Shear Wave Velocity Structure Research Articles

Fine Shear-Wave Velocity Structures of Subsurface beneath the Guangdong–Hong Kong–Macao Greater Bay Area with Dense Seismic Array and SPAC Method

Abstract We apply the spatial autocorrelation (SPAC) method to construct the 3D subsurface shear-wave velocity structure model using the short-period dense seismic array (containing 725 nodal geophones) located at the Guangdong–Hong Kong–Macao Greater Bay area (GBA). We first divided the dense array into numerous subarrays, with each subarray consisting of nine nodal geophones, and obtained 562 subarrays that can provide 1D VS profiles of the same quantity. Then, the SPAC method and genetic algorithm are utilized to extract the dispersion curve of the Rayleigh wave from the raw microtremor data and invert VS structure, respectively. Finally, a 3D VS structure model from the surface to 3.3 km depth is derived by combining all 1D VS structures. Relatively low-velocity anomalies above 700 m are considered unconsolidated shallow sediments as well as relatively high-velocity anomalies beneath 1100 m are attributed to consolidated granite bedrock. Meanwhile, low-velocity anomalies that are identified through the vertical VS profile at a depth of about 900–3000 m can be contributed to the fractured zone, and striped low-velocity anomalies in the horizontal VS maps reveal the location of the deeply buried faults in the study area. The results also mean that the SPAC method combined with the records of short-period dense seismic array can be effectively applied to image subsurface structures in high-populated urban area. The development of this noise-resistance and environment-friendly geophysical technique provides a reliable and effective way to explore the complicated subsurface geological structures, which is of great significance to urban engineering construction and earthquake disaster reduction work in densely populated urban agglomerations.

Multimodal surface wave inversion with automatic differentiation

Summary Investigating subsurface shear-wave velocity (vs) structures using surface wave dispersion data involves minimizing a misfit function that is commonly solved through gradient-based optimization. Sensitivity kernels for model updates are commonly estimated using numerical differentiation, variational methods or implicit functions which however, may involve numerical instability and computational challenges when dealing with complex velocity models and large datasets. In this study, we propose a novel surface wave inversion framework in which error-free gradients are calculated by automatic differentiation (AD) and forward modeling is implemented by convenient computational graphs in the state-of-the-art deep learning framework. The AD-based inversion approach is first validated using two synthetic datasets. Then the subsurface structures at three distinct locations, namely the Great Plains and the Long Beach in the US and Tong Zhou in China, are also derived using this method with seismic ambient noise data, which show nice consistency with those obtained using traditional methods. With the significantly improved computational efficiency, a great number of initial models can be inverted simultaneously to mitigate the impact of local minima and to estimate the uncertainty in the invert models. We have developed a new surface wave inversion package named ADsurf based on automatic differentiation and computational graphs in the deep learning framework, and its computational efficiency is also compared with the traditional finite-difference based gradient estimation approach. While a great number of intriguing studies on the geophysical inverse problems have been conducted recently using deep learning for end-to-end mapping, the use of AD provided in the in the deep learning frameworks to assist and expedite the gradient computations are still underexploited in geophysics. Thus, it is expected that various geophysical inverse problems in many different areas beyond the surface wave inversion can also be tackled with this new paradigm in the future.

Significant variations in the structure and composition of the crust beneath northwestern India: Imprints of magmatism

Northwestern India was affected by the Neoproterozoic Malani and Erinpura magmatism, Cretaceous rifting, and magmatism associated with the Deccan precursors. Receiver function analysis was done using 1704 RFs from 18 stations to image and decipher the imprints of the magmatic events and comprehend the crustal modifications in terms of variations in structure and composition. The H-κ grid search and neighbourhood inversion techniques are used to retrieve the crustal structure and Vp/Vs ratios (κ). The study reveals significant variations in the crustal thickness (H = 34 km to 43 km), composition (κ=1.74 to 1.92) and shear wave velocity structure across northwestern India. The terrains encompassing the Erinpura granite and the Malani igneous suite in the vicinity of the Barmer rift are characterized by a thick (H ≥ 41 km) crust with felsic to intermediate composition (κ ≤ 1.81). The crust beneath the southern part of the Marwar basin is ≈ 38–40 km thick with mafic composition (κ> 1.81). The region around the Barmer rift has a thin crust (H = 34–36 km) with intermediate to mafic composition. The region hosting the Early Cretaceous to Paleogene alkaline complexes exhibits a high Vp/Vs ratio (κ= 1.91) that may be associated with mafic cumulates emplaced by magmatic events that overprint the signatures of the Malani event. The crust is heterogeneous with low/high velocity intracrustal layers that reflect the fractionation of magma at different depths. The mafic residue, together with magmatic intrusions, results in a mafic lowermost crust with high shear velocities of 3.9–4.0 km/s beneath most stations. Overall, northwestern India is characterized by a thick crust with intermediate crustal composition and intracrustal layering resulting from large scale magmatic events linked to the Neoproterozoic reorganization of plates and younger magmatic events.

Incorporating topographic effects in surface wave tomography based on shortest-path ray tracing

SUMMARY We propose a new method to obtain 3-D shear wave velocity structures for regions with undulating topography based on surface wave dispersion data. In our method, we assume that surface waves propagate along a curved free surface, and the dispersion effects of these waves can be modelled as a frequency-dependent ray tracing problem. We use the shortest-path method to address off-great-circle propagation arising from inhomogeneous elastic parameters and topographic variations. We then apply our method to both synthetic and real-data inversions and demonstrate that ignoring topographic effects may significantly distort the inverted images. Finally, we analysed the accuracy of our method and provided a rule-of-thumb principle to quantitatively assess the need to account for topographic effects in surface wave tomography for a region.

Ambient Noise Interferometry Using Ocean Bottom Seismometer Data From Active Source Experiments Conducted in the Southernmost Mariana Trench

AbstractOcean bottom seismometers (OBSs) have been used to detect submarine structural and tectonic information for decades. According to signal source controllability, OBS data have generally been classified into active and passive source data categories. The former mainly focuses on the compressional wave (P‐wave) velocity inversion and always lacks valid information about the shear wave (S‐wave) velocity structure. While the latter provides structural information with limited resolution due to the aperture of the stations. Overcoming the barriers between processing these two data types will allow the reuse of a vast amount of data from active source experiments to explore the submarine S‐wave velocity structural properties. Here, we creatively applied ambient noise interferometry to invert the S‐wave velocity structure using data from active source OBS deployment conducted in the southernmost Mariana subduction zone, which had already been utilized to detect submarine P‐wave velocity structure. Considering the short time duration and relatively low quality of this type of data, a combined method of short‐segment cross‐correlation and selected time‐frequency domain phase‐weighted stacking was adopted to obtain stable cross‐correlation functions, which were subsequently used to invert S‐wave velocity structures. Compared to previous studies using different methods, our result sheds new light on the crust and upper mantle structure of the southernmost Mariana subduction zone. This method could be used to detect more information based on the reutilization of existing active source OBS data.

A frequency-Hankel transform method to extract multimodal Rayleigh wave dispersion spectra from active and passive source surface wave data

Rayleigh wave dispersion energy spectra have been widely used to extract dispersion curves and invert underground S-wave velocity structures for engineering geophysics and seismology. We develop a frequency-Hankel (F-H) transform method to extract high-quality multimodal Rayleigh wave dispersion energy spectra from active and passive source Rayleigh wave data. The F-H transform method is inspired by the frequency-Bessel (F-J) transform method and considers the physical meaning of Green’s functions for Rayleigh wave dispersion analysis. The F-H transform method can naturally avoid crossed artifacts caused by converging waves on F-J spectrograms and obtain more multimodal dispersion spectra of the same quality with fewer Rayleigh wave data than the F-J transform method. Synthetic and field Rayleigh wave data from active and passive sources for near-surface exploration and ambient noise tomography are used to demonstrate the validity, accuracy, and applicability of the F-H transform method. The F-H transform method unifies the formulas of the F-J transform method and its modifications for active and passive sources of Rayleigh wave data. The F-H transform method is a robust and efficient multimodal Rayleigh wave dispersion analysis method for active and passive source Rayleigh wave data.

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.

Shear-Wave Velocity Model from Site Amplification Using Microtremors on Jeju Island

This study examines shear-wave velocity structures in the Jeju region utilizing horizontal-to-vertical spectral ratios (HVSRs) of environmental noise, focusing on identifying significant low-velocity layers (LVLs). Although conventional methodologies predominantly involve borehole and active seismic exploration, recent advancements in the diffuse-field theory of seismic waves have offered a theoretical foundation for this approach. In the volcanic region of Jeju Island characterized by unique geological features, a pervasive LVL composed of quaternary marine sediments and the Seoguipo sedimentary layer has been observed. These components are crucial for site amplification and attenuation in seismic microzonation. The present study introduces a novel discovery of a distinct LVL, specifically at the UDO site, suggesting that its origin may be attributable to a local magmatic intrusion event. Advanced algorithms and HVSR curve analysis have enabled reliable inversion processes, enhancing the comprehension of the subsurface geology of Jeju. These insights are essential for seismic microzonation practices and contribute significantly to the development of seismic design standards in the Jeju region.

Sedimentary structures of the western part of the Indo-Gangetic Plain and Siwalik Himalaya inferred from receiver function inversion

SUMMARY We investigated sedimentary thickness and shear wave velocity structure in the western part of the Indo-Gangetic Plain (Punjab and Haryana Plain) and adjoining Siwalik Himalaya with the help of receiver function inversion at 20 broad-band seismological stations. This region is one of the most seismically vulnerable zones of the world due to the presence of thick surface sediments in the foreland basin that can amplify seismic waves and cause huge damage due to the earthquakes of the Himalaya. The study reveals a progressive thickening of sediments from southwest to northeast. The basement depth varies from ∼1.5 to 1.7 km in the Central Alluvium Plain, ∼1.8 to 2.8 km in the Zone of Terminal Fans, and attains a maximum of ∼3.8 km near the Himalayan Frontal Thrust. The inverted models show the presence of soft alluvial with extremely low Vs (< 0.5 km s−1) and high Vp/Vs (∼2.5–3.0) at the top ∼400–700 m of the surface at most of the stations. A comparatively higher velocity of surface sediments observed at northern stations suggests the presence of compact sediments at the surface. The layered sedimentary structure revealed by the S-wave velocity models supports the previous geophysical investigations using borehole data. The velocity–depth structure obtained in this study is important for evaluating the seismic hazard of the densely populated urban areas spread over this region.

New constraints on the shear wave velocity structure of the Ivrea geophysical body from seismic ambient noise tomography (Ivrea-Verbano Zone, Alps)

SUMMARY We performed seismic ambient noise tomography to investigate the shallow crustal structure around the Ivrea geophysical body (IGB) in the Ivrea-Verbano Zone (IVZ). We achieved higher resolution with respect to previous tomographic works covering the Western Alps, by processing seismic data collected by both permanent and temporary seismic networks (61 broad-band seismic stations in total). This included IvreaArray, a temporary, passive seismic experiment designed to investigate the IVZ crustal structure. Starting from continuous seismic ambient noise recordings, we measured and inverted the dispersion of the group velocity of surface Rayleigh waves (fundamental mode) in the period range 4–25 s. We obtained a new, 3-D vS model of the IVZ crust via the stochastic neighbourhood algorithm (NA), with the highest resolution between 3 to 40 km depth. The fast and shallow shear wave velocity anomaly associated with the IGB presents velocities of 3.6 km s−1 directly at the surface, in remarkable agreement with the location of the exposed lower-to-middle crustal and mantle outcrops. This suggests a continuity between the surface geological observations and the subsurface geophysical anomalies. The fast IGB structure reaches vS of 4 km s−1 at 20–25 km depth, at the boundary between the European and Adriatic tectonic plates, and in correspondence with the earlier identified Moho jump in the same area. The interpretation of a very shallow reaching IGB is further supported by the comparison of our new results with recent geophysical investigations, based on receiver functions and gravity anomaly data. By combining the new geophysical constraints and the geological observations at the surface, we provide a new structural interpretation of the IGB, which features lower crustal and mantle rocks at upper crustal depths. The comparison of the obtained vS values with the physical properties from laboratory analysis of local rock samples suggests that the bulk of the IGB consists of a combination of mantle peridotite, ultramafic and lower crustal rocks, bound in a heterogeneous structure. These new findings, based on vS tomography, corroborate the recent interpretation for which the Balmuccia peridotite outcrops are continuously linked to the IGB structure beneath. The new outcomes contribute to a multidisciplinary framework for the interpretation of the forthcoming results of the scientific drilling project DIVE. DIVE aims at probing the lower continental crust and its transition to the mantle, with two ongoing and one future boreholes (down to 4 km depth) in the IVZ area, providing new, complementary information on rock structure and composition across scales. In this framework, we constrain the upper crustal IGB geometries and lithology based on new evidence for vS, connecting prior crustal knowledge to recent active seismic investigations.

Detection of Low-Velocity Layer Using Passive Surface-Wave Data and Deep Learning: An Application in The Urban Area of Hangzhou, China

Passive surface-wave methods using dense seismic arrays have gained growing attention in near-surface high-resolution imaging in urban environments. Deep learning (DL) can release a tremendous workload brought by dense seismic arrays. We presented a case study of shear-wave velocity (Vs) structure imaging in the Hangzhou urban area (eastern China) using DL inversion. Noise data were recorded by dense linear arrays with approximately 5 m spacing deployed along two crossing roads for investigating the top 80 m of the subsurface. Phase-velocity dispersion curves are extracted from virtual shot gathers using multichannel analysis of surface waves. We divided the area where the low-velocity layer (LVL) may exist into three layers with a thickness of 5 m. We gave the four layers weak constraints to generate training dataset and adopted a convolutional neural network to directly invert fundamental-mode Rayleigh-wave phase velocity for 1D Vs models. To improve the accuracy, we further applied the sensitivities to weight the loss function in DL inversion. The obtained pseudo-2D Vs profiles correspond to the velocities estimated from logging data and previous survey. The well-trained neural network successfully identified that the LVL is located at 50-60 m deep. And this network was also achieved accurately the inversion of a dense seismic network nearby. The results of this survey demonstrate the accuracy and efficiency of delineating near-surface structures from traffic-induced noise using the DL technique, which has great potential for monitoring subsurface changes in urban areas.

3D shear-wave velocity structure of the Eastern Indian Shield from ambient noise tomography: Ultramafic lower crust underneath the Singhbhum Craton

Singhbhum Craton (SC) in the Eastern Indian Shield (EIS) is known to have preserved Archean crust which might provide insights into the Precambrian tectonics. In this study, ambient noise cross-correlation of about 1 year of continuous data recorded in 15 broadband seismic stations across EIS is performed. Rayleigh wave group velocities at 4–22 s periods obtained by frequency-time analysis of the cross-correlograms were subjected to tomographic inversion to produce group velocity maps. The spatial resolution of these maps was found to be ∼100 km. Inverted 1D shear-wave velocity (VS) models at regular grid points of these tomographic maps were combined to construct the first 3D VSmodel of the SC and adjoining regions within the EIS. The 3D model highlights a high VS(3.6–3.8 km/s) structure in the 4–20 km depth range and an ultra-high VS(∼4.0–4.2 km/s) structure at depths ≥ 20 km within the SC only. The VS of the upper layer and its regionalisation match with the well-studied granite batholith within the SC. The ultra-high velocitylayer in the lower crust, spatially spanning ∼100 km × 150 km, is distinctly imaged for the first time and its VS indicates probable ultramafic composition. The existence of ultramafic composition at ∼800 MPa lithostatic pressure requires ultra-high temperature metamorphism, possible through mantle upwelling/melting. The previous studies of upwarped crust-lithosphere beneath the SC and the lower-crustal ultramafic composition inferred in this study probably confirm the delamination of the lithosphere beneath the SC at some point during the Precambrian eon.

Shear-wave velocity determination by combining data from passive and active source field investigations in Kumamoto city, Japan

We present the 1D subsoil structure and local site effects at KUMA strong ground motion station in Kumamoto City, Japan. We analyze data from a field campaign conducted in the framework of the Blind Prediction BP1 test of the 6th IASPEI/IAEE International Symposium: Effects of Surface Geology on Seismic Motion. In parallel with other participants of the BP1 test, we process data from passive and active source measurements aiming to determine the shear-wave velocity, Vs, structure and the site response at KUMA station. Passive measurements are associated to five microtremor arrays. In each array, seven seismometers have been deployed in a common-center triangle shape, recording microtremors simultaneously for 1 to 2 h. The vertical component of microtremors was analyzed using the spatial autocorrelation (SPAC) method. Cross-correlation coefficients were computed for all station pairs available for each array. By fitting the average SPAC’s coefficients to the first-kind zero-order Bessel function, J0, and assuming that microtremors primarily comprise fundamental mode Rayleigh waves, phase velocity dispersion curves were determined. Phase velocity values for frequencies > 15 Hz were obtained from data of a close-by active source geophone profile. We integrated the data with those of the passive measurements and obtained an experimental phase velocity dispersion curve. The resulting curve shows low velocity variation, from 150 to 200 m/s, in the surface layers, whereas significant dispersion appears in frequencies below 2.5 Hz. By inverting this curve, we achieved to determine the 1D shear-wave velocity structure at KUMA station. Site response characteristics were determined by applying the Horizontal-to-Vertical-Spectral-Ratios method. Significantly amplified peaks in the frequency range between 0.3 to 1.5 Hz dominate HVSR spectral ratios. These peaks correspond to resonant frequencies of soils and originate from different impedance contrasts within the substratum of the site.Graphical

Effects of shear wave velocity and soil structure interaction on the structural pounding during earthquakes

It has been found that in the event of a strong earthquake, and due to insufficient distance between two adjacent structures, the lateral movement at the top of structures may cause collisions between them. This phenomenon, commonly known as seismic collision, can generate impact forces that were not considered during the initial design of the structure. These forces can cause significant structural damage or lead to complete collapse of the structure. The main purpose of this paper is to study the coupled effects of soil flexibility and impact between adjacent buildings undergoing seismic excitation. To capture the impact forces between the structures during the collision, a modified linear viscoelastic model was used effectively. Particular attention has been paid to studying the effects of shear wave velocity, first on the soil structure interaction and then on the collision response of adjacent structures. Three configurations of adjacent structures were analyzed: light-light, light-heavy, and heavy-heavy structures. The results obtained through this analysis showed that the dynamic response and the impact force of the structures depend essentially on the interaction between the structure, the foundation, and the soil.

Applying Feature Transformation-Based Domain Confusion to Neural Network for the Denoising of Dispersion Spectrograms

Abstract Ambient noise tomography has been widely used to estimate the shear-wave velocity structure of the Earth. A key step in this method is to pick dispersions from dispersion spectrograms. Using the frequency–Bessel (F-J) transform, the generated spectrograms can provide more dispersion information by including higher modes in addition to the fundamental mode. With the increasing availability of these spectrograms, manually picking dispersion curves is highly time and energy consuming. Consequently, neural networks have been used for automatically picking dispersions. Dispersion curves are picked based on deep learning mainly for denoising these spectrograms. In several studies, the neural network was solely trained, and its performance was verified for the denoising. However, they all learn single-source data in the training of neural network. It will lead the regionality of trained neural network. Even if we can use domain adaptation to improve its performance and achieve some success, there are still some spectrograms that cannot be solved effectively. Therefore, multisources training is useful and could reduce the regionality in training stage. Normally, dispersion spectrograms from multisources have feature differences of dispersion curves, especially for higher modes in F-J spectrograms. Thus, we propose a training strategy based on domain confusion through which the neural network effectively learns spectrograms from multisources. After domain confusion, the trained neural network can effectively process large number of test data and help us easily obtain more dispersion curves automatically. The proposed study can provide a deep insight into the denoising of dispersion spectrograms by neural network and facilitate ambient noise tomography.

Shear wave crustal velocity structure in the Garhwal-Kumaon Himalaya based on noise cross-correlation of Rayleigh wave

The tectonics of the Garwal-Kumaon Himalaya is characterized by thrusts, tectonic windows, and klippen. We investigate crustal shear wave velocity variations using ambient noise cross-correlation tomography. The studied region encompasses the Kali River valley in the east to Satluj valley in the west, with the adjoining Indo-Gangetic Plain in the south covering the northern part of the Delhi-Haridwar ridge. The fundamental mode group velocities of Rayleigh waves are extracted from cross-correlation data of 33 broadband seismological stations from a regional seismic network. A total of 374 dispersion curves with a period range of 4–29 s show a group velocity variation between ∼2.3 and ∼ 3.4 km/s. The shear wave velocity structure of the uppermost lithosphere down to ∼50 km obtained by non-linear inversion of the Rayleigh wave dispersion data provides new insight into the geometry of the crust. A large variation in Vs in the range of ∼2.8 to ∼4.7 km/s corresponds to a variety of changes in the tectonic deformation, structure, and crustal thickness of the Himalayan wedge. Thick low-velocity sedimentary formations are identified beneath the Indo-Gangetic Plain and the frontal Himalaya. Anomalous low Vs zones are also observed in the mid-crust beneath the higher Himalaya and southern Tibet, indicating partial melting or the presence of aqueous fluid zones. The high-velocity anomalies may be correlated with duplex structures beneath the Lesser Himalaya and with lithospheric flexure.

High-resolution shallow crustal shear wave velocity structure of Anyuan mining area and its adjacent region in Jiangxi Province, China

High-resolution seismic image is critically important for mining minerals. In this study, we collected seismic data from a local dense seismic array consisting of 154 stations around the Anyuan mining area and its adjacent region of Pingxiang City, Jiangxi Province in South China, and applied the ambient noise tomography (ANT) method to image the shear wave velocity structure in the study area. Shallow crustal velocities at depths less than 3.3 km were determined by direct inversion of Rayleigh wave group velocity dispersion curves at the period range of 0.5–5.0 s. Overall, the S-wave velocity structure has a tight correlation with surface geological and tectonic features in the study area. The shear wave velocity structure in the shallow crust of the Anyuan Mine and its adjacent areas displayed distinct low-velocity anomalies, which can be attributed to the depression of sedimentary structures and coal mining activities in the Pingxiang-Leping region. The zones surrounding the Anyuan fault (AYF) and Wangkeng fault (WKF) zones exhibited low-velocity anomalies from the ground surface to ~ 3.3 km underground. And the low-velocity anomalies at depths less than 1.2 km could be related to the sedimentary environment of coal mine and the coal mining activities, while the low-velocity anomalies at depths below 1.2 km are caused by the presence of fracture medium, oil and gas in the fault zone. The shear wave velocity changes sharply across the AYF, and the characteristics of the velocity change interface indicate that the AYF is inclined toward the northwest, with its extension reaching depths of approximately 3 km underground.Graphical

Monitoring performance of slopes via ambient seismic noise recordings: Case study in a colluvium deposit

Long-term monitoring of slopes is of significance for engineering geology research and geo-disaster prevention. There is a growing need to develop fast, nondestructive and affordable techniques that can detect gradual and cyclic changes inside slopes. For this purpose, we propose a fast and low-cost computational framework based on processing the single-point ambient seismic noise recordings by the horizontal-to-vertical spectral ratio (HVSR) method. To test the efficiency of the proposed framework, we conduct a demonstration study in a road-cut slope in colluvium deposits in Southwest China. First, we carry out short-term ambient seismic noise surveys on the slope, delineate the shear wave velocity (Vs) structure of the slope, verify and establish this structure as the reference Vs model for the slope. Then we conduct the long-term monitoring of the ambient seismic noise on two profiles of the slope, calculate the HVSR curves and observe the time-dependent variations of the predominant peaks that reflect temporal changes of subsurface interfaces. Finally, we perform the Vs inversion to investigate changes in the Vs structure with rainfall. Through the monitoring, we identify the rainfall-induced slope failure and discover that both predominant frequency and shallow subsurface Vs of slope are negatively correlated with rainfall. The HVSR calculation, the predominant peak identification and the Vs inversion can all be implemented in minutes, which is much faster than the array-based surface wave method. The theoretical analysis and the demonstration application show that the framework we proposed in this study has great potential for monitoring changes in performance of slopes.

The upper crustal shear wave structure of the Zagros collision zone from local earthquake Rayleigh wave tomography

The Zagros fold-and-thrust belt is the principal response to the Arabia–Eurasia continental collision, which generates significant seismic activity in Iran. Surface wave tomography can be a proper tool for crustal structure imaging in this region. We used observed dispersion curves of Rayleigh wave derived from high-quality local-earthquake waveforms to obtain 2D tomographic maps in the period range 1–28 s beneath the Zagros collision zone. Subsequently, local pseudo-dispersion curves for individual grid points are inverted for shear-wave velocity structure, in order to obtain a 3D velocity model of the crust. Our results indicate shallow low-velocity structures near the suture zone. Moreover, a low-velocity flexure shape in the south of the Zagros Mountain Front Fault could be due to the thick sedimentary basin beneath the Dezful embayment. Relatively an uplifting of the deeper structures beneath Sanandaj-Sirjan and the high Zagros might could be led to high-velocity structures at mid-crustal depths. The variation of shear-wave velocity along the Zagros indicates the inhomogeneous structures along this folding and thrust belt.

From ambient vibration data analysis to 1D ground-motion prediction of the Mj 5.9 and the Mj 6.5 Kumamoto earthquakes in the Kumamoto alluvial plain, Japan

We present horizontal ground motion predictions at a soft site in the Kumamoto alluvial plain for the Mj 5.9 and Mj 6.5 Kumamoto earthquakes of April 2016, in the framework of an international blind prediction exercise. Such predictions were obtained by leveraging all available information which included: (i) analysis of earthquake ground motions; (ii) processing of ambient vibration data (AMV); and (iii) 1D ground response analysis. Spectral analysis of earthquake ground-motion data were used to obtain empirical estimates of the prediction site amplification function, with evidence of an amplification peak at about 1.2 Hz. Horizontal-to-vertical spectral ratio analysis of AMV confirmed this resonance frequency and pointed out also a low-frequency resonance around 0.3 Hz at the prediction site. AMV were then processed by cross-correlation, modified spatial autocorrelation and high-resolution beamforming methods to retrieve the 1D shear-wave velocity (Vs) structure at the prediction site by joint inversion of surface-wave dispersion and ellipticity curves. The use of low frequency dispersion curve and ellipticity data allowed to retrieve a reference Vs profile down to few thousand meters depth which was then used to perform 1D equivalent-linear simulations of the M 5.9 event, and both equivalent-linear and nonlinear simulations of the M 6.5 event at the target site. Adopting quantitative goodness-of-fit metrics based on time–frequency representation of the signals, we obtained fair-to-good agreement between 1D predictions and observations for the Mj 6.5 earthquake and a poor agreement for the Mj 5.9 earthquake. In terms of acceleration response spectra, while ground-motion overpredictions were obtained for the Mj 5.9 event, simulated ground motions for the Mj 6.5 earthquake severely underestimate the observations, especially those obtained by the nonlinear approach.Graphical