Rayleigh Wave Dispersion Curves Research Articles

Upper mantle structure beneath the Mongolian region from multimode surface waves: Implications for the western margin of Amurian plate

Multimode phase speeds of surface waves are used to build a new radially anisotropic S wave model in the eastern Eurasian and Mongolian regions. Our dataset includes seismic waveforms of over 1655 teleseismic events (Mw≥5.8) from 2009 to 2021, recorded at permanent and temporary stations in and around Mongolia. The multimode dispersion curves of Love and Rayleigh waves were extracted using the nonlinear waveform fitting method for individual seismograms. Then, we retrieved phase speed maps for each mode and frequency, incorporating finite-frequency effects. Finally, localized multimode dispersion curves extracted from the phase speed maps were inverted for local 1-D SV and SH wave profiles, which are combined into a radially anisotropic 3-D shear wave model. Our new model exhibits significant lateral variations of S wave speeds at 70–100 km depth beneath Mongolia, i.e., slow anomalies in the tectonically active western Mongolia in contrast to fast anomalies in stable eastern Mongolia. In the radial anisotropy model, SH waves are faster than SV waves in most areas of the Mongolian lithosphere above 100 km depth, except for the northeast of the Altay Mountains. The Hangay Dome region is characterized by significantly slower velocities that may relate to its uplifting. A large-scale low velocity beneath the northeast of the Hangay Dome with a slower SV wave speed than SH may indicate the existence of partially molten layers. This study also reveals distinct lateral variations of S wave speeds across the boundary between the Amurian and Eurasian plates, characterized by the fast anomaly in eastern Mongolia, corresponding to the lithosphere in the western Amurian plate.

Seismic structure of Iceland revealed by ambient noise Rayleigh wave tomography

As it is an ideal location for studying plume–ridge interactions, a clear image of the Icelandic upper mantle structure is necessary. We collect continuous seismic records from 164 stations and extract Rayleigh wave dispersion curves via the frequency-Bessel (F-J) transform method. Based on ambient noise tomography, we provide a new shear-wave velocity model of the Icelandic crust and uppermost mantle, extending to a depth of 120 km. The model is validated by the waveform simulation method and reveals extensive crustal low-velocity zones (LVZs) across both the neovolcanic and nonvolcanic zones of Iceland. These crustal LVZs may be attributed to elevated temperatures, partial melting, and lithological variations. A distinct LVZ beneath a depth of 60 km, mainly on the North American Plate, may correspond to Icelandic plume material. Additionally, hot plume material may be delivered to the crust through low-velocity conduits beneath the spreading mid-ocean ridge. There is a clear contrast between the uppermost mantle low-velocity zones (UMLVZs) in the western region and the uppermost mantle high-velocity zones in the eastern region, which may indicate asymmetric tectonic plates on both sides of the mid-ocean ridge. This asymmetry may be attributed to the multiple eastward jumps of the ridge systems. The eastern high-velocity body, meaning a cooler uppermost mantle than that of the western region, may act as a barrier to obstruct the eastward plume flow. Under plume–ridge interactions, plume material can affect crustal accretion and feed volcanic activity on the surface along the spreading Mid-Atlantic Ridge.

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.

Lithospheric structure of the Paraná, Chaco-Paraná, and Pantanal basins: Insights from ambient noise and earthquake-based surface wave tomography

To investigate the lithospheric seismic structure of southern South America beneath the Paraná, Chaco-Paraná, and Pantanal basins, we measure two distinct sets of dispersion curves of Rayleigh waves: the first corresponds to 25,986 phase velocity dispersion curves in the period range 5–50 s, extracted from cross-correlation of the ambient noise between pairs of stations and thus sensitive mainly to crustal structure; the second set is derived from group velocities extracted from earthquake recordings, resulting in 33,888 dispersion curves with periods ranging from 8 to 200 s, allowing us to probe deeper Earth structure. From these data sets, we derive two independent S-wave velocity models following a two-step inversion procedure, resulting in a crustal and a lithospheric mantle model with depths extending up to 50 and 200 km, respectively. Features imaged in our crustal model include low velocity anomalies in agreement with the surface position of the major sedimentary basins and high velocity anomalies within fold-thrust provinces and the basement of the Pantanal basin. In the uppermost mantle, we identify high velocities in the region of the Paraná basin, São Francisco and Amazonian cratons, and Luiz Alves block, and a low velocity feature beneath the Pantanal basin, which suggests a potentially thinner lithosphere under the basin. We also illuminate a strong velocity gradient boundary between the eastern border of the Pantanal basin with the Paraná basin, from crustal to lithospheric mantle depths, which seems to delineate the western and southern borders of the Paraná basin, coinciding with the Western Paraná Suture. Additionally, we construct a Moho depth proxy map for the study area, which has an overall good agreement with previous results, except for a thicker crust beneath the southern Chaco-Paraná basin. This area, together with a thicker crust counterpart in the Paraná basin, has a remarkable correlation with the position of a volcanic layer related to the Paraná Magmatic Province, which leads us to suggest that magmatic processes played an important role in the south Chaco-Paraná basin as well.

Site Characterization and Shallow Shear Velocity through HVSR Measurements around the Hyderabad Metropolitan Region, India

ABSTRACT We investigate the site characterization and shallow shear velocity profiles from the analysis of the Horizontal to Vertical Spectral ratio (HVSR) around the Hyderabad metropolitan region (HMR), which falls under the southern Indian shield. This work uses both the ambient noise and microearthquake data to compute the HVSR, and additionally the Random Decrement technique to compute the HVSR of extracted Rayleigh waves. This study indicates comparable HVSR curves at each station with the three different datasets, from which we obtain the average dominant frequency (f0) and amplification value (A0). They are further used to calculate the seismic vulnerability index value (Kg). We observe that the value of f0 around the HMR is not fixed, but is varying in the range of 3.4 to 18 Hz, whereas the value of A0 is in the range of 1.7 to 12 approximately and Kg in the range of 0.16 to 1.68 approximately, with an exception of ∼ 33 at VKB (Vikarabad) station, which may be due to a local unconsolidated sub-surface structure. Based on the Diffused Field Assumption (DFA), we invert the average HVSR curves and average dispersion curves of Rayleigh waves, and provide the shallow shear velocity profiles up to 300 m, along with an approximate estimate of VS30 (in upper 30 m depth). The estimated VS30 values vary between 911 to 3143 m/s, falling under the classifications A and B (mostly Hard Rock and Rock type) of National Earthquake Hazards Reduction Program (NEHRP) (BSSC, 2001). However, our study shows some stations with shear velocity inversions at shallow depths within 300 m, indicating layers of low velocity, needing further study. In the absence of detailed near-surface findings, these findings are valuable inputs for geotechnical engineering studies and urban-city planning around the HMR, and emphasizes the effectiveness the HVSR method to determine sub-surface topography and/or unknown soil structures as an economical investigation viability.

Research and application of Rayleigh wave imaging based on the Born–Jordan time‐frequency distribution

AbstractCurrently, the horizontal resolution of Rayleigh wave exploration is low. In this study, we propose the Born–Jordan time‐frequency distribution to analyse Rayleigh waves. The seismic signal was filtered with a wavelet transform for denoising, and the Rayleigh wave was separated in the time domain. Using the Born–Jordan time‐frequency distribution, the time waveform of each frequency comprising the Rayleigh wave from every seismic channel was obtained, and the time difference of the Rayleigh wave with the same frequency was calculated, based on which the dispersion curve between the two channels was obtained. Combined with the multichannel Rayleigh wave dispersion curve, phase velocity and frequency imaging under the seismic arrangement were obtained. Applying this method to detect abnormal geological bodies in engineering investigations showed that hard geologic bodies, such as comcrete rocks, have high velocity and frequency, whereas weak ones have low velocity and frequency. This strategy facilitated the detection of fractured zones, underground goafs and obstacles during pipe‐jacking construction near the surface.

Lithospheric Evolution of the South‐Central United States Constrained by Joint Inversion of Receiver Functions and Surface Wave Dispersion

AbstractIn the present study, we use broadband seismic data recorded by 190 stations of the EarthScope program's Transportable Array to construct a 3‐D shear wave velocity model for the upper 180 km using a non‐linear Bayesian Monte‐Carlo joint inversion of receiver functions (RFs) and Rayleigh wave dispersion curves. Ambient noise and teleseismic data are used for obtaining Rayleigh wave phase velocity dispersion curves. A resonance removal filtering technique is applied to the RFs contaminated by reverberations from the thick sedimentary layers that cover most of the region. Our observations of the higher crustal shear velocities (∼3.40 km/s) beneath the Sabine Block (SB), along with the estimated relatively thicker crust (∼34.0 km) and lower crustal Vp/Vs estimates (∼1.80) in comparison with the rest of the Gulf Coastal Plain (GCP) (∼3.10 km/s for crustal shear velocities, ∼29.0 km for crustal thickness, and ∼1.90 for crustal Vp/Vs estimates), indicating that this crustal block has different crustal properties from the surrounding coastal plain regions. The southern Ouachita Mountains have a thin crust (∼30.0 km) and low mean crustal Vp/Vs value (∼1.73), suggesting that lower crustal delamination has occurred in this region. Low velocities in the upper mantle beneath most of the GCP are interpreted as a combined result of thin lithosphere, higher‐than‐normal temperatures, and possibly compositional variations.

Near-Surface Rayleigh Wave Dispersion Curve Inversion Algorithms: A Comprehensive Comparison

Near-Surface Rayleigh Wave Dispersion Curve Inversion Algorithms: A Comprehensive Comparison

Multi‐mode non‐linear inversion of Rayleigh wave dispersion curves with grey wolf optimization and cuckoo search algorithm

AbstractDispersion curve inversion is one of the core contents of Rayleigh wave data processing. However, the dispersion curve inversion has the characteristics of multi‐parameter, multi‐extremum as well as nonlinearity. In the face of Rayleigh wave data processing under complex seismic‐geological conditions, it is difficult to reconstruct an underground structure quickly and accurately apply a single global‐searching non‐linear inversion algorithm. For this reason, we proposed a strategy to invert multi‐order mode Rayleigh wave dispersion curves by combining with grey wolf optimization (GWO) and cuckoo search (CS) algorithms. On the basis of introducing the mechanism of iterative chaotic map with infinite collapses (ICMIC) and the strategy of dimension learning–based hunting (DLH), an improved GWO was developed that was called IDGWO (ICMIC and DLH GWO). After searching the near‐optimal region through IDGWO, the CS with a variable step‐size Lévy flight search mechanism was switched adaptively to complete the final inversion. The correctness of our method was verified by the multi‐order mode dispersion curve inversion of a six‐layer high‐velocity interlayer model. Then it was further applied to the processing of real seismic datasets. The research results show that our method fully utilizes the advantages of each of the two global‐searching non‐linear algorithms after integrating IDGWO and CS, while effectively balancing the ability between global search and local exploitation, further improving the convergence speed and inversion accuracy and having good anti‐noise performance.

Crust and upper mantle S wave velocity structure in eastern Turkey based on ambient noise tomography

Eastern Turkey is an ideal location to study continental collision, which is manifested here by the presence of extensive volcanism and rapid tectonic uplift. To better understand the relationship between these surface phenomena and underground structures, we collect continuous waveform data from 121 seismic stations in the region to investigate the S wave velocity structure of the crust and upper mantle using ambient noise tomography. We obtain Rayleigh wave dispersion curves for periods between 5 and 100 s based on frequency-Bessel (F-J) transform dispersion analysis. We then perform quasi-Newton inversion to calculate the S wave velocity structure between 0 and 200 km. The results indicate the presence of two high-velocity anomalies: one in the southern Black Sea that may represent stable and cold lithospheric mantle and another near Lake Van that may represent the subduction remnant of the southern branch of the Neo-Tethys Ocean. In addition, the results demonstrate that the overall velocity in the study area is lower than the global average and reveal extensive upper mantle low-velocity zones (UMLVZs) which represent asthenospheric materials, and mid-crustal low-velocity zones (MCLVZs). In this study, we propose two possible formation mechanisms of the MCLVZs: one is partial melting, and the other is the formation of low-velocity compositional layering due to complex refining and differentiation processes and subsequent continent reworking. Considering that the spatial distribution of the MCLVZs and UMLVZs is well correlated with regional volcanic activity, the MCLVZs and UMLVZs may provide the driving force and are the material sources for volcanism in the area. Furthermore, the extremely thin and weak lithospheric mantle lid and the exceedingly shallow lithosphere asthenosphere boundary (LAB) indicate that the lower part of the lithosphere has delaminated and that the asthenosphere intrudes into a very shallow location, which then supports regional tectonic uplift.

Higher-order thin layer method as an efficient forward model for calculating dispersion curves of surface and Lamb waves in layered media

The thin layer method (TLM) is an effective semi-discrete numerical tool for analyzing wave motion in stratified media. The TLM can calculate the eigenvalues and eigenvectors for both propagating and decaying waves, which is essential to simulate waves near the source. The eigenvectors are particularly useful in various applications, such as estimating modal contributions, calculating synthetic seismograms, determining Green’s function, analyzing site response, and solving wave amplification problems in earthquake engineering, to name a few. In practice, most engineering applications use linear element-based TLM. However, the overall performance of TLM depends upon the type of element and the number of thin layers. The linear element TLM requires relatively thin sub-layers to obtain an accurate solution, making it computationally expensive and time-consuming. This study explores the potential of using non-linear higher-order TLM (HTLM) for forward modeling. The mathematical framework of TLM is used to formulate the generalized HTLM in determining the multimodal dispersion curve of Rayleigh, Love, and Lamb waves. The convergence and computational efficiency of different orders of HTLM have been investigated on a diverse set of published soil profiles and plate-like structures. Since non-linear interpolation functions better capture the change in displacement and stress between the layers, the findings reveal that the HTLM consistently outperforms the linear element TLM in terms of accuracy. Furthermore, the computational efficiency of different order HTLM is analyzed by comparing the total degrees of freedom and CPU time. Remarkably, higher-order HTLMs exhibit improved accuracy without increasing the computational burden, making them an attractive option for practical applications such as global search-based inversion and full spectrum inversions where fast and economical forward modeling is essential. Therefore, the HTLM will become helpful in seismic wavefield modeling, dynamic soil characterization, non-destructive evaluation methods, wave propagation analysis through waveguides, seismic site response analysis, etc.

Seismic Evidence for Craton Formation by Underplating and Development of the MLD

AbstractInconsistencies between observations from long and short period seismic waves and geochemical data mean craton formation and evolution remains enigmatic. Specifically, internal layering and radial anisotropy are poorly constrained. Here, we show that these inconsistencies can be reconciled by inverting cratonic Rayleigh and Love surface wave dispersion curves for shear‐wave velocity and radial anisotropy using a flexible Bayesian scheme. This approach requires no explicit vertical smoothing and only adds anisotropy to layers where required by the data. We show that all cratonic lithospheres are comprised of a positively radially anisotropic upper layer, best explained by Archean underplating, and an isotropic layer beneath, indicative of two‐stage formation. Within the positively radially anisotropic upper layer, we find a variable amplitude low velocity zone within 9 of 12 cratons studied, that is well correlated with observed Mid‐Lithospheric Discontinuities (MLDs). The MLD is best explained by metasomatism after craton formation.

A simple and fast method to calculate dispersion curves of Rayleigh waves due to models with a low-velocity half-space

For layered models with a low-velocity half-space, Rayleigh waves can exhibit inversely dispersive patterns and thus are advantageous for providing useful information on the seismic properties of such structures. However, the calculation of dispersion curves usually encounters some difficulties such as root skipping or numerical instability due to the presence of leaky modes. The full-wavefield method and spectral element method can offer better solutions but at the cost of a significant computational effort. For practical applications, most researchers treat the zeros of the real part of the secular function as an approximation to the leaky modes, which may lead to relatively large errors that cannot be ignored. Instead, in this paper, we propose a simple and efficient method to approximate the leaky mode based on the local minima of the absolute value of the secular function. Compared with the dispersion curve generated from the real part of the secular function, the proposed method can provide a more accurate approximation. We further apply a new efficient Monte Carlo search technique to two synthetic datasets and a permafrost example. Results show that our forward and inverse methods are effective, efficient, and robust. Moreover, our numerical experiments indicate that the modified Thomson-Haskell method is more suitable for calculating leaky modes compared with the fast delta method and the generalized reflection/transmission method, and that the inversion results are less sensitive to the effect of the limited spread length on the measured dispersion curves. The present study emphasizes the high efficiency of the proposed method in imaging permafrost structures and foundation features of buildings or roads.

Trans-Dimensional Bayesian Inversion of Rayleigh Wave Dispersion Curves for Shallow Surface Two-Dimensional Imaging

In this study, we achieve 2-D spatial structure directly from dispersion measurements using the reversible jump Markov chain Monte Carlo (rj-McMC) algorithm with a fully 2-D model parameterization. New parsimonious parameterization with low dimensional representation is introduced, which enable us to accurately reproduce the wave velocity field and the associated uncertainties. Field data test demonstrates that the proposed method estimates directly the shear-wave velocity profile with better lateral resolution. The associated uncertainties provide directly interpretable uncertainty on structures of interest.

Application of Deep Learning in Rayleigh Dispersion Curves Inversion

The inversion of the Rayleigh wave dispersion curve is a fundamental step in determining the shear wave velocity profile. However, existing inversion methods, such as linear and nonlinear approaches, have inherent limitations. In order to overcome these challenges and enhance the speed and accuracy of the inversion, this paper proposes a novel deep learning-based approach. We establish a method to construct sample data that ensures ergodicity and evolutive orderliness of the near-surface velocity profile using a constrained Markov decision process to address these limitations. We then design a deep learning model to capture the nonlinear mapping relationship between the dispersion sequence data and velocity structure. Training the model on the generated sample data accurately predicts the subsurface velocity profile based on the dispersion curve. The proposed method is evaluated through theoretical model experiments, demonstrating high computational accuracy.

Joint Inversion of Rayleigh Wave Dispersion and Ellipticity Based on Deep Learning

The joint inversion of Rayleigh wave dispersion curves and ellipticity curves is an important method to obtain information about the velocity structure of the stratum, which has the advantages of high resolution and speed. However, there are problems in the application of this technique, such as complicated data, low inversion efficiency, and large influence factors of nonlinear inversion. Deep learning has excellent nonlinear approximation capability, which can compensate for most of the above defects, and has higher efficiency and accuracy compared with traditional methods. In this paper, in order to obtain the near-surface stratigraphic structure quickly and accurately, we propose to introduce the deep learning method into the joint inversion, use the CNN-LSTM network to perform the joint inversion of Rayleigh wave dispersion curves and ellipticity curves. And in order to expand the variety of data set, we propose to add noise to the sample data set, through which the empirical risk can be reduced, and the generalization performance of the network can be increased. Model experiments show that the deep learning network can effectively and accurately perform the joint inversion of Rayleigh waves, and adding noise to the training set is an effective way to improve the generalization and stability of the network.

Joint Inversion of SPREE Receiver Functions and Surface Wave Dispersion Curves for 3‐D Crustal and Upper Mantle Structure Beneath the U.S. Midcontinent Rift

AbstractBroadband seismograms from the EarthScope Transportable Array and Superior Province Rifting EarthScope Experiment (SPREE) deployments are used to map the crust and uppermost mantle structures beneath the failed Midcontinent Rift (MCR) of Minnesota/Wisconsin, USA. The results suggest the existence of a variable zone of mafic underplating that is up to 20 km thick (40–60 deep). We jointly invert receiver functions and Rayleigh wave dispersion curves to quantify the region's crustal and mantle shear‐wave velocity structure. Basin sediment thicknesses are mildly asymmetric about the rift axis, with thickest regions immediately beneath the rift. 3‐D modeling shows anomalous lower crust and crust‐mantle transitions beneath the MCR. Sub‐MCR crustal thicknesses are generally >50 km with lower crust Vs of 4.0–4.2 km/s. Away from the MCR, the crust is typically ∼40 km thick. Strong variations in apparent crustal thickness are found along the MCR, increasing significantly in places. An additional layer of shear velocities intermediate between typical lower crust and upper mantle velocities (4.1–4.6 km/s) exists beneath most of the MCR which is thickest beneath the rift axis and pinches out away from the rift. This structure corroborates previous proposals of the presence of an underplated layer near the Moho. Results cannot distinguish between different mechanisms of emplacement (e.g., mafic interfingering within a subsequently down‐dropped lower crust vs. development of a high‐density pyroxenitic residuum at the top of the mantle). Also observed are anomalously high (>4.7 km/s) sub‐rift shear‐wave velocities at ∼70–90‐km depths, suggesting the presence of cold, depleted upper mantle material.

Experimental study on various factors contributing to poor microtremor data processing results using ESAC method

In practical applications, microtremor data often encounter contamination from various sources of noise, leading to suboptimal processing outcomes. This study aimed to provide a comprehensive analysis of the impact of noise on the microtremor method. The focus was on presenting explicit and quantitative evidence to demonstrate the extent to which these noises influence the accuracy and reliability of the method. Initially, a ten-station triangular array was employed to gather a sequence of high-precision microtremor data. Next, various influencing factors, such as high-amplitude random noise, were generated and incorporated into the meticulously collected real data in order to simulate the actual perturbation process. Based on both the raw and the artificially contaminated data, a series of dispersion curves of the fundamental-mode Rayleigh waves were obtained by applying the extended spatial autocorrelation method (ESAC). Based on the obtained results, several valuable conclusions have been derived that can effectively inform the procedures for acquiring and processing microtremor data. If the amplitude and duration of sudden onset vibrations are considerable, more data should be collected to reduce the Negative impact. If non-random noise exhibits similar frequency characteristics as microtremor data, and the duration and energy of the noise are relatively small, it can be ignored; otherwise, more data should be collected. The dispersion curve, determined by the principle of the ESAC method, remains unaffected by any changes in the amplitude of one or more traces. The likelihood of frequency dispersion curve distortion increases as the impact of entire data anomalies on the coherence curve becomes greater. In general, when the collection positions of abnormal data are more widely spaced, the impact on the frequency dispersion curve tends to decrease. It is important to note that, in a nested-triangular array configuration, the central geophone plays a crucial role in ensuring accurate and dependable outcomes.

A Weighted Average of Multiple Inversions of Rayleigh Wave Dispersion Curve Using Particle Swarm Optimization for Geotechnical Site Characterization

<p class="Keywords">Shear wave velocity is an important parameter in geotechnical engineering for studying liquefaction, finding bedrock for the basement of a building, and figuring out the presence of subsurface cavities. This study aims to develop and evaluate the accuracy of the multiple inversions by the Particle Swarm Optimization (MI-PSO) algorithm with a weighted average solution. This algorithm is applied to Rayleigh wave dispersion data for geotechnical site characterization. Two synthetic models, the HVL model and the complex model (i.e., a combination of models with LVL and HVL characteristics), are used to conduct algorithm tests. These synthetic models replicate subsurface characteristics that are frequently encountered in geotechnical cases. Synthetic data tests show that the MI-PSO algorithm with a weighted average solution works excellently. The MI-PSO technique with a weighted average solution resolves the model better than the conventional average solution. When applied to two field data sets, the MI-PSO algorithm with a weighted average solution can delineate target models that are consistent with the qualitative interpretation based on the observed dispersion curve characteristics.</p>

Magma and hydrothermal sources below the northern part of Paramushir Island (Kuril Arc) inferred from ambient noise tomography

Paramushir is the northernmost island of the Kuril Arc comprising several Holocene volcanoes, of which two are presently active: Ebeko and Chikurachki. We present the first crustal-scale three-dimensional seismic shear-wave velocity model of the northern part of Paramushir derived with the use of data of 20 temporary stations operated in 2021–2022 and one permanent station. The continuous seismic data were used to obtain the Rayleigh wave dispersion curves for periods ranging from 0.5 s to 12 s through computing ambient seismic noise cross-correlation functions. Then the 3D shear wave velocity distribution was constructed by seismic tomography. The synthetic tests demonstrate that the model has sufficient resolution down to 7–10 km depth. We observe a series of low-velocity anomalies at the depths of 4–6 km below the volcano-origin Vernadsky Ridge. They may likely represent magma reservoirs that are responsible for Holocene eruptions along the ridge. These anomalies are overlain by the high-velocity anomalies associated with the rigid cover consisting of an upper-crustal granite-granodiorite body and consolidated magma intrusions. Along the eastern flank of Ebeko we reveal a thin low-velocity anomaly at a depth of ∼1 km that can be interpreted as a water-saturated layer. The contact of this layer with hot magma intrusions leads to the formation of a large amount of steam that is ejected during the frequent phreatic eruptions of Ebeko and from numerous fumaroles in the summit area. On the other hand, this layer, which is following down to the Pacific Coast, might be promising for the geothermal energy exploitation.