scholarly journals An adapted eigenvalue-based filter for ocean ambient noise processing

Geophysics ◽  
2020 ◽  
Vol 85 (1) ◽  
pp. KS29-KS38 ◽  
Author(s):  
Guoli Wu ◽  
Hefeng Dong ◽  
Ganpan Ke ◽  
Junqiang Song
Keyword(s):  
Covariance Matrix ◽  
Ambient Noise ◽  
Green's Functions ◽  
Green’S Functions ◽  
Homogeneous Distribution ◽  
Noise Sources ◽  
Data Set ◽  
Recording Time ◽  
Receiver Array ◽  
Sample Covariance

Accurate approximations of Green’s functions retrieved from the correlations of ambient noise require a homogeneous distribution of random and uncorrelated noise sources. In the real world, the existence of highly coherent, strong directional noise generated by ships, earthquakes, and other human activities can result in biases in the ambient-noise crosscorrelations (NCCs). We have developed an adapted eigenvalue-based filter to attenuate the interference of strong directional sources. The filter is based on the statistical model of the sample covariance matrix and can separate different components of the data covariance matrix in the eigenvalue spectrum. To improve the effectiveness and make it adaptable for different data sets, a weight is introduced to the filter. Then, the NCCs can be calculated directly from the filtered data covariance matrix. This approach is applied to a 1.02 h data set of ambient noise recorded by a permanent reservoir monitoring receiver array installed on the seabed. The power spectral density indicates that the noise recordings were contaminated by strong directional noise over nearly half of the whole observation period. Beamforming and crosscorrelation results indicate that the interference still exists even after applying traditional temporal and spectral normalization techniques, whereas the adapted eigenvalue-based filter can significantly attenuate it and help to obtain improved crosscorrelations. The approach makes it possible to retrieve reliable approximations of Green’s functions over a much shorter recording time.

scholarly journals Automatic 3D illumination-diagnosis method for large-N arrays: Robust data scanner and machine-learning feature provider

Geophysics ◽  
2019 ◽  
Vol 84 (3) ◽  
pp. Q13-Q25 ◽  
Author(s):  
Michał Chamarczuk ◽  
Michał Malinowski ◽  
Yohei Nishitsuji ◽  
Jan Thorbecke ◽  
Emilia Koivisto ◽  
...  
Keyword(s):  
Ambient Noise ◽  
Body Wave ◽  
Synthetic Data ◽  
Large Data ◽  
Body Waves ◽  
Support Vector ◽  
Full Data ◽  
Noise Sources ◽  
Data Set ◽  
Receiver Array

The main issues related to passive-source reflection imaging with seismic interferometry (SI) are inadequate acquisition parameters for sufficient spatial wavefield sampling and vulnerability of surface arrays to the dominant influence of the omnipresent surface-wave sources. Additionally, long recordings provide large data volumes that require robust and efficient processing methods. We address these problems by developing a two-step wavefield evaluation and event detection (TWEED) method of body waves in recorded ambient noise. TWEED evaluates the spatiotemporal characteristics of noise recordings by simultaneous analysis of adjacent receiver lines. We test our method on synthetic data representing transient ambient-noise sources at the surface and in the deeper subsurface. We discriminate between basic types of seismic events by using three adjacent receiver lines. Subsequently, we apply TWEED to 600 h of ambient noise acquired with an approximately 1000-receiver array deployed over an active underground mine in Eastern Finland. We develop the detection of body-wave events related to mine blasts and other routine mining activities using a representative 1 h noise panel. Using TWEED, we successfully detect 1093 body-wave events in the full data set. To increase the computational efficiency, we use slowness parameters derived from the first step of TWEED as input to a support vector machine (SVM) algorithm. Using this approach, we detect 94% of the TWEED-evaluated body-wave events indicating the possibility to limit the illumination analysis to only one step, and therefore increase the time efficiency at the price of lower detection rate. However, TWEED on a small volume of the recorded data followed by SVM on the rest of the data could be efficiently used for a quick and robust (real-time) scanning for body-wave energy in large data volumes for subsequent application of SI for retrieval of reflections.

scholarly journals The Effects of Atmospheric Models on the Estimation of Infrasonic Source Functions at the Source Physics Experiment

2020 ◽  
Vol 110 (3) ◽  
pp. 998-1010 ◽  
Author(s):  
Christian Poppeliers ◽  
Lauren Bronwyn Wheeler ◽  
Leiph Preston
Keyword(s):  
Green's Functions ◽  
Time Window ◽  
Green’S Functions ◽  
Atmospheric Model ◽  
Weather Data ◽  
Atmospheric Models ◽  
Data Set ◽  
Model Type ◽  
Physics Experiment ◽  
Source Physics

ABSTRACT We invert infrasound signals for an equivalent seismoacoustic source function using different atmospheric models to produce the necessary Green’s functions. The infrasound signals were produced by a series of underground chemical explosions as part of the Source Physics Experiment (SPE). In a previous study, we inverted the infrasound data using so-called predictive atmospheric models, which were based on historic, regional-scaled, publicly available weather observations interpolated onto a 3D grid. For the work presented here, we invert the same infrasound data, but using atmospheric models based on weather data collected in a time window that includes the approximate time of the explosion experiments, which we term postdictive models. We build two versions of the postdictive models for each SPE event: one that is based solely on the regional scaled observations, and one that is based on both regional scaled observations combined with on-site observations obtained by a weather sonde released at the time of the SPE. We then invert the observed data set three times, once for each atmospheric model type. We find that the estimated seismoacoustic source functions are relatively similar in waveform shape regardless of which atmospheric model that we used to construct the Green’s functions. However, we find that the amplitude of the estimated source functions is systematically dependent on the atmospheric model type: using the predictive atmospheric models to invert the data generally yields estimated source functions that are larger in amplitude than those estimated using the postdictive models.

scholarly journals Seismic interferometry: Reconstructing the earth’s reflection response

Geophysics ◽  
2006 ◽  
Vol 71 (4) ◽  
pp. SI61-SI70 ◽  
Author(s):  
Deyan Draganov ◽  
Kees Wapenaar ◽  
Jan Thorbecke
Keyword(s):  
Particle Velocity ◽  
Green's Functions ◽  
Green’S Functions ◽  
Reciprocity Theorem ◽  
Depth Image ◽  
Acoustic Medium ◽  
S Wave ◽  
Noise Sources ◽  
Subsurface Structures ◽  
Area Of Interest

In 1968, Jon Claerbout showed that the reflection response of a 1D acoustic medium can be reconstructed by autocorrelating the transmission response. Since then, several authors have derived relationships for reconstructing Green’s functions at the surface, using crosscorrelations of (noise) recordings that were taken at the surface and that derived from subsurface sources. For acoustic media, we review relations between the reflection response and the transmission response in 3D inhomogeneous lossless media. These relations are derived from a one-way wavefield reciprocity theorem. We use modeling results to show how to reconstruct the reflection response in the presence of transient subsurface sources with distinct excitation times, as well as in the presence of simultaneously acting noise sources in the subsurface. We show that the quality of reconstructed reflections depends on the distribution of the subsurface sources. For a situation with enough subsurface sources — that is, for a distribution that illuminates the subsurface area of interest from nearly alldirections — the reconstructed reflection responses and the migrated depth image exhibit all the reflection events and the subsurface structures of interest, respectively. With only a few subsurface sources, that is, with insufficient illumination, the reconstructed reflection responses are noisy and can even become kinematically incorrect. At the same time, however, the depth image, which was obtained from their migration, still shows clearly all the illuminated subsurface structures at their correct positions. For the elastic case, we review a relationship between the reflection Green’s functions and the transmission Green’s functions derived from a two-way wavefield reciprocity theorem. Using modeling examples, we show how to reconstruct the different components of the particle velocity observed at the surface and resulting from a surface traction source. This reconstruciton is achieved using crosscorrelations of particle velocity components measured at the surface and resulting from separate P- and S-wave sources in the subsurface.

Seabed property estimation from ambient-noise recordings: Part 2 — Scholte-wave spectral-ratio inversion

Geophysics ◽  
2007 ◽  
Vol 72 (4) ◽  
pp. U47-U53 ◽  
Author(s):  
Everhard Muyzert
Keyword(s):  
Ambient Noise ◽  
Spectral Ratio ◽  
Shear Velocity ◽  
Velocity Model ◽  
Surface Shear ◽  
Data Set ◽  
Recording Time ◽  
Near Surface ◽  
Scholte Waves ◽  
Scholte Wave

Having knowledge of the near-surface shear-velocity model is useful for various seismic processing methods such as shear-wave static estimation, wavefield separation, and geohazard prediction. I present a new method to derive a 2D near-surface shear-velocity model from ambient-noise recordings made at the seafloor. The method relies on inverting horizontal- and vertical-amplitude spectra of Scholte waves propagating in the seafloor. I compare the commonly used horizontal-over-vertical spectral ratio with three alternative spectral-ratio definitions through modeling. The modeling shows that the vertical-over-total spectral ratio has some favorable properties for inversion. I describe a nonlinear inversion method for the vertical-to-total spectral ratio of the Scholte waves and apply it to an ambient-noise data set recorded by an ocean-bottom-cable (OBC) system. A 1D near-surface shear-velocity model is derived through a joint inversion of the spectral-ratio and phase-velocity data. A 2D shear-velocity model is obtained through a local inversion of the spectral ratios averaged over small groups of receivers and shows evidence for lateral heterogeneity. The newly developed method for deriving near-surface shear-velocity distribution by inverting the Scholte-wave spectral ratio measured from seabed noise provides great opportunities for estimating the shallow-seabed shear velocity in deep water. Another benefit of the method is that, with the OBC system, no additional hardware is needed; only additional recording time is required. In this case, half an hour is sufficient.

scholarly journals Seismic interferometry from correlated noise sources

2021 ◽  
Author(s):  
Daniella Ayala ◽  
Andrew Curtis ◽  
Michal Branicki
Keyword(s):  
Ambient Noise ◽  
Real Life ◽  
Seismic Energy ◽  
Seismic Interferometry ◽  
Correlated Noise ◽  
Highway Traffic ◽  
Noise Sources ◽  
Estimation Errors ◽  
Space And Time ◽  
Receiver Array

<p>It is a well-established principle that cross-correlating seismic observations at different receiver locations yields new seismic responses that, under certain conditions, provide a useful estimate of the Green's function between the given receiver locations (that is, the medium response at one receiver location, had there been an impulsive source located at the other receiver). This principle, known as seismic interferometry, is a powerful technique that transforms previously discarded data such as seismic codas or background noise into useful signals that allow us to remotely illuminate subsurface Earth structures.</p><p> </p><p>In practice it is often necessary and even desirable to rely on noise already present in the environment, since this type of seismic energy is freely and widely available in many regions around the globe.  Across many applications of ambient noise interferometry there exists a persistent assumption that the noise sources in question are uncorrelated in space and time, and that energy arrives at the receiver array more-less equally from all directions. That this assumption is so tenaciously made comes as no surprise since the underlying theory unambiguously requires that the noise sources be uncorrelated for interferometry to work.</p><p> </p><p>However, many real-world noise sources such as trains or highway traffic are inherently correlated both in space and time, in direct contradiction to these theoretical foundations. Violating the uncorrelatedness condition makes the Green’s function and associated phases liable to estimation errors that so far have not been accounted for. We show that these errors are indeed significant for commonly used noise sources, in some cases completely obscuring the phase one wishes to retrieve. Furthermore, we perform analysis that explains why stacking has the potential to reduce these errors in the interferometric estimate, as well as some limitations of this approach. This analytical insight allowed us to develop a novel workflow that mitigates or even completely removes the spurious effects arising from the use of correlated noise sources. Our methodology can be used in conjunction with already existing approaches, and hence we expect it to be widely applicable in real life ambient noise studies.</p>

Emergence of Acoustic Green's Functions from Time Averages of Ambient Noise

2011 ◽  
Vol 97 (1) ◽  
pp. 44-53 ◽  
Author(s):  
Nikolay A. Zabotin ◽  
Oleg A. Godin
Keyword(s):  
Ambient Noise ◽  
Green's Functions ◽  
Green’S Functions ◽  
Time Averages

Estimation of surface wave Green's functions from correlation of direct waves, coda waves, and ambient noise in SE Tibet

2009 ◽  
Vol 177 (1-2) ◽  
pp. 1-11 ◽  
Author(s):  
Huajian Yao ◽  
Xander Campman ◽  
Maarten V. de Hoop ◽  
Robert D. van der Hilst
Keyword(s):  
Surface Wave ◽  
Ambient Noise ◽  
Green's Functions ◽  
Green’S Functions ◽  
Coda Waves ◽  
Se Tibet

Green’s function implementation of common‐offset, wave‐equation migration

Geophysics ◽  
1996 ◽  
Vol 61 (6) ◽  
pp. 1813-1821 ◽  
Author(s):  
Andreas Ehinger ◽  
Patrick Lailly ◽  
Kurt J. Marfurt
Keyword(s):  
Wave Equation ◽  
Green's Functions ◽  
Wave Equations ◽  
Green’S Functions ◽  
Data Set ◽  
Kirchhoff Migration ◽  
Migration Velocity Analysis ◽  
Standard Wave ◽  
The Cost ◽  
Wavefield Extrapolation

Common‐offset migration is extremely important in the context of migration velocity analysis (MVA) since it generates geologically interpretable migrated images. However, only a wave‐equation‐based migration handles multipathing of energy in contrast to the popular Kirchhoff migration with first‐arrival traveltimes. We have combined the superior treatment of multipathing of energy by wave‐equation‐based migration with the advantages of the common‐offset domain for MVA by implementing wave‐equation migration algorithms via the use of finite‐difference Green’s functions. With this technique, we are able to apply wave‐equation migration in measurement configurations that are usually considered to be of the realm of Kirchhoff migration. In particular, wave‐equation migration of common offset sections becomes feasible. The application of our wave‐equation, common‐offset migration algorithm to the Marmousi data set confirms the large increase in interpretability of individual migrated sections, for about twice the cost of standard wave‐equation common‐shot migration. Our implementation of wave‐equation migration via the Green’s functions is based on wavefield extrapolation via paraxial one‐way wave equations. For these equations, theoretical results allow us to perform exact inverse extrapolation of wavefields.

Sign in / Sign up

Export Citation Format

Share Document