Transdimensional ambient-noise tomography of the Zabargad Fracture Zone, Red Sea

With the advancements of three-component seismic instruments, much valuable information about source distributions and subsurface structures can be utilized to improve passive surface-wave imaging with anthropogenic seismic noise in urban environments. Current passive surface-wave methods, however, are mainly concerned with the Rayleigh waves in the vertical (Z) component, often neglecting the useful dispersion information in the radial (R) and transverse (T) components, particularly in higher modes of surface waves. So, we introduced the common-midpoint two-station analysis (CMP-TS) to extract the multimode dispersion curves of Rayleigh and Love waves from three-component noise recordings using multicomponent ambient seismic noise cross-correlations (ZZ, RR, and TT components). Results from synthetic data sets from given models show that the CMP-TS method is able to retrieve higher-mode Rayleigh waves from the RR component and improve the multimode dispersion measurements of Rayleigh waves by the summation of the ZZ and RR spectrograms. Besides, this method can extract multimode dispersion curves of Love waves with high resolution from the TT component. We applied the CMP-TS method to process three-component field data and retrieved the dispersion curves of Rayleigh waves with the fundamental and the first higher modes, as well as Love waves with the fundamental, the first, and second higher modes. The S-wave velocity model is constructed by inverting the fundamental and higher mode data and validated through borehole S-wave velocity measurements.

Love waves are generally less employed than Rayleigh waves in near-surface shear-wave (S-wave) investigations because acquiring S-wave data is not as easy as acquiring P-wave data. The dispersion of Love waves, however, is independent of Poisson’s ratio. This character can be the valuable basis to improve the S-wave velocity imaging. The sensitivity of earth properties to the dispersion curve of surface waves (Love and Rayleigh waves) is fundamental to determining the accuracy of an S-wave velocity model. By analyzing sensitivities to the dispersion curves of Love and Rayleigh waves comparatively, modes and frequency ranges of surface-wave dispersion data can be chosen appropriately during the inverse procedure, which helps to improve the accuracy of S-wave velocity profiles. For layered earth models, the sensitivities are defined by the percentage changes instead of partial derivatives. Analysis based on a regular model and an irregular model shows that, for earth properties such as the S-wave velocity and the thickness of each layer, Love waves are more sensitive than Rayleigh waves in most frequency ranges. Although the low velocity layer can dramatically influence the sensitivities of both Love and Rayleigh waves, the sensitivity of Love-wave to changes in S-wave velocity is much higher at low frequencies. Furthermore, Love waves are more sensitive than Rayleigh waves to the change of Swave velocity in half-space. That means Love waves can recovery the S-wave velocity of the half-space more easily. All these indicate that the joint inversion of multimode Love waves with Rayleigh waves would obtain a more accurate S-wave profile.

SUMMARY Both synthetic and observed ambient vibration array data are analysed using high-resolution beam-forming. In addition to a classical analysis of the vertical component, this paper presents results derived from processing horizontal components. We analyse phase velocities of fundamental and higher mode Rayleigh and Love waves, and particle motions (ellipticity) retrieved from H/V spectral ratios. A combined inversion with a genetic algorithm and a strategy for selecting possible model parameters allow us to define structural models explaining the data. The results from synthetic data for simple models with one or two layers of sediments suggest that, in most cases, the number of layers has to be reduced to a few sediment strata to find the original structure. Generally, reducing the number of soft-sediment layers in the inversion process with genetic algorithms leads to a class of models that are less smooth. They have a stronger impedance contrast between sediments and bedrock. Combining Love and Rayleigh wave dispersion curves with the ellipticity of the fundamental mode Rayleigh waves has some advantages. Scatter is reduced when compared to using structural models obtained only from Rayleigh wave phase velocity curves. By adding information from Love waves some structures can be excluded. Another possibility for constraining inversion results is to include supplementary geological or borehole information. Analysing radial components also can provide segments of Rayleigh wave dispersion curves for modes not seen on the vertical component. Finally, using ellipticity information allows us to confine the total depth of the soft sediments. For real sites, considerable variability in the measured phase velocity curves is observed. This comes from lateral changes in the structure or seismic sources within the array. Constraining the inversion by combining Love and Rayleigh wave information can help reduce such problems. Frequency bands in which the Rayleigh wave dispersion curves show considerable scatter are often better resolved by Love waves. Information from the horizontal component can be used to correctly assign the mode number to the different phase–velocity curve segments, especially when two modes seem to merge at osculation points. Such merging of modes is usually observed for Rayleigh waves and thus can be partly solved if additional information from the Love waves and the horizontal component of Rayleigh waves is considered. Whenever a site presents a velocity inversion below the top layer, Love wave data clearly helps to better constrain the solution.

SUMMARY Analysis of seismic ambient vibrations is becoming a widespread approach to estimate subsurface shear wave velocity profiles. However, the common restriction to vertical component wavefield data does not allow investigations of Love wave dispersion and the partitioning between Rayleigh and Love waves. In this study we extend the modified spatial autocorrelation technique (MSPAC) to three-component analysis (3c-MSPAC). By determination of Love wave dispersion curves, this technique provides additional information for the determination of shear wave velocity–depth profiles. Furthermore, the relative fraction of Rayleigh waves in the total portion of surface waves on the horizontal components is estimated. Tests of the 3c-MSPAC method are presented using synthetic ambient vibration waveform data. Different types of surface waves are simulated as well as different modes. In addition, different spatial distributions of sources are used. We obtain Rayleigh and Love wave dispersion curves for broad frequency bands in agreement with the models used for waveform simulation. The same applies for the relative fraction of Rayleigh waves. Dispersion curves are observed at lower frequencies for Love waves than for Rayleigh waves. While 3c-MSPAC has clear advantages for determination of Love waves velocities, 3c-MSPAC and conventional vertical frequency–wavenumber analysis complement each other in estimating the Rayleigh wave dispersion characteristics. Inversions of the dispersion curves for the shear wave velocity–depth profile show that the use of Love wave velocities confirms the results derived from Rayleigh wave velocities. In the presence of higher mode surface waves, Love waves even can improve results. Application of 3c-MSPAC to ambient vibration data recorded during field measurements (Pulheim, Germany) show dominance of Love waves in the wavefield. Existing shear wave profiles for this site are consistent with models obtained from inversion of Rayleigh and Love wave dispersion curves.

<p>We present a 3D probabilistic model of shear wave velocity and radial anisotropy of the European crust and uppermost mantle mainly focusing on the Alps and the Apennines.</p><p>The model is built using continuous seismic noise recorded between 2010 and 2018 at 1521 broadband stations, including the AlpArray network (Hetényi et al., 2018).</p><p>We use a large dataset of more than 730 000 couples of stations representing as many virtual source-receiver pairs. For each path, we calculate the cross-correlation of continuous vertical- and transverse-components of the noise records in order to get the Green’s function. From the Green’s function, we then obtain the group velocity dispersion curves of Love and Rayleigh waves in the period range 5 to 149 s.</p><p>Our 3D model is built in two steps. First, the dispersion data are used in a linearized least square inversion providing 2D maps of group velocity in Europe at each period. These maps are obtained using the same coverage for Love and Rayleigh waves. Dispersion curves for both Love and Rayleigh waves are then extracted from the maps, at each geographical point. In a second step, these curves are jointly inverted to depth for shear velocity and radial anisotropy. The inversion in done within a Bayesian Monte-Carlo framework integrating some a priori information coming either from PREM (Dziewonski and Anderson 1961) or the recent 3D shear wave model of Lu et al. 2018 performed for the same region.</p><p>Therefore, this joint inversion of Rayleigh and Love data allows us to derive a new 3D model of shear velocity and radial anisotropy of the European crust and uppermost mantle. The isotropic part of our model is consistent with the shear velocity model of Lu et al. 2018. The 3D radial anisotropy model of the region adds new constraints on the deformation of the lithosphere in Europe. Here we present and discuss this new radial anisotropy model, with particular emphasis on the Apennines.</p>

Seismic interferometry is one of the noteworthy new subsurface imaging methods that have been used in recent years in many tomographic studies. In this study, the interstation estimated Green functions extracted from the Rayleigh and Love wave ambient noise cross-correlations are used to obtain the Love and Rayleigh wave group velocity maps in different periods across the study area in order to determine the velocity anomalies and their corresponding geological structures within the crust. In the ambient noise method, the quality of the cross-correlations increases when longer time series of data are used, so we used 22 months of continuous data from the 17 broadband seismic stations as the primary database. After performing the initial corrections of the ambient noise data and obtaining the 3-month cross-correlation time series, the longer time series were obtained by stacking the 3-month time series with acceptable quality. In the next step, by using the time-frequency analysis and the estimated Green functions, the Love and Rayleigh wave dispersion curves were determined. Then, using the 2D tomography method, the Love and Rayleigh wave group velocity maps for the periods 10–25 s were produced. In the short periods, a low-velocity zone is observed in the Love and Rayleigh wave group velocity maps from the south Caspian basin toward central Iran, which is likely due to the effect of the thick alluvial deposits and the intermountain sediments in Alborz. At the longer periods, the Rayleigh and Love wave group velocity maps show a low-velocity zone in central Alborz, while in the eastern and western parts of Alborz, they show higher velocities, which is consistent with the prior studies. The dense path coverage permits to produce images that have substantially higher lateral resolution than is currently available from global and regional group velocity studies. Tomographic maps at high frequencies are well correlated with the upper crust structure and especially with sediment layer thickness.

We present a shear velocity model of the crust and uppermost mantle under the Aegean region by simultaneous inversion of Rayleigh and Love waves. The database consists of regional earthquakes recorded by portable broadband three-component digital stations that were installed for a period of 6 months in the broader Aegean region. For each epicentre–station ray path group velocity dispersion curves are measured using appropriate frequency time analysis (FTAN). The dispersion measurements for more than 600 Love wave paths have been used. We have also incorporated previous results for c . 700 Rayleigh wave paths for the study area. The single-path dispersion curves of both waves were inverted to regional group velocity maps for different values of period (6–32 s) via a tomographic method. The local dispersion curves of discrete grid points for both surface waves were inverted nonlinearly to construct 1D models of shear-wave velocity v. depth. In most cases the joint inversion of Rayleigh and Love waves resulted in a single model (from the multiple models compatible with the data) that could interpret both Rayleigh and Love wave data. Around 60 local dispersion curves for both Rayleigh and Love waves were finally jointly inverted. As expected, because of the complex tectonic environment of the Aegean region the results show strong lateral variations of the S-wave velocities for the crust and uppermost mantle. Our results confirm the presence of a thin crust typically less than 28–30 km in the whole Aegean Sea, which in some parts of the southern and central Aegean Sea becomes significantly thinner (20–22 km). In contrast, a large crustal thickness of about 40–45 km exists in western Greece, and the remaining part of continental Greece is characterized by a mean crustal thickness of about 35 km. A significant sub-Moho upper mantle low-velocity zone (LVLmantle) with velocities as low as 3.7 km s −1 , is clearly identified in the southern and central Aegean Sea, correlated with the high heat flow in the mantle wedge above the subducted slab and the related active volcanism in the region. The results obtained results are compared with independent body-wave tomographic information on the velocity structure of the study area and exhibit a generally good agreement, although significant small-scale differences are also identified.

Prevention of earthquake disaster is critical in the densely populated Kanto Basin, Japan, because of the presence of thick sediments that amplify strong ground motion. For this, a high‐resolution three‐dimensional S‐wave velocity model is essential to understand the complex amplification of strong ground motion. We constructed a three‐dimensional S‐wave velocity model using the joint inversion of multimodal dispersion curves of Rayleigh and Love waves. Inclusion of higher modes significantly improved the accuracy and precision of the surface‐wave inversion by more than 50% compared to using only fundamental mode. Our proposed model revealed a low‐velocity anomaly of sediments toward the east and a curved velocity contour of the basement rock, thereby better explaining the observed complex surface‐wave dispersion curves that have been unexplained in past homogeneous multilayered models. Estimated S‐wave velocity model better reflected subsurface heterogeneity, providing vital information for hazard assessment in a metropolitan area where huge earthquakes are expected.

Radial anisotropy measures the difference between horizontally and vertically polarized shear waves, which can give important constraints on crustal deformation with depth. Ambient noise surface wave tomography has become a main tool to effectively study the radial anisotropy structure in which Rayleigh and love dispersion curves extracted from ambient noise data are utilized to invert radial anisotropy parameter. However, only fundamental-mode dispersion curves are used in traditional methods, for which the inversion results could be easily affected by severe non-uniqueness of inversion. Recently, frequency-Bessel transform method has been validated that it can effectively extract multimodal Rayleigh and Love wave dispersion curves from multi-component ambient noise data. Here, we develop a joint inversion method for radial anisotropy with multimodal Rayleigh and Love wave dispersion curves, and validate its effectiveness with synthetic and realistic examples. Specifically, we modified a Markov chain Monte Carlo (McMC) Bayesian inversion tool, BayHunter, to conduct the joint inversion of multimodal Rayleigh and Love wave dispersion curves. During the inversion, the initial model is introduced and the misfit value from single objective function in each iteration process is utilized to adaptively weight the joint likelihood. We proved that the incorporation of higher-modes dispersion curves can effectively reduce the non-uniqueness of inversion with synthetic tests. Currently, we are applying this joint inversion method to realistic data from eastern South China Block and validate its effectiveness in realistic applications.

ABSTRACTGeophysical data were acquired during a survey of the Hluboká Fault in the Czech Republic, Central Europe. The recorded surface waves are studied in the frequency range 8‐200 Hz. Phase velocity dispersion curves of Rayleigh and Love waves are determined from pairs of three‐component seismograms with a 5 m receiver spacing by means of a frequency‐time analysis along the profile. Rayleigh waves are analysed on the vertical (Z) and radial (R) components and Love waves on the transversal (T) component. Dispersion curves from the vertical component are then inverted to 1‐D S‐wave velocity models using the isometric method. A set of 1‐D S‐wave velocity models representing a pseudo 2‐D S‐wave velocity distribution along the profile is obtained.This velocity distribution is compared with the results of other geophysical methods and also with direct observation from a shallow paleoseismic trenching. A combination of the S‐wave velocities obtained from the surface wave analysis and P‐wave velocities from refraction tomography is used to estimate the Poisson ratio distribution. It is shown that the resolution capabilities of surface waves are comparable in this case with electric resistivity tomography in near surface medium and with P‐wave tomography in the depths exceeding approx. 15 metres.

A set of two hundred shear-wave velocity models of the crust and uppermost mantle in southeast Europe is determined by application of a sequence of methods for surface-waves analysis. Group velocities for about 350 paths have been obtained after analysis of more than 600 broadband waveform records. Two-dimensional surface-wave tomography is applied to the group-velocity measurements at selected periods and after regionalisation, two sets of local dispersion curves (for Rayleigh and Love waves) are constructed in the period range 8–40 s. The shear-wave velocity models are derived by applying non-linear iterative inversion of local dispersion curves for grid cells predetermined by the resolving power of data. The period range of observations limits the velocity models to depths of 70 km in accordance to the penetration of the surface waves with a maximum period of 40 s. Maps of the Moho boundary depth, velocity distribution above and below Moho boundary, as well as velocity distribution at different depths are constructed. Well-known geomorphologic units (e.g. the Pannonian basin, southeastern Carpathians, Dinarides, Hellenides, Rodophean massif, Aegean Sea, western Turkey) are delineated in the obtained models. Specific patterns in the velocity models characterise the southeast Carpathians and adjacent areas, coast of Albania, Adriatic coast of southern Italy and the southern coast of the Black Sea. The models obtained in this study for the western Black Sea basin shows the presence of layers with shear-wave velocities of 3.5 km/s–3.7 km/s in the crust and thus do not support the hypothesis of existence of oceanic structure in this region.

<p><strong>Taupō volcano lies beneath the waters of Lake Taupō within the rohe (region) of Ngāti Tūwharetoa in the centre of North Island, Aotearoa New Zealand. It is a frequently active rhyolitic caldera volcano that was the site of Earth’s most recent supereruption (Ōruanui, ∼25.5 ka), as well as one of the most violent eruptions globally of the last 5000 years (Taupō, 232±10 CE). Taupō has erupted 28 times since Ōruanui, and displays elevated unrest activity (seismicity and surface deformation) on roughly decadal timescales, most recently in 2019 and 2022–23. This elevated activity resulted in the Volcanic Alert Level for Taupō being raised to Level 1 for the first time on 2022-09-22. Any resumption of eruptive activity at the volcano poses a major source of hazard, and the magma reservoir and its interactions with the regional tectonics that lead to unrest and possible eruption are not well understood.</strong></p><p>In working to understand the current state of Taupō volcano, we deployed the temporary ECLIPSE seismometer network (October 2019 to May 2022) to complement the permanent national GeoNet network. A core part of the planning, deployment, and management of the ECLIPSE network involved partnering with local Indigenous Māori Iwi and Hapū communities and with emergency management. We reflected upon this atypical co-production approach to geophysical network deployment, that has improved outcomes both for communities and researchers, identifying a central theme of creating and holding space for researchers and communities to engage. We built the co-production approach into the project from the start by involving a broad team including representatives of local Iwi Ngāti Tūwharetoa and Te Arawa as supported key researchers. We worked to respect communities’ time, protocols, and decisions; and to exchange knowledge about the research and results with landowners, community leaders, schools, and young people. Time spent kanohi ki te kanohi (face-to-face) built relationships and trust within and outside the research team with the potential to last beyond the scope of the research project.</p><p>To investigate the processes controlling seismicity at Taupō, we characterised the earthquakes near Taupō between October 2019 to September 2022 in detail, using automatic picking and association; locations; relative relocations; and focal mechanisms. We developed a Taupō-specific one-dimensional velocity model, and inverted for local magnitude scales (horizontal and vertical components). The seismicity outside the northern part of the lake was tectonically-controlled with minor aqueous fluid involvement, with the exception of the swarms beneath the geothermal power plants that we interpret as being induced by anthropogenic activity. Stresses from rifting processes also caused slip on pre-existing structures to the east of the lake. The seismicity in the northern part of the lake was directly related to the magmatic system. It revealed a change in the magmatic system after the end of the 2019 unrest, with seismicity occurring in the centre of the lake for the first time in a decade as the shallow magma system reacted to the 2019 intrusion. In May 2022, there was a seven-fold increase in the seismicity rate as well as uplift beneath the lake, attributed to an intrusion. Seismicity throughout the catalogue defined an arcuate shape with depth 6±1 km, representing the interaction between the magma system and a ring-fault structure. Ongoing seismicity related to the magma system between the two uplift episodes in 2019 and 2022 indicate that this activity can be considered as one four-year unrest episode. We used ambient noise interferometry to investigate the magma reservoir beneath Taupō. We calculated empirical Green’s functions for broadband station pairs by correlating pre-processed seismic ambient noise; used automated frequency-time analysis to extract surface wave dispersion curves for Rayleigh (ZZ, RR components) and Love (TT components) waves; and calculated mean dispersion curves for station-pair paths that passed beneath different areas. We identified a −20% to −30% surface wave velocity anomaly beneath the northeastern part of the lake for periods 5.5–10 s (∼2–10 km depth), compared to the paths that pass outside the Taupō Rift (2.6 km s−1) that we interpret as the active magma system. This anomaly does not extend into Western Bay or the Taupō Fault Belt. First order estimates of melt percentage (1–23%) from these velocity anomalies are broadly consistent with previous estimates. On average, Love waves are slower than Rayleigh waves beneath the northern part of the lake, indicating possible negative radial anisotropy and vertical internal structure of the magma system. We propose that this signal is the result of the long-term control of the rift on magma pathways. Finally, we discuss the implications of these results on our understanding of the volcano, suggest avenues for future research, and offer some recommendations on monitoring Taupō for future unrest and possible eruption.</p>

This study presents a three‐dimensional (3D) model of the crustal and uppermost mantle shear wave velocity and radial anisotropy beneath the Iran Plateau constructed by Rayleigh and Love waves from ambient noise. We correlate three years of continuous seismic ambient noise data recorded in 98 stations to obtain cross correlation functions. Then, we measure Rayleigh (8–60 s) and Love (8–50 s) wave dispersion curves from these cross‐correlation functions to generate two‐dimensional dispersion maps using a fast marching surface tomography method. Finally, we build a quasi‐3D shear wave velocity and radial anisotropy model by jointly inverting Rayleigh and Love local phase velocity dispersion curves using a Bayesian Markov chain Monte Carlo inversion method. Observed radial anisotropy beneath Central Iran is weaker than adjacent areas. Negative radial anisotropy is observed in the shallow structures across our study region, which is most likely attributed to vertically aligned cracks in the upper crust. Strong positive radial anisotropy in the middle to the lower crust beneath the Zagros is imaged, which is associated with ductile shear zones in the crust. Radial anisotropy changes from positive values in the crust to negative values in the upper mantle beneath the Zagros, which may be evidence for the decoupling of the crust from the upper mantle beneath the Zagros.

SUMMARYWe use broad-band stations of the ‘Los Angeles Syncline Seismic Interferometry Experiment’ (LASSIE) to perform a joint inversion of the Horizontal to Vertical spectral ratios (H/V) and multimode dispersion curves (phase and group velocity) for both Rayleigh and Love waves at each station of a dense line of sensors. The H/V of the autocorrelated signal at a seismic station is proportional to the ratio of the imaginary parts of the Green’s function. The presence of low-frequency peaks (∼0.2 Hz) in H/V allows us to constrain the structure of the basin with high confidence to a depth of 6 km. The velocity models we obtain are broadly consistent with the SCEC CVM-H community model and agree well with known geological features. Because our approach differs substantially from previous modelling of crustal velocities in southern California, this research validates both the utility of the diffuse field H/V measurements for deep structural characterization and the predictive value of the CVM-H community velocity model in the Los Angeles region. We also analyse a lower frequency peak (∼0.03 Hz) in H/V and suggest it could be the signature of the Moho. Finally, we show that the independent comparison of the H and V components with their corresponding theoretical counterparts gives information about the degree of diffusivity of the ambient seismic field.

The joint inversion of Rayleigh and Love waves plays a crucial role in mitigating the non‐uniqueness of surface wave inversion results and enhancing the stability of these inversions. Existing approaches to the joint inversion of Rayleigh and Love wave dispersion curves, which rely on conventional objective functions, often struggle with complex stratigraphic configurations and yield results of limited accuracy. This study introduces two novel nonlinear joint inversion techniques for Rayleigh and Love waves: Pearson correlation coefficient and thickness mean sharing. The Pearson correlation coefficient approach employs the Pearson correlation coefficient and alternating iterative objective functions to synchronize the shear wave velocity structures derived from Rayleigh and Love waves, thereby enhancing the accuracy of the joint inversion. Conversely, the thickness mean sharing method computes an average of the thickness values obtained in each iteration of the inversion, utilizing the traditional joint inversion objective function. Tests on three distinct stratigraphic structures—characterized by increasing velocity, high‐speed hard interlayers and low‐speed soft interlayers—as well as on measured data, demonstrate that the proposed methods significantly improve the stability and accuracy of nonlinear joint inversion.