scholarly journals Variations of crustal elastic properties during the 2009 L'Aquila earthquake inferred from cross-correlations of ambient seismic noise

2011 ◽  
Vol 38 (24) ◽  
pp. n/a-n/a ◽  
Author(s):  
L. Zaccarelli ◽  
N. M. Shapiro ◽  
L. Faenza ◽  
G. Soldati ◽  
A. Michelini
Keyword(s):  
Elastic Properties ◽  
Seismic Noise ◽  
Ambient Seismic Noise ◽  
L’Aquila Earthquake ◽  
2009 L’Aquila Earthquake ◽  
Cross Correlations

scholarly journals Delineation of sedimentary basin structure beneath the Banyumas Basin, Central Java, Indonesia, using ambient seismic noise tomography

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ahmad Setiawan ◽  
Zulfakriza Zulfakriza ◽  
Andri Dian Nugraha ◽  
Shindy Rosalia ◽  
Awali Priyono ◽  
...  
Keyword(s):  
Seismic Noise ◽  
Sedimentary Basin ◽  
Sedimentary Rocks ◽  
Vertical Component ◽  
Seismic Exploration ◽  
Ambient Seismic Noise ◽  
Central Java ◽  
Sediment Deposits ◽  
Cross Correlations ◽  
Grid Points

AbstractSubsurface images of an area with a thick volcanic layer generally cannot be well-imaged with conventional seismic exploration (seismic reflection) due to seismic wave scattering. Another method is needed to obtain an accurate subsurface image in a thick volcanic layer area. In this study, we applied ambient noise tomography (ANT) to image the shear-wave velocity (Vs) structure in the Banyumas Basin, Central Java, Indonesia, which has relatively thick volcanic layers. We aimed to delineate the sediment deposits and the sedimentary thickness in this area through the utilization of ambient seismic noise. The application of cross-correlations from ambient seismic noise has been widely applied in numerous locations to obtain a greater understanding of subsurface structures. In this study, more than 1000 pairs of vertical component cross-correlations were used to estimate the Green's Function of the Rayleigh wave. The Neighbourhood Algorithm (NA) was utilized to invert the dispersion curves at 121 grid points which were used to obtain a vertical depth profile of 1D Vs. The Vs map results show that the low Vs tend to trend in a northwest–southeast direction associated with two areas: the Majenang low, and the Citanduy low. The presence of low Vs values corresponds with Middle Miocene–Pliocene sedimentary rocks. Meanwhile, the high Vs values in this area might correspond with Oligocene–Early Miocene volcanic products and Eocene sediment. Our study was also able to reveal the thickness of sedimentary rocks in the Banyumas sedimentary basin, which is believed to have hydrocarbon potential.

scholarly journals Determining OBS clock drift using ambient seismic noise 

2021 ◽  
Author(s):  
David Naranjo ◽  
Laura Parisi ◽  
Philippe Jousset ◽  
Cornelis Weemstra ◽  
Sigurjón Jónsson
Keyword(s):  
Seismic Noise ◽  
Temperature Drop ◽  
Time Lapse ◽  
Ambient Seismic Noise ◽  
Ocean Bottom ◽  
Timing Error ◽  
Clock Drift ◽  
Ocean Bottom Seismometers ◽  
Cross Correlations ◽  
Reykjanes Peninsula

<p>Accurate timing of seismic records is essential for almost all applications in seismology. Wrong timing of the waveforms may result in incorrect Earth models and/or inaccurate earthquake locations. As such, it may render interpretations of underground processes incorrect. Ocean bottom seismometers (OBSs) experience clock drifts due to their inability to synchronize with a GNSS signal (with the correct reference time), since electromagnetic signals are unable to propagate efficiently in water. As OBSs generally operate in relatively stable ambient temperature, the timing deviation is usually assumed to be linear. Therefore, the time corrections can be estimated through GPS synchronization before deployment and after recovery of the instrument. However, if the instrument has run out of power prior to recovery (i.e., due to the battery being dead at the time of recovery), the timing error at the end of the deployment cannot be determined. In addition, the drift may not be linear, e.g., due to rapid temperature drop while the OBS sinks to the seabed. Here we present an algorithm that recovers the linear clock drift, as well as a potential timing error at the onset.</p><p>The algorithm presented in this study exploits seismic interferometry (SI). Specifically, time-lapse (averaged) cross-correlations of ambient seismic noise are computed. As such, virtual-source responses, which are generally dominated by the recorded surface waves, are retrieved. These interferometric responses generate two virtual sources: a causal wave (arriving at a positive time) and an acausal wave (arriving at a negative time). Under favorable conditions, both interferometric responses approach the surface-wave part of the medium's Green's function. Therefore, it is possible to calculate the clock drift for each station by exploiting the time-symmetry between the causal and acausal waves. For this purpose, the clock drift is calculated by measuring the differential arrival times of the causal and acausal waves for a large number of receiver-receiver pairs and computing the drift by carrying-out a least-squares inversion. The methodology described is applied to time-lapse cross-correlations of ambient seismic noise recorded on and around the Reykjanes peninsula, SW Iceland. The stations used for the analysis were deployed in the context of IMAGE (Integrated Methods for Advanced Geothermal Exploration) and consisted of 30 on-land stations and 24 ocean bottom seismometers (OBSs).  The seismic activity was recorded from spring 2014 until August 2015 on an area of around 100 km in diameter (from the tip of the Reykjanes peninsula).</p>

scholarly journals Source location of the 26 sec microseism from cross-correlations of ambient seismic noise

2006 ◽  
Vol 33 (18) ◽  
pp. n/a-n/a ◽  
Author(s):  
N. M. Shapiro ◽  
M. H. Ritzwoller ◽  
G. D. Bensen
Keyword(s):  
Seismic Noise ◽  
Source Location ◽  
Ambient Seismic Noise ◽  
Cross Correlations

scholarly journals A Linear Inversion Approach to Measuring the Composition and Directionality of the Seismic Noise Field

2021 ◽  
Vol 13 (16) ◽  
pp. 3097
Author(s):  
Patrick M. Meyers ◽  
Tanner Prestegard ◽  
Vuk Mandic ◽  
Victor C. Tsai ◽  
Daniel C. Bowden ◽  
...  
Keyword(s):  
Real World ◽  
Rayleigh Waves ◽  
Seismic Noise ◽  
Angular Resolution ◽  
Three Dimensional ◽  
Body Waves ◽  
Ambient Seismic Noise ◽  
Linear Inversion ◽  
Modal Composition ◽  
Cross Correlations

We develop a linear inversion technique for measuring the modal composition and directionality of ambient seismic noise. The technique draws from similar techniques used in astrophysics and gravitational-wave physics, and relies on measuring cross-correlations between different seismometer channels in a seismometer array. We characterize the sensitivity and the angular resolution of this technique using a series of simulations and real-world tests. We then apply the technique to data acquired by the three-dimensional seismometer array at the Homestake mine in Lead, SD, to estimate the composition and directionality of the seismic noise at microseism frequencies. We show that, at times of low-microseism amplitudes, noise is dominated by body waves (P and S), while at high-microseism times, the noise is dominated by surface Rayleigh waves.

scholarly journals Imaging of a Magma System Beneath the Merapi Volcano Complex, Indonesia, using Ambient Seismic Noise Tomography

2021 ◽  
Author(s):  
T Yudistira ◽  
J-P Metaxian ◽  
M Putriastuti ◽  
S Widiyantoro ◽  
N Rawlinson ◽  
...  
Keyword(s):  
Rayleigh Wave ◽  
Group Velocity ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Seismic Noise ◽  
Ambient Seismic Noise ◽  
Wave Group ◽  
Velocity Anomaly ◽  
Cross Correlations

Summary Mt. Merapi, which lies just north of the city of Yogyakarta in Java, Indonesia, is one of the most active and dangerous volcanoes in the world. Thanks to its subduction zone setting, Mt Merapi is a stratovolcano, and rises to an elevation of 2968 m above sea level. It stands at the intersection of two volcanic lineaments, Ungaran–Telomoyo–Merbabu–Merapi (UTMM) and Lawu–Merapi–Sumbing–Sindoro–Slamet, which are oriented north-south and west-east, respectively. Although it has been the subject of many geophysical studies, Mt Merapi's underlying magmatic plumbing system is still not well understood. Here, we present the results of an ambient seismic noise tomography study, which comprise of a series of Rayleigh wave group velocity maps and a 3-D shear wave velocity model of the Merapi-Merbabu complex. A total of 10 months of continuous data (October 2013–July 2014) recorded by a network of 46 broadband seismometers were used. We computed and stacked daily cross-correlations from every pair of simultaneously recording stations to obtain the corresponding inter-station empirical Green's functions. Surface wave dispersion information was extracted from the cross-correlations using the multiple filtering technique, which provided us with an estimate of Rayleigh wave group velocity as a function of period. The group velocity maps for periods 3–12 s were then inverted to obtain shear wave velocity structure using the neighbourhood algorithm. From these results, we observe a dominant high velocity anomaly underlying Mt. Merapi and Mt. Merbabu with a strike of 152° N, which we suggest is evidence of old lava dating from the UTMM double-chain volcanic arc which formed Merbabu and Old Merapi. We also identify a low velocity anomaly on the southwest flank of Merapi which we interpret to be an active magmatic intrusion.

scholarly journals Using multicomponent ambient seismic noise cross-correlations to identify higher mode Rayleigh waves and improve dispersion measurements

2020 ◽  
Vol 222 (3) ◽  
pp. 1590-1605 ◽  
Author(s):  
Avinash Nayak ◽  
Clifford H Thurber
Keyword(s):  
Rayleigh Wave ◽  
Group Velocity ◽  
Particle Motion ◽  
Fundamental Mode ◽  
Rayleigh Waves ◽  
Seismic Noise ◽  
Vertical Plane ◽  
Ambient Seismic Noise ◽  
Cross Correlations ◽  
The Individual

SUMMARY Ambient seismic noise cross-correlation with three-component sensors yields a nine-component empirical Green's tensor, in which four components of the radial–vertical plane contain Rayleigh waves. We exploit the retrograde elliptical nature of particle motion of the fundamental mode Rayleigh wave to correct the phase of the four radial–vertical components and stack them to obtain an average fundamental mode Rayleigh-wave time-series. This technique can suppress incoherent noise and wave packets that do not follow the targeted elliptical particle motion. The same technique can be used to isolate the first higher mode Rayleigh wave that follows prograde elliptical particle motion. We first demonstrate the effectiveness of the method on synthetic waveforms and then apply it on noise cross-correlations computed in Central California. Using this method, we isolate 1st higher mode Rayleigh waves on noise cross-correlations in the Great Valley, California, which provides new phase velocity constraints for estimating velocity structure in the sedimentary basin. We also obtain improved estimates of fundamental mode Rayleigh-wave dispersion for surface-wave tomography. The waveforms stacked assuming retrograde particle motion return at least ∼20 per cent more group velocity dispersion measurements satisfying a minimum signal-to-noise ratio (SNR) criterion than the individual components for periods ∼4–18 s. For equivalent group velocity measurements, SNR for the stacked estimate of the fundamental mode Rayleigh wave is on average 40 per cent greater than that measured on the individual components at periods less than 10 s. The technique also provides an easy way to detect large errors in sensor orientation.

S-wave velocity model for several regions of the Kamchatka Peninsula from the cross correlations of ambient seismic noise

2017 ◽  
Vol 53 (3) ◽  
pp. 341-352 ◽  
Author(s):  
S. Ya. Droznina ◽  
N. M. Shapiro ◽  
D. V. Droznin ◽  
S. L. Senyukov ◽  
V. N. Chebrov ◽  
...  
Keyword(s):  
Wave Velocity ◽  
Seismic Noise ◽  
Kamchatka Peninsula ◽  
Velocity Model ◽  
Ambient Seismic Noise ◽  
S Wave ◽  
Cross Correlations ◽  
The Cross
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