Extraction of Surface-Wave Dispersion Curves from Ambient Noise Data in a Mineral Exploration Site in Finland

ABSTRACT The subsurface shear-wave structure primarily determines the characteristics of the surface-wave dispersion curve theoretically and observationally. Therefore, surface-wave dispersion curve inversion is extensively applied in imaging subsurface shear-wave velocity structures. The frequency–Bessel transform method can effectively extract dispersion spectra of high quality from both ambient seismic noise data and earthquake events data. However, manual picking and semiautomatic methods for dispersion curves lack a unified criterion, which impacts the results of inversion and imaging. In addition, conventional methods are insufficiently efficient; more precisely, a large amount of time is required for curve extraction from vast dispersion spectra, especially in practical applications. Thus, we propose DisperNet, a neural network system, to extract and discriminate the different modes of the dispersion curve. DisperNet consists of two parts: a supervised network for dispersion curve extraction and an unsupervised method for dispersion curve classification. Dispersion spectra from ambient noise and earthquake events are applied in training and validation. A field data test and transfer learning test show that DisperNet can stably and efficiently extract dispersion curves. The results indicate that DisperNet can significantly improve multimode surface-wave imaging.

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Extracting surface wave dispersion curves from asynchronous seismic stations: method and application

Geophysical Journal International ◽

10.1093/gji/ggab153 ◽

2021 ◽

Author(s):

Hao Rao ◽

Yinhe Luo ◽

Kaifeng Zhao ◽

Yingjie Yang

Keyword(s):

Surface Wave ◽

Phase Velocity ◽

Ambient Noise ◽

Wave Dispersion ◽

Surface Wave Dispersion ◽

Seismic Stations ◽

Seismic Arrays ◽

Single Station ◽

Path Coverage ◽

Noise Data

Summary Correlation of the coda of Empirical Green's functions from ambient noise can be used to reconstruct Empirical Green's function between two seismic stations deployed different periods of time. However, such method requires a number of source stations deployed in the area surrounding a pair of asynchronous stations, which limit its applicability in cases where there are not so many available source stations. Here, we propose an alternative method, called two-station C2 method, which uses one single station as a virtual source to retrieve surface wave phase velocities between a pair of asynchronous stations. Using ambient noise data from USArray as an example, we obtain the interstation C2 functions using our C2 method and the traditional cross-correlation functions (C1 functions). We compare the differences between the C1 and C2 functions in waveforms, dispersion measurements, and phase velocity maps. Our results show that our C2 method can obtain reliable interstation phase velocity measurements, which can be used in tomography to obtain reliable phase velocity maps. Our method can significantly improve ray path coverage from asynchronous seismic arrays and enhance the resolution in ambient noise tomography for areas between asynchronous seismic arrays.

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COMPARISON OF AMBIENT NOISE METHODS TO FIND SURFACE - WAVE DISPERSION CURVES AT PACAYA VOLCANO, GUATEMALA

10.37099/mtu.dc.etdr/377 ◽

2017 ◽

Author(s):

Simone Puel

Keyword(s):

Surface Wave ◽

Ambient Noise ◽

Wave Dispersion ◽

Dispersion Curves ◽

Surface Wave Dispersion ◽

Pacaya Volcano

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Processing seismic ambient noise data to obtain reliable broad-band surface wave dispersion measurements

Geophysical Journal International ◽

10.1111/j.1365-246x.2007.03374.x ◽

2007 ◽

Vol 169 (3) ◽

pp. 1239-1260 ◽

Cited By ~ 987

Author(s):

G. D. Bensen ◽

M. H. Ritzwoller ◽

M. P. Barmin ◽

A. L. Levshin ◽

F. Lin ◽

...

Keyword(s):

Surface Wave ◽

Ambient Noise ◽

Broad Band ◽

Wave Dispersion ◽

Surface Wave Dispersion ◽

Seismic Ambient Noise ◽

Noise Data

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A Feasibility Study on Monitoring Crustal Structure Variations by Direct Comparison of Surface Wave Dispersion Curves from Ambient Seismic Noise

International Journal of Geophysics ◽

10.1155/2020/5269537 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

K. Muhumuza

Keyword(s):

Surface Wave ◽

Rayleigh Wave ◽

Crustal Structure ◽

Upper Mantle ◽

Ambient Noise ◽

Seismic Velocity ◽

Wave Dispersion ◽

Dispersion Curves ◽

Wave Group ◽

Surface Wave Dispersion

This work assesses the feasibility of the direct use of surface-wave dispersion curves from seismic ambient noise to gain insight into the crustal structure of Bransfield Strait and detect seasonal seismic velocity changes. We cross-correlated four years of vertical component ambient noise data recorded by a seismic array in West Antarctica. To estimate fundamental mode Rayleigh wave Green’s functions, the correlations are computed in 4-hr segments, stacked over 1-year time windows and moving windows of 3 months. Rayleigh wave group dispersion curves are then measured on two spectral bands—primary (10–30 s) and secondary (5–10 s) microseisms—using frequency-time analysis. We analyze the temporal evolution of seismic velocity by comparing dispersion curves for the successive annual and 3-month correlation stacks. Our main assumption was that the Green’s functions from the cross-correlations, and thus the dispersion curves, remain invariant if the crustal structure remains unchanged. Maximum amplitudes of secondary microseisms were observed during local winter when the Southern Ocean experiences winter storms. The Rayleigh wave group velocity ranges between 2.1 and 3.7 km/s, considering our period range studied. Interannual velocity variations are not much evident. We observe a slight velocity decrease in summer and increase in winter, which could be attributed to the pressure melting of ice and an increase in ice mass, respectively. The velocity anomalies observed within the crust and upper mantle structure correlate with the major crustal and upper mantle features known from previous studies in the area. Our results demonstrate that the direct comparison of surface wave dispersion curves extracted from ambient noise might be a useful tool in monitoring crustal structure variations.

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Phase-weighted slant stacking for surface wave dispersion measurement

Geophysical Journal International ◽

10.1093/gji/ggab101 ◽

2021 ◽

Vol 226 (1) ◽

pp. 256-269

Author(s):

Feng Cheng ◽

Jianghai Xia ◽

Kai Zhang ◽

Changjiang Zhou ◽

Jonathan B Ajo-Franklin

Keyword(s):

Surface Wave ◽

Ambient Noise ◽

Wave Dispersion ◽

Source Surface ◽

Coherent Signals ◽

Surface Wave Dispersion ◽

Data Set ◽

Dispersion Measurement ◽

Noise Data ◽

Slant Stacking

SUMMARY Surface wave retrieval from ambient noise records using seismic interferometry techniques has been widely used for multiscale shear wave velocity (Vs) imaging. One key step during Vs imaging is the generation of dispersion spectra and the extraction of a reliable dispersion curve from the retrieved surface waves. However, the sparse array geometry usually affects the ability for high-frequency (>1 Hz) seismic signals’ acquisition. Dispersion measurements are degraded by array response due to sparse sampling and often present smeared dispersion spectra with sidelobe artefacts. Previous studies usually focus on interferograms’ domain (e.g. cross-correlation function) and attempt to enhance coherent signals before dispersion measurement. We propose an alternative technique to explicitly deblur dispersion spectra through use of a phase-weighted slant-stacking algorithm. Numerical examples demonstrate the strength of the proposed technique to attenuate array responses as well as incoherent noise. Three different field examples prove the flexibility and superiority of the proposed technique: the first data set consists of ambient noise records acquired using a nodal seismometer array; the second data set utilizes distributed acoustic sensing (DAS) and a marine fibre-optic cable to acquire a similar ambient noise data set; the last data set is a vibrator-based active-source surface wave data. The enhanced dispersion measurements provide cleaner and higher-resolution spectra without distortions which will assist both human interpreters as well as ML algorithms in efficiently picking curves for subsequent Vs inversion.

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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

Geophysical Journal International ◽

10.1093/gji/ggaa230 ◽

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.

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