The tectonic of northern Taiwan is in a post-collisional stage and has undergone a subduction polarity flip between the Eurasian Plate (EP) and Philippine Sea Plate (PSP). The shallow crust of northern Taiwan features the Tatun Volcano Group (TVG) and the Turtle Island magma reservoirs, with their proximity to Taipei metropols highlighting the volcanic risks to densely populated regions and critical infrastructure. However, it is challenging to image all these structures from the surface down to several hundred kilometers depth with classical passive tomographic approaches. Here, we present tomographic models of density, P-wave velocity (Vp), S-wave velocity (Vs), and the Vp/Vs ratio beneath northern Taiwan, obtained by inverting complete teleseismic waveforms from 18 P and 9 SH events recorded by 175 broadband stations from the Formosa Array and the permanent stations. In our final model, the plate boundary between EP and PSP is clearly depicted as a west-dipping plane, consistent with the western boundary of slab seismicities. Our model identifies two distinct low-velocity, high VP/VS bodies beneath the TVG and Turtle Island, indicative of underlying magma reservoirs. The reservoir beneath the TVG is beaker-shaped, extending from a depth of 6 to 20 kilometers. The reservoir beneath Turtle Island, located on the island’s eastern side, is larger than TVG's but less well defined due to sparse station coverage. The crust north of the Hsueshan Range is thinner, likely related to the post-collisional delamination of the lower crust. This process leads to increased mantle heat flow, providing the heat source for the TVG. With the new 3-D model, we also relocate the local events by utilizing a nonlinear location method, in order to improve their spatial accuracy and get better constraints on the seismogenic structure.

How to cite: Kan, L.-Y., Kuo-Chen, H., Chevrot, S., Sibuet, J.-C., Lin, C.-H., and Monteiller, V.: Lithospheric Imaging of Northern Taiwan Using Teleseismic Full Waveform Inversion: from Volcanic Reservoirs to Plate Boundaries, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13737, https://doi.org/10.5194/egusphere-egu24-13737, 2024.

Accurate imaging of crustal structures necessitates sophisticated inverse modeling utilizing extensive waveform data, including high-frequency signals from minor seismic events, an aspect traditionally underused in full waveform inversion (FWI). Integrating these intricate elements into novel tomographic models using FWI for the Western Mediterranean region, we address the challenge of enhancing model resolution while acknowledging the increased computational demands associated with inversion, an endeavor made feasible only through High-Performance Computing (HPC).

This study incorporates new considerations by comparing diverse reference models and updating a substantial dataset of earthquakes spanning the time interval from 2007 to 2022. While station deployment in the Mediterranean region is notably dense, the uneven geographical distribution of ray coverage from far-field waveforms necessitates the inclusion of lower magnitude earthquakes (M<4.5). This demands the determination of additional moment tensor solutions not readily available in public databases, alongside efforts to enhance signal-to-noise ratios. Our approach employs an iterative multiscale FWI approach, initially prioritizing the inversion of lower frequencies (period band of 100-120 s), and as the model refines, higher frequencies are progressively incorporated. The final goal is targeting a minimum period of 12 seconds or less. This incremental strategy aims to continuously enhance waveform fitting throughout each iteration, facilitated by an intensive computational workflow.

This contribution centers on the technical construction of the model, primarily focusing on S-velocity, and provides a comprehensive discussion of the employed data processing methods. We address the benefits, limitations and uncertainties inherent in this approach. Recognizing the pivotal role of higher-resolution velocity models in precise forward waveform modeling, we anticipate that the advancement of these inversion strategies will also contribute to refining earthquake-induced shake maps at regional to local scales. This research is funded by the Horizon Europe Project DT-GEO: A Digital Twin for GEOphysical extremes (ID 101058129).

How to cite: Herrero-Barbero, P., Schimmel, M., Martí, D., Krischer, L., and Carbonell, R.: Refinement of tomographic models in the Western Mediterranean region through full waveform inversion, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12010, https://doi.org/10.5194/egusphere-egu24-12010, 2024.

Petit-spot volcanoes, recently discovered volcanic structures, have significantly enriched our understanding of intraplate volcanism, particularly occuring in response to plate flexure during subduction. Discovery of these volcanoes in the vicinity of the Japan Trench marked a milestone showcasing the profound impact of tectonic processes on the intraplate volcanism and supporting the existence of small-degree melts at the base of the lithosphere.

One of the key question marks surrounding the petit-spot volcanoes is the extraction and ascent of melts to the seabed that would required development of lithospheric-scale fractures. As for now, no physical model has been devised to validate this hypothesis. The complexities involved in understanding the intricate genesis of petit-spot volcanism underline the need for its further investigation with innovative approaches.

In 2017 Japan Agency for Marine-Earth Science and Technology (JAMSTEC) carried out an active seismic survey to investigate the geological setting impacted by petit-spot volcanism in the trench-outer-rise region of the Japan Trench. During the survey 40 ocean-bottom seismometers (OBS) were deployed at 2 km intervals along an 80-km long 2D receiver profile, coupled with the firing of 983 air-gun shots at 100 m intervals along an extensive 100-km shooting profile. The resulting dataset creates an opportunity for in-depth analysis of subsurface and holds the potential for constructing a high-resolution velocity model with full-waveform inversion (FWI).

In this work we use first arrival traveltime tomography and time-domain acoustic FWI to reconstruct P-wave velocity model at the wavelet resolution. We push the inversion up to 8 Hz, which allows us to delineate sharp velocity contrasts within the incoming plate that are likely related to the petit-spot volcanism phenomenon occurring in this region. The resulting velocity model promises to contribute to our comprehension of intraplate volcanism, offering a perspective on the broadening of our understanding the underlying processes causing intraplate volcanism.

How to cite: Górszczyk, A. and Amirzadeh, Y.: Full-waveform inversion of the OBS data from the Japan Trench area affected by the petit-spot volcanism, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4699, https://doi.org/10.5194/egusphere-egu24-4699, 2024.

Waveform inversion is a robust geophysical tool for reconstructing subsurface structures, involving an iterative process that begins with an initial model and utilizes complete information from observed data to update the target model. However, its wide field applications have been impeded by challenges such as strong nonlinearity, nonconvexity of the misfit function, and complexity of the propagation medium. To mitigate these issues and enhance the linearity and simplicity of the inversion process, we employ early arrival wavefields to construct misfit function that promotes  global convergence.

High-resolution structure imaging of active faults within urban areas is vital for earthquake hazard mitigation, so we perform a seismic survey line crossing the Pearl River Estuary Fault (PREF) in Guangzhou, China. First, ten shots of a new and environmentally friendly gas explosion source are excited with about 1 km spacing and recorded by 241 nodal short-period seismometers with an average spacing of 60 m. Then, based on these acquisition data, we adopt waveform inversion to explore the kinematic and dynamic information of early arrival wave-fields to recover the subsurface structures. Here, the early arrival wavefields were defined as those events that arrived within a few periods of the first arrivals. The inversion results indicate that while the low-velocity zone (LVZ) in depth surrounding the PREF is 2.5 km in width and extended to 0.7 km, another LVZ of 1.5 km in width and extended to 0.7 km in depth is surrounded by the Beiting-Nancun fault. We observe that the analysis of evolution and activities of the fault systems reveal no historical earthquakes in our study area; we interpret that the two LVZs controlled by the faults are probably attributed to the fluid dynamics, sediment source, and fault motion at different geological times, rather than fault-related damage zones.

Summarily, the results can provide significant basis for earthquake prevention and hazard assessment in Guangzhou. The finding also shows that the waveform inversion can effectively explore the fine structure of active faults in urban area with dense linear array and spare active source excitations. This acquisition and inversion methods should have broad applications in other cities.

How to cite: Ma, X., Wang, W., Xu, S., Yang, W., and Zhang, Y.: The research of early arrival waveform inversion and its application in imaging the shallow fault zone structure, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7020, https://doi.org/10.5194/egusphere-egu24-7020, 2024.

The island of Sulawesi is located within a complex tectonic region at the confluence of the Eurasian, Indo-Australian and Philippine plates. The recent geological history in the area reflects the ongoing subduction, extension, obduction, and collision of continental fragments. The island consists of four elongated arms (the north, east, southeast, and southern arms) that are composed of distinct lithological assemblages. Based on local and regional earthquake travel-time tomography, we present a new 3-D P-wave velocity model of the crust and upper mantle beneath Sulawesi. We used the Fast Marching Tomography (FMTOMO) package to retrieve 3-D P-wave velocity variations relative to a 1-D starting velocity model based on ak135. The catalogue and phase data were taken from the Agency for Meteorology, Climatology, and Geophysics (BMKG) of Indonesia for the period 2018 through to 2023, recorded by 126 seismic stations in Sulawesi and its neighbourhood. Our preliminary results reveal clear evidence of subducted slabs as indicated by high-velocity anomalies penetrating into the mantle along the Molucca Sea Collision Zone and to the north of Sulawesi; we also see a low-velocity anomaly beneath volcanoes located at the eastern end of the North Arm of Sulawesi.

How to cite: Supendi, P., Rawlinson, N., Han, J., Widiyantoro, S., and Karnawati, D.: Preliminary Results of P-wave tomographic imaging beneath Sulawesi, Indonesia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3393, https://doi.org/10.5194/egusphere-egu24-3393, 2024.

Located in the eastern region of Indonesia, Sulawesi exhibits a distinctive K-shaped configuration due to the Cretaceous to present day tectonic interaction of the Indian-Australian, Sunda, and Philippine plates. This tectonic interaction has delineated two main tectonic provinces of Sulawesi: the Western Sulawesi Province, including the South and North Arms with large plutono-volcanic rocks generated during the Paleogene, and the Eastern Sulawesi Province, comprising the East and Southeast Arms characterized by the ophiolite complex and metamorphic belt emerging after the Early Miocene collision between the northern part of the Australian continental plate and the North Arm of Sulawesi. The present configuration of Sulawesi is attributed to the Sulawesi Orogeny, the attachment of eastern Sulawesi and Buton-Tukang Besi as well as Banggai-Sula Islands by subduction, accretion, and collision that led to the development of two major active tectonic structures in Sulawesi: the left-lateral Palu-Koro strike-slip fault to the west and the Celebes Sea subduction zone to the north.

We present preliminary P-wave tomographic images of the crust and upper mantle beneath Sulawesi, obtained by exploiting teleseismic earthquake data. Passive-seismic data are recorded by approximately 89 seismic stations of the Agency for Meteorology, Climatology, and Geophysics (BMKG) network running from January 2020 to July 2023. We employ an adaptive stacking technique to extract relative P-wave traveltime residuals from nearly a thousand teleseismic events recorded across the network. The relative arrival-time residuals from first-arriving, core and reflected P phases are then utilized to map 3-D P-wave perturbations using an inversion technique implemented in FMTOMO.

The final tomographic model reveals several distinct features, including a south-dipping, high-velocity anomaly beneath northern Sulawesi that we associated to the subducting slab of the Celebes Sea. 

How to cite: Kesumastuti, L. and Pilia, S.: Teleseismic Traveltime Tomography of Sulawesi, Indonesia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4417, https://doi.org/10.5194/egusphere-egu24-4417, 2024.

Surface wave tomography has proven to be a very powerful tool for discerning complex crustal and upper mantle structures since it bypasses the necessity for local seismic sources and crustal corrections. This study presents a refined 3D model encompassing the crust and uppermost mantle in Southern Italy and the broader southern Central Mediterranean region, achieved through the joint inversion of ambient noise and earthquake data.

Our dataset comprises 11,900 phase velocity dispersion curves, spanning 2 to 100 seconds, derived from ambient noise cross-correlations. Additionally, we incorporate 81,000 phase velocity curves covering 8 to 250 seconds, obtained through inter-station cross-correlations and averaging over single earthquake measurements. A thorough quality control process ensures the reliability of both datasets, which are seamlessly integrated using a correction factor derived from inter-station paths with overlapping measurements.

Azimuthally anisotropic Rayleigh wave phase velocity maps are computed using a regularized least-square approach. These maps, showcasing directional variations in wave velocities, serve as the foundation for our 3D model. The inversion process employs a stochastic particle swarm optimization algorithm, enhancing the robustness and accuracy of the final model.

The resulting 3D velocity model brings to light significant subsurface features, notably the subducted Calabrian and Hellenic slabs, alongside the identification of a delaminated high-velocity anomaly beneath Sicily. Additionally, the model captures details such as the transition from the Ionian Lithosphere to the Calabrian Slab, deformation of the Adriatic Lithosphere, and the dynamic flow of the asthenosphere beneath the Tyrrhenian Sea.

How to cite: Eckel, F., El-Sharkawy, A., Barberi, G., Scarfì, L., Langer, H., Lebedev, S., and Meier, T.: Imaging the Crust and Upper Mantle in the Southern Central Mediterranean with Joint Ambient Noise and Earthquake Surface Wave Tomography, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8390, https://doi.org/10.5194/egusphere-egu24-8390, 2024.

The Mount Isa region in Northern Australia is a world-class mining complex, yielding significant combined outputs of lead, silver, copper and zinc. This zone consists of sedimentary layers from lower to middle Proterozoic era with a mix of bimodal volcanic rocks and plutonic formations. A significant crustal structure, known as the Gidyea Suture zone, exists within the Mt Isa succession, and its geological history and association with the mineralisation at Mt Isa are unclear.  Previous surveys highlighted the distinct geophysical characteristics of region, in terms of magnetic and gravity anomalies, magnetotellurics conductivity anomalies, and structural features from deep seismic refraction. The characteristics of the mid-crustal zone has been implicated in mineralisation models, and imaging mid to deep crustal structures is important for mineral exploration. In this depth zone, passive seismic surveys show apparent advantages due to their low cost and continuous recording, when contrasted with active seismic surveys, or indeed earthquake tomography in this typically low-activity seismic zone.  In this study, we use the legacy noise data collected from 53 3-component temporary seismic sensors with 50km spacing covering the Mount Isa area deployed from June 2009 to March 2011, and perform ambient noise tomography (ANT) to model the shear wave velocity (Vs) crustal structure. 681 cross-correlations (CCs) of recordings over two weeks between each pairwise stations are used to calculate the empirical Green’s function (EGF) to construct the impulse wavefield. The dispersion curves of the fundamental mode of Rayleigh surface waves are extracted from vertical components of the CCs. Separating the fundamental mode from the other higher modes in the group-velocity map is usually hard to identify. A modified frequency-time analysis (FTAN) based on the global seismology code CPS is used for digitising dispersion curves here. Then the dispersion curve is inverted using a bespoke Markov-Chain Monte Carlo approach to build 1D Vs profiles, which are finally used to construct a 3D shear wave velocity model across the area of interest. We discuss the comparison of the legacy passive seismic data to the results of other geophysical measurements, to provide a new understanding of terrane evolution and crustal structure of Northern Queensland.

How to cite: Chang, A. and O'Neill, C.: Ambient noise tomography of Mount Isa, Northern Queensland in Australia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7001, https://doi.org/10.5194/egusphere-egu24-7001, 2024.

The Southeast Coastal Areas of China (SCAC) is part of the Cathaysia block, the convergence area of the Eurasian, Pacific and India-Australian plates. The Cathaysia and the Yangtze blocks collided and merged into the South China block in the Neoproterozoic. These areas have experienced complex geological evolution and multiple periods of intense magmatic events since the Paleozoic, mainly manifested as a large number of Paleozoic and Mesozoic granitic rocks and Cenozoic mafic magmatism. Large-scale structural deformation caused by emplacements is very strong to the stratum reconstruction, forming a series of faults with different scales and directions. The development of large-scale fault systems has led to the potential risk of strong earthquake disasters in the region. In addition, the urban agglomeration with its dense population, is located in SCAC. Thus, seismic risk assessment and seismic research are very urgent and critical. The high-resolution crustal structure model is especially necessary for understanding the geological processes and seismic hazards in and around the areas. We collected ambient noise data from fixed and temporary seismic stations in the SCAC, and used the frequency-Bessel transform (F-J) method to extract Rayleigh wave dispersion curves and performed multimodal ambient noise dispersion curves inversion. We constructed a 3D high-resolution S-wave velocity model for this area. Specifically, we extracted reliable multimodal dispersion curves (up to the 8th higher-order in some sub-areas) with a broad frequency band range (0.03Hz-0.65Hz). Preliminary results show a widespread mid-crustal low-velocity zone, and we will further discuss the crustal structural anomalies and their related tectonic implications and evolution mechanisms.

How to cite: Li, H., Chen, X., and Cai, H.: Shear‐Wave Velocity Structure beneath Southeast Coastal Areas of China from the F-J Multimodal Ambient Noise Tomography, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5355, https://doi.org/10.5194/egusphere-egu24-5355, 2024.

Turkey belongs to the initial collision stage of the Tethys tectonic domain. The western part of Turkey experiences the subduction of the African plate, while the eastern part suffers the collision with the Arabian plate. In addition, extensive volcanic activity and tectonic uplift are also distributed in this region. To understand the relationship between these surface phenomena and underground structure, it is necessary to obtain reliable and precise velocity structure in the region. We collect continuous waveform data from 688 stations in the region and obtain Rayleigh wave dispersion curves for periods between 4 and 100 s based on frequency-Bessel transform dispersion analysis. We then perform quasi-Newton inversion to calculate the S wave velocity structure between 0 and 200 km. Subsequently, the reliability of the results is verified using a model validation method based on waveform simulation. Our results elucidate the layered structure and distribution of the lithosphere asthenosphere boundary beneath the region, which is of great significance for a profound understanding of the tectonic evolution process in the region. At the same time, it also provides reliable data support for subsequent waveform inversion and earthquake mechanism research in the region.

How to cite: Wang, P., Chen, J., and Chen, X.: Crust and Upper Mantle S Wave Velocity Structure in Turkey Based on Ambient Noise Tomography, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4339, https://doi.org/10.5194/egusphere-egu24-4339, 2024.

We present the latest update of the global SV model developed by our team, in Lyon. It is based on the waveform modeling of more than 3 millions Rayleigh waves recorded since 1976. The tomographic model is built using the same automated scheme as was presented in Debayle et al., GRL 2016, while the number of data has increased by a factor larger than 2. For each seismogram, we obtain a path average shear velocity and quality factor model, and a set of fundamental and higher-mode dispersion and attenuation curves from 40s to 250s. We incorporate the resulting set of path average shear velocity models into a tomographic inversion. Due to the drastic increase of data, this second step of inversion became too computationally costly. We rewrote it so that the largest matrix to invert has now a size (number of geographical point)**2 instead of (number of data)**2. Thanks to these improvements we reduced the correlation lengths from 4 deg down to 1deg.  We will focus in several geographical areas and geological objects, to emphasize the improvement in precision of this new model. We will also present our new online tool (https://fascil.univ-lyon1.fr/) available to explore this tomographic model and to compare with existing ones.

How to cite: durand, S., ricard, Y., dubuffet, F., and debayle, E.: Global SV wave upper mantle model., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10073, https://doi.org/10.5194/egusphere-egu24-10073, 2024.

Receiver functions are a powerful tool to image lithospheric stratigraphy. For flat lying structures, receiver functions can be stacked azimuthally to achieve high signal-to-noise ratios and h-κ-stacks allow to estimate the depth of interfaces (h) and P-to-S wave velocity ratio of the hanging layers (κ). For dipping layers, characteristic for the slab structure in a subduction zone forearc, these methods fail, because the moveout of phases arriving from different azimuths violates the basic assumptions of these methods.

We here present a simple routine to simultaneously search for the depth of the top of slab and of the oceanic Moho, for strike and dip of the downgoing slab, as well as for the S-wave velocities and the P‑to-S wave velocity ratios of multiple layers of the overriding and downgoing plates in subduction zone forearcs. Our approach is based on the recent Python port PyRaysum of Frederiksen and Bostock's classic (2000) code for modeling ray-theoretical plane body-wave propagation in dipping anisotropic media, and on SciPy's simulated annealing global parameter search.

We applied the routine to hundreds of azimuthally-dependent receiver function sections from the subduction zones of Cascadia (North America) and the central Andes (South America) and retrieved laterally coherent station measurements of the depth and orientation of the top of the subducting slab and the subducting Moho, with only weakly constrained seismic velocities. In Cascadia, we interpolated a regional slab model through fitting of regularized spline surfaces. Small scale structures that are not present in previous slab models can be resolved, e.g. under Olympic Peninsula (Cascadia) and Mejillones Peninsula (northern Chile). Where the receiver functions are more complex than can be accounted for by our model, the labeling of the modeled receiver function phases and comparison to the observed receiver functions allows us to confidently interpret the additional subsurface complexities and reconcile them with our interpretations.

How to cite: Bloch, W., Schurr, B., Faccenna, C., Tilmann, F., Audet, P., and Bostock, M.: Multi-parameter Receiver Function Modeling: Application to the Subduction Zones of Cascadia and the Central Andes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9514, https://doi.org/10.5194/egusphere-egu24-9514, 2024.

The Mongol-Baikal region is located in the western segment of the Central Asian Orogenic Belt. This area experienced multiple periods of extensive compression starting from the Proterozoic. In the Cenozoic, the study region was modified by neotectonics, featuring large extensional rifting (the Baikal rift zone) and plateau uplift (the Hangai Dome). However, the deep mechanisms of the onset of rifting and doming are still debated. We performed high-resolution 3-D P-wave tomography under the southwestern Baikal and western Mongolia. The images show distinct low-velocity anomalies under the Baikal Rift at ~60 km depth, the Hangai Dome at ~200 km depth, and beneath the Siberian craton. This may indicate that potential mantle flows ascended from the deep Siberian MTZ to shallower levels, influencing the rifting of the Baikal rift zone and the lithospheric process of the Hangai Dome. We then further determined the seismic anisotropy of the upper mantle under western Mongolia using SKS splitting measurements. The study region is dominated by NW-SE trending fast polarization directions (FPD), which indicates consistent compressional and transitional stress among the whole study area. Small delay times in the Hangai Dome spatially coincide with the low-velocity anomalies, supporting remarkable asthenosphere upwelling. However, the local Hangai upwelling did not affect the general anisotropic structures significantly, indicating that the lithospheric process only occurred in a limited area.

References

Wu, H., Huang, Z., 2022. Upper mantle anisotropy and deformation beneath the western Mongolian Plateau revealed by SKS splitting. Tectonophysics 835, 229376. https://doi.org/10.1016/j.tecto.2022.229376

Wu, H., Huang, Z., Zhao, D., 2021. Deep structure beneath the southwestern flank of the Baikal rift zone and adjacent areas. Physics of the Earth and Planetary Interiors 310, 106616. https://doi.org/10.1016/j.pepi.2020.106616

How to cite: Wu, H. and Huang, Z.: Upper-mantle velocity structures and anisotropy under the Mongol-Baikal region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11392, https://doi.org/10.5194/egusphere-egu24-11392, 2024.

Nowadays multimodal dispersion spectra can be easily obtained from surface wave data, but sometimes they are actually intractable for the traditional curve-based inversion methods due to the existences of mode-kissing phenomenon and uneven mode-energy distribution. We developed a new spectrum inversion method to circumvent these possible curve-related troubles by directly minimizing the image dissimilarity between the observed and synthetic dispersion spectra. Wherein the synthetic spectrum would be straightforwardly calculated by the Generalized Reflection and Transmission Coefficient method. The new inversion method could rapidly obtain a stable and reliable subsurface velocity structure, even without curve-extracting and mode-identifying in data processing, because it could exploit dispersion energy distribution features to constrain further the velocity structure. As an example of application, we applied this method to deep seismic reflection data in Beijing, and resolved a 2-D S-wave velocity profile above 200 m depth. The strong consistency of structural features between the inversed results of the new and traditional methods shows that the former is effective and practical for realistic data.

How to cite: Liu, Q., Chen, X., and Guo, W.: Direct image inversion of multimodal dispersion spectra and its application to deep seismic reflection data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8837, https://doi.org/10.5194/egusphere-egu24-8837, 2024.

Carried out in 2021, the wide-angle reflection-refraction (WARR) SHIELD’21 profile crosses, from SW to NE, the main tectonic structures of Ukraine. It has targeted the crustal and uppermost mantle structure underlying the Archaean and Paleoproterozoic crystalline complexes of the Ukrainian Shield and the adjacent platformal areas. To the SW of the Ukrainian Shield, the crystalline basement is overlain by Vendian through Paleozoic strata of the Volhyno-Podolian Homocline, plunging at its SW end below the Carpathian belt and its Neogene foredeep. To the NE, the crystalline cratonic basement is covered by Devonian and Carboniferous successions of the Dnieper-Donets rift basin. The ~650 km long SHIELD’21 profile is a northeasterly extension of the RomUkrSeis profile carried out in 2014 and running from Romania to the southwestern part of the Ukrainian Shield (Starostenko et al., 2020). The WARR study along the SHIELD’21 profile provided high-quality seismic records. The main recorded seismic waves are refractions of P- and S-waves in the sedimentary layer, crystalline basement, middle and lower crust and uppermost mantle, as well as reflections from crustal boundaries, the Moho interface and boundaries in the uppermost mantle. The correlation picking of their arrival times allowed us to build a velocity model not only for the P-, but also for S-waves and Vp/Vs ratio. The model reveals that over the entire thickness of the crust, the Vp in the crystalline basement nowhere exceeds 6.85 km/s, which – particularly in the context of the lower crust – represent low values, but similar to those known from the other nearby deep seismic profiles (e.g. TTZ-South, and DOBRE-4). Patterns of crustal boundaries combined with velocity differences across them, permit hypothesizing on Proterozoic large-scale subhorizontal extensional faulting in the crystalline upper crust. A prominent dome-like structure in the lower crust may represent a longitudinal section of a major duplex resulting from Paleoproterozoic overthrusting to the NW, comparable to those interpreted on the TTZ-South profile (Janik et al., 2022). The Moho shows strong variability of a depth (~32-50 km), and is underplated by lenticular horizontal ca. 10 km thick high velocity mantle bodies with Vp>8.36 to 8.40 km/s, also present deeper in the upper mantle of Vp between 8.15 and 8.25 km/s. The Moho is prominent and marked by the Vp velocity contrast of c. 1.4 to 1.8 km/s between the upper mantle and lower crust. It is characteristically undulated with successive downward and upward bends, with the amplitude locally exceeding 15 km and wavelength of the order of 150 to 250 km. A similar Moho undulation form was described along the DOBRE-4 profile and was interpreted as Mesozoic(?) buckle mega-folds (Starostenko et al, 2013).

• Janik, T. et al. (2022). TTZ-South, Minerals, 12, 112, doi.org/10.3390/min12020112
• Starostenko, V. et al. (2013). DOBRE-4, Geophys. J. Int. 195, 740–766, doi.10.1093/gji/ggt292
• Starostenko, V. et al. (2020). RomUkrSeis, Tectonophysics, 794, 228620, doi.org/10.1016/j.tec to.2020.228620

How to cite: Janik, T., Starostenko, V., Murovskaya, A., Czuba, W., Środa, P., Yegorova, T., Aleksandrowski, P., Verpakhovska, O., Kolomiyets, K., Lysynchuk, D., Amashukeli, T., Wójcik, D., Omelchenko†, V., Legostaeva, O., Gryn, D., and Chulkov, S.: The SHIELD’21 deep seismic profile across Ukraine, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4597, https://doi.org/10.5194/egusphere-egu24-4597, 2024.

The evolution of the Icelandic crust has been significantly influenced by magmatism associated with the Icelandic hotspot and the Mid-Atlantic Ridge. The spreading mid-ocean ridge and fissure swarms create favorable conditions for magma migration, feeding active volcanic activities on the surface. Previous receiver function studies have reported the mid-crustal low-velocity zone (MCLVZ) as a crucial characteristic. However, it is absent in the previous model representing the overall features of Iceland. Recently, the frequency-Bessel transform method (F-J method) has been proposed, enabling the effective extraction of multi-mode dispersion curves from ambient noise data. We collect continuous seismic data from the HOTSPOT network in Iceland for 2 years, as well as other supplementary data, covering the main regions of Iceland. Using the F-J method, we extract multi-mode dispersion curves of 0.02-0.4 Hz. Subsequently, we obtain an Icelandic average Vs model, including an MCLVZ with an amplitude of 3%. Moreover, through the analysis of local region data, we identify MCLVZs in the western fjords and the central volcanic zone of Iceland. Our results supplement the previously lacking MCLVZ feature in the Icelandic average structure, suggesting the presence of MCLVZs in both volcanic and non-volcanic regions of Iceland. The elevated temperature and partial melting associated with the volcanic activity may be not the sole reasons for MCLVZs. Further research on the distribution of MCLVZs in Iceland is needed in the future.

How to cite: Zhang, S. and Chen, X.: The mid-crustal low-velocity zones in Iceland revealed by multimodal surface wave, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4967, https://doi.org/10.5194/egusphere-egu24-4967, 2024.

The simulation of wave propagation is an essential part of several cutting-edge geophysical techniques, such as Full-Waveform Inversion (FWI) and Reverse Time Migration (RTM). Due to constraints in computational resources and memory capacity, wave propagation simulations are typically conducted within truncated media. In order to effectively absorb unwanted reflected waves at the boundaries of these simulations, specialized boundary layers are implemented. The Perfectly Matched Layer (PML) is widely acknowledged as a commonly employed technique in the field of seismology for its effectiveness as an absorbing boundary layer.

Conventional PML suffers from some well-known drawbacks, including instabilities in long-time simulations and inadequate absorption in cases involving grazing incident and evanescent waves. These limitations can hinder the accuracy and reliability of numerical modeling of seismic wave propagation. CFS-PML (Complex Frequency Shifted Perfectly Matched Layer) addresses the limitations of traditional PML approaches and offers improved absorption and stability in numerical modeling. The CPML technique is widely regarded as highly effective when applied in the context of first-order systems of equations. Nevertheless, this method is not specifically designed for application in second-order displacement formulations. In such cases, alternative numerical techniques, such as finite-element methods and spectral-element methods, have demonstrated greater suitability. Previous studies have primarily focused on expanding the first-order formulation to the second-order.

Another approach that to incorporate the CFS technique in the wave conventional PML is using ADEs (auxiliary differential equations). The ADE-CFS-PML method incorporates ADEs to drive wave equations equipped with PML in a more simple and straightforward manner than recursive convolution approach.

Our contribution is to develop a general scheme that not only satisfies the first-order (velocity-displacement) staggard-grid system, but can easily incorporate in second-order wave equation and address the drawbacks of conventional PML effectively. The proposed scheme demonstrates comparable performance to CPML while avoiding the need for recursive convolution operations. Instead, it introduces the PML into the wave equation through ADEs, which is easily implementable, efficient, and compatible with existing codes and simplifies the implementation process.

Our proposed scheme for implementation of the ADE-CFS-PML method has been tested on benchmark models with complex geological structures and has shown excellent performance by demonstrating its effectiveness in absorbing grazing incident waves and maintaining stability in long-term simulations. It effectively dampens grazing incidence waves and remains stable for long-term simulations. The scheme is suitable for large 3D models due to its on-the-fly computation capabilities, and its memory efficiency, since the coefficients are only varying in the PML area and are constant in the interior media.

Overall, it offers an improved method for numerical modeling in various media and PDE orders, while addresses the limitations of traditional PML approaches. The proposed scheme demonstrates enhanced absorption and stability, making it a valuable tool for seismic wave propagation studies and other applications in geophysics and physics.

How to cite: Rezaei Nevisi, A. and Bohlen, T.: A perfectly matched layer for first- and second order time-domain wave equation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2333, https://doi.org/10.5194/egusphere-egu24-2333, 2024.

Advanced seismic imaging techniques play a crucial role in generating reliable, high-resolution subsurface images across diverse applications, such as exploring hydrocarbons and minerals, characterizing geothermal reservoirs, and selecting sites for radioactive waste disposal. In this study we present the extension of the seismic imaging technique, Fresnel volume migration (FVM), to anisotropic and anelastic media.

A wavefront construction method for 3D anisotropic (TTI) velocity models was employed to compute the Green's functions required for FVM. This wavefront construction method was further developed by calculating complex traveltime fields (t*) for predefined quality factor (Q) models, describing the anelastic attenuation of seismic waves. Subsequently, these resulting traveltime fields (t*) were used in the migration process to facilitate the corresponding anelastic compensation of the amplitudes.

The developed method was applied to synthetic 2D data and a real 3D dataset obtained over the "Asse" salt structure in Lower Saxony, Germany (2020). The migration with anelastic compensation demonstrated a correct enhancement of amplitudes in the synthetic data. Furthermore, the application of the anisotropic FVM to the real 3D dataset resulted in a significant improvement in the imaging quality of reflectors throughout the area surrounding the salt structure.

Our findings underscore the pivotal role played by considering both anisotropy and anelastic attenuation in complex 3D models for achieving a reliable and high-resolution subsurface image using the further developed FVM approach. The latter lays the foundation for subsequent quantitative analyses of reflectors and hence supporting dependable geological interpretations.

How to cite: Kühne, N., Hlousek, F., Buske, S., Ding, H., and Schulze, M.: Advancing seismic imaging: Fresnel volume migration in anisotropic and anelastic media, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5241, https://doi.org/10.5194/egusphere-egu24-5241, 2024.

Teleseismic P-wave coda autocorrelation has been increasingly applied to subsurface structure detection and has shown potential for inverting subsurface velocity models. However, it has yet to be extensively investigated in terms of inversion and practical field data application strategy and initial model dependence. Compared with the receiver function, teleseismic P-wave coda autocorrelation can be used to invert the subsurface velocity model using only single-component data. This will significantly improve the application areas and reduce the costs of passive source seismology methods. Here, we propose a new inversion scheme for teleseismic P-wave coda autocorrelation based on the particle swarm optimization.

The teleseismic P-wave coda autocorrelations are binned according to the ray parameters and then stacked to construct the observed waveforms. Our featured method, the particle swarm optimization, is then used to find the velocity model that minimizes the fitting error to the observed waveforms. It is a global optimization algorithm that simulates the feeding of a natural population. Each particle in the population has two parameters: position and velocity. The optimization space is a multi-dimensional space comprising various stratum thicknesses and velocities. Thus, a particle's position in the optimization space represents a set of parameters for the subsurface velocity distribution. We assume that the maximum number of layers within the crust above the mantle (a homogeneous half-space) is 10. The thickness of each layer ranges from 0 to 10 km, and if the thickness of a layer is 0 km, this corresponds to a reduction of one layer. The method thus allows the number of layers within the crust to be obtained by inversion without a priori information. Together with the P-wave velocity of each layer (including the mantle), the optimization space is 21-dimensional.

While we still assume horizontal layers, our method is capable of inverting a wide range of crustal models, including those containing surface sedimentary layers, upper crustal low-velocity layers, and lower crustal low-velocity layers, among others. Notably, the method does not require prior knowledge of the number of layers in the model, making it highly robust. Furthermore, field data tests demonstrate the method's potential for practical application.

How to cite: Zhou, J. and Tkalčić, H.: Earth structure from P-wave coda autocorrelation using particle swarm optimization, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4874, https://doi.org/10.5194/egusphere-egu24-4874, 2024.

A 3D seismic velocity model of the Iranian plateau was constructed using adjoint tomography. The initial model used in this study is taken from a previous adjoint noise tomography investigation in the region, and was refined through the inversion of waveforms recorded at seismic broadband stations from 320 local/regional earthquakes for which a Centroid-Moment-Tensor (CMT) has been computed. The synthetic waveforms were calculated using the spectral-element method (SEM) through a mesh of 12 million grid points representing the Iran region. The model parameters were iteratively updated by Newton's method, using misfit and Hessian kernels to minimize the difference between observed and synthetic waveforms. The forward and adjoint simulations were computed on the HRL GPU-cluster in Frankfurt, requiring a total of 6720 simulations and approximately 13,000 node hours to achieve the final model after 11 iterations. Comparison of the synthetic waveforms produced by the proposed model to the observed waveforms for a subset of selected earthquakes indicates improved fit in the period range of 10-50 seconds confirms the model's ability to accurately predict actual waveforms. The identification of previously undetected anomaly features beneath the Iranian plateau was also observed, which could be attributed to geological features with sufficient data coverage and resolution.

How to cite: komeazi, A., Kaviani, A., and Rümpker, G.: Investigating the Subsurface of the Iranian Plateau using Adjoint Tomography: A 3D Seismic Velocity Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5347, https://doi.org/10.5194/egusphere-egu24-5347, 2024.

Joint inversion of multiple datasets is an effective approach for high-precision imaging of the crustal and upper mantle velocity structures. In this study, we propose a novel deep learning-based method called JointNet for jointly inverting Rayleigh wave phase velocity and ellipticity data to obtain high-precision shear wave velocity models. JointNet, a multimodal deep neural network, is designed to analyze these independent physical parameters and generate outputs that include a velocity model and layer thicknesses. The network is trained using a large dataset of randomly generated 1D models along with their corresponding calculated phase velocities and ellipticities. Our tests using synthetic and observed data demonstrate that JointNet produces inversion results that are highly comparable to those obtained through a Markov Chain Monte Carlo-based method. This indicates that the network effectively captures the nonlinear relationship between phase velocity, ellipticity data, and the 1D Vs model. In addition, JointNet eliminates the need for prior information input and significantly reduces the computational time for inversion compared to traditional nonlinear methods. Training using synthetic data based on a global model ensures its wide applicability in various regions with different velocity structures. Furthermore, JointNet can be readily adapted to incorporate additional datasets, such as receiver functions, to further enhance imaging resolution. Essentially, JointNet can also function as a novel inversion framework for more extensive model inversion studies.

How to cite: Huang, X., Yu, Z., Wang, W., and Wang, F.: JointNet: A multimodal deep-learning-based approach for joint inversion of Rayleigh wave dispersion and ellipticity, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6980, https://doi.org/10.5194/egusphere-egu24-6980, 2024.

Africa’s lithosphere hosts the longest-lived cratons on our planet and records a rich and diverse tectonic history: plate subduction to the North, a long rift system in the East, the super swell in the South, and a record of continental breakup to the West. However, gaps remain in our current efforts to study its lithospheric layering due to sparse coverage and noisy short-term seismic deployments. Here, we present a body-wave dataset and model assessment products for investigating Africa’s lithosphere (ADAMA). We address the challenge of lithospheric imaging on the continent using sparse and noisy teleseismic body wavefields, i.e., receiver functions and SS precursors. The latter extends lithospheric illumination in regions without station coverage. In both cases, we explore novel denoising approaches: (1) CRISP-RF (Clean Receiver Function Imaging with Sparse Radon Filters), which uses sparse Radon transforms to interpolate the sparse receiver function data and eliminate incoherent noise, and (2) FADER (Fast Automated Detection and Elimination of Echoes and Reverberations), which deconvolves thin-layer reflections buried in long-period SS precursors. We improve constraints on bulk crustal structure and lithospheric layering, e.g., from H-k stacking, following CRISP-RF denoising. We extend spatial sampling and detections of lithospheric layering by jointly interpreting receiver functions and SS precursors following cepstral deconvolution of long-period SS precursor waveforms. Our final model, ACE-ADAMA-BW (Africa’s Continental Layering Evaluated with ADAMA’s Body Waves), will improve 3-D resolution of lithospheric layering spanning the cratons (West Africa, Tanzania, Congo, Kaapvaal, Zimbabwe), rifts (Gourma, East African Rift System) and basins (Taoudeni, Goo, Congo) of Africa.

How to cite: Legre, J.-J. and Olugboji, T.: Africa's Lithospheric Architecture with Multi-mode Body Wave Imaging, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4249, https://doi.org/10.5194/egusphere-egu24-4249, 2024.

We present a high-resolution 3D shear wave velocity (Vsv) model for the North-East India Region (NER) and Indo-Burmese Subduction Zone (IBSZ) down to an 80 km depth by inverting a new and extensive Rayleigh wave dispersion dataset. We retrieved vertical component dataset of more than 900 seismic events recorded by 26 global and regional seismic broadband networks. We picked and analyzed ~20,000 paths of Rayleigh wave fundamental mode group velocity dispersion curves across a wide period range of 4-70s. The methodology involved a two-step inversion approach: a 2D continuous regionalization incorporating azimuthal anisotropy was utilized to produce tomographic images from the local velocity dispersion curves. Subsequently, a Markov Chain Monte Carlo scheme within a trans-dimensional Bayesian framework was employed for the inversion process. The resulting 2D tomograms of the regionalized dispersion data at different periods and the subsequent inverted 3D model are consistent with the velocity values associated with the known geologic features, accurately outlining the primary tectonic boundaries in the study area. The region under study encompasses the Bengal basin (BB), Shillong plateau (SP), Mikir Hills, Assam-Brahmaputra-valley (BV), North-Eastern Himalayas, Indo-Burma ranges (IBR) and western Myanmar. Sediment thickness is highest in the southern delta region (18-22km), increasing from west to east towards the northern part of the BB and thinnest at the Dauki fault zone (8-10km). Crustal thickness under BB varies widely, from 33-46km. The velocity model reveals an undulating mantle and higher upper-crustal shear wave velocity (Vsv~3.3-3.6km/s) under the SP than its surrounding regions. Moho thickness varies across the region: ~33km in Garo Hills, ~38km in Assam valley, and ~42km beneath the Lesser Himalayan foredeep. There is a clear eastward dipping subduction geometry along ~22°N from under BB towards the Central Myanmar Basin (CMB) (at ~95°E). Sediment thickness in the CMB varies from 10-12km. The Main Himalayan Thrust (MHT) depth is ~18km under the Arunachal Himalaya, which moving northward dips down to ~28km under the Greater Himalaya. The crustal thickness at the syntaxial corner and BV is significantly greater than its surficial topography. The maximum crustal thickness of ~52km is on the southern IBR, along its eastern side.

Keywords: Crustal Structure, North-East India, Indo-Burma subduction zone, Rayleigh wave, group velocity, 3D shear wave velocity model, Bayesian inversion.

How to cite: Mishra, A., Maurya, S., Mohanty, W. K., and Gollapally, M.: 3D Crustal Imaging of North-East India and Indo-Burmese Subduction Zone, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11052, https://doi.org/10.5194/egusphere-egu24-11052, 2024.