Multimode Inversion Research Articles

Shear-Wave Velocity Model for the Dead Sea Transform from Multimode Inversion of Surface Waves Excited by the February 2023 Southeast Türkiye Earthquake Sequence

Abstract The dispersive nature of surface waves can be used for shear-wave velocity inversion at different scales. We show that four large earthquakes from the 2023 Türkiye earthquake sequence generate visible surface waves recorded by a dense strong-motion network deployed along the Dead Sea Transform (DST) in Israel. Thanks to favorable geometrical conditions and source radiation patterns, we observe both Rayleigh and Love waves that travel predominantly parallel to the network. We can reliably compute the dispersion of three Love-wave modes and two Rayleigh-wave modes. Using these dispersion curves, we invert for a 1D S-wave velocity model of the entire DST, outperforming an existing model. Statistical and kernel sensitivity analysis show high certainty down to a depth of 30 km thanks to the multimode joint inversion in the frequency band of 0.03–0.3 Hz. Using a multiwindow approach, we invert for an along-strike laterally varying velocity model of the DST. Although it is limited to the fundamental Love-wave mode, using the 1D model as a constraint allows us to recover a shallow (10 km) velocity structure in agreement with previous studies of the area. Despite the simplicity of our used approach, it can be used as a basis for more advanced studies.

Uncertainty quantification of multimodal surface wave inversion using artificial neural networks

An inversion of surface wave dispersion curves (DCs) is a nonunique and ill-conditioned problem. The inversion result has a probabilistic nature, which becomes apparent when simultaneously restoring the S-wave velocity and layer thickness. Therefore, the problem of uncertainty quantification is relevant. Existing methods through deterministic or global optimization approaches of uncertainty quantification via posterior probability density (PPD) of the model parameters are not computationally efficient because they demand multiple solutions to the inverse problem. We have developed an alternative method based on a multilayer fully connected artificial neural network (ANN). We improve the current unimodal approach, which is known from publications, to multimodal inversion. We use Cox’s and Teague’s algorithm to determine optimal parameterization (number of layers) and the ranges of possible model parameters. We uniformly draw training data sets within estimated ranges and train the ANN. Saved ANN weights map the phase velocity DCs to values of the S-wave velocity and layers thickness. To estimate the uncertainties, we adapt the Monte Carlo simulation strategy, according to which the frequency dependent data noise and the inverse operator errors are projected onto the resulting velocity model. The inverse operator errors are evaluated by the prediction of the training data set. The proposed combination of surface wave data processing methods, configured with each other, provides a novel surface wave multimodal dispersion data inversion and uncertainty quantification approach. We first test our approach on synthetic experiments for various velocity models: a positive gradient velocity, a low-velocity layer, and a high-velocity layer. This is done considering unimodal inversion at first and then compared with the multimodal inversion. Afterward, we apply our approach to field data and compare the resulting models with the body S-wave processing by the generalized reciprocal method. The experiments indicate high-potential results — using ANN yields the possibility to accurately estimate the PPD of restored model parameters without a significant computational effort. The PPD-based comparison demonstrates the advantages of a multimodal inversion over a unimodal inversion. The trained ANN provides reasonable model parameters predictions and related uncertainties in real time.

Multimodal 4DVarNets for the Reconstruction of Sea Surface Dynamics From SST-SSH Synergies

The space-time reconstruction of sea surface dynamics from satellite observations is a challenging inverse problem due to the associated irregular sampling. Satellite altimetry provides a direct observation of the sea surface height (SSH), which relates to the divergence-free component of sea surface currents. The associated sampling pattern prevents operational schemes from retrieving fine-scale dynamics, typically below 10 days. By contrast, other satellite sensors provide higher-resolution observations of sea surface tracers such as sea surface temperature (SST). Multimodal inversion schemes then arise as appealing approaches. Though theoretical evidence supports the existence of an explicit relationship between sea surface temperature and sea surface dynamics under specific dynamical regimes, the generalization to the variety of upper ocean dynamical regimes is complex. Here, we investigate this issue from a physics-informed learning perspective. We introduce a trainable multimodal inversion scheme for the reconstruction of sea surface dynamics from multi-source satellite-derived observations, namely satellite-derived SSH and SST data. The proposed multimodal 4DVarNet schemes combine a variational formulation involving trainable observation and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> terms with a trainable gradient-based solver. An observing system simulation experiment for a Gulf Stream region supports the relevance of our approach compared with state-of-the-art schemes. We report a relative improvement greater than 60% compared with the operational altimetry product in terms of root mean square error and resolved space-time scales. We discuss further the potential and the limitations of the proposed approach for the reconstruction and forecasting of geophysical dynamics from irregularly-sampled satellite observations.

Multimodal inversion of Rayleigh wave dispersion curves based on a generalized misfit function

Fundamental mode dispersions, particularly higher ones obtained from weak and hard interlayers, are usually used for inversion to obtain the S-wave velocity profile. Higher mode dispersions are more informative for surface-wave inversion, thus benefiting both inversion stability and model resolution. In the multimodal inversion of the Rayleigh wave dispersion curves, the conventional root-mean-square based misfit function (RMF) is often deviated when the dispersion modes are misidentified. Alternatively, the inversion based on the determinant misfit function (DMF) may avoid misidentifications, but will still be limited to its inherent local minima. In order to reduce this complexity, the paper presents a generalized misfit function (GMF) for estimating the S-wave velocity profile. The generalized function allows for the inversion of both fundamental and higher modes dispersion, without mode-numbering. Before the inversion, the dispersion points are divided into the recognizable fundamental mode dispersion points and others. The RMF is then calculated using data from distinct fundamental mode dispersion, while the DMF was calculated using data from other points. Lastly, the RMF and the DMF were combined to form the GMF by normalizing. The results are based on both theoretical and real data sets and confirm the advantages of GMF in mode identification and inversion accuracy compared to both RMF and DMF.

A niching particle swarm optimization strategy for the multimodal inversion of surface waves

SUMMARY In practice, near-surface structures with shear wave velocity inversions or strong shear wave velocity contrasts may cause the phase velocity spectra of surface waves to be complex. Hence, it is sometimes difficult to identify mode numbers in the phase velocity spectrum. To avoid numbering different modes, the determinant misfit function has been applied to invert multimodal dispersion curves with a very limited computational cost due to the absence of the root-seeking procedure. However, this function presents a complicated relation with modal parameters and thus has multiple minima, resulting in an increase in model ambiguity. Therefore, it is more appropriate to adopt a multimodal optimization algorithm to find multiple minima instead of obtaining one optimal solution. In this study, we use a niching particle swarm optimization to find multiple minima with an enhanced fine search ability. Subsequently, we performed cluster analysis to distinguish different clusters in the inverted solutions and find the best-fitting profiles from multiple minima based on the Euclidean distance between the measured and inverted dispersion curves. Moreover, a modified Thomson–Haskell transfer matrix method is used to calculate the determinant misfit function for a better constraint on inversion because it can only resolve the surface wave modes possessing energy at the free surface, where both the sources and geophones are commonly deployed for active and passive surface wave exploration. Tests of synthetic and field data demonstrate that our inversion method is both effective and robust and emphasize its great potential in urban subsurface exploration and geotechnical characterization applications.

Extracting high-resolution, multi-mode surface wave dispersion data from distributed acoustic sensing measurements using the multichannel analysis of surface waves

Distributed acoustic sensing (DAS) is a rapidly expanding tool to sense wave propagation and system deformations in many engineering applications. In terms of site characterization, DAS presents the ability to make dynamic strain measurements at scales (e.g., kilometers) and spatial resolutions (e.g., meters) that were previously unattainable with traditional measurement technologies. In this study, we use both vibroseis truck and sledgehammer sources to demonstrate that DAS can be used to make multichannel analysis of surface waves (MASW) dispersion measurements that are equivalent in quality to geophones when: (a) a tight-buffered or strain-sensing fiber-optic cable is used, (b) the cable is buried in a shallow trench to enhance coupling, and (c) short gauge lengths and small channel separations are used. We analytically demonstrate the impact gauge length has on DAS measurements in terms of phase and amplitude for off-end MASW surveys. We further show that the choice of waveform units (i.e., phase, strain, strain-rate, displacement, velocity) do not impact the dispersion results when frequency-dependent normalization is applied to the dispersion images. Finally, we show that shear wave velocity profiles recovered from the DAS data using an uncertainty-consistent, multi-mode inversion agree favorably with cone penetration tests performed at the site. This study demonstrates that DAS, when appropriate considerations are made, can be used instead of traditional sensors (i.e., geophones) for making high-resolution, multi-mode measurements of surface wave dispersion data using the MASW technique.

Field and Synthetic Waveform Tests on Using Large‐Offset Seismic Streamer Data to Derive Shallow Seabed Shear‐Wave Velocity and Geotechnical Properties

AbstractCharacterizing properties of marine subsurface sediment helps with siting for offshore infrastructure. Shear‐wave velocity (Vs) provides information on the geotechnical properties of the seabed. We present our initial efforts to obtain a detailed two‐dimensional model of Vs for a large‐offset multi‐channel seismic (MCS) transect collected in shallow waters across the Taiwan Strait using surface waves excited by a large volume airgun. We derived the dispersion curves of the Scholte waves along the 37.5‐km‐long transect using the phase‐shift method and then conducted multimodal inversion to obtain a Vs model down to a depth of 150 m. To estimate the dynamic Poisson's ratio across the transect, we combined the Vs model with a compressional wave velocity model derived from the traditional MCS semblance velocity analysis. Lastly, we approximated the seismic attenuation of the profile. Our results show a large lateral variation in shear‐wave velocity. In the north, a low‐velocity zone with shear‐wave velocities of about 150 m/s was identified, while in the south, the shear‐wave velocity was found to be 300 m/s. With synthetic data, several sensitivity tests were performed to derive optimal parameters for offshore large‐offset streamer data. We particularly focused on the depth of the streamer and source and the water depth in combination with different seabed properties. Our results show that we can robustly derive the shear‐wave velocity, along with the Poisson's ratio, using large‐offset streamer data elsewhere based on the criteria we have tested using field and synthetic data sets.

Uncertainty quantification of phase velocity surface waves multy-modal inversion using machine learning

The paper is devoted to uncertainty quantification of the inverse problem solution of the multichannel analysis of surface waves method - the inversion of the curves of the phase velocity via frequency dependence. The uncertainty estimation approach is based on the Monte Carlo sampling strategy and a multilayer fully connected artificial neural network to approximate nonlinear dependence of shear wave velocity and layers thickness via values of phase velocity surface waves. Frequency-dependent noise in the data and errors of the inverse operator are projected onto the inverse problem solution. The results of unimodal and multimodal inversion are compared on the example of synthetic data processing. The experimental results show that using of machine learning approaches makes it possible to quickly and accurately estimate the posterior probability density of the reconstructed velocity model parameters.

Advances in surface-wave tomography for near-surface applications

Surface-wave (SW) tomography is a technique that has been widely used in the field of seismology. It can provide higher resolution relative to the classical multichannel SW processing and inversion schemes that are usually adopted for near-surface applications. Nevertheless, the method is rarely used in this context, mainly due to the long processing times needed to pick the dispersion curves as well as the inability of the two-station processing to discriminate between higher SW modes. To make it efficient and to retrieve pseudo-2D/3D S-wave velocity (VS) and P-wave velocity (VP) models in a fast and convenient way, we develop a fully data-driven two-station dispersion curve estimation, which achieves dense spatial coverage without the involvement of an operator. To handle higher SW modes, we apply a dedicated time-windowing algorithm to isolate and pick the different modes. A multimodal tomographic inversion is applied to estimate a VS model. The VS model is then converted to a VP model with the Poisson's ratio estimated through the wavelength-depth method. We apply the method to a 2D seismic exploration data set acquired at a mining site, where strong lateral heterogeneity is expected, and to a 3D pilot data set, recorded with state-of-the-art acquisition technology. We compare the results with the ones retrieved from classical multichannel analysis.

A residual-driven adaptive Gaussian mixture approximation for Bayesian inverse problems

In this article, we develop a residual-driven adaptive Gaussian mixture approximation (RD-AGMA) for Bayesian inverse problems. The posterior distribution is often non-Gaussian in practical Bayesian inference. To obtain a good approximation of the posterior, we provide the adaptive Gaussian mixture approximation (GMA) based on a residual. For GMA, the clustering of ensemble samples provides the predictor of means, covariances and weights by smoothed expectation–maximization (SmEM). SmEM can overcome the singularity of covariance matrix for small ensemble size. Then the parameters of GMA are updated by an iterative ensemble smoother (IES). To enhance clustering efficiency, the ensemble samples for the clustering are updated by IES as well. Since the goal of inverse problems is to minimize the residual between the observation data and the model response, the adaptive GMA of the posterior is constructed through a residual threshold. The mixture components with large residuals will be discarded in the adaptive procedure. When the prior is incorporated into the likelihood model, small residuals can drive the AGMA close to the true posterior. In the proposed method, a large number of samples can be efficiently drawn from the posterior distribution of GMA. A few numerical examples are presented to demonstrate the efficacy of RD-AGMA with applications in multimodal inversion and channel identification for subsurface flow problems in porous media.

A widespread mid-crustal low-velocity layer beneath Northeast China revealed by the multimodal inversion of Rayleigh waves from ambient seismic noise

In this study, we re-examined the crustal and uppermost mantle shear velocity structure beneath Northeast China using the broadband ambient seismic noise data from several temporary and permanent arrays in this region. By employing our newly developed interferometric analysis method (F-J method) for array noises and the corresponding multi-mode dispersion curve inversion approach, we obtained an integrated crustal and uppermost mantle shear velocity model of this region. We divided the study area into 39 overlapping sub-areas and then applied the F-J method to each sub-area to obtain a 3D shear velocity model. We extracted the fundamental mode and up to 4 higher-mode dispersion curves of Rayleigh waves in frequencies ranging 0.02–0.45 Hz from the ambient seismic noise data, inverted multi-mode dispersion curves, and obtained local 1D shear velocity model for each sub-area. By integrating all 1D shear velocity models of the 39 overlapping sub-areas, we obtained a 3D crustal and upper mantle model of Northeast China. Our integrated 3D model shows a widely distributed low-velocity zone at a depth of approximately 10–20 km located beneath the northern part of Xing-Mong orogenic belt and Songliao basin and the Changbaishan Mountain. Moreover, a large high-velocity zone underneath the region around the northern edge of Songliao basin is observed at a depth of approximately 40–50 km depth. These results provide evidence to improve our understanding of the tectonics of Northeast China.

The upper mantle beneath the South Atlantic Ocean, South America and Africa from waveform tomography with massive data sets

SUMMARYWe present a tomographic model of the crust, upper mantle and transition zone beneath the South Atlantic, South America and Africa. Taking advantage of the recent growth in broadband data sampling, we compute the model using waveform fits of over 1.2 million vertical-component seismograms, obtained with the automated multimode inversion of surface, S and multiple S waves. Each waveform provides a set of linear equations constraining perturbations with respect to a 3-D reference model within an approximate sensitivity volume. We then combine all equations into a large linear system and solve it for a 3-D model of S- and P-wave speeds and azimuthal anisotropy within the crust, upper mantle and uppermost lower mantle. In South America and Africa, our new model SA2019 reveals detailed structure of the lithosphere, with structure of the cratons within the continents much more complex than seen previously. In South America, lower seismic velocities underneath the transbrasilian lineament (TBL) separate the high-velocity anomalies beneath the Amazon Craton from those beneath the São Francisco and Paraná Cratons. We image the buried portions of the Amazon Craton, the thick cratonic lithosphere of the Paraná and Parnaíba Basins and an apparently cratonic block wedged between western Guyana and the slab to the west of it, unexposed at the surface. Thick cratonic lithosphere is absent under the Archean crust of the São Luis, Luis Álves and Rio de La Plata Cratons, next to the continental margin. The Guyana Highlands are underlain by low velocities, indicating hot asthenosphere. In the transition zone, we map the subduction of the Nazca Plate and the Chile Rise under Patagonia. Cratonic lithosphere beneath Africa is more fragmented than seen previously, with separate cratonic units observed within the West African and Congo Cratons, and with cratonic lithosphere absent beneath large portions of Archean crust. We image the lateral extent of the Niassa Craton, hypothesized previously and identify a new unit, the Cubango Craton, near the southeast boundary of the grater Congo Craton, with both of these smaller cratons unexposed at the surface. In the South Atlantic, the model reveals the patterns of interaction between the Mid-Atlantic Ridge (MAR) and the nearby hotspots. Low-velocity anomalies beneath major hotspots extend substantially deeper than those beneath the MAR. The Vema Hotspot, in particular, displays a pronounced low-velocity anomaly under the thick, high-velocity lithosphere of the Cape Basin. A strong low velocity anomaly also underlies the Cameroon Volcanic Line and its offshore extension, between Africa and the MAR. Subtracting the global, age-dependent VS averages from those in the South Atlantic Basins, we observe areas where the cooling lithosphere is locally hotter than average, corresponding to the location of the Tristan da Cunha, Vema and Trindade hotspots. Beneath the anomalously deep Argentine Basin, we image unusually thick, high-velocity lithosphere, which suggests that its anomalously great depth can be explained, at least to a large extent, by isostatic, negative lithospheric buoyancy.

Geophysics Bright Spots

An explosion of Bright Spots illuminate the July–August issue of Geophysics. In this issue, we learn an efficient technique for up/down and P/S elastic wavefield separation in reverse time migration (RTM) that yields more accurate Poynting vectors while suppressing RTM artifacts. We see the derivation of an efficient formula for correlative least squares in RTM. We learn about a Q-compensated RTM with one-step wave extrapolation. We are introduced to a new approach to isotropic elastic wavefield imaging using an energy norm. We also see the derivation of a seismoelectric Green's function for homogeneous media. We learn a new method by which P- and S-wave velocities are extracted via surface-wave multimodal inversion. We also learn about a new method for finding the relative location of microseismic events with multiple master events, and we see how to apply frequency cycling for the compensation for undesired off-resonance effects in surface nuclear magnetic resonance (NMR).

P- and S-wave velocity models of shallow dry sand formations from surface wave multimodal inversion

Surficial formations composed of loose, dry granular materials constitute a challenging target for seismic characterization. They exhibit a peculiar seismic behavior, characterized by a nonlinear seismic velocity gradient with depth that follows a power-law relationship, which is a function of the effective stress. The P- and S-wave velocity profiles are then characterized by a power-law trend, and they can be defined by two power-law exponents [Formula: see text] and two power-law coefficients [Formula: see text]. In case of depth-independent Poisson’s ratio, the P-wave velocity profile can be defined using the [Formula: see text] power-law parameters and Poisson’s ratio. Because body wave investigation techniques (e.g., P-wave tomography) may perform ineffectively on such materials because of high attenuation, we addressed the potential of surface-wave method for a reliable seismic characterization of shallow formations of dry, uncompacted granular materials. We took into account the dependence of seismic wave velocity on effective pressure and performed a multimodal inversion of surface-wave data, which allowed the [Formula: see text] and [Formula: see text] profiles to be retrieved. The method requires the selection of multimodal dispersion curve points referring to surface-wave frequency components traveling within the granular media formation and their inversion for the S-wave power-law parameters and Poisson’s ratio. We have tested our method on a synthetic dispersion curve and applied it to a real data set. In both cases, the surficial layer was made of loose dry sand. The test on the synthetic data set confirmed the reliability of the proposed procedure because the thickness and the [Formula: see text], [Formula: see text] profiles of the sand layer were correctly estimated. For the real data, the outcomes were validated by other geophysical measurements conducted at the same site and they were in agreement with similar studies regarding loose sand formations.

Mode-multiplexed waveguide sensor

Optical sensors enable quantification of analyte concentrations non-invasively without being affected from electromagnetic fields. The development of single-mode waveguides (WGs) is simple approach; however, limitations in the spatial distribution of the refractive index and the inability to sense multiple samples at the same time. Here, we demonstrate a multi-mode WG model and matrix inversion method (MIM) to improve spatial information in at least one dimension. This method is used to optimize and estimate three external stimulus properties in TE00, TE01 and TM00 multiplexed modes for a semi-triangular ring resonator configuration. The multi-mode WG and MIM may have applications in the development of biosensors for multi-analyte detection.

Upper-mantle shear-wave structure under East and Southeast Asia from Automated Multimode Inversion of waveforms

Upper-mantle shear-wave structure under East and Southeast Asia from Automated Multimode Inversion of waveforms

Reply on “Implications of surface wave data measurement uncertainty on seismic ground response analysis”

We are thankful to Comina and Foti [1] (‘The Discussers’), who showed their interest in our research and raised some issues on our paper on the “Implications of surface wave data measurement uncertainty on seismic ground response analysis”. Their main concerns [1] are on the selection criteria that we adopted to select the equivalent profiles, requirement of multimodal inversion and the seismic response of those equivalent profiles. We have prepared here a detailed explanation on all the raised issues and it is shown that the variations in spectral parameters are not merely due to the so-called discrepancies raised by ‘The Discussers’.

Global shear speed structure of the upper mantle and transition zone

S U M M A R Y The rapid expansion of broad-band seismic networks over the last decade has paved the way for a new generation of global tomographic models. Significantly improved resolution of global upper-mantle and crustal structure can now be achieved, provided that structural information is extracted effectively from both surface and body waves and that the effects of errors in the data are controlled and minimized. Here, we present a new global, vertically polarized shear speed model that yields considerable improvements in resolution, compared to previous ones, for a variety of features in the upper mantle and crust. The model, SL2013sv, is constrained by an unprecedentedly large set of waveform fits (∼3/4 of a million broad-band seismograms), computed in seismogram-dependent frequency bands, up to a maximum period range of 11– 450 s. Automated multimode inversion of surface and S-wave forms was used to extract a set of linear equations with uncorrelated uncertainties from each seismogram. The equations described perturbations in elastic structure within approximate sensitivity volumes between sources and receivers. Going beyond ray theory, we calculated the phase of every mode at every frequency and its derivative with respect to Sand P-velocity perturbations by integration over a sensitivity area in a 3-D reference model; the (normally small) perturbations of the 3-D model required to fit the waveforms were then linearized using these accurate derivatives. The equations yielded by the waveform inversion of all the seismograms were simultaneously inverted for a 3-D model of shear and compressional speeds and azimuthal anisotropy within the crust and upper mantle. Elaborate outlier analysis was used to control the propagation of errors in the data (source parameters, timing at the stations, etc.). The selection of only the most mutually consistent equations exploited the data redundancy provided by our data set and strongly reduced the effect of the errors, increasing the resolution of the imaging. Our new shear speed model is parametrized on a triangular grid with a ∼280 km spacing. In well-sampled continental domains, lateral resolution approaches or exceeds that of regionalscale studies. The close match of known surface expressions of deep structure with the distribution of anomalies in the model provides a useful benchmark. In oceanic regions, spreading ridges are very well resolved, with narrow anomalies in the shallow mantle closely confined near the ridge axis, and those deeper, down to 100–120 km, showing variability in their width and location with respect to the ridge. Major subduction zones worldwide are well captured, extending from shallow depths down to the transition zone. The large size of our waveform fit data set also provides a strong statistical foundation to re-examine the validity field of the JWKB approximation and surface wave ray theory. Our analysis shows that the approximations are likely to be valid within certain time–frequency portions of most seismograms with high signal-to-noise ratios, and these portions can be identified using a set of consistent criteria that we apply in the course of waveform fitting.

A shear wave velocity model of the European upper mantle from automated inversion of seismic shear and surface waveforms

SUMMARY We present a new, S-velocity model of the European upper mantle, constrained by inversions of seismic waveforms from broad-band stations in Europe and surrounding regions. We collected seismograms for the years 1990–2007 from all permanent stations in Europe for which data were available. In addition, we incorporated data from temporary experiments. Automated multimode inversion of surface and S-wave forms was applied to extract structural information from the seismograms, in the form of linear equations with uncorrelated uncertainties. The equations were then solved for seismic velocity perturbations in the crust and mantle with respect to a 3-D reference model with a realistic crust. We present two versions of the model: one for the entire European upper mantle and another, with the highest resolution, focused on the upper 200 km of the mantle beneath western and central Europe and the circum Mediterranean. The mantle lithosphere and asthenosphere are well resolved by both models. Major features of the lithosphere–asthenosphere system in Europe and the Mediterranean are indentified. The highest velocities in the mantle lithosphere of the East European Craton (EEC) are found at about 150 km depth. There are no indications for a deep cratonic root below about 330 km depth. Lateral variations within the cratonic mantle lithosphere are resolved as well. The locations of kimberlites correlate with reduced S-wave velocities in the shallow cratonic mantle lithosphere. This anomaly is present in regions of both Proterozoic and Archean crust, pointing to an alteration of the mantle lithosphere after the formation of the craton. Strong lateral changes in S-wave velocity are found at the northwestern margin of the EEC and may indicate erosion of cratonic mantle lithosphere beneath the Scandes by hot asthenosphere. The mantle lithosphere beneath western Europe and between the Tornquist–Teisseyre Zone and the Elbe Line shows moderately high velocities and is of an intermediate character, between cratonic lithosphere and the thin lithosphere of central Europe. In central Europe, Caledonian and Variscian sutures are not associated with strong lateral changes in the lithosphere–asthenosphere system. Cenozoic anorogenic intraplate volcanism in central Europe and the circum Mediterranean is found in regions of shallow asthenosphere and close to changes in the depth of the lithosphere–asthenosphere boundary. Very low velocities at shallow upper-mantle depths are present from eastern Turkey towards the Dead Sea transform fault system and Sinai, beneath locations of recent volcanism. Low-velocity anomalies extending vertically from shallow upper mantle down to the transition zone are found beneath the Massif Central, Sinai and the Dead Sea, the Canary Islands and Iceland.

Bayesian Inversion of Multimode Interface-Wave Dispersion From Ambient Noise

This paper applies nonlinear Bayesian inversion to estimate seabed shear-wave speed profiles and their uncertainties using interface-wave dispersion curves extracted from ocean ambient noise, and compares the resolution of seabed structure for fundamental mode and multimode data. Ambient noise recordings were collected for 2.15 h at hydrophones of an entrenched ocean bottom cable in the North Sea. Scholte-wave dispersion curves for the fundamental mode and several higher order modes within the frequency range 0.25–3.9 Hz are extracted from cross correlations of noise recordings at sensor pairs via the slowness-frequency transform. The Bayesian information criterion is used to determine the preferred model parameterizations in terms of the number of sediment layers supported by the data for inversions based on the fundamental mode alone and on the first three modes. Adaptive hybrid optimization and Metropolis–Hastings sampling are applied to estimate the optimum a posteriori shear-wave speed models and to compute marginal posterior probability distributions and profiles. The results show quantitatively that multimode inversion provides higher resolution of shallow shear-wave speed structure with smaller uncertainties at all depths than inversion of the fundamental mode alone.