Rayleigh Wave Signals Research Articles

Quantitative detection of the width and depth of subsurface defects in curved structures using ultrasonic Rayleigh waves

ABSTRACT Rayleigh waves are highly sensitive to surface and subsurface defects. However, the varying surface profiles of complex structures induce constant changes in the Rayleigh waveform. Furthermore, Rayleigh waves encompass multi-dimensional characteristics of subsurface defects, significantly increasing the difficulty of quantitatively detecting the sizes of subsurface defects in different dimensions. This paper presents a machine learning (ML)-based method for quantitatively detecting subsurface defect depth and width in curved structures using laser-generated Rayleigh waves. An experimentally validated finite element model is first established to construct a dataset of Rayleigh wave signals labeled with multidimensional subsurface defect sizes. The dataset is augmented from 188 to 368 samples using the Mix-up algorithm, with additional experimental signals incorporated. Feature parameters of Rayleigh waves are extracted in both time and frequency domains through waveform analysis and sliding window wavelet transform (SWT). These features are used to train ML models: Gaussian Process (GP), Extreme Gradient Boosting (XGBoost), and Random Forest (RF). Comparative results demonstrate GP achieves the highest prediction accuracy for subsurface defect depth and width on the test set, reaching 98.64% and 97.73%, respectively. The study highlights the integration of experimental and simulated data in training, which reduces costs while enhancing model robustness in practical applications.

Broadband Love Wave Phase Velocity Maps Based on Modified Double‐Beamforming of Ambient Noise Cross‐Correlations

AbstractAmbient noise tomography has become a popular method in the past two decades to image the crust and uppermost mantle structure. To date, broadband Rayleigh wave signals can be obtained from ambient noise, which can be utilized to study the earth's interior structure from the surface down to ∼200–300 km depths. However, it is hard to extract intermediate‐ and long‐period (>50 s) Love wave signals from ambient noise using conventional data processing techniques for ambient noise. Array‐based data processing techniques can enhance weak signals. In this study, we adopt a modified algorithm of the double‐beamforming method to extract broadband Love wave signals from ambient noise. We validate the accuracy of the dispersion curves measured from our method by comparing them to those measured from the conventional method. Then, we use a finite frequency ambient noise tomography method to construct broadband Love wave phase velocity maps across the contiguous USA. These phase velocity maps are consistent with those obtained from conventional methods at short periods (<40 s). Finally, we analyze the resolution of our double‐beamforming method based on checkerboard tests and find that the resolution of phase velocity maps based on our method is close to the aperture of the subarrays used in our double‐beamforming method.

Real-Time Ambient Seismic Noise Tomography of the Hillside Iron Oxide–Copper–Gold Deposit

We conduct an exploration-scale ambient noise tomography (ANT) survey over the Hillside Iron Oxide–Copper–Gold (IOCG) deposit in South Australia, leveraging Fleet’s direct-to-satellite technology for real-time data analysis. The acquisition array consisted of 100 sensors spaced 260 m apart which recorded continuous vertical-component seismic ambient noise for 14 days. High quality Rayleigh wave signals, with a mean signal-to-noise ratio (SNR) of 40, were recovered in the frequency band 1–4 Hz after processing the recorded data between 0.1–9 Hz. Our modelling results capture aspects of the deposit’s known geology, including depth of cover, structures linked to mineralisation, and the mineralised host rock, down to approximately 1 km depth. We compare our velocity model with existing magnetic, gravity, induced polarisation and drilling data, showing strong correlation with each. We identify several new features of the local geology, including the behaviour of key structures down to 1 km, and highlight the significance of a Cambrian-age dolomite that cuts across the main structural corridor that hosts the Hillside deposit. An analysis of model convergence rates with respect to Rayleigh wave SNRs shows that real-time data analysis can reduce recording duration at the site by 65% compared to traditional deployment durations, from ∼14 days to ∼5 days. Finally, we conclude by commenting on the efficacy of the ANT technique for the exploration of IOCG systems more broadly.

Machine learning enhanced characterization of surface defects using ultrasonic Rayleigh waves

Rayleigh waves are successfully employed for the characterization of surface defects through the utilization of both time and frequency domain analysis. However, a significant challenge arises when attempting to cover a broad range of the defect depth to Rayleigh wavelength ratio with a single method. This paper presents an enhanced methodology that integrates optimized machine learning (ML) algorithms to expand the coverage of defect depth assessment. An experimentally validated numerical model is firstly established to simulate the interaction of Rayleigh waves with surface defects and utilized to generate a significant quantity of labeled Rayleigh wave signals. The features of received Rayleigh wave patterns are effectively extracted and serve as indicators of defect parameters. The ML framework encompasses the extraction and selection of optimal features, the construction of model architecture, and the tuning of hyperparameters. Three dimensionality optimization techniques—principal component analysis, correlation coefficient analysis, and expert knowledge integration—are compared. Additionally, six machine learning models (decision tree, support vector machine, k-nearest neighbors, random forest, gradient-boosting machine, and artificial neural network) are optimized using particle swarm optimization and subjected to comparison. The results reveal that: (1) principal component analysis yields the highest performance, suggesting the potential for feature dimensionality reduction without relying on expert knowledge; (2) among the models examined, the random forest model and artificial neural network with hyperparameter tuning demonstrate superior performance in predicting defect depth; and (3) the optimized machine learning methods consistently outperform empirical approaches in accurately predicting defect depths across a wide range. Furthermore, the proposed method shows promise for extension to determine additional parameters associated with surface defects, including their width and angle of inclination.

Crustal Anisotropy in the Martian Lowlands From Surface Waves

AbstractThe largest seismic event ever recorded on Mars, with a moment magnitude of 4.7 ± 0.2, is the first event to produce both Love and Rayleigh wave signals. We measured their group velocity dispersion between about 15 and 40 s period and found that no isotropic depth‐dependent velocity model could explain the two types of waves wave simultaneously, likely indicating the presence of seismic anisotropy. Inversions of Love and Rayleigh waves yielded velocity models with horizontally polarized shear waves traveling faster than vertically polarized shear waves in the top 10–25 km. We discuss the possible origins of this signal, including the preferred orientation of anisotropic crystals due to shear deformation, alignment of cracks, layered intrusions due to an impact, horizontal layering due to the presence of a large‐scale sediment layer on top of the crust, and alternation of sedimentation and basalt layers deposits due to large volcanic eruptions.

Seismic Noise Interferometry and Distributed Acoustic Sensing (DAS): Inverting for the Firn Layer S‐Velocity Structure on Rutford Ice Stream, Antarctica

AbstractFirn densification profiles are an important parameter for ice‐sheet mass balance and palaeoclimate studies. One conventional method of investigating firn profiles is using seismic refraction surveys, but these are difficult to upscale to large‐area measurements. Distributed acoustic sensing (DAS) presents an opportunity for large‐scale seismic measurements of firn with dense spatial sampling and easy deployment, especially when seismic noise is used. We study the feasibility of seismic noise interferometry (SI) on DAS data for characterizing the firn layer at the Rutford Ice Stream, West Antarctica. Dominant seismic energy appears to come from anthropogenic noise and shear‐margin crevasses. The DAS cross‐correlation interferometry yields noisy Rayleigh wave signals. To overcome this, we present two strategies for cross‐correlations: (a) hybrid instruments—correlating a geophone with DAS, and (b) stacking of selected cross‐correlation panels picked in the tau‐p domain. These approaches are validated with results derived from an active survey. Using the retrieved Rayleigh wave dispersion curve, we inverted for a high‐resolution 1D S‐wave velocity profile down to a depth of 100 m. The profile shows a “kink” (velocity gradient inflection) at ∼12 m depth, resulting from a change of compaction mechanism. A triangular DAS array is used to investigate directional variation in velocity, which shows no evident variations thus suggesting a lack of azimuthal anisotropy in the firn. Our results demonstrate the potential of using DAS and SI to image the near‐surface and present a new approach to derive S‐velocity profiles from surface wave inversion in firn studies.

Directional Sensitivity of DAS and Its Effect on Rayleigh-Wave Tomography: A Case Study in Oxnard, California

AbstractDistributed acoustic sensing (DAS) provides dense arrays ideal for seismic tomography. However, DAS only records average axial strain change along the cable, which can complicate the interpretation of surface-wave observations. With a rectangular DAS array located in the City of Oxnard, California, we compare phase velocity dispersion at the same location illuminated by differently oriented virtual sources. The dispersion curves are consistent for colinear and noncolinear virtual sources, suggesting that surface-wave observations in most of the cross-correlations are dominated by Rayleigh waves. Our measurements confirm that colinear channel pairs provide higher Rayleigh-wave signal-to-noise ratio (SNR). For cross-correlations of noncolinear channel pairs, the travel time of each connecting ray path can still be obtained despite the lower SNR of Rayleigh wave signals. The inverted Rayleigh-wave dispersion map reveals an ancient river channel consistent with the local geologic map. Our results demonstrate the potential of DAS-based 2D surface-wave tomography without special treatment of directional sensitivity in areas where one type of wave is dominating or can be identified.

Numerical simulation of laser ultrasonic detection of the surface microdefects on laser powder bed fusion additive manufactured 316L stainless steel

A numerical model is presented in this article to investigate the interactions between laser generated ultrasonic and the microdefects (0.01 to 0.1 mm), which are on the surface of the laser powder bed fusion additive manufactured 316L stainless steel. Firstly, the influence of the transient sound field and detection positions on Rayleigh wave signals are investigated. The interactions between the varied microdefects and the laser ultrasonic are studied. It is shown that arrival time of reflected Rayleigh (RR) waves wave is only related to the location of defects. The depth can be checked from the feature point Q, the displacement amplitude and time delay of converted transverse (RS) wave, while the width information can be evaluated from the RS wave time delay. With the aid of fitting curves, it is found to be linearly related. This simulation study provides a theoretical basis for quantitative detection of surface microdefects of additive manufactured 316L stainless steel components.

Seafloor depth controls seismograph orientation uncertainty

SUMMARY This study evaluates the seafloor ambient noise environment that varies with the water depth based on a correction analysis of the horizontal sensor orientation for ocean-bottom seismographs. As ocean-bottom seismographs are mainly deployed as ‘free-fall’ installations, we have no information on which direction a horizontal sensor faces at the seafloor. An accurate sensor orientation is crucial for data processing based on seismic wavefields. Among several seismological approaches that use passive sources to correct the horizontal sensor azimuth, the particle motion of teleseismic Rayleigh waves is widely used for broad-band ocean-bottom seismographs. We performed seafloor seismic observations in the Hyuga-nada region at the western end of the Nankai subduction zone and deployed broad-band and short-period seismographs. However, studies have yet to investigate whether orientation correction via the Rayleigh-wave polarization method is valid for short-period data. The results of the Rayleigh wave method from our campaign observation data showed that the estimation uncertainty of short-period sensor orientations increased with a decreasing water depth; we observed a transition depth for the uncertainty at 2200–2600 m. The measurement quality, or the cross-correlation coefficient between the radial and Hilbert-transformed vertical components, also decreased at depths shallower than 2000 m. Moreover, an analysis of the noise power spectral densities showed that ambient noise levels during long periods (>10 s) increased with decreasing depth. Infragravity waves controlled vertical long-period noise levels, while ocean currents dominated horizontal long-period noise; both of these reduced the Rayleigh-wave signals as a function of environmental noise. Infragravity waves also likely distorted the Rayleigh waveforms. Both mechanisms contributed to the sudden rise in orientation uncertainty and low measurement quality at shallow stations (i.e. <2000 m). We confirmed that the variation in orientation uncertainty with the water depth can be used as an index for the ambient noise environment of the seafloor.

Surface wave tomography using dense 3D data around the Scrovegni Chapel in Padua, Italy

A dense single-node 3D seismic survey has been carried out around the Scrovegni Chapel in Padua (Italy), in order to give new insights about the archaeological setting of the area. The survey made use of nearly 1500 vertical nodes deployed over two rectangular grids. 38 shot positions were fired all around the two receiver patches. The fundamental mode Rayleigh wave signal is here analysed: traveltimes are directly inferred from the signal phases, and phase velocity maps are obtained using Eikonal tomography. Also surface wave amplitudes are used, to produce autospectrum gradient maps. The joint analysis of phase velocity and autospectrum gradient allowed the identification of several buried features, among which possible remains of radial walls of the adjacent Roman amphitheater, structures belonging to a medieval convent, and the root area of an eradicated tree. Finally, depth inversion of 1D dispersion curves allowed the reconstruction of a quasi-3D shear-wave velocity model.

Combining Dense Seismic Arrays and Broadband Data to Image the Subsurface Velocity Structure in Geothermally Active South‐Central Utah

AbstractSouth‐central Utah is characterized by Quaternary volcanism, current geothermal activity, and unusually high surface heat flux across the region. Additionally, there are three operating geothermal power plants in this region, known as the Sevier thermal area. This setting is very similar to that found in the Coso geothermal area. However, the source and lateral extent of subsurface heat in Utah is poorly understood. We use temporally separated geophone arrays combined with regional broadband data to perform ambient noise seismic tomography to study the subsurface shear velocity structure in the region. We find good recovery of ocean‐seism excited Rayleigh wave signals between the periods of 5 and 10 s. For each period, we measure the Rayleigh wave phase velocity and ellipticity across the region using the beamforming methodology and horizontal to vertical (H/V) amplitude ratio. Finally, we combine spline‐based 1D shear velocity models inverted using Rayleigh wave phase velocity and H/V measurements on a 2D, beamforming discretized grid to construct a final 3D model. The final 3D model has many similarities to imaged velocity models at the Coso geothermal area. We find a strong correlation between low velocity anomalies and high surface heat flux and geothermal activity. Additionally, we find a laterally continuous low‐velocity anomaly between 5 and 15 km depth, which may represent elevated temperature of hundreds of degrees Celsius, partial melt, and/or the presence of water, and a common heat source across the region which likely originates from the mantle.

Non-contact detection of railhead defects and their classification by using convolutional neural network

Railhead defects must be detected and classified intelligently in order for railway transportation systems to operate safely. Rail defect identification and categorization can be automated by using machine learning models to process rail image data (acquired using cameras). However, such an automated method has significant drawbacks: it cannot detect subsurface defects, picture data requires a high-end GPU with a long computational time, and machine learning model training can be influenced by image quality, which is dependent on light intensity and shooting altitude. Rayleigh waves are a potential candidate for rail inspection because they can detect both surface and subsurface defects and travel long distances on curved surfaces (like a rail) at high speed. This article looks into the possibility of combining fully non-contact laser ultrasonic technology (LUT) and a deep learning approach for intelligent detection and classification of railhead surface and subsurface defects. The fully non-contact LUT was used to actuate and capture laser-generated Rayleigh wave signals on railhead specimens in order to create a database of A-scan signals from healthy, surface, subsurface, and edge defect railheads. The classification capabilities of a support vector machine (SVM), a fully connected deep neural network (DNN), and a convolutional neural network (CNN) were examined after they were applied to the preprocessed signals without extracting any statistical/signal processing-based characteristics. The comparative analysis demonstrates that CNN is robust in classifying railhead defects. As a result, when combined with CNN, the laser ultrasonic technology may ensure automatic defection and classification of railhead surface and subsurface flaws.

High‐Resolution 3‐D Shear Wave Velocity Model of Northern Taiwan via Bayesian Joint Inversion of Rayleigh Wave Ellipticity and Phase Velocity With Formosa Array

AbstractThe Formosa array, with 137 broadband seismometers and ∼5 km station spacing, was deployed recently in Northern Taiwan. Here by using eight months of continuous ambient noise records, we construct the first high‐resolution three‐dimensional (3‐D) shear wave velocity model of the crust in the area. We first calculate multi‐component cross‐correlations to extract robust Rayleigh wave signals. We then determine phase velocity maps between 3 and 10 s periods using Eikonal tomography and measure Rayleigh wave ellipticity at each station location between 2 and 13 s periods. For each location, we jointly invert the two types of Rayleigh wave measurements with a Bayesian‐based inversion method for a one‐dimensional shear wave velocity model. All piecewise continuous one‐dimensional models are then used to construct the final 3‐D model. Our 3‐D model reveals upper crustal structures that correlate well with surface geological features. Near the surface, the model delineates the low‐velocity Taipei and Ilan Basins from the adjacent fast‐velocity mountainous areas, with basin geometries consistent with the results of previous geophysical exploration and geological studies. At a greater depth, low velocity anomalies are observed associated with the Linkou Tableland, Tatun Volcano Group, and a possible dyke intrusion beneath the Southern Ilan Basin. The model also provides new geometrical constraints on the major active fault systems in the area, which are important to understand the basin formation, orogeny dynamics, and regional seismic hazard. The new 3‐D shear wave velocity model allows a comprehensive investigation of shallow geologic structures in the Northern Taiwan.

Non-Contact Inspection of Railhead via Laser-Generated Rayleigh Waves and an Enhanced Matching Pursuit to Assist Detection of Surface and Subsurface Defects.

Laser ultrasonic technology can provide a non-contact, reliable and efficient inspection of train rails. However, the laser-generated signals measured at the railhead are usually contaminated with a high level of noise and unwanted wave components that complicate the identification of defect echoes in the signal. This study explores the possibility of combining laser ultrasonic technology (LUT) and an enhanced matching pursuit (MP) to achieve a fully non-contact inspection of the rail track. A completely non-contact laser-based inspection system was used to generate and sense Rayleigh waves to detect artificial surface horizontal, surface edge, subsurface horizontal and subsurface vertical defects created at railheads of different dimensions. MP was enhanced by developing two novel dictionaries, which include a finite element method (FEM) simulation dictionary and an experimental dictionary. The enhanced MP was used to analyze the experimentally obtained laser-generated Rayleigh wave signals. The results show that the enhanced MP is highly effective in detecting defects by suppressing noise, and, further, it could also overcome the deficiency in the low repeatability of the laser-generated signals. The comparative analysis of MP with both the FEM simulation and experimental dictionaries shows that the enhanced MP with the FEM simulation dictionary is highly efficient in both noise removal and defect detection from the experimental signals captured by a laser-generated ultrasonic inspection system. The major novelty contributed by this research work is the enhanced MP method with the developments of, first, an FEM simulation dictionary and, second, an experimental dictionary that is especially suited for Rayleigh wave signals. Third, the enhanced MP dictionaries are created to process the Rayleigh wave signals generated by laser excitation and received using a 3D laser scanner. Fourth, we introduce a pioneer application of such laser-generated Rayleigh waves for inspecting surface and subsurface detects occurring in train rails.

Shallow Damage Zone Structure of the Wasatch Fault in Salt Lake City from Ambient-Noise Double Beamforming with a Temporary Linear Array

Abstract We image the shallow structure across the East Bench segment of the Wasatch fault system in Salt Lake City using ambient noise recorded by a month-long temporary linear seismic array of 32 stations. We first extract Rayleigh-wave signals between 0.4 and 1.1 s period using noise cross correlation. We then apply double beamforming to enhance coherent cross-correlation signals and at the same time measure frequency-dependent phase velocities across the array. For each location, based on available dispersion measurements, we perform an uncertainty-weighted least-squares inversion to obtain a 1D VS model from the surface to 400 m depth. We put all piece-wise continuous 1D models together to construct the final 2D VS model. The model reveals high velocities to the east of the Pleistocene Lake Bonneville shoreline reflecting thinner sediments and low velocities particularly in the top 200 m to the west corresponding to the Salt Lake basin sediments. In addition, there is an ∼400-m-wide low-velocity zone that narrows with depth adjacent to the surface trace of the East Bench fault, which we interpret as a fault-related damage zone. The damage zone is asymmetric, wider on the hanging wall (western) side and with greater velocity reduction. These results provide important constraints on normal-fault earthquake mechanics, Wasatch fault earthquake behavior, and urban seismic hazard in Salt Lake City.

Local-scale phase velocity estimation using ambient seismic noise: comparison between passive seismic interferometry and conventional frequency–wavenumber methods

SUMMARYWe present a new processing scheme that uses passive seismic interferometry (PSI) followed by multichannel analysis of surface waves (MASW), which we call the 2-D PSI-MASW method, to obtain Rayleigh wave phase velocity dispersion (PVD) information. In this scheme, we first use the principles of PSI to form multidirectional cross-correlations (CCs) then project the CCs onto a 1-D virtual array and apply the phase-shift transform as in MASW processing. We compare PVD information obtained by this method with those of the conventional beam-power based frequency–wavenumber decomposition (CVFK) method using ambient seismic noise (ASN) data collected by local-scale 2-D arrays deployed at three selected sites in Bursa, Turkey. By analysing the ASN data from these sites, we show that similar multimodal PVD curves can be obtained with the two methods over a broad frequency range (∼2–23 Hz) within the wavenumber resolution and aliasing limits. However, in one of our sites where the 2-D array configuration has a considerable antisymmetry, we show that the 1-D virtual array used in the 2-D PSI-MASW method has a better array response function in terms of wavenumber resolution and suppression of side-lobes leading to superior mode resolution and separation than that of the CVFK method, which shows strong directional variations. Furthermore, unlike the CVFK method, the 2-D PSI-MASW method takes advantage of temporal stacking of CCs ensuring weak but coherent Rayleigh wave signals present in the ASN wavefield to be strengthened and has the potential for better extraction of PVD information. We conclude that by using a 2-D array with spatial coverage providing a wide range of directions and distances, reliable PVD information can be obtained even if the ASN sources are not concentrated in the stationary phase zones. Thus, we suggest that the 2-D PSI-MASW method is highly advantageous for the extraction of reliable PVD information owing to the multidirectional CCs provided by the 2-D array configurations. We also report that using only a single receiver line in the interferometric approach results in biased and/or incomplete PVD information due to the non-isotropic ASN source distribution at all three sites we analysed. In conclusion, our results clearly indicate that the 2-D PSI-MASW method can be used as complementary or alternative to the CVFK method to extract multimodal Rayleigh wave PVD information in local-scale seismological studies.

Rayleigh Wave Reconstruction from Ambient Noise Cross-Correlation Function by Radon-Wigner Transform

The ambient noise tomography is a powerful underground-structure-detection method developed in recent years. The key of this technique is to extract Rayleigh-wave signals from cross-correlation functions (CCF). It is important to suppress the noise in Rayleigh wave signals to increase the accuracy of dispersion-curve extraction. In this paper, a Radon-Wigner Transform (RWT) method is used for Rayleigh-wave reconstruction from CCF. We first introduce the principles of RTW, and then use a multi-component synthetic seismic data to show the processing steps of RWT. Finally, we process actual ambient noise CCF with RWT to reconstruct Rayleigh waves. The results of the synthetic and actual data examples indicate that RWT successfully reconstructed the Rayleigh waves from CCF and improved the signal-to-ratio of the signal.

Surface-wave dispersion spectrum inversion method applied to Love and Rayleigh waves recorded by distributed acoustic sensing

Recently, distributed acoustic sensing (DAS) has been applied to shallow seismic structure imaging providing dense spatial sampling at a relatively low cost. DAS on a standard straight fiber-optic cable mostly records axial dynamic strain, which makes it difficult to separate the Rayleigh and Love wavefields. As a result, the mixed Rayleigh and Love wave signals cannot be used in the conventional surface-wave dispersion inversion method. Therefore, it is often ensured that the source and the cable are in the same line and only Rayleigh wave dispersion is used, which limits the constraints on structure and model resolution. We have inverted surface-wave dispersion spectra instead of dispersion curves. This inversion method can use mixed Rayleigh and Love waves recorded when the source and receiver array are not aligned. The multiple-channel records are transformed to the frequency domain, and a slant stack method is used to construct the dispersion spectra. The genetic algorithm method is used to obtain an optimal S-wave velocity model that minimizes the difference between theoretical and observed dispersion spectra. A series of synthetic tests are conducted to validate our method. The results suggest that our method not only improves the flexibility of the acquisition system design, but the Love wave data also provide additional constraints on the structure. Our method is applied to the active source and ambient noise data sets acquired at a geothermal site and provides consistent results for different data sets and acquisition geometries. The sensitivity of the dispersion spectra to layer thickness, density, and P-wave velocity is also discussed. With our method, the amount of usable data can be increased, helping deliver better subsurface images.

Near-surface structure from ambient-noise tomography and horizontal-to-vertical spectral ratio beneath the Nankou-Sunhe fault*

Active faults pose a great threat to urban security. As the largest NW-trend active fault in Beijing area, the Nankou-Sunhe fault plays an important role in earthquake disaster and city construction. In this study, we collect continuous ambient noise data recorded by 43 temporary short-period seismograph between September 21th to October 12th 2019 to investigate the near-surface structure beneath the Nankou-Sunhe fault by using ambient noise tomography (ANT) and horizontal-to-vertical spectral ratio (HVSR) method. From ambient noise processing, fundamental-mode Rayleigh wave signals are clearly observed in the frequency band of 0.4–2.5 Hz. Then direct surface-wave inversion algorithm is applied to calculate the 3D shear-wave velocity model. Our results show that there is a sharp velocity contrast across the Nankou-Sunhe fault, with low velocities down to about 2 km on the hanging wall and high velocity on the footwall of the fault. According to the geological investigation, the low velocities are related to thicker sediments and Jurassic volcanic rock below which are the cap rock of the hydrothermal system. From the HVSR analysis, the HVSR curves of the sites near the fault shows double-peak, one less than 1 Hz and the other centered 7 Hz. After converting frequency to depth by the empirical equation, the results show that the thickness of sediments is thinned from southwest to northeast, which generally agrees well to field survey. Our results provide high-resolution near-surface structure for future study on disaster risk reduction and urban planning.

Ambient noise correlation analysis of S-net records: extracting surface wave signals below instrument noise levels

SUMMARY We applied ambient noise cross-correlation analysis to the cabled ocean bottom seismic network offshore northeast Japan (Seafloor observation network for earthquakes and tsunamis along the Japan Trench: S-net) to extract surface waves propagating in the ocean area of the forearc region. We found two types of peculiar pulses in the cross-correlation functions (CCFs) of ambient seismic noise records: periodic pulses mainly every minute and sharp pulses around the lag time zero. These pulses strongly contaminate the surface wave signals in the CCFs at frequencies below ∼0.1 Hz. The periodic pulses originate from periodic instrument noises, while the zero-lag pulses originate from random instrument noises which are coherent within station pairs. By developing solutions to remove the periodic and zero-lag pulses based on the characteristics of the pulses, we succeeded in extracting Rayleigh and Love wave signals from the S-net records at 0.03–0.3 Hz, while the surface wave signals at 0.03–0.1 Hz were not visible without the application of these solutions. These solutions widen the frequency range of analysis, and may be applicable to other seismic networks, particularly to recent dense but non-broad-band networks. We identified the fundamental and first higher modes of Rayleigh waves and the fundamental mode of the Love wave. The extracted surface wave signals can constrain the shear wave velocity structure from the sediment to seismogenic zone around the megathrust plate boundary in the forearc region.