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

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.

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

2007 ◽  
Vol 169 (3) ◽  
pp. 1239-1260 ◽  
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

scholarly journals Phase-weighted slant stacking for surface wave dispersion measurement

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.

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.

Multiple geophysical investigations to characterize massive slope failure deposits: application to the Balta rockslide, Carpathians

2021 ◽  
Vol 225 (2) ◽  
pp. 1032-1047
Author(s):  
A-S Mreyen ◽  
L Cauchie ◽  
M Micu ◽  
A Onaca ◽  
H-B Havenith
Keyword(s):  
Surface Wave ◽  
Ambient Noise ◽  
Slope Failure ◽  
Seismic Refraction ◽  
P Wave ◽  
Surface Wave Dispersion ◽  
Seismic Region ◽  
Valley Bottom ◽  
Single Station ◽  
Landslide Body

SUMMARY Origins of ancient rockslides in seismic regions can be controversial and must not necessarily be seismic. Certain slope morphologies hint at a possible coseismic development, though further analyses are required to better comprehend their failure history, such as modelling the slope in its pre-failure state and failure development in static and dynamic conditions. To this effect, a geophysical characterization of the landslide body is crucial to estimate the possible failure history of the slope. The Balta rockslide analysed in this paper is located in the seismic region of Vrancea-Buzau, Romanian Carpathian Mountains and presents a deep detachment scarp as well as a massive body of landslide deposits. We applied several geophysical techniques on the landslide body, as well as on the mountain crest above the detachment scarp, in order to characterize the fractured rock material as well as the dimension of failure. Electrical resistivity measurements revealed a possible trend of increasing fragmentation of rockslide material towards the valley bottom, accompanied by increasing soil moisture. Several seismic refraction surveys were performed on the deposits and analysed in form of P-wave refraction tomographies as well as surface waves, allowing to quantify elastic parameters of rock. In addition, a seismic array was installed close to the detachment scarp to analyse the surface wave dispersion properties from seismic ambient noise; the latter was analysed together with a colocated active surface wave analysis survey. Single-station ambient noise measurements completed all over the slope and deposits were used to further reveal impedance contrasts of the fragmented material over in situ rock, representing an important parameter to estimate the depth of the shearing horizon at several locations of the study area. The combined methods allowed the detection of a profound contrast of 70–90 m, supposedly associated with the maximum landslide material thickness. The entirety of geophysical results was used as basis to build up a geomodel of the rockslide, allowing to estimate the geometry and volume of the failed mass, that is, approximately 28.5–33.5 million m3.

A crust and upper mantle model for Novaya Zemlya from Rayleigh-wave dispersion data

1978 ◽  
Vol 68 (6) ◽  
pp. 1651-1662
Author(s):  
Douglas W. McCowan ◽  
Peter Glover ◽  
Shelton S. Alexander
Keyword(s):  
Phase Velocity ◽  
Rayleigh Wave ◽  
Shear Wave ◽  
Upper Mantle ◽  
Wave Dispersion ◽  
Surface Wave Dispersion ◽  
Crust And Upper Mantle ◽  
Novaya Zemlya ◽  
Phase Velocity Dispersion ◽  
Single Station

abstract We derive a shear-wave crust and upper mantle structure for the southern part of Novaya Zemlya by an application of the two-event, single-station method of Rayleigh-wave phase-velocity dispersion analysis. This method provides a means of isolating the surface-wave dispersion characteristics of a remote source region using only teleseismic recordings. The observed phase-velocity data are then systematically inverted to obtain a best-fitting model. Our preferred model has a 45-km thick crust with no shear-wave low-velocity zone in the upper mantle. It is similar to published structures for the southern Ural mountains and is therefore compatible with the premise that Novaya Zemlya is a nothern extension of the Urals.

DisperNet: An Effective Method of Extracting and Classifying the Dispersion Curves in the Frequency–Bessel Dispersion Spectrum

2021 ◽  
Author(s):  
Sheng Dong ◽  
Zhengbo Li ◽  
Xiaofei Chen ◽  
Lei Fu
Keyword(s):  
Surface Wave ◽  
Dispersion Curve ◽  
Shear Wave ◽  
Wave Structure ◽  
Wave Dispersion ◽  
Dispersion Curves ◽  
Surface Wave Dispersion ◽  
Transform Method ◽  
Practical Applications ◽  
Noise Data

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.

scholarly journals Accurate Sparse Recovery of Rayleigh Wave Characteristics Using Fast Analysis of Wave Speed (FAWS) Algorithm for Soft Soil Layers

2018 ◽  
Vol 8 (7) ◽  
pp. 1204
Author(s):  
Zhuoshi Chen ◽  
Baofeng Jiang ◽  
Jingjing Song ◽  
Wentao Wang
Keyword(s):  
Surface Wave ◽  
Phase Velocity ◽  
Dispersion Curve ◽  
Wave Speed ◽  
Soft Soil ◽  
Wave Dispersion ◽  
Surface Wave Dispersion ◽  
Fast Analysis ◽  
Soil Layers ◽  
Norm Minimization

This paper presents a novel fast analysis of wave speed (FAWS) algorithm from the waveforms recorded by a random-spaced geophone array based on a compressive sensing (CS) platform. Rayleigh-type seismic surface wave testing is excited by a hammer source and conducted to develop the phase velocity characteristics of the subsoil layers in Shenyang Metro line 9. Data are filtered by a bandpass filter bank to pursue the dispersive profiles of phase velocity at various frequencies. The Rayleigh-type surface-wave dispersion curve for the soil layers at each frequency is conducted by the ℓ1-norm minimization algorithm of CS theory. The traditional frequency-wavenumber transform technique and in-site downhole observation are employed as the comparison of the proposed technique. The experimental results indicate the proposed FAWS algorithm has a good agreement with both the results of conventional even-spaced geophone array and the in-site measurements, which provides an effective and efficient way for accurate non-destructive evaluation of the surface wave dispersion curve of the soil.

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