, benefiting from the long-term continuous data of Broadband Array in Taiwan for Seismology (BATS) that has been operated since 1994, we analyze continuous seismic data from 1998 to 2019 by applying single-station cross-component (SC) technique to investigate the temporal variations of crust on seismic velocity. We process the continuous waveforms of BATS stations, construct the empirical Green&amp;#8217;s functions, and compute daily seismic velocity changes by the stretching technique in a frequency band of 0.1 to 0.9 Hz. We observe co-seismic velocity drops associated with the inland moderate earthquakes. Furthermore, clear seasonal cycles, with a period of near one-year, are also revealed at most stations, but with different characteristics. Systematic spectral and time-series analyses with the weather data are conducted and show that the rainfall-induced pore-pressure change is likely the main cause to the seasonal variations with high correlations. The strong site-dependency of these seasonal variations also precludes air pressure and temperature which varies smoothly in space from being dominant sources and suggests spatially-varying complex hydro-mechanical interaction across the orogenic belt in Taiwan.&lt;/p&gt;

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>

scholarly journals Toward Using Seismic Interferometry to Quantify Landscape Mechanical Variations after Earthquakes

2021 ◽  
Author(s):  
Odin Marc ◽  
Christoph Sens-Schönfelder ◽  
Luc Illien ◽  
Patrick Meunier ◽  
Manuel Hobiger ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Seismic Velocity ◽  
Landslide Hazard ◽  
Seismic Velocities ◽  
Surface Strength ◽  
Near Surface ◽  
Single Station ◽  
Seismic Velocity Changes ◽  
Velocity Changes ◽  
Large Earthquakes

ABSTRACT In mountainous terrain, large earthquakes often cause widespread coseismic landsliding as well as hydrological and hydrogeological disturbances. A subsequent transient phase with high landslide rates has also been reported for several earthquakes. Separately, subsurface seismic velocities are frequently observed to drop coseismically and subsequently recover. Consistent with various laboratory work, we hypothesize that the seismic-velocity changes track coseismic damage and progressive recovery of landscape substrate, which modulate landslide hazard and hydrogeological processes, on timescales of months to years. To test this, we analyze the near-surface seismic-velocity variations, obtained with single-station high-frequency (0.5–4 Hz) passive image interferometry, in the epicentral zones of four shallow earthquakes, for which constraints on landslide susceptibility through time exist. In the case of the 1999 Chi-Chi earthquake, detailed landslide mapping allows us to accurately constrain an exponential recovery of landslide susceptibility with a relaxation timescale of about 1 yr, similar to the pattern of recovery of seismic velocities. The 2004 Niigata, 2008 Iwate, and 2015 Gorkha earthquakes have less-resolved constraints on landsliding, but, assuming an exponential recovery, we also find matching relaxation timescales, from ∼0.1 to ∼0.6  yr, for the landslide and seismic recoveries. These observations support our hypothesis and suggest that systematic monitoring of seismic velocities after large earthquakes may help constrain and manage the evolution of landslide hazard in epicentral areas. To achieve this goal, we end by discussing several ways to improve the link between seismic velocity and landscape mechanical properties, specifically by better constraining time-dependent near-surface strength and hydrogeological changes. Hillslopes displaying coseismic surface fissuring and displacement may be an important target for future geotechnical analysis and coupled to passive geophysical investigations.

Single-station seismic monitoring of permafrost on Mt. Zugspitze (Germany) over the past 15 years

2021 ◽  
Author(s):  
Fabian Lindner ◽  
Joachim Wassermann
Keyword(s):  
Travel Time ◽  
Seismic Velocity ◽  
Temperature Record ◽  
Lapse Time ◽  
Resistivity Tomography ◽  
Single Station ◽  
Velocity Variations ◽  
Spatio Temporal ◽  
Velocity Changes ◽  
Permafrost Thawing

&lt;p&gt;Permafrost thawing affects mountain slope stability and can trigger hazardous rock falls. As rising temperatures promote permafrost thawing, spatio-temporal monitoring of long-term and seasonal variations in the perennially frozen rock is therefore crucial in regions with high hazard potential. With various infrastructure in the summit area and population in the close vicinity, Mt. Zugspitze in the German/Austrian Alps is such a site and permafrost has been monitored with temperature logging in boreholes and lapse-time electrical resistivity tomography. Yet, these methods are expensive and laborious, and are limited in their spatial and/or temporal resolution.&lt;/p&gt;&lt;p&gt;Here, we analyze continuous seismic data from a single station deployed at an altitude of 2700 m a.s.l. in a research station, which is separated by roughly 250 m from the permafrost affected ridge of Mt. Zugspitze. Data are available since 2006 (with some gaps) and reveal high-frequency (&gt;1 Hz) anthropogenic noise likely generated by the cable car stations at the summit. We calculate single-station cross-correlations between the different sensor components and investigate temporal coda wave changes by applying the recently introduced wavelet-based cross-spectrum method. This approach provides time series of the travel time relative to the reference stack as a function of frequency and lag time in the correlation functions. In the frequency and lag range of 1-10 Hz and 0.5-5 s respectively, we find various parts in the coda that show clear annual variations and an increasing trend in travel time over the past 15 years of consideration. Converting the travel time variations to seismic velocity variations (assuming homogeneous velocity changes affecting the whole mountain) results in seasonal velocity changes of up to a few percent and on the order of 0.1% decrease per year. Yet, estimated velocity variations do not scale linearly with lag time, which indicates that the medium changes are localized rather than uniform and that the absolute numbers need to be taken with caution. The annual velocity variations are anti-correlated with the temperature record from the summit but delayed by roughly one month.&lt;/p&gt;&lt;p&gt;The phasing of the annual seismic velocity change (relative to the temperature record) is in agreement with a previous study employing lapse-time electrical resistivity tomography. Furthermore, the decreasing trend in seismic velocity happens concurrently with an increasing trend in temperature. The results therefore suggest that the velocity changes are related to seasonal thaw and refreeze and permafrost degradation and thus highlight the potential of seismology for permafrost monitoring. By adding additional receivers and/or a fiber-optic cable for distributed acoustic sensing, hence increasing the spatial resolution, the presented method holds promise for lapse-time imaging of permafrost bodies with high spatio-temporal resolution from passive measurements.&lt;/p&gt;

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

&lt;p&gt;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.&amp;#160; 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&lt;sub&gt;2&lt;/sub&gt; wellbore storage, and geothermal development.&lt;/p&gt;

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

&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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 Aut&amp;#243;noma de M&amp;#233;xico 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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;

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