Near-surface Shear-wave Velocity Research Articles

Passive Seismic Imaging of Urban Environments Using Distributed Acoustic Sensing: A Case Study from Melbourne, Australia

Abstract Distributed acoustic sensing (DAS) offers a cost effective, nonintrusive method for high-resolution near-surface characterization in urban environments where conventional geophysical surveys are limited or nonexistent. However, passive imaging with DAS in urban settings presents challenges such as strong diurnal traffic noise, nonlinear array geometry, and poor fiber coupling to the ground. We repurposed a dark fiber in Melbourne, Australia, into a 25 km DAS array that traces busy arterial roads, tram routes, and orthogonal sections. By employing noise cross correlation and array beamforming, we calculated dispersion curves and successfully inverted for a near-surface shear-wave velocity model down to 100 meters. Stationary seismic sources are maximized by selecting daytime traffic signals, thereby recovering surface waves and reducing interference from acoustic waves from man-made structures in the subsurface. Poorly coupled channels, which are linked to fiber maintenance pits, are identified through cross-correlation amplitudes. The dispersion curve calculation further considers the channel orientation to avoid mixing Rayleigh and Love waves. Using a trans-dimensional Markov chain Monte Carlo sampling approach, we achieved effective model inversion without a prior reference model. The resulting near-surface profile aligns with mapped lithology and reveals previously undocumented lithological variation.

Characterization of Shallow Sedimentary Layers in the Oran Region Using Ambient Vibration Data

This study investigates the structure of shear-wave velocities (Vs) in the shallow layers of the Oran region, north-west of Algeria, using non-invasive techniques based on ambient vibration arrays. The region has experienced several moderate earthquakes, including the historical Oran earthquake of 1790. Ambient vibration measurements were carried out at 15 sites throughout the study area. Two methods were used: spatial autocorrelation (SPAC) and frequency–wavenumber analysis (f-k), which allowed us to better constrain Rayleigh wave dispersion curves. The inversion of the dispersion curves derived from the f-k analysis allowed for estimating the shear-wave velocity profiles and the Vs30 value at the sites under study. The other important result of the present study is an empirical equation that has been proposed to predict Vs30 in the Oran region. The determination of near-surface shear-wave velocity profiles is an important step in the assessment of seismic hazard. This study has demonstrated the effectiveness of using ambient vibration array techniques to estimate the soil Vs structure.

Distributed acoustic sensing for seismic surface wave data acquisition in an intertidal environment

The application of distributed acoustic sensing (DAS) for shallow marine seismic investigations is assessed, in particular with respect to the collection of seismic surface wave data in an intertidal setting. Appropriate selection and directional sensitivity of fiber-optic cables is considered and the measured data is validated with respect to conventional seismic data acquisition approaches, using geophones and hydrophones, along with independent borehole and seismic cone penetration test (SCPT) data. In terms of cable selection, a reduction in amplitude and frequency response of an armored cable is observed, when compared with an unarmored cable. For seismic surface wave surveys in an offshore environment where the cable would need to withstand significant stresses, the use of the armored variant with limited loss in frequency response may be acceptable from a practical perspective. The DAS approach also has indicated good consistency with conventional means of surface wave data acquisition, and the inverted [Formula: see text] also is very consistent with downhole SCPT data. Observed differences in phase velocity between high tide (Scholte wave propagation) and low tide (Rayleigh wave propagation) are not thought to be related to the particular type of interface wave due to shallow water depth. These differences are more likely to be related to the development of capillary forces in the partially saturated granular medium at low tide. Overall, this study demonstrates that our novel approach of DAS using seabed fiber-optic cables in the intertidal environment is capable of rapidly providing near-surface S-wave velocity data across considerable spatial scales (multikilometer) at high resolution, which is beneficial for the design of subsea cables routes and landfall locations. The associated reduction in deployment and survey duration, when compared with conventional approaches, is particularly important when working in the marine environment due to potentially short weather windows and expensive downtime.

Subsurface Imaging Using Interferometry of Distributed Acoustic Sensing Ambient Noise Measurement along a Dark Fiber Line: A Case Study in Downtown Reno, Nevada

ABSTRACT Distributed acoustic sensing (DAS) technology is an emerging field of seismic sensing that enables recording ambient noise seismic data along the entire length of a fiber-optic cable at meter-scale resolution. Such a dense spatial resolution of recordings over long distances has not been possible using traditional methods because of limited hardware resources and logistical concerns in an urban environment. The low spatial resolution of traditional passive seismic acquisition techniques has limited the accuracy of the previously generated velocity profiles in many important urban regions, including the Reno-area basin, to the top 100 m of the underlying subsurface. Applying the method of seismic interferometry to ambient noise strain rate data obtained from a dark-fiber cable allows for generating noise cross correlations, which can be used to infer shallow and deep subsurface properties and basin geometry. We gathered DAS ambient noise seismic data for this study using a 12 km portion of a dark-fiber line in Reno, Nevada. We used gathered data to generate and invert dispersion curves to estimate the near-surface shear-wave velocity structure. Comparing the generated velocity profiles with previous regional studies shows good agreement in determining the average depth to bedrock and velocity variations in the analyzed domain. A synthetic experiment is also performed to verify the proposed framework further and better understand the effect of the infrastructural cover along the cable. The results obtained from this research provide insight into the application of DAS using dark-fiber lines in subsurface characterization in urban environments. It also discusses the potential effects of the conduit that covers such permanent fiber installations on the produced inversion results.

Distributed acoustic sensing for shallow structure imaging using mechanical noise: A case study in Guangzhou, China

Distributed Acoustic Sensing (DAS) is a novel seismic detection technology that transforms fiber-optic cables into densely sampled seismic stations, showing immense potential in both urban seismology and the oil and gas industry. We deployed a ~ 182-m-long single-core fiber-optic cable in a campus under construction to record human activity and demonstrated the application for seismic imaging. The spectrogram analysis revealed significant differences in ambient noise between the daytime and nighttime at the construction site. During the daytime, the noise energy exhibited dominant frequencies ranging from 2 to 11 Hz, approximately one order of magnitude higher than that at night, which had frequencies ranging from 2 to 5 Hz. Beamforming analysis demonstrated that noise sources exhibited distinct directional characteristics and propagation velocities during daytime and nighttime periods. We extracted the cross-correlation functions between DAS channels from the mechanical noise generated by a large excavator and enhanced signal-to-noise ratio by using the phase-weighted stacking method. The multichannel analysis of surface waves (MASW) method was then employed to obtain 2D near-surface shear-wave velocity. Based on five 60-m-deep boreholes drilled along the DAS array, we established the correlation between shear-wave velocity information and shallow structure. We obtained the unbiased cross-correlation function in the presence of continuous mechanical noise along the DAS array direction, demonstrating the importance of site noise source distribution analysis in ambient noise tomography. Our research is informative for research related to the construction of accurate, high-resolution near-surface shear wave velocity models.

Evaluation of the 3D Near-Surface Velocity Structure in an Urban Environment from Ambient Noise Array Tomography: The Case of the City of Thessaloniki (Northern Greece)

Abstract We examine the implementation of ambient noise array tomography in an urban environment to assess the 3D near-surface shear wave velocity (VS) structure at an intermediate spatial scale (∼1 km2, depth range 200–300 m). The application employs cross correlation traces of vertical component ambient noise recordings from a local network installed in Thessaloniki city (Northern Greece), allowing the determination of Rayleigh wave travel times for the frequency range of 1.5–14 Hz. The results confirm the presence of a complex subsurface with strong lateral variations in the geology, with travel times varying up to almost one order of magnitude. A surface wave travel time tomography approach was applied for each frequency to determine the spatial variability of the group velocity, involving the use of approximate Fresnel volumes, as well as damping and spatial smoothing constraints to stabilize the results. We also employed an interfrequency smoothing scheme to obtain smooth but data-compatible dispersion curves at the cost of inverting all travel time data simultaneously. Following the application of several quality cutoff criteria, we reconstructed local group slowness dispersion curves for a predefined tomographic grid in the study area. The final 3D velocity model was determined by a modified Monte Carlo inversion of these dispersion curves and the spatial integration of the obtained 1D VS profiles. Different model parameterizations were tested for the inversion to determine the optimal datafit. The final 3D velocity model is in a very good agreement with the local geology, previous larger scale studies, and other geophysical surveys, providing additional structural constraints (such as hidden fault identification) for the complex sedimentary deposits and bedrock formation in Thessaloniki, up to the depth of ∼250–300 m. The introduction of the aforementioned modifications to the ambient noise array tomography suggests that it can be efficiently adjusted and employed as a reliable tool for imaging the 3D seismic structure in urban environments with complex geology.

Near-surface characterization using urban traffic noise recorded by fiber-optic distributed acoustic sensing

The recently developed fiber-optic distributed acoustic sensing (DAS) technology has attracted widespread attention in engineering applications, oil exploration, and seismological research. Compared with the conventional geophones, DAS can acquire high-resolution data due to a dense sampling and can be deployed conveniently in the complex acquisition environment. These advantages of DAS make it promising for near-surface characterization in the urban city. In this study, a DAS line was utilized to record traffic noise seismic data in the urban city and to investigate the near-surface characterization. Seismic surface waves were reconstructed from the acquired traffic noises using seismic interferometry. Thereafter, we obtain the near-surface shear wave velocity profile below the DAS line by surface wave dispersion curve inversion using a Bayesian Markov Chain Monte Carlo method. The results demonstrate the effectiveness of DAS-based urban traffic noise in near-surface characterization.

Seismic imaging of mine tunnels by ambient noise along linear arrays

Seismic imaging using ambient noise has been widely used to image subsurface structures. The spatial autocorrelation (SPAC) method using ambient noise recorded by seismic arrays can image subsurface velocity structures at various scales. In addition, an approximation of zero-offset reflection responses of the structure can be achieved by station autocorrelations of the ambient noise. In this study, to detect an abandoned mine tunnel that is expected to have low velocity anomalies, we jointly apply the SPAC method and station autocorrelation method along linear arrays on the surface above the tunnel. One hundred and thirty-six nodal seismometers were deployed linearly along three lines with station intervals of 1.5 and 5 m, respectively. The ambient seismic noise was continuously recorded for two hours by these stations. Through the SPAC analysis, the Rayleigh wave dispersion curves for the frequency band of 3 Hz to 15 Hz were extracted from the ambient noise, from which the 2D near-surface shear-wave velocity models were estimated. A low velocity zone is obvious at a depth of about 60–100 m in the velocity profile along line 2. In addition, clear diffraction signals can be seen at similar positions on station autocorrelation profile along line 3, which is very close to line 2. This anomalous area is interpreted to be the abandoned tunnel, which is further verified by drilling. Our study shows that the combination of SPAC and station autocorrelation methods can be effective in near surface exploration.

Liquefaction resistance evaluation of soils using artificial neural network for Dhaka City, Bangladesh

Soil liquefaction resistance evaluation is an important site investigation for seismically active areas. To minimize the loss of life and property, liquefaction hazard analysis is a prerequisite for seismic risk management. Liquefaction potential index (LPI) is widely used to determine the severity of liquefaction quantitatively and spatially. LPI is estimated from the factor of safety of liquefaction that is the ratio of cyclic resistance ratio (CRR) to cyclic stress ratio calculated applying simplified procedure. Artificial neural network (ANN) algorithm has been used in the present study to predict CRR directly from the normalized standard penetration test blow count (SPT-N) and near-surface shear wave velocity (Vs) data of Dhaka City. It is observed that ANN models have generated accurate CRR data. Three liquefaction hazard zones are identified in Dhaka City on the basis of the cumulative frequency (CF) distribution of the LPI of each geological unit. The liquefaction hazard maps have been prepared for the city using the liquefaction potential index (LPI) and its cumulative frequency (CF) distribution of each liquefaction hazard zone. The CF distribution of the SPT-N based LPI indicates that 15%, 53%, and 69% of areas, whereas the CF distribution of the Vs based LPI indicates that 11%, 48%, and 62% of areas of Zone 1, 2, and 3, respectively, show surface manifestation of liquefaction for an earthquake of moment magnitude, Mw 7.5 with a peak horizontal ground acceleration of 0.15 g.

Near-Surface Characterization Using High-Speed Train Seismic Data Recorded by a Distributed Acoustic Sensing Array

A high-speed train can be regarded as a moving seismic source when it travels along a railway. Seismic waves from such sources have strong energy and can be used for near-surface characterization, safety monitoring of high-speed railways, and detection of urban underground spaces. Distributed acoustic sensing (DAS) is a newly developed seismic acquisition technology. It has attracted widespread attention due to its advantages of low cost, high sensitivity, high efficiency, and dense sampling. This study investigated near-surface characterization using high-speed train seismic data recorded by DAS. The data were processed to obtain surface waves by seismic interferometry. Thereafter, the extracted surface waves were inverted to obtain the near-surface shear-wave velocity model using a multichannel analysis method. The inverted model is consistent with the subsurface geology of the study area. Our results demonstrate the effectiveness and reliability of DAS-based acquisition and data analysis in near-surface characterization using the high-speed train type of moving sources.

Direct shear-wave seismic survey in Sanhu area, Qaidam Basin, west China

The formation containing shallow gas clouds poses a major challenge for conventional P-wave seismic surveys in the Sanhu area, Qaidam Basin, west China, as it dramatically attenuates seismic P-waves, resulting in high uncertainty in the subsurface structure and complexity in reservoir characterization. To address this issue, we proposed a workflow of direct shear-wave seismic (S-S) surveys. This is because the shear wave is not significantly affected by the pore fluid. Our workflow includes acquisition, processing, and interpretation in calibration with conventional P-wave seismic data to obtain improved subsurface structure images and reservoir characterization. To procure a good S-wave seismic image, several key techniques were applied: (1) a newly developed S-wave vibrator, one of the most powerful such vibrators in the world, was used to send a strong S-wave into the subsurface; (2) the acquired 9C S-S data sets initially were rotated into SH-SH and SV-SV components and subsequently were rotated into fast and slow S-wave components; and (3) a surface-wave inversion technique was applied to obtain the near-surface shear-wave velocity, used for static correction. As expected, the S-wave data were not affected by the gas clouds. This allowed us to map the subsurface structures with stronger confidence than with the P-wave data. Such S-wave data materialize into similar frequency spectra as P-wave data with a better signal-to-noise ratio. Seismic attributes were also applied to the S-wave data sets. This resulted in clearly visible geologic features that were invisible in the P-wave data.

A high-resolution dispersion imaging method of seismic surface waves based on chirplet transform

Abstract The surface-wave analysis method is widely adopted to build a near-surface shear-wave velocity structure. Reliable dispersion imaging results form the basis for subsequent picking and inversion of dispersion curves. In this paper, we present a high-resolution dispersion imaging method (CSFK) of seismic surface waves based on chirplet transform (CT). CT introduces the concept of chirp rate, which could focus surface-wave dispersion energy well in time-frequency domain. First, each seismic trace in time-distance domain is transformed to time-frequency domain by CT. Thus, for each common frequency gather, we obtain a series of 2D complex-valued functions of time and distance, which are called pseudo-seismograms. Then, we scan a series of group velocities to obtain the slanting-phase function and perform a spatial Fourier transform on the slanting-phase function to get its amplitude. In addition, power operation is adopted to increase the amplitude difference between dispersion energy and noise. Finally, we generate the dispersion image by searching for the maximum amplitude of a slanting-phase function. Because the CSFK method considers the position of surface-wave energy in the time-frequency domain, this largely eliminates the noise interference from other time locations and improves the resolution and signal-to-noise ratio of the dispersion image. The results of synthetic test and field dataset processing demonstrate the effectiveness of the proposed method. In addition, we invert all 120 sets of dispersion curves extracted from reflected wave seismic data acquired for petroleum prospecting. The one-dimensional inversion shear-wave velocity models are interpolated into a two-dimensional profile of shear-wave velocity, which is in good agreement with the borehole data.

Near-surface velocity inversion from Rayleigh wave dispersion curves based on a differential evolution simulated annealing algorithm

The utilization of urban underground space in a smart city requires an accurate understanding of the underground structure. As an effective technique, Rayleigh wave exploration can accurately obtain information on the subsurface. In particular, Rayleigh wave dispersion curves can be used to determine the near-surface shear-wave velocity structure. This is a typical multiparameter, high-dimensional nonlinear inverse problem because the velocities and thickness of each layer must be inverted simultaneously. Nonlinear methods such as simulated annealing (SA) are commonly used to solve this inverse problem. However, SA controls the iterative process though temperature rather than the error, and the search direction is random; hence, SA always falls into a local optimum when the temperature setting is inaccurate. Specifically, for the inversion of Rayleigh wave dispersion curves, the inversion accuracy will decrease with an increasing number of layers due to the greater number of inversion parameters and large dimension. To solve the above problems, we convert the multiparameter, high-dimensional inverse problem into multiple low-dimensional optimizations to improve the algorithm accuracy by incorporating the principle of block coordinate descent (BCD) into SA. Then, we convert the temperature control conditions in the original SA method into error control conditions. At the same time, we introduce the differential evolution (DE) method to ensure that the iterative error steadily decreases by correcting the iterative error direction in each iteration. Finally, the inversion stability is improved, and the proposed inversion method, the block coordinate descent differential evolution simulated annealing (BCDESA) algorithm, is implemented. The performance of BCDESA is validated by using both synthetic data and field data from western China. The results show that the BCDESA algorithm has stronger global optimization capabilities than SA, and the inversion results have higher stability and accuracy. In addition, synthetic data analysis also shows that BCDESA can avoid the problems of the conventional SA method, which assumes the S-wave velocity structure in advance. The robustness and adaptability of the algorithm are improved, and more accurate shear-wave velocity and thickness information can be extracted from Rayleigh wave dispersion curves. • We convert the high-dimensional inverse problem into multiple low-dimensional optimizations to improve accuracy by using BCD. • We ​use the error as control conditions different from that in SA to avoid the inversion falling into local optima. • We introduce DE to ensure that the iterative error steadily decreases by correcting the iterative error direction.

Seismic Risk Assessment of Chania, Greece, Using an Integrated Computational Approach

This study develops a comprehensive seismic risk model for the city of Chania, in Greece, which is located ina highly seismic-prone region due to the occurrenceof moderate to large earthquakes because of the nearby major subduction zone between African and Eurasian tectonic plates. The main aim is to reduce the seismic risk for the study area by incorporating the spatial distribution of the near-surface shear wave velocity model and the soil classification, along with all possible seismic sources, taking into account historical events. The study incorporates and correlates various ground motion scenarios and geological fault zones as well as information on existing buildings to develop a seismic risk model using QuakeIST software, and then the seismic hazard and a realistic prediction of resulting future adverse effects are assessed. The developed model can assist the municipal authorities of Chania to be prepared for potential seismic events, as well as city planners and decisionmakers, who can use the model as an effective decision-making tool to identify the seismic vulnerability of the city buildings and infrastructure. Thus, this study enables the implementation of an appropriate and viable earthquake-related hazards strategy to mitigate damage and losses in future earthquakes.

Automatic picking of multi-mode surface-wave dispersion curves based on machine learning clustering methods

The surface-wave analysis method is widely adopted to build near-surface shear-wave velocity structure. Automatic picking of surface-wave dispersion curves is a problem that needs to be solved, especially when dealing with a large amount of data. In this paper, we proposed an automatic picking method of multi-mode surface-wave dispersion curves based on unsupervised machine learning methods. Firstly, seismic data is transformed to two-dimensional dispersion image by dispersion imaging method. The discrete dispersion image points are separated into two clusters: dispersion energy and background noise by Gaussian mixture model clustering method. Then the dispersion energy points are projected into frequency-velocity domain, and are distinguished as different surface-wave modes by the density-based spatial clustering of applications with noise algorithm. Finally, multi-mode dispersion curves are obtained by searching for the local peaks in different mode areas of surface waves. Particle filter is used to smooth the picked dispersion curves to eliminate the influence of noise. The synthetic tests show that the automatically picked dispersion curves can match the theoretical dispersion curves well. We also applied the proposed method to the picking of dispersion curves of two field data. The automatically picked dispersion curves are applied to the subsequent inversion. In the second field data, the one-dimensional inversion results are interpolated to obtain the near-surface three-dimensional velocity model of the work area. The inversion results are in good agreement with borehole data, which further proves the accuracy and efficiency of the automatic picking method for multi-mode dispersion curves.

Estimation of near-surface shear-wave velocity by joint inversion of dispersion and HVSR curves of Rayleigh waves from multicomponent active-seismic records

The multichannel analysis of surface waves (MASW) method has been widely used for near-surface geophysical and geotechnical studies by using the dispersive characteristics of Rayleigh waves to determine subsurface shear-wave velocity. Commonly, Rayleigh waves are obtained only from the vertical component of P-SV waves. However, the multimodal Rayleigh waves estimated from the horizontal (radial) component data have a different energy distribution compared with those estimated from the vertical component data. The horizontal-to-vertical spectral ratio (HVSR) of Rayleigh waves can be calculated from multicomponent active-seismic records after mode separation. The HVSR peak and trough frequencies are very sensitive to model parameters. We propose to estimate near-surface shear-wave velocity by joint inversion of dispersion and HVSR curves of Rayleigh waves from multicomponent active-seismic records. The proposed joint inversion method can combine the strengths of MASW and HVSR analysis methods. A synthetic example demonstrates that our proposed joint inversion method can provide better constrained and more reliable S-wave velocity in the shallow subsurface.

Simultaneous Inversion of Multiwindow Surface Wave Dispersion Data in 2D Layered Media

Seismic Surface Wave Methods (SWMs) have been one of the effective tools for retrieving near-surface shear-wave velocity (Vs). However, a critical problem associated with the SWMs is that using a horizontally layered model assumption in inversion may introduce blurred errors into 2D Vs imaging due to the presence of lateral variations. In addition, the increase of investigation depth necessary for the SWMs may cause an increase in errors since a wider acquisition array is essential to acquire longer wavelength data precisely. In this study, we analyzed the relationship between the output power, calculated by an amplitude normalized frequency-domain beamforming and the local phase velocity following the ray-theory approximation. Based on this relationship, we calculated the theoretical dispersion curves of multichannel surface wave data using a local maximum search algorithm. Then, we presented an algorithm for simultaneously inverting surface wave dispersion data extracted from multi-size spatial windows. We parametrized the inverted model in a 2D layered pattern. Finally, we used synthetic and field data sets to test the effectivity of the proposed method. Both results showed that the lateral resolution of the Vs model retrieved by the simultaneous inversion is improved compared to the horizontally layered model-based inversion.

Modified frequency–Bessel transform method for dispersion imaging of Rayleigh waves from ambient seismic noise

SUMMARYAmbient noise surface wave methods have gained much attention among geophysical and civil engineering communities because of their capability of determining near-surface shear wave velocities in highly populated urban areas. Higher mode information of surface waves is important in dispersion curve inversion for shear wave velocity structure. The frequency–Bessel (F-J) transform method is an effective tool for multimode surface wave extraction, which has been applied to multiscale investigations of the Earth structure. The measured dispersion energy with the F-J method, however, would usually be contaminated by a type of ‘crossed’ artefacts at high frequencies, which are caused by spatial aliasing and bidirectional velocity scan of dispersion analysis methods. The ‘crossed’ artefacts usually cross and smear the true dispersion energy in the frequency–velocity domain. We propose a modified F-J (MFJ) transform method in which the Bessel function is replaced by the Hankel function for dispersion analysis of empirical Green's function. The MFJ method performs a unidirectional velocity scanning on the outgoing wave to avoid the ‘crossed’ artefacts. Synthetic and real-world examples demonstrate the effectiveness of the proposed MFJ method in improving the accuracy of Rayleigh wave multimode dispersion measurements.

Spurious signals attenuation using SVD-based Wiener filter for near-surface ambient noise surface wave imaging

In recent years, high-frequency passive surface wave methods based on seismic interferometry have been applied to estimate near-surface shear wave velocity structure in urban geophysical investigations. One of key steps of using surface-wave methods is to image the dispersion energy and identify different modes of surface waves. In the dispersion image generated by multichannel analysis of passive surface waves method, however, a type of “crossed” artifacts usually overlaps and smears the true dispersion energy in a high frequency range. The “crossed” artifacts are spatial aliasing of a reverse-directional propagating wave with a directional velocity scan. In urban areas, the coherent noise signals could be repetitively induced by traffic in a short time segment, which will produce repetitive signals in the cross-correlation function, leading to spurious reverse-directional propagating waves in the causal or acausal part. We propose to use the Wiener filter based on singular value decomposition (SVD) to attenuate the spurious signals in the cross-correlation functions so as to reduce “crossed” artifacts in the dispersion image. A synthetic example demonstrates that the “crossed” artifacts related to spurious signals in the dispersion image can be well attenuated by applying the SVD-based Wiener filter (SVDWF) to cross-correlation functions. Two field examples suggest that high-mode dispersion energy in the dispersion images generated from the SVDWF denoised cross-correlation functions becomes more continuous and clearer in high frequency bands, which benefits from the advantages of both SVD and Wiener filters.

Deep Learning for Extracting Dispersion Curves

High-frequency surface-wave methods have been widely used for surveying near-surface shear-wave velocities. A key step in high-frequency surface-wave methods is to acquire dispersion curves in the frequency–velocity domain. The traditional way to acquire the dispersion curves is to identify the dispersion energy and manually pick phase velocities by following energy peaks at different frequencies. A large number of dispersion curves need to be extracted for inversion, especially for surveys with long two-dimensional sections or large three-dimensional (3D) coverages. Human–machine interaction-based dispersion curves extraction, however, is still common, which is time-consuming. We developed a deep learning model, termed Dispersion Curves Network (DCNet), that can rapidly extract dispersion curves from dispersion images by treating dispersion curves extraction as an instance segmentation task. The accuracy of the dispersion curves extracted by our DCNet model is demonstrated by theoretical data. We used a 3D field application of ambient seismic noise to demonstrate the effectiveness and robustness of our method. The real-world results showed that the accuracy of the dispersion curves extracted from the field data using our method can achieve human-level performance and our method can meet the requirement of geoengineering surveys in rapidly extracting massive dispersion curves of surface waves.