Surface Wave Retrieval from Ambient Noise using Multi–dimensional Deconvolution
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Seismic interferometry is an effective tool to retrieve surface waves between two receiver stations by cross-correlating ambient background noise over sufficiently long recording times. This method assumes an azimuthally uniform distribution of noise sources. Unfortunately this assumption is not always fulfilled in practice. If noise sources are located on one side of a receiver array only, surface waves can also be retrieved by multi-dimensional deconvolution of passive records. We show how this method can effectively correct for azimuthal variations in the noise source distribution. We do not take backscattering of the surface waves into account, but this can be overcome if wavefield decomposition is incorporated.Keywords:
Seismic interferometry
Ambient noise level
Reflection
Ambient noise-based seismology is fast expanding and has been widely applied to global and regional Earth′s interior imaging,near-surface investigation,and oil and gas exploration and production.The review article briefly introduced the origins of ambient noises and traced the root and development history of ambient noise-based seismology.Based on numerous work of modeling and observation,we reviewed the effects of source distribution and station separation on Green′s function retrieved for full fields and single mode surface-wave.The theoretical connection and difference between two-station correlation and spatial auto-correlation are also discussed.We then described the methods of ambient noise-based imaging,including ambient noise-based tomography,ambient noise-based eikonal tomography,and seismic interferometry or virtual source method.Finally we summarized its various but emphasizing on near-surface applications and gave an outlook for its future development.
Ambient noise level
Seismic interferometry
Seismic Noise
Mode (computer interface)
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Deconvolution interferometry successfully recovers the impulse response between two receivers without the need for an independent estimate of the source function. Here we extend the method of interferometry by deconvolution to multicomponent data in elastic media. As in the acoustic case, elastic deconvolution interferometry retrieves only causal scattered waves that propagate between two receivers as if one acts as a pseudosource of the point-force type. Interferometry by deconvolution in elastic media also generates artifacts because of a clamped-point boundary condition imposed by the deconvolution process. In seismic-while-drilling (SWD) practice, the goal is to determine the subsurface impulse response from drill-bit noise records. Most SWD technologies rely on pilot sensors and/or models to predict the drill-bit source function, whose imprint is then removed from the data. Interferometry by deconvolution is of most use to SWD applications in which pilot records are absent or provide unreliable estimates of bit excitation. With a numerical SWD subsalt example, we show that deconvolution interferometry provides an image of the subsurface that cannot be obtained by correlations without an estimate of the source autocorrelation. Finally, we test the use of deconvolution interferometry in processing SWD field data acquired at the San Andreas Fault Observatory at Depth (SAFOD). Because no pilot records were available for these data, deconvolution outperforms correlation in obtaining an interferometric image of the San Andreas fault zone at depth.
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Impulse response
Point spread function
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One of the applications of seismic interferometry (SI) by cross-correlation is the retrieval of the reflection response of the subsurface from ambient seismic noise recorded at the surface. We apply SI to ambient-noise data recorded in a desert area in No
Ambient noise level
Seismic interferometry
Seismic Noise
Reflection
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Seismic interferometry can be used to extract useful information about Earth's subsurface from the ambient noise wave field. It is an important new tool for exploring seismically quiescent areas. The method involves extraction of empirical Green's function from the background ambient vibrations of the Earth, followed by computation of group or phase velocity and tomographic imaging. Here we provide a review of seismic interferometry and ambient noise tomography (ANT) and present an example of the method in south India.
Ambient noise level
Seismic interferometry
Seismic Noise
Seismic Tomography
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Seismic While Drilling(SWD)is a new technology applied in the field of petroleum.It uses the continuous random vibration to make bit forecast by cross-correlation between the source function of thousands meters underground and the wave recorded on the surface.The key issue of the SWD is how to obtain the seismic source function at such a depth which blocks the wide application of SWD.The deconvolution interferometry,developed based on cross-correlation,is another technology that can be used in SWD.It doesn't need the source function and can derive the Green's function of station pairs by the ambient noise,and image the structure and velocity property of the subsurface.It can eliminate the effects of the source and get good image result.This paper reviews the developments of the deconvolution interferometry,and analyzes the theory and characters of the cross-correlation and deconvolution interferometry.With an example of deconvolution interferometry application to the SWD data processing,it demonstrates the great potential of deconvolution interferometry in SWD,and provides important information for new technology developments of SWD.
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Seismic interferometry is a field of growing interest in exploration seismology. In this paper we provide the theoretical basis for performing interferometry by deconvolution. We argue that for general models, deconvolution interferometry gives only the causal scattering response between any two receivers, as opposed to cross‐correlation which gives both causal and acausal scattering responses. Deconvolution interferometry also gives rise to a spurious event not present in cross‐correlation. Through a simple model, we gain physical insight about the meaning of each term in deconvolution interferometry. We also show deconvolution interferometry can also be accomplished after summation over sources. We demonstrate the feasibility of deconvolution interferometry with numerical examples on with impulsive sources, and show that the deconvolution interferometry artifacts are not mapped onto the image space. Finally, we show that deconvolution interferometry can successfully image drill‐bit source data without independent estimates of the source function with quality comparable to impulsive source data.
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Spurious relationship
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Summary In recent years there has been increasing interest in the study of so-called ambient noise seismic interferometry. This method is used to extract meaningful information from long recordings (hours to days) of ambient seismic noise. This meaningful information is extracted in the form of wavefields propagating between those receiver positions at which the noise was recorded, i.e., as if a source had been placed at one of those locations - a so-called “virtual source”. The method has found most success in global/regional seismology where low-frequency (sub-1 Hz) fundamental mode surface waves are extracted by cross-correlating months of ambient noise recorded on two or more receiver stations. Whereas the most successful applications of the method have been in recovering surface waves propagating between receiver locations, other successful applications have seen the recovery of body waves. Another very appealing aspect of the ambient noise interferometry is the possibility to use it for time-lapse or continuous un-invasive monitoring of the subsurface properties.
Seismic interferometry
Ambient noise level
Seismic Noise
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We discuss seismic interferometry by multi-dimensional deconvolution for controlled-source data as well as for passive data and compare both approaches with the corresponding correlation-based interferometric methods. For the controlled-source situation we derive the virtual source method as an approximation of the multi-dimensional deconvolution method. For the passive data situation we show that the deconvolution method and the cross-correlation method are essentially different, and we discuss the merits and drawbacks of both approaches.
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Passive seismic interferometry is a new promising methodology for seismic exploration. Interferometry allows information about the subsurface structure to be extracted from ambient seismic noise. In this study, we apply the cross-correlation technique to approximately 25 hr of recordings of ambient seismic noise at the Ketzin experimental CO2 storage site, Germany. Common source gathers were generated from the ambient noise for all available receivers along two seismic lines by cross-correlation of noise records. This methodology isolates the interstation Green's functions that can be directly compared to active source gathers. We show that the retrieved response includes surface waves, refracted waves and reflected waves. We use the dispersive behaviour of the retrieved surface waves to infer geological properties in the shallow subsurface and perform passive seismic imaging of the subsurface structure by processing the retrieved reflected waves.
Seismic interferometry
Seismic Noise
Ambient noise level
Passive seismic
Vertical seismic profile
Cross-correlation
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Seismic Interferometry (SI) is the process of generating seismic traces from the crosscorrelation of existing traces. One application of SI is the retrieval of surface-wave arrivals between two passive stations at the Earth’s surface from the crosscorrelation of ambient noise. Another application is the retrieval of body-wave reflections from the crosscorrelation of ambient noise recorded at the Earth's surface. Retrieved reflections would afford the construction of subsurface velocity models and subsurface reflection images with higher resolution than provided by surface-wave tomography. So far the extraction of body-wave reflections has proven to be more challenging. Several factors contribute to this difficulty: e.g., the difference in geometrical spreading between body and surface waves and the reliance on a random distribution of noise sources in the subsurface, as opposed to the ubiquitous and well-studied surface noise.
Seismic interferometry
Ambient noise level
Reflection
Seismic Noise
Passive seismic
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