Journal articles: 'Seismic ambient noise'

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Krebes, Edward S. "Seismic Ambient Noise." Journal of the Acoustical Society of America 146, no. 1 (July 2019): 532–33. http://dx.doi.org/10.1121/1.5118247.

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Ermert, Laura, Jonas Igel, Korbinian Sager, Eléonore Stutzmann, Tarje Nissen-Meyer, and Andreas Fichtner. "Introducing noisi: a Python tool for ambient noise cross-correlation modeling and noise source inversion." Solid Earth 11, no. 4 (August 28, 2020): 1597–615. http://dx.doi.org/10.5194/se-11-1597-2020.

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Abstract. We introduce the open-source tool noisi for the forward and inverse modeling of ambient seismic cross-correlations with spatially varying source spectra. It utilizes pre-computed databases of Green's functions to represent seismic wave propagation between ambient seismic sources and seismic receivers, which can be obtained from existing repositories or imported from the output of wave propagation solvers. The tool was built with the aim of studying ambient seismic sources while accounting for realistic wave propagation effects. Furthermore, it may be used to guide the interpretation of ambient seismic auto- and cross-correlations, which have become preeminent seismological observables, in light of nonuniform ambient seismic sources. Written in the Python language, it is accessible for both usage and further development and efficient enough to conduct ambient seismic source inversions for realistic scenarios. Here, we introduce the concept and implementation of the tool, compare its model output to cross-correlations computed with SPECFEM3D_globe, and demonstrate its capabilities on selected use cases: a comparison of observed cross-correlations of the Earth's hum to a forward model based on hum sources from oceanographic models and a synthetic noise source inversion using full waveforms and signal energy asymmetry.

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Hong, Tae-Kyung, Jeongin Lee, Giha Lee, Junhyung Lee, and Seongjun Park. "Correlation between Ambient Seismic Noises and Economic Growth." Seismological Research Letters 91, no. 4 (June 3, 2020): 2343–54. http://dx.doi.org/10.1785/0220190369.

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Abstract Human activity is a major source of high-frequency seismic noise. Long-term ambient seismic noise levels and their influencing factors are investigated. The diurnal seismic noise level in 5–15 Hz display high correlation with human activities including traffic and industrial operations that are related to economic conditions. The temporal noise-level variations are consistent among three components. Analysis with seismic noises in three consecutive months of each year enables us to estimate the noise levels without seasonal effects. The daytime seismic noise-level changes in major cities of 11 countries are assessed using the 3 month records for decades. The annual seismic noise levels present strong correlations with gross domestic product (GDP), particularly with manufacturing and industrial GDP. The seismic noise levels increase quickly with GDP in low-GDP regions but slowly in high-GDP regions. This is because high-GDP regions already have large volumes of existing noise-inducing sources and because added sources contribute weakly. The seismic noise levels increased by 14%–111% for 5–23 yr depending on the economic conditions. The correlation between ambient seismic noise level and economy growth is a global feature. The high-frequency noise level may be a proxy to present the economic condition. Economic growth affects the Earth environment in a wide range of aspects.

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Draganov, Deyan, Xander Campman, Jan Thorbecke, Arie Verdel, and Kees Wapenaar. "Reflection images from ambient seismic noise." GEOPHYSICS 74, no. 5 (September 2009): A63—A67. http://dx.doi.org/10.1190/1.3193529.

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One application of seismic interferometry is to retrieve the impulse response (Green’s function) from crosscorrelation of ambient seismic noise. Various researchers show results for retrieving the surface-wave part of the Green’s function. However, reflection retrieval has proven more challenging. We crosscorrelate ambient seismic noise, recorded along eight parallel lines in the Sirte basin east of Ajdabeya, Libya, to obtain shot gathers that contain reflections. We take advantage of geophone groups to suppress part of the undesired surface-wave noise and apply frequency-wavenumber filtering before crosscorrelation to suppress surface waves further. After comparing the retrieved results with data from an active seismic exploration survey along the same lines, we use the retrieved reflection data to obtain a migrated reflection image of the subsurface.

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Sens-Schönfelder, Christoph. "Synchronizing seismic networks with ambient noise." Geophysical Journal International 174, no. 3 (September 2008): 966–70. http://dx.doi.org/10.1111/j.1365-246x.2008.03842.x.

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de Ridder, Sjoerd A. L., and Biondo L. Biondi. "Ambient seismic noise tomography at Ekofisk." GEOPHYSICS 80, no. 6 (November 2015): B167—B176. http://dx.doi.org/10.1190/geo2014-0558.1.

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Laske, Gabi. "Book Review of ‘Seismic Ambient Noise’." Geophysical Journal International 221, no. 3 (March 3, 2020): 1667–68. http://dx.doi.org/10.1093/gji/ggaa101.

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Shirzad, Taghi, and Zaher‐Hossein Shomali. "Extracting Stable Seismic Core Phases from Ambient Seismic Noise." Bulletin of the Seismological Society of America 106, no. 1 (December 15, 2015): 307–12. http://dx.doi.org/10.1785/0120150031.

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Vassallo, M., G. Festa, and A. Bobbio. "Seismic Ambient Noise Analysis in Southern Italy." Bulletin of the Seismological Society of America 102, no. 2 (March 29, 2012): 574–86. http://dx.doi.org/10.1785/0120110018.

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Saygin, Erdinc, and Brian L. N. Kennett. "Ambient seismic noise tomography of Australian continent." Tectonophysics 481, no. 1-4 (January 2010): 116–25. http://dx.doi.org/10.1016/j.tecto.2008.11.013.

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de Ridder, S. A. L., and J. R. Maddison. "Full wavefield inversion of ambient seismic noise." Geophysical Journal International 215, no. 2 (August 7, 2018): 1215–30. http://dx.doi.org/10.1093/gji/ggy328.

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Stephen, Ralph A. "Ambient seismic noise below the deep seafloor." Leading Edge 19, no. 3 (March 2000): 276–81. http://dx.doi.org/10.1190/1.1438590.

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Overduin, Pier P., Christian Haberland, Trond Ryberg, Fabian Kneier, Tim Jacobi, Mikhail N. Grigoriev, and Matthias Ohrnberger. "Submarine permafrost depth from ambient seismic noise." Geophysical Research Letters 42, no. 18 (September 16, 2015): 7581–88. http://dx.doi.org/10.1002/2015gl065409.

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Weemstra, Cornelis, Lapo Boschi, Alexander Goertz, and Brad Artman. "Seismic attenuation from recordings of ambient noise." GEOPHYSICS 78, no. 1 (January 1, 2013): Q1—Q14. http://dx.doi.org/10.1190/geo2012-0132.1.

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We applied seismic interferometry to data from an ocean-bottom survey offshore Norway and found that ambient seismic noise can be used to constrain subsurface attenuation on a reservoir scale. By crosscorrelating only a few days of recordings by broadband ocean bottom seismometers, we were able to retrieve empirical Green’s functions associated with surface waves in the frequency range between 0.2 and 0.6 Hz and acoustic waves traveling through the sea water between 1.0 and 2.5 Hz. We discovered that the decay of these surface waves cannot be explained by geometrical spreading alone and required an additional loss of energy with distance. We quantified this observed attenuation in the frequency domain using a modified Bessel function to describe the cross-spectrum in a stationary field. We averaged cross-spectra of equally spaced station couples and sorted these azimuthally averaged cross-spectra with distance. We then obtained frequency-dependent estimates of attenuation by minimizing the misfit of the real parts to a damped Bessel function. The resulting quality factors as function of frequency are indicative of the depth variation of attenuation and correlated with the geology in the survey area.

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Olivier, Gerrit, and Florent Brenguier. "Interpreting seismic velocity changes observed with ambient seismic noise correlations." Interpretation 4, no. 3 (August 1, 2016): SJ77—SJ85. http://dx.doi.org/10.1190/int-2015-0203.1.

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Recent results have shown that crosscorrelating ambient seismic noise recorded in underground mines can successfully extract the seismic Green’s function between sensors. We have revisited an earlier experiment that showed that these virtual seismic sources can be used to measure changes in seismic velocity accurately enough to monitor the short- and long-term influences of a blast in an underground mine. To use this method routinely, it is important to determine the cause of velocity variations in the absence of large dynamic stress perturbations (such as blasts). It also is important to calibrate the seismic velocity changes in terms of known stress changes so the effect of mining activities can be quantified in units that can be used by geotechnical engineers. To this end, we used coda-wave interferometry to measure relative velocity variations during times where no significant blasting or microseismic activity occurred and compared it to atmospheric air pressure changes, temperature variations, and modeled tidal strain. The results indicate that atmospheric air pressure changes have a measurable influence on the long-term seismic velocity variations at depth in the absence of large dynamic stress perturbations. This influence enabled us to determine the sensitivity of the relative velocity changes to stress, where a value of [Formula: see text] was found. This calibration essentially enables us to turn each sensor pair in an underground mine into a stress meter, paving the way for geotechnical engineers to use ambient seismic noise correlations to monitor the evolution of stress and to assess seismic hazard in conjunction with conventional microseismic methods.

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Edme, Pascal, and David F. Halliday. "Near-surface imaging using ambient-noise body waves." Interpretation 4, no. 3 (August 1, 2016): SJ55—SJ65. http://dx.doi.org/10.1190/int-2016-0002.1.

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We have introduced a workflow that allows subsurface imaging using upcoming body-wave arrivals extracted from ambient-noise land seismic data. Rather than using the conventional seismic interferometry approach based on correlation, we have developed a deconvolution technique to extract the earth response from the observed periodicity in the seismic traces. The technique consists of iteratively applying a gapped spiking deconvolution, providing multiple-free images with higher resolution than conventional correlation. We have validated the workflow for zero-offset traces with simple synthetic data and real data recorded during a small point-receiver land seismic survey.

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Xu, Zongbo, T. Dylan Mikesell, Josefine Umlauft, and Gabriel Gribler. "Rayleigh-wave multicomponent crosscorrelation-based source strength distribution inversions. Part 2: a workflow for field seismic data." Geophysical Journal International 222, no. 3 (June 11, 2020): 2084–101. http://dx.doi.org/10.1093/gji/ggaa284.

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SUMMARY Estimation of ambient seismic source distributions (e.g. location and strength) can aid studies of seismic source mechanisms and subsurface structure investigations. One can invert for the ambient seismic (noise) source distribution by applying full-waveform inversion (FWI) theory to seismic (noise) crosscorrelations. This estimation method is especially applicable for seismic recordings without obvious body-wave arrivals. Data pre-processing procedures are needed before the inversion, but some pre-processing procedures commonly used in ambient noise tomography can bias the ambient (noise) source distribution estimation and should not be used in FWI. Taking this into account, we propose a complete workflow from the raw seismic noise recording through pre-processing procedures to the inversion. We present the workflow with a field data example in Hartoušov, Czech Republic, where the seismic sources are CO2 degassing areas at Earth’s surface (i.e. a fumarole or mofette). We discuss factors in the processing and inversion that can bias the estimations, such as inaccurate velocity model, anelasticity and array sensitivity. The proposed workflow can work for multicomponent data across different scales of field data.

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Ermert, L. A., K. Sager, T. Nissen-Meyer, and A. Fichtner. "Multifrequency inversion of global ambient seismic sources." Geophysical Journal International 225, no. 3 (February 6, 2021): 1616–23. http://dx.doi.org/10.1093/gji/ggab050.

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SUMMARY We develop and apply a method to constrain the space- and frequency-dependent location of ambient noise sources. This is based on ambient noise cross-correlation inversion using numerical wavefield simulations, which honour 3-D crustal and mantle structure, ocean loading and finite-frequency effects. In the frequency range from 3 to 20 mHz, our results constrain the global source distribution of the Earth’s hum, averaged over the Southern Hemisphere winter season of 9 yr. During Southern Hemisphere winter, the dominant sources are largely confined to the Southern Hemisphere, the most prominent exception being the Izu-Bonin-Mariana arc, which is the most active source region between 12 and 20 mHz. Generally, strong hum sources seem to be associated with either coastlines or bathymetric highs. In contrast, deep ocean basins are devoid of hum sources. While being based on the relatively small number of STS-1 broad-band stations that have been recording continuously from 2004 to 2013, our results demonstrate the practical feasibility of a frequency-dependent noise source inversion that accounts for the complexities of 3-D wave propagation. It may thereby improve full-waveform ambient noise inversions and our understanding of the physics of noise generation.

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Lei, Xiaoqiong, Jun Zhang, Wenyuan Jin, Chen Han, and Xiwei Xu. "The application of ambient noise and reflection seismic exploration in an urban active fault survey." Interpretation 8, no. 4 (November 1, 2020): SU1—SU10. http://dx.doi.org/10.1190/int-2020-0085.1.

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As the detection of urban active faults becomes increasingly important, high-resolution detection of urban blind active faults is very important for urban planning, land use, and disaster risk reduction. However, it is difficult to determine the corresponding surface positions in the city environment for noise and building restrictions. The active source reflection seismic technique is considered the best technique to image faults with a high resolution and deep penetration. However, urban geophysical exploration must often consider the complex urban environment, which includes moving vehicles, dense power grids, and irregular buildings. These features make active source reflection seismic exploration difficult for wide application due to its drawbacks of high cost and the necessary use of explosives. In contrast, ambient noise seismic surveys have the advantages of continuous ambient noise sources, low cost, and fast deployment. These advantages are good for urban exploration. Although ambient noise seismic surveys have a lower resolution than active seismic surveys, their ultrahigh-density layout can improve the resolution. We have conducted two active source seismic lines and two ambient noise seismic lines near the Huangzhuang-Gaoliying fault (HGF) in a northern suburb of Beijing. The autocorrelation and crosscorrelation results are consistent with the active source reflection seismic results. They revealed the location of the HGF, which is composed of a set of steep dip faults. The study of the combination of the two techniques demonstrates that ambient noise seismic surveys are effective for urban active fault detection, especially for larger scale area surveys, and active source reflection seismic surveys can be used for detailed surveys. The combination of the two techniques has a higher efficiency and lower costs and can be widely used in blind urban active fault surveys.

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Ritzwoller, Michael H., Fan-Chi Lin, and Weisen Shen. "Ambient noise tomography with a large seismic array." Comptes Rendus Geoscience 343, no. 8-9 (September 2011): 558–70. http://dx.doi.org/10.1016/j.crte.2011.03.007.

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Ayala-Garcia, Daniella, Andrew Curtis, and Michal Branicki. "Seismic Interferometry from Correlated Noise Sources." Remote Sensing 13, no. 14 (July 9, 2021): 2703. http://dx.doi.org/10.3390/rs13142703.

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It is a well-established principle that cross-correlating seismic observations at different receiver locations can yield estimates of band-limited inter-receiver Green’s functions. This principle, known as Green’s function retrieval or seismic interferometry, is a powerful technique that can transform noise into signals which enable remote interrogation and imaging of the Earth’s subsurface. In practice it is often necessary and even desirable to rely on noise already present in the environment. Theory that underpins many applications of ambient noise interferometry assumes that the sources of noise are uncorrelated in time. However, many real-world noise sources such as trains, highway traffic and ocean waves are inherently correlated in space and time, in direct contradiction to the these theoretical foundations. Applying standard interferometric techniques to recordings from correlated energy sources makes the Green’s function liable to estimation errors that so far have not been fully accounted for theoretically nor in practice. We show that these errors are significant for common noise sources, always perturbing or entirely obscuring the phase one wishes to retrieve. Our analysis explains why stacking may reduce the phase errors, but also shows that in commonly encountered circumstances stacking will not remediate the problem. This analytical insight allowed us to develop a novel workflow that significantly mitigates effects arising from the use of correlated noise sources. Our methodology can be used in conjunction with already existing approaches, and improves results from both correlated and uncorrelated ambient noise. Hence, we expect it to be widely applicable in ambient noise studies.

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Gritto, Roland, Ali Elobaid Elnaiem, Fateh Alrahman Mohamed, and Fadhil Sadooni. "Seismic detection and characterization of a man-made karst analog — A feasibility study." GEOPHYSICS 86, no. 3 (March 19, 2021): WA35—WA48. http://dx.doi.org/10.1190/geo2020-0377.1.

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At the site of a water drainage shaft on the campus of Qatar University that serves as a man-made karst analog, two seismic imaging techniques were adapted to use resonant scattered waves recorded during active-source seismic surveys and during passive ambient-noise surveys. Data acquisition included two seismic transmission surveys that encompassed the shaft and a passive ambient-noise survey that extended across the top of the shaft. Seismic imaging of band-pass-filtered resonance waves correctly estimated the location and dimension of the shaft. Furthermore, the method detected the presence and the location of a horizontal drainage pipe and gravel bed connecting neighboring water shafts. Ambient-noise data were analyzed by computing amplitude values of the seismic records in spectral passbands. The results indicated an amplification of seismic amplitudes above the shaft for low-frequency passbands and a sharp decrease in amplitude values for high-frequency passbands. The high- and low-amplitude values displayed as a function of the receiver position allowed for accurate detection and location of the shaft in space. Ground truthing of the imaging results confirmed the accuracy of the seismic techniques, whereas numerical modeling supported the interpretation of the ambient-noise data. The techniques used do not require knowledge of the seismic velocities in the subsurface, but they depend on a priori information about the approximate location of the target.

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Jiang, Chengxin, and Marine A. Denolle. "NoisePy: A New High-Performance Python Tool for Ambient-Noise Seismology." Seismological Research Letters 91, no. 3 (April 1, 2020): 1853–66. http://dx.doi.org/10.1785/0220190364.

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Abstract The fast-growing interests in high spatial resolution of seismic imaging and high temporal resolution of seismic monitoring pose great challenges for fast, efficient, and stable data processing in ambient-noise seismology. This coincides with the explosion of available seismic data in the last few years. However, the current computational landscape of ambient seismic field seismology remains highly heterogeneous, with individual researchers building their own homegrown codes. Here, we present NoisePy—a new high-performance python tool designed specifically for large-scale ambient-noise seismology. NoisePy provides most of the processing techniques for the ambient field data and the correlations found in the literature, along with parallel download routines, dispersion analysis, and monitoring functions. NoisePy takes advantage of adaptable seismic data format, a parallel input and output enabled HDF5 data format designed for seismology, for a structured organization of the cross-correlation data. The parallel computing of NoisePy is performed using Message Passing Interface and shows a strong scaling with the number of cores, which is well suited for embarrassingly parallel problems. NoisePy also uses a small memory overhead and stable memory usage. Benchmark comparisons with the latest version of MSNoise demonstrate about four-time improvement in compute time of the cross correlations, which is the slowest step of ambient-noise seismology. NoisePy is suitable for ambient-noise seismology of various data sizes, and it has been tested successfully at handling data of size ranging from a few GBs to several tens of TBs.

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Wu, Guoli, Hefeng Dong, Ganpan Ke, and Junqiang Song. "Shear-Wave Tomography Using Ocean Ambient Noise with Interference." Remote Sensing 12, no. 18 (September 11, 2020): 2969. http://dx.doi.org/10.3390/rs12182969.

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Ambient noise carries abundant subsurface structure information and attracts ever-increasing attention in the past decades. However, there are lots of interference factors in the ambient noise in the real world, making the noise difficult to be utilized in seismic interferometry. The paper performs shear-wave tomography on a very short recording of ocean ambient noise with interference. An adapted eigenvalue-based filter is adopted as a pre-processing method to deal with the strong, directional interference problem. Beamforming and the noise crosscorrelation analyses show that the filter works well on the noise recorded by the array. Directional energy is significantly suppressed and the background diffuse component of the noise is relatively enhanced. The shear-wave tomography shows a 4-layer subsurface structure of the area covered by the array, with relatively homogeneous distribution of the shear-wave velocity values in the top three layers and a complicated structure in the bottom layer. Moreover, 3 high-velocity zones can be recognized in the bottom layer. The result is compared with several other tomography results using different methods and data. It demonstrates that, although the ambient noise used in this paper is very short and severely contaminated, a reasonable tomography result can be obtained by applying the adapted eigenvalue-based filter. Since it is the first application of the adapted eigenvalue-based filter in seismic tomography using ambient noise, the paper proves the effectiveness of this technique and shows the potential of the technique in ambient noise processing and passive seismic interferometry.

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Kementzetzidou, D., P. Paradisopoulou, K. Gkogkas, E. Arampatzi, E. Kyriakidou, E. Melissanidou, and N. Theodoulidis. "USE OF AMBIENT VIBRATIONS IN UNDERSTANDING LOCAL SITE EFFECTS AT BROADBAND SEISMIC STATIONS OF THE HELLENIC UNIFIED SEISMOLOGICAL NETWORK (HUSN)." Bulletin of the Geological Society of Greece 50, no. 3 (July 27, 2017): 1505. http://dx.doi.org/10.12681/bgsg.11863.

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The evaluation of ambient seismic noise at the Hellenic Unified Seismic Network (HUSN) stations is investigated in this study. Ambient vibration recordings combined with the horizontal to vertical (H/V) spectral ratio technique helps in characterizing local site effects. This technique was applied at 17 sites ambient noise measurements. We selected a number of 1-hr waveform segments during day and night for summer and winter. For each site the H/V spectral ratio was calculated and the results were combined with geological and geophysical information. The goal was to show the network performance as far as the station quality and noise level at each site concerns in order to provide possible structural improvements, seismic station relocations or to detecting operational problems.

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Seats, Kevin J., and Jesse F. Lawrence. "The seismic structure beneath the Yellowstone Volcano Field from ambient seismic noise." Geophysical Research Letters 41, no. 23 (December 5, 2014): 8277–82. http://dx.doi.org/10.1002/2014gl061913.

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Anderson, Richard G., and George A. McMechan. "Noise‐adaptive filtering of seismic shot records." GEOPHYSICS 53, no. 5 (May 1988): 638–49. http://dx.doi.org/10.1190/1.1442498.

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Ambient noise can obscure reflections on deep crustal seismic data. We use a spectral subtraction method to attenuate stationary noise. Our procedure, called noise‐adaptive filtering, is to Fourier transform the noise before the first arrivals, subtract the amplitude spectrum of the noise from the amplitude spectrum of the noisy data, and inverse Fourier transform. The phase spectrum is not corrected, but the method attenuates noise if the phase shift between the signal and noise is random. The algorithm can be implemented as a frequency filter, as a frequency‐wavenumber filter, or as two separate frequency and wavenumber filters. Noise‐adaptive filtering is often superior to conventional frequency or frequency‐wavenumber filtering because it adapts to spatial variations in the noise without parameter testing. Noise‐adaptive filters can achieve noise rejection ratios of up to 45 dB; their dynamic range is about 25 dB. These filters work best when the input signal‐to‐noise ratio is on the order of 0 dB and there are significant differences between the frequency‐wavenumber amplitude spectra of the signal and noise. Application of the method to field data can enhance events that are not visible in the input data.

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Valero, Maria, Fangyu Li, Jose Clemente, and Wenzhan Song. "Distributed and Communication-Efficient Spatial Auto-Correlation Subsurface Imaging in Sensor Networks." Sensors 19, no. 11 (May 28, 2019): 2427. http://dx.doi.org/10.3390/s19112427.

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A wireless seismic network can be effectively used as a tool for subsurface monitoring and imaging. By recording and analyzing ambient noise, a seismic network can image underground infrastructures and provide velocity variation information of the subsurface that can help to detect anomalies. By studying the variation in the noise cross-correlation function of the noise, it is possible to determine the subsurface seismic velocity and image underground infrastructures. Ambient noise imaging can be done in a decentralized fashion using Distributed Spatial Auto-Correlation (dSPAC). In dSPAC over sensor networks, the cross-correlation is the most intensive communication process since nodes need to communicate their data with neighbor nodes. In this paper, a new communication-reduced method for cross-correlation is presented to meet bandwidth and cost of communication constraints in networks while ambient noise imaging is performed using dSPAC method. By applying the proposed communication-reduced method, we show that energy and computational cost of the nodes is also preserved.

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Zhan, Z., S. Wei, S. Ni, and D. Helmberger. "Earthquake Centroid Locations Using Calibration from Ambient Seismic Noise." Bulletin of the Seismological Society of America 101, no. 3 (May 29, 2011): 1438–45. http://dx.doi.org/10.1785/0120100118.

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Shapiro, N. M. "High-Resolution Surface-Wave Tomography from Ambient Seismic Noise." Science 307, no. 5715 (March 11, 2005): 1615–18. http://dx.doi.org/10.1126/science.1108339.

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Zheng, DingChang, Erdinc Saygin, Phil Cummins, Zengxi Ge, Zhaoxu Min, Athanasius Cipta, and Runhai Yang. "Transdimensional Bayesian seismic ambient noise tomography across SE Tibet." Journal of Asian Earth Sciences 134 (February 2017): 86–93. http://dx.doi.org/10.1016/j.jseaes.2016.11.011.

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Gerstoft, Peter, Karim Sabra, Phillippe Roux, W. A. Kuperman, and William S. Hodgkiss. "Passive acoustic and seismic tomography with ocean ambient noise." Journal of the Acoustical Society of America 118, no. 3 (September 2005): 1845. http://dx.doi.org/10.1121/1.4778534.

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Kumar, Santosh, Sumer Chopra, Pallabee Choudhury, A. P. Singh, R. B. S. Yadav, and B. K. Rastogi. "Ambient noise levels in Gujarat State (India) seismic network." Geomatics, Natural Hazards and Risk 3, no. 4 (November 2012): 342–54. http://dx.doi.org/10.1080/19475705.2011.611952.

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De Plaen, Raphael S. M., Thomas Lecocq, Corentin Caudron, Valérie Ferrazzini, and Olivier Francis. "Single-station monitoring of volcanoes using seismic ambient noise." Geophysical Research Letters 43, no. 16 (August 28, 2016): 8511–18. http://dx.doi.org/10.1002/2016gl070078.

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Saygin, Erdinc, and B. L. N. Kennett. "Crustal structure of Australia from ambient seismic noise tomography." Journal of Geophysical Research: Solid Earth 117, B1 (January 2012): n/a. http://dx.doi.org/10.1029/2011jb008403.

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Daskalakis, E., C. P. Evangelidis, J. Garnier, N. S. Melis, G. Papanicolaou, and C. Tsogka. "Robust seismic velocity change estimation using ambient noise recordings." Geophysical Journal International 205, no. 3 (April 12, 2016): 1926–36. http://dx.doi.org/10.1093/gji/ggw142.

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Liu, Xin, and Yehuda Ben-Zion. "Estimating correlations of neighbouring frequencies in ambient seismic noise." Geophysical Journal International 206, no. 2 (May 23, 2016): 1065–75. http://dx.doi.org/10.1093/gji/ggw196.

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D’yakonov, B. P., P. S. Martyshko, A. K. Troyanov, V. A. Gavrilov, Yu G. Astrakhantsev, N. I. Nachapkin, and N. A. Beloglazova. "Temporal variability of ambient seismic noise at great depths." Doklady Earth Sciences 433, no. 1 (July 2010): 944–47. http://dx.doi.org/10.1134/s1028334x10070214.

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Stankiewicz, Jacek, Michael H. Weber, Ayman Mohsen, and Rami Hofstetter. "Dead Sea Basin Imaged by Ambient Seismic Noise Tomography." Pure and Applied Geophysics 169, no. 4 (June 18, 2011): 615–23. http://dx.doi.org/10.1007/s00024-011-0350-y.

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Li, Hongyi, Fabrizio Bernardi, and Alberto Michelini. "Love wave tomography in Italy from seismic ambient noise." Earthquake Science 23, no. 5 (October 2010): 487–95. http://dx.doi.org/10.1007/s11589-010-0747-1.

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de Ridder, S. A. L., and B. L. Biondi. "Daily reservoir-scale subsurface monitoring using ambient seismic noise." Geophysical Research Letters 40, no. 12 (June 18, 2013): 2969–74. http://dx.doi.org/10.1002/grl.50594.

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Sari, Endah Puspita, Melda Panjaitan, Hery Sunandar, and Erida Wati. "Spectral Probability Density for Broadband Seismic Ambient Noise Level." Journal of Computation Physics and Earth Science (JoCPES) 1, no. 1 (April 1, 2021): 17–21. http://dx.doi.org/10.53842/jocpes.v1i1.4.

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The time-series approach is commonly utilized to get to the estimation of the likelihood thickness work of control ghostly densities (PDF PSD) of waveform information. This paper is concerned with the introduction of the evaluation of waveform commotion to degree the likelihood thickness work (PDF) be done inside, we utilized the metadata from a stock, a parser occurrence of DNP (Denpasar, Bali, Indonesia), IGBI (Ingas, Bali, Indonesia), and PLAI (Plampang, NTB, Indonesia) from BMKG IA-Networks and computations are based on the schedule utilized by McNamara Demonstrate. The point of this paper to characterize the current and past execution of the stations and recognizing the data on clamor levels at BMKG IA-Networks Station. The result of this paper shows the consistency of the unearthly is displayed the DNP, IGBI, and PLAI organize to confirm the quality of information conjointly acts as a test execution broadband arrange to the time taken by the broadband organize within the field and examination the Lombok earthquake in 2018.

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Liner, Christopher L., Gokay Bozkurt, and V. Dale Cox. "Shooting direction and crosswell seismic data acquisition." GEOPHYSICS 61, no. 5 (September 1996): 1489–98. http://dx.doi.org/10.1190/1.1444074.

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Four crosswell seismic surveys were acquired in the Glenn Pool Field of northeastern Oklahoma as part of a multidisciplinary reservoir characterization project. The acquisition goal was to generate data suitable for tomographic traveltime inversion. Acquisition parameters and shooting geometry were selected by conducting a parameter test at the site. Following the parameter test, the first survey resulted in high quality data showing clear first arrivals, low ambient noise, some reflection events, and strong source‐generated tube waves. The second survey involved a different receiver well and encountered high ambient noise levels. The noise was strong enough to prohibit first‐arrival picking for much of the data. On‐site analysis of the second survey revealed tube waves emanating from a perforated interval in the receiver well. This well was shut in and was not flowing fluid or gas at the surface. We interpret the source of ambient tube waves as borehole‐to‐formation fluid flow (circulation) associated with the perforations. Since this image plane was important for characterization of the reservoir, the survey was reshot (third survey) by reversing sources and receivers in the two wells. The resulting high‐quality data indicates that shooting direction can be an important factor in crosswell seismic acquisition. This experience influenced acquisition of a previously planned fourth survey so that the ambient noise problem would be avoided.

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Mordret, Aurélien, T. Dylan Mikesell, Christopher Harig, Bradley P. Lipovsky, and Germán A. Prieto. "Monitoring southwest Greenland’s ice sheet melt with ambient seismic noise." Science Advances 2, no. 5 (May 2016): e1501538. http://dx.doi.org/10.1126/sciadv.1501538.

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The Greenland ice sheet presently accounts for ~70% of global ice sheet mass loss. Because this mass loss is associated with sea-level rise at a rate of 0.7 mm/year, the development of improved monitoring techniques to observe ongoing changes in ice sheet mass balance is of paramount concern. Spaceborne mass balance techniques are commonly used; however, they are inadequate for many purposes because of their low spatial and/or temporal resolution. We demonstrate that small variations in seismic wave speed in Earth’s crust, as measured with the correlation of seismic noise, may be used to infer seasonal ice sheet mass balance. Seasonal loading and unloading of glacial mass induces strain in the crust, and these strains then result in seismic velocity changes due to poroelastic processes. Our method provides a new and independent way of monitoring (in near real time) ice sheet mass balance, yielding new constraints on ice sheet evolution and its contribution to global sea-level changes. An increased number of seismic stations in the vicinity of ice sheets will enhance our ability to create detailed space-time records of ice mass variations.

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Stehly, Laurent, Paul Cupillard, and Barbara Romanowicz. "Towards improving ambient noise tomography using simultaneously curvelet denoising filters and SEM simulations of seismic ambient noise." Comptes Rendus Geoscience 343, no. 8-9 (September 2011): 591–99. http://dx.doi.org/10.1016/j.crte.2011.03.005.

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Li, Zhengbo, Jie Zhou, Gaoxiong Wu, Jiannan Wang, Gongheng Zhang, Sheng Dong, Lei Pan, et al. "CC-FJpy: A Python Package for Extracting Overtone Surface-Wave Dispersion from Seismic Ambient-Noise Cross Correlation." Seismological Research Letters 92, no. 5 (June 9, 2021): 3179–86. http://dx.doi.org/10.1785/0220210042.

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Abstract In the past two decades, seismic ambient-noise cross correlation (CC) has been one of the most important technologies in seismology. Usually, only the fundamental-mode surface-wave dispersion was extracted from the ambient noise. Recently, with the frequency–Bessel transform (F-J) method, overtone dispersion can also be extracted from the ambient noise and it adds significant value in inversion. This method has also been verified to be effective for array seismic records of earthquake events. In this article, we describe our algorithm and a Python package called CC-FJpy. For the F-J method, we use the Nvidia’s graphics processing unit to accelerate the computation, which can achieve a 100-fold computational efficiency. We have encapsulated our experiences and technologies into CC-FJpy and tested the CC-FJpy by ambient-noise and earthquake data to ensure its speed and ease of use. Our open-source package CC-FJpy can benefit the development of surface-wave studies using ambient noise and make it easier to start with high-mode surface waves.

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Takagi, Ryota, Genti Toyokuni, and Naotaka Chikasada. "Ambient noise correlation analysis of S-net records: extracting surface wave signals below instrument noise levels." Geophysical Journal International 224, no. 3 (November 17, 2020): 1640–57. http://dx.doi.org/10.1093/gji/ggaa548.

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SUMMARY We applied ambient noise cross-correlation analysis to the cabled ocean bottom seismic network offshore northeast Japan (Seafloor observation network for earthquakes and tsunamis along the Japan Trench: S-net) to extract surface waves propagating in the ocean area of the forearc region. We found two types of peculiar pulses in the cross-correlation functions (CCFs) of ambient seismic noise records: periodic pulses mainly every minute and sharp pulses around the lag time zero. These pulses strongly contaminate the surface wave signals in the CCFs at frequencies below ∼0.1 Hz. The periodic pulses originate from periodic instrument noises, while the zero-lag pulses originate from random instrument noises which are coherent within station pairs. By developing solutions to remove the periodic and zero-lag pulses based on the characteristics of the pulses, we succeeded in extracting Rayleigh and Love wave signals from the S-net records at 0.03–0.3 Hz, while the surface wave signals at 0.03–0.1 Hz were not visible without the application of these solutions. These solutions widen the frequency range of analysis, and may be applicable to other seismic networks, particularly to recent dense but non-broad-band networks. We identified the fundamental and first higher modes of Rayleigh waves and the fundamental mode of the Love wave. The extracted surface wave signals can constrain the shear wave velocity structure from the sediment to seismogenic zone around the megathrust plate boundary in the forearc region.

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Lecocq, T., C. Caudron, and F. Brenguier. "MSNoise, a Python Package for Monitoring Seismic Velocity Changes Using Ambient Seismic Noise." Seismological Research Letters 85, no. 3 (May 1, 2014): 715–26. http://dx.doi.org/10.1785/0220130073.

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Dalen, Karel N., T. Dylan Mikesell, Elmer N. Ruigrok, and Kees Wapenaar. "Retrieving surface waves from ambient seismic noise using seismic interferometry by multidimensional deconvolution." Journal of Geophysical Research: Solid Earth 120, no. 2 (February 2015): 944–61. http://dx.doi.org/10.1002/2014jb011262.

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Draganov, Deyan, Xander Campman, Jan Thorbecke, Arie Verdel, and Kees Wapenaar. "Seismic exploration-scale velocities and structure from ambient seismic noise (>1 Hz)." Journal of Geophysical Research: Solid Earth 118, no. 8 (August 2013): 4345–60. http://dx.doi.org/10.1002/jgrb.50339.

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Journal articles on the topic 'Seismic ambient noise'

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