Extracting subsurface information from ambient seismic noise — A case study from Germany

Geophysics ◽  
2012 ◽  
Vol 77 (4) ◽  
pp. KS13-KS31 ◽  
Author(s):  
Alexander Goertz ◽  
Barbara Schechinger ◽  
Benjamin Witten ◽  
Matthias Koerbe ◽  
Paul Krajewski
Keyword(s):  
Seismic Noise ◽  
Body Wave ◽  
Source Region ◽  
Low Frequency ◽  
Spectral Ratio ◽  
Velocity Model ◽  
Body Waves ◽  
Seismic Survey ◽  
Ambient Seismic Noise ◽  
Oil Reservoir

We analyzed ambient seismic noise from a broadband passive seismic survey acquired in an urban area in Germany. Despite a high level of anthropogenic noise, we observe lateral variations in the quasi-stationary spectra that are of natural origin and indicative of the subsurface in the survey area. The best diagnostic is the ellipticity spectrum which is the spectral ratio of the vertical over the horizontal components. Deviations of the observed spectra from a pure Rayleigh-wave ellipticity allow an approximate separation of surface-wave from body-wave components in the analyzed frequency range, distinguishing shallow (upper tens of meters) from deeper (upper three kilometers) subsurface effects. We observe an increase of vertically polarized body waves between 1 and 4 Hz that is correlated to a subsurface structure that contains an oil reservoir at about 2-km depth. We located the source of the observed body wave microtremor in depth by applying an elastic wavefield back projection and imaging technique. The method includes normalization by the impulse response of the velocity model, effects of the receiver geometry, and lateral variation of incoherent noise. The source region of the low-frequency body wave microtremor is centered above the location of the oil reservoir. Two possible explanations for the deep microtremor are elastic body-wave scattering due to the impedance contrast of the structural trap, and viscoelastic scattering due to poroelastic effects in the partially saturated reservoir.

scholarly journals Microseisms in geothermal exploration—studies in Grass Valley, Nevada

Geophysics ◽  
1979 ◽  
Vol 44 (6) ◽  
pp. 1097-1115 ◽  
Author(s):  
Alfred L. Liaw ◽  
T. V. McEvilly
Keyword(s):  
Seismic Noise ◽  
Velocity Structure ◽  
Hot Spring ◽  
Body Wave ◽  
Sedimentary Cover ◽  
Source Region ◽  
Body Waves ◽  
Rock Properties ◽  
Noise Field ◽  
Short Period

Frequency(f)‐wavenumber(k) spectra of seismic noise in the bands 1 ⩽ f ⩽ 10 Hz in frequency and |k| ⩽ 35.7 cycles/km in wavenumber, measured at several places in Grass Valley, Nevada, exhibit numerous features which can be correlated with variations in surface geology and sources associated with hot spring activity. Exploration techniques for geothermal reservoirs, based upon the spatial distribution of the amplitude and frequency characteristics of short‐period seismic noise, are applied and evaluated in a field program at this potential geothermal area. A detailed investigation of the spatial and temporal characteristics of the noise field was made to guide subsequent data acquisition and processing. Contour maps of normalized noise level derived from judiciously sampled data are dominated by the hot spring noise source and the generally high noise levels outlining the regions of thick alluvium. Major faults are evident when they produce a shallow lateral contrast in rock properties. Conventional seismic noise mapping techniques cannot differentiate noise anomalies due to buried seismic sources from those due to shallow geologic effects. The noise radiating from a deep reservoir ought to be evident as body waves of high‐phase velocity with time‐invariant source azimuth. A small two‐dimensional (2-D) array was placed at 16 locations in the region to map propagation parameters. The f‐k spectra reveal shallow local sources, but no evidence for a significant body wave component in the noise field was found. With proper data sampling, array processing provides a powerful method for mapping the horizontal component of the vector wavenumber of the noise field. This information, along with the accurate velocity structure, will allow ray tracing to locate a source region of radiating microseisms. In Grass Valley, and probably in most areas of sedimentary cover, the 2–10 Hz microseismic field is predominantly fundamental‐mode Rayleigh waves controlled by the very shallow structure.

Retrieval of reflectivity images from ambient seismic noise correlations using machine learning as a noise-panel classification tool

2020 ◽  
Author(s):  
Boris Boullenger ◽  
Merijn de Bakker ◽  
Arie Verdel ◽  
Stefan Carpentier
Keyword(s):  
Machine Learning ◽  
Seismic Noise ◽  
Body Wave ◽  
The Body ◽  
Body Waves ◽  
Ambient Seismic Noise ◽  
Depth Range ◽  
Classification Models ◽  
Coherent Noise ◽  
Noise Interferometry

<p>The theory of ambient seismic noise interferometry offers techniques to retrieve estimates of inter-receiver responses from continuously recorded ambient seismic noise. This is usually achieved by correlating and stacking successive noise panels over sufficiently long periods of time. If the noise panels contain significant body-wave energy, the stacked correlations expected to result in retrieved estimates of the body-wave responses, including reflections. Such application combined with a dense surface seismic array is promising for imaging the subsurface structures at lower cost and lower environmental impact as compared to with controlled seismic sources. Subsequently, this technique can be an alternative to active-source surveys in a range of challenging scenarios and locations, and can also be used to perform time-lapse subsurface characterization.</p><p>In this study, we apply seismic body-wave noise interferometry to 30-days of continuous records from a surface line of 31 receivers spaced by 25 meters in the South of the Netherlands with the aim to image subsurface reflectors, at depths from a few hundreds of meters to a few kilometers. As a first step, we compute stacked auto-correlations and compare the retrieved zero-offset section with a co-located stacked section from a past active reflection survey on the site.</p><p>Yet, the retrieval of reflectivity estimates relies on the identification and collection of a sufficient number of noise panels with recorded body waves that have travelled from the subsurface towards the array. Even in the case of favorable body-wave noise conditions, the panels are most often contaminated with stronger anthropogenic coherent seismic noise, mainly in the form of surface waves, which in turn prevents the stacked correlations to reveal reflectivity. Because of the limited effect of frequency filtering, the application of seismic body-wave noise interferometry requires in fact extensive effort to identify noise panels without prominent coherent noise from the surface activity. Typically, this leads to disregard a significant amount of actually useful data.</p><p>For this reason, we designed, trained and tested a deep convolutional neural network to perform this classification task more efficiently and facilitate the repetition of the retrieval method over long periods of time. We tested several supervised learning schemes to classify the panels, where two classes are defined, according to the presence or absence of prominent coherent noise. The retained classification models achieved close to 90% of prediction accuracy on the test set.</p><p>We used the trained classification models to correlate and stack panels which were predicted in the class with coherent noise absent. The resulting stacked correlations exhibit potential reflectors in a larger depth range than previously achieved. The results show the benefits of using machine learning to collect efficiently a maximum amount of favorable noise panels and a way forward to the upscaling of seismic body-wave noise interferometry for reflectivity imaging.</p>

Seismic Monitoring of Permafrost in Svalbard, Arctic Norway

2021 ◽  
Author(s):  
Julie Albaric ◽  
Daniela Kühn ◽  
Matthias Ohrnberger ◽  
Nadège Langet ◽  
Dave Harris ◽  
...  
Keyword(s):  
Surface Waves ◽  
Seismic Noise ◽  
Green's Functions ◽  
Seismic Velocity ◽  
Green’S Functions ◽  
Velocity Model ◽  
Body Waves ◽  
Ambient Seismic Noise ◽  
Seasonal Temperature ◽  
Velocity Variations

Abstract We analyze data from passive and active seismic experiments conducted in the Adventdalen valley of Svalbard in the Norwegian Arctic. Our objective is to characterize the ambient wavefield of the region and to investigate permafrost dynamics through estimates of seismic velocity variations. We are motivated by a need for early geophysical detection of potentially hazardous changes to permafrost stability. We draw upon several data sources to constrain various aspects of seismic wave propagation in Adventdalen. We use f-k analysis of five years of continuous data from the Spitsbergen seismic array (SPITS) to demonstrate that ambient seismic noise on Svalbard consists of continuously present body waves and intermittent surface waves appearing at regular intervals. A change in wavefield direction accompanies the sudden onset of surface waves when the average temperature rises above the freezing point, suggesting a cryogenic origin. This hypothesis is supported further by our analysis of records from a temporary broadband network, which indicates that the background wavefield is dominated by icequakes. Synthetic Green’s functions calculated from a 3D velocity model match well with empirical Green’s functions constructed from the recorded ambient seismic noise. We use a shallow shear-wave velocity model, obtained from active seismic measurements, to estimate the maximum depth of Rayleigh wave sensitivity to changes in shear velocity to be in the 50–100 m range. We extract seasonal variations in seismic velocities from ambient noise cross-correlation functions computed over three years of SPITS data. We attribute relative velocity variations to changes in the ice content of the shallow (2–4 m depth) permafrost, which is sensitive to seasonal temperature changes. A linear decreasing trend in seismic velocity is observed over the years, most likely due to permafrost warming.

Causes of regional variation of magnitudes

1971 ◽  
Vol 61 (3) ◽  
pp. 649-670 ◽  
Author(s):  
Ronald W. Ward ◽  
M. Nafi Toksöz
Keyword(s):  
Upper Mantle ◽  
Body Wave ◽  
Source Region ◽  
Spectral Ratio ◽  
Relative Difference ◽  
Body Waves ◽  
Regional Variations ◽  
Underground Nuclear Explosions ◽  
Short Period ◽  
Q Model

abstract Data from the short and long-period seismographs at the NORSAR in Norway are used to investigate the discrimination of earthquakes and underground nuclear explosions using surface-wave versus body-wave magnitude (Ms versus mb). Earthquakes and explosions occurring within the western United States and recorded in Norway exhibit either anomalously large surface waves or anomalously low compressional body waves compared to events from central Asia. These data, as well as the results of other investigators, indicate an anomaly of 0.8 to 1.0 in Ms or 0.6 to 0.8 in mb or some linear combination of the two. The mechanism producing anomalously large Ms values from a region for explosions and the cause of lower mb values are investigated in terms of stress relaxation triggered by an explosion and regional variations in attenuation in the upper mantle beneath both the source region and the receiver region. The method of short-period amplitude spectral ratio is applied to the records of the waves from five deep events to determine the difference in attenuation beneath different receivers. The relative Q model inferred from these data for the upper mantle from 50 to 750 km depth is QLASA = 75, QTFSO = 175, and QNORSAR = 390. The circum-Pacific island arc exhibits an apparent source attenuation asymmetry. The data from the mid-Atlantic ridge indicate that strong attenuation may be associated with parts of the ridge. The relative difference of the Q model between LASA and NORSAR results in a difference in mb of 0.40 for distances of 60° to 80°, which agrees well with the observed differences in mb. We conclude that regional variations of attenuation in the upper mantle play an important role in regional differences in Ms versus mb relationship.

The Search of Diffusive Properties in Ambient Seismic Noise

2021 ◽  
Author(s):  
José Piña-Flores ◽  
Martín Cárdenas-Soto ◽  
Antonio García-Jerez ◽  
Michel Campillo ◽  
Francisco J. Sánchez-Sesma
Keyword(s):  
Green’S Function ◽  
Surface Waves ◽  
Green's Function ◽  
Seismic Noise ◽  
Elastic Field ◽  
Spectral Ratio ◽  
Ambient Seismic Noise ◽  
Data Sets ◽  
Original Experiment ◽  
Imaging Theory

ABSTRACT Ambient seismic noise (ASN) is becoming of interest for geophysical exploration and engineering seismology, because it is possible to exploit its potential for imaging. Theory asserts that the Green’s function can be retrieved from correlations within a diffuse field. Surface waves are the most conspicuous part of Green’s function in layered media. Thus, the velocities of surface waves can be obtained from ASN if the wavefield is diffuse. There is widespread interest in the conditions of emergence and properties of diffuse fields. In the applications, useful approximations of the Green’s function can be obtained from cross correlations of recorded motions of ASN. An elastic field is diffuse if the background illumination is azimuthally uniform and equipartitioned. It happens with the coda waves in earthquakes and has been verified in carefully planned experiments. For one of these data sets, the 1999 Chilpancingo (Mexico) experiment, there are some records of earthquake pre-events that undoubtedly are composed of ASN, so that the processing for coda can be tested on them. We decompose the ASN energies and study their equilibration. The scheme is inspired by the original experiment and uses the ASN recorded in an L-shaped array that allows the computation of spatial derivatives. It requires care in establishing the appropriate ranges for measuring parameters. In this search for robust indicators of diffusivity, we are led to establish that under certain circumstances, the S and P energy equilibration is a process that anticipates the diffusion regime (not necessarily isotropy), which justifies the use of horizontal-to-vertical spectral ratio in the context of diffuse-field theory.

A linear algorithm for ambient seismic noise double beamforming without explicit crosscorrelations

Geophysics ◽  
2021 ◽  
Vol 86 (1) ◽  
pp. F1-F8
Author(s):  
Eileen R. Martin
Keyword(s):  
Correlation Functions ◽  
Seismic Noise ◽  
Ambient Noise ◽  
Computational Cost ◽  
Body Waves ◽  
Ambient Seismic Noise ◽  
Noise Correlation ◽  
Dense Array ◽  
Near Surface ◽  
Noise Interferometry

Geoscientists and engineers are increasingly using denser arrays for continuous seismic monitoring, and they often turn to ambient seismic noise interferometry for low-cost near-surface imaging. Although ambient noise interferometry greatly reduces acquisition costs, the computational cost of pair-wise comparisons between all sensors can be prohibitively slow or expensive for applications in engineering and environmental geophysics. Double beamforming of noise correlation functions is a powerful technique to extract body waves from ambient noise, but it is typically performed via pair-wise comparisons between all sensors in two dense array patches (scaling as the product of the number of sensors in one patch with the number of sensors in the other patch). By rearranging the operations involved in the double beamforming transform, I have developed a new algorithm that scales as the sum of the number of sensors in two array patches. Compared to traditional double beamforming of noise correlation functions, the new method is more scalable, easily parallelized, and it does not require raw data to be exchanged between dense array patches.

Ambient seismic noise suppression in COST action G2Net

2020 ◽  
Author(s):  
Velimir Ilić ◽  
Alessandro Bertolini ◽  
Fabio Bonsignorio ◽  
Dario Jozinović ◽  
Tomasz Bulik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gravitational Waves ◽  
Seismic Noise ◽  
Noise Suppression ◽  
Real Life ◽  
Low Frequency ◽  
Ambient Seismic Noise ◽  
Cost Action ◽  
The Cost ◽  
Life Challenge

<p>The analysis of low-frequency gravitational waves (GW) data is a crucial mission of GW science and the performance of Earth-based GW detectors is largely influenced by ability of combating the low-frequency ambient seismic noise and other seismic influences. This tasks require multidisciplinary research in the fields of seismic sensing, signal processing, robotics, machine learning and mathematical modeling.<br><br>In practice, this kind of research is conducted by large teams of researchers with different expertise, so that project management emerges as an important real life challenge in the projects for acquisition, processing and interpretation of seismic data from GW detector site. A prominent example that successfully deals with this aspect could be observed in the COST Action G2Net (CA17137 - A network for Gravitational Waves, Geophysics and Machine Learning) and its seismic research group, which counts more than 30 members. </p><div>In this talk we will review the structure of the group, present the goals and recent activities of the group, and present new methods for combating the seismic influences at GW detector site that will be developed and applied within this collaboration.</div><div> <p> </p> <p>This publication is based upon work from CA17137 - A network for Gravitational Waves, Geophysics and Machine Learning, supported by COST (European Cooperation in Science and Technology).</p> </div>

Shear wave velocity model of the ABANICO formation underlying The santiago City Metropolitan Area, chile, using ambient seismic noise tomography

2020 ◽  
Author(s):  
J Salomón ◽  
C Pastén ◽  
S Ruiz ◽  
F Leyton ◽  
M Sáez ◽  
...  
Keyword(s):  
Shear Wave ◽  
Wave Velocity ◽  
Metropolitan Area ◽  
Shear Wave Velocity ◽  
Seismic Noise ◽  
Velocity Model ◽  
Dispersion Curves ◽  
Ambient Seismic Noise ◽  
Travel Times ◽  
Abanico Formation

Summary The seismic response of the Santiago City, the capital of Chile with more than 5.5 million inhabitants, is controlled by the properties of the shallower quaternary deposits and the impedance contrast with the underlying Abanico formation, among other factors. In this study, we process continuous records of ambient seismic noise to perform an ambient seismic noise tomography with the aim of defining the shallower structure of the Abanico formation underneath the densely populated metropolitan area of Santiago, Chile. The seismic signals were recorded by a network consisting of 29 broadband seismological stations and 12 accelerograph stations, located in a 35 × 35 km2 quadrant. We used the average coherency of the vertical components to calculate dispersion curves from 0.1 to 5 Hz and Bootstrap resampling to estimate the variance of the travel times. The reliable frequency band of the dispersion curves was defined by an empirical method based on sign normalization of the coherency real part. The ambient noise tomography was solved on a domain discretized into 256 2 × 2 km2 cells. Using a regularized weighted least squares inversion, we inverted the observed travel-times between stations, assuming straight ray paths, in order to obtain 2D phase velocity maps from 0.2 Hz to 1.1 Hz, linearly spaced every 0.05 Hz, in 157 of the 256 square cells of the domain. In each square cell with information, dispersion curves were assembled and used to invert shear wave velocity profiles, which were interpolated using the ordinary Kriging method to obtain a 3D shear wave velocity model valid from 0.6 to 5 km depth. The 3D velocity model shows that the Abanico formation is stiffer in the south of the study area with larger velocity anomalies towards the shallower part of the model. The value of the shear wave velocity narrows with depth, reaching an average value of 3.5 km/s from 3 to 5 km depth.

Evidence for the existence of locally-generated body waves in the short-period noise at the Large Aperture Seismic Array, Montana

1972 ◽  
Vol 62 (1) ◽  
pp. 13-29 ◽  
Author(s):  
H. M. Iyer ◽  
John H. Healy
Keyword(s):  
Statistical Analysis ◽  
Phase Velocity ◽  
High Frequency ◽  
Seismic Noise ◽  
Average Velocity ◽  
Low Frequency ◽  
Body Waves ◽  
Frequency Noise ◽  
High Frequency Noise ◽  
Short Period

Abstract The approximate hexagonal configuration of LASA subarrays enables their use as omnidirectional arrays. This property is used to study the phase velocity of short-period seismic noise at different frequencies. It is found that the noise in the low-frequency band consists mainly of surface waves traveling with average velocities in the range 3.0 to 3.5 km/sec. The high-frequency noise, in the band 0.45 to 1.0 Hz, has an average velocity of about 6.0 km/sec. It is quite likely that the high-frequency noise has the nature of locally-generated body waves. Statistical analysis of Pg velocities observed during a crustal refraction experiment at LASA lends support to this hypothesis.

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