scholarly journals Three-station interferometry and tomography: coda versus direct waves

2020 ◽  
Vol 221 (1) ◽  
pp. 521-541
Author(s):  
Shane Zhang ◽  
Lili Feng ◽  
Michael H Ritzwoller
Keyword(s):  
Green’S Function ◽  
Green's Function ◽  
Ambient Noise ◽  
Signal To Noise Ratio ◽  
Wave Dispersion ◽  
Correction Method ◽  
Coda Wave ◽  
Surface Wave Dispersion ◽  
Direct Wave ◽  
Noise Interferometry

SUMMARY Traditional two-station ambient noise interferometry estimates the Green’s function between a pair of synchronously deployed seismic stations. Three-station interferometry considers records observed three stations at a time, where two of the stations are considered receiver–stations and the third is a source–station. Cross-correlations between records at the source–station with each of the receiver–stations are correlated or convolved again to estimate the Green’s function between the receiver–stations, which may be deployed asynchronously. We use data from the EarthScope USArray in the western United States to compare Rayleigh wave dispersion obtained from two-station and three-station interferometry. Three three-station interferometric methods are distinguished by the data segment utilized (coda-wave or direct-wave) and whether the source–stations are constrained to lie in stationary phase zones approximately inline with the receiver–stations. The primary finding is that the three-station direct wave methods perform considerably better than the three-station coda-wave method and two-station ambient noise interferometry for obtaining surface wave dispersion measurements in terms of signal-to-noise ratio, bandwidth, and the number of measurements obtained, but possess small biases relative to two-station interferometry. We present a ray-theoretic correction method that largely removes the bias below 40 s period and reduces it at longer periods. Three-station direct-wave interferometry provides substantial value for imaging the crust and uppermost mantle, and its ability to bridge asynchronously deployed stations may impact the design of seismic networks in the future.

scholarly journals Tutorial on seismic interferometry: Part 1 — Basic principles and applications

Geophysics ◽  
2010 ◽  
Vol 75 (5) ◽  
pp. 75A195-75A209 ◽  
Author(s):  
Kees Wapenaar ◽  
Deyan Draganov ◽  
Roel Snieder ◽  
Xander Campman ◽  
Arie Verdel
Keyword(s):  
Surface Wave ◽  
Plane Wave ◽  
Green’S Function ◽  
Green's Function ◽  
Ambient Noise ◽  
Source Function ◽  
Stationary Points ◽  
Seismic Interferometry ◽  
Direct Wave ◽  
Reflected Wave

Seismic interferometry involves the crosscorrelation of responses at different receivers to obtain the Green’s function between these receivers. For the simple situation of an impulsive plane wave propagating along the [Formula: see text]-axis, the crosscorrelation of the responses at two receivers along the [Formula: see text]-axis gives the Green’s function of the direct wave between these receivers. When the source function of the plane wave is a transient (as in exploration seismology) or a noise signal (as in passive seismology), then the crosscorrelation gives the Green’s function, convolved with the autocorrelation of the source function. Direct-wave interferometry also holds for 2D and 3D situations, assuming the receivers are surrounded by a uniform distribution of sources. In this case, the main contributions to the retrieved direct wave between the receivers come from sources in Fresnel zones around stationary points. The main application of direct-wave interferometry is theretrieval of seismic surface-wave responses from ambient noise and the subsequent tomographic determination of the surface-wave velocity distribution of the subsurface. Seismic interferometry is not restricted to retrieving direct waves between receivers. In a classic paper, Claerbout shows that the autocorrelation of the transmission response of a layered medium gives the plane-wave reflection response of that medium. This is essentially 1D reflected-wave interferometry. Similarly, the crosscorrelation of the transmission responses, observed at two receivers, of an arbitrary inhomogeneous medium gives the 3D reflection response of that medium. One of the main applications of reflected-wave interferometry is retrieving the seismic reflection response from ambient noise and imaging of the reflectors in the subsurface. A common aspect of direct- and reflected-wave interferometry is that virtual sources are created at positions where there are only receivers without requiring knowledge of the subsurface medium parameters or of the positions of the actual sources.

Interactive processing to obtain interstation surface-wave dispersion

1991 ◽  
Vol 81 (3) ◽  
pp. 931-947
Author(s):  
E. A. Dean ◽  
G. R. Keller
Keyword(s):  
Surface Wave ◽  
Green’S Function ◽  
Least Squares ◽  
Group Velocity ◽  
Green's Function ◽  
Wave Dispersion ◽  
Standard Errors ◽  
Phase Matching ◽  
Surface Wave Dispersion ◽  
Processing Scheme

Abstract A processing scheme for the analysis of surface-wave dispersion is presented. This scheme involves preprocessing seismograms, computing the interstation Green's function, and determining the self-consistent phase and group dispersion with standard errors for one or more events for a two-station path. Time-variable filtering is employed, based on group velocity that is computer-selected by the multiple filter technique and refined by phase matching iteration. The interstation Green's function is frequency filtered to remove spikes from the spectrum. The interstation group velocity, perturbed by standard errors and refined by phase matching, is used to determine phase velocity and its uncertainty. Self-consistency between phase and group velocity is obtained by a simultaneous least-squares method, which ensures the correct functional relation for the two dispersed velocities. The uncertainty in dispersion is computed from the covariance matrix of the simultaneous least-squares solution. The technique is evaluated by comparing the analyzed dispersion for a path along the Andean Cordillera with results employing other techniques.

scholarly journals Imaging the subsurface using induced seismicity and ambient noise: 3-D tomographic Monte Carlo joint inversion of earthquake body wave traveltimes and surface wave dispersion

2020 ◽  
Vol 222 (3) ◽  
pp. 1639-1655
Author(s):  
Xin Zhang ◽  
Corinna Roy ◽  
Andrew Curtis ◽  
Andy Nowacki ◽  
Brian Baptie
Keyword(s):  
Surface Wave ◽  
Ambient Noise ◽  
Velocity Structure ◽  
Joint Inversion ◽  
Body Wave ◽  
Source Parameters ◽  
Wave Dispersion ◽  
Inversion Method ◽  
Surface Wave Dispersion ◽  
Wave Data

SUMMARY Seismic body wave traveltime tomography and surface wave dispersion tomography have been used widely to characterize earthquakes and to study the subsurface structure of the Earth. Since these types of problem are often significantly non-linear and have non-unique solutions, Markov chain Monte Carlo methods have been used to find probabilistic solutions. Body and surface wave data are usually inverted separately to produce independent velocity models. However, body wave tomography is generally sensitive to structure around the subvolume in which earthquakes occur and produces limited resolution in the shallower Earth, whereas surface wave tomography is often sensitive to shallower structure. To better estimate subsurface properties, we therefore jointly invert for the seismic velocity structure and earthquake locations using body and surface wave data simultaneously. We apply the new joint inversion method to a mining site in the United Kingdom at which induced seismicity occurred and was recorded on a small local network of stations, and where ambient noise recordings are available from the same stations. The ambient noise is processed to obtain inter-receiver surface wave dispersion measurements which are inverted jointly with body wave arrival times from local earthquakes. The results show that by using both types of data, the earthquake source parameters and the velocity structure can be better constrained than in independent inversions. To further understand and interpret the results, we conduct synthetic tests to compare the results from body wave inversion and joint inversion. The results show that trade-offs between source parameters and velocities appear to bias results if only body wave data are used, but this issue is largely resolved by using the joint inversion method. Thus the use of ambient seismic noise and our fully non-linear inversion provides a valuable, improved method to image the subsurface velocity and seismicity.

Linearly filtered estimation of the time-domain Green’s function from measurements of ambient noise

2008 ◽  
Vol 124 (5) ◽  
pp. 2699-2701 ◽  
Author(s):  
S. A. Albahrani ◽  
M. R. Frater ◽  
E. H. Huntington
Keyword(s):  
Green’S Function ◽  
Green's Function ◽  
Time Domain ◽  
Ambient Noise ◽  
The Time Domain

Estimation of group velocity of Green’s function in the sedimentary basin from crosscorrelation of ambient noise around

2019 ◽  
Author(s):  
K. Chimoto ◽  
H. Yamanaka ◽  
S. Tsuno ◽  
M. Korenaga ◽  
H. Miyake ◽  
...  
Keyword(s):  
Green’S Function ◽  
Group Velocity ◽  
Green's Function ◽  
Ambient Noise ◽  
Sedimentary Basin
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