Surface Wave Tomography Research Articles

Rayleigh-Wave Dispersion Curves from Energetic Hurricanes in the Southeastern United States

ABSTRACT Seismic records during energetic storms, similar to earthquake signals, carry information about the Earth’s interior. However, the utility of these records is rare in seismic imaging, because signal onsets in storm-generated microseisms are difficult to pick. The exciting seismic sources are also unsteady. Here, we present a new method for extracting accurate Rayleigh-wave dispersion curves from broadband seismic recordings made during the passage of hurricanes by incorporating Shen et al. (2012) cross-correlation strategy with Wang et al. (2019) frequency–Bessel transform array stacking technique. We show that surface-wave dispersion curves are observed in the seismic recordings made of four hurricanes. Compared with dispersion curves from hurricanes traveling onshore or in the deepwater, dispersion curves have higher signal-to-noise ratios from hurricanes moving near the coast or along the continental shelf. The average dispersion curve obtained from stacked cross correlations of all the four hurricanes agrees closely with that obtained from yearly ambient noise. We utilize the average dispersion curve from hurricanes to obtain a reliable shear-wave velocity (VS) model. Our VS model matches that derived from annual ambient seismic noise results with an average difference less than 0.1 km/s in the crust and uppermost mantle. This study suggests that hurricane-based dispersion curves can be an effective supplement to surface-wave tomography.

On the method of surface-wave tomography and perspectives for its application in engineering seismic exploration

The upper part of the seismic section is studied using refracted waves, as well as surface waves using the MASW method during engineering seismic surveys. This work is devoted to consideration of a relatively new near-surface approach, which is actively used in seismology for studying the upper mantle and deep part of the earth's crust, the method of surface wave tomography. This method is of great practical interest, since it allows to obtain 3D subsurface models and conduct remote researches; also it potentially has better spatial resolution than the widely used MASW method. Within the framework of this work, tests of the developed algorithm for surface-wave tomography using direct rays were carried out on modeled data. The performance of the algorithm was assessed and the resolution of the method was estimated. Also the optimal observation schemes were considered as well as the influence of the regularization parameter value on the inversion result. Basing on the results of the current study, it can be concluded that the method of surface-wave tomography and its realization via the developed algorithm can be effectively used to solve engineering-geological problems.

Capturing spatial variability in the regional Ground Motion Model of Groningen, the Netherlands

Abstract Long-term exploration of the Groningen gas field in the Netherlands led to induced seismicity. Over the past nine years, an increasingly sophisticated Ground Motion Model (GMM) has been developed to assess the site response and the related seismic hazard. The GMM output strongly depends on the shear-wave velocity (V S ), among other input parameters. To date, V S model data from soil profiles (Kruiver et al., Bulletin of Earthquake Engineering, 15(9): 3555–3580, 2017; Netherlands Journal of Geosciences, 96(5): s215–s233, 2017) have been used in the GMM. Recently, new V S profiles above the Groningen gas field were constructed using ambient noise surface wave tomography. These so-called field V S data, even though spatially limited, provide an independent source of V S to check whether the level of spatial variability in the GMM is sufficient. Here, we compared amplification factors (AF) for two sites (Borgsweer and Loppersum) calculated with the model V S and the field V S (Chmiel et al., Geophysical Journal International, 218(3), 1781–1795, 2019 and new data). Our AF results over periods relevant for seismic risk (0.01–1.0 s) show that model and field V S profiles agree within the uncertainty range generally accepted in geo-engineering. In addition, we compared modelled spectral accelerations using either field V S or model V S in Loppersum to the recordings of an earthquake that occurred during the monitoring period (ML 3.4 Zeerijp on 8 January 2018). The modelled spectral accelerations at the surface for both field V S and model V S are coherent with the earthquake data for the resonance periods representative of most buildings in Groningen (T = 0.2 and 0.3 s). These results confirm that the currently used V S model in the GMM captures spatial variability in the site response and represents reliable input for the site response calculations.

Constructing shear velocity models from surface wave dispersion curves using deep learning

Surface wave tomography has been widely used to determine shear wave velocities by inverting surface wave dispersion curves. Conventional least-squares inversions strongly depend on an initial model and Monte Carlo inversion algorithms are usually time-consuming. In this study, we apply a deep neural network (DNN) to surface wave dispersion curves to investigate whether the initial model can be relaxed and whether reliable shear velocity models can be constructed. By applying our method to synthetic and field data, our results show that: (1) by constructing a well-trained DNN model from the global continental CRUST1.0 data, the DNN approach is effective and efficient to determine shear velocity structures using Rayleigh wave dispersion curves; (2) using the well-trained DNN model, no prior model is required, relaxing the requirement of an initial model; (3) the well-trained DNN model can be used to construct pseudo 3D seismic models across different continental areas.

Data–driven prospectivity modelling of sediment–hosted Zn–Pb mineral systems and their critical raw materials

Demand for critical raw materials is expected to accelerate over the next few decades due to continued population growth and the shifting consumption patterns of the global economy. Sedimentary basins are important sources for critical raw materials and new discoveries of sediment–hosted Mississippi Valley–type (MVT) and/or clastic–dominated (CD) Zn–Pb deposits are likely required to mitigate future supply chain disruptions for Zn, Pb, Ag, Cd, Ga, Ge, Sb, and In. Herein we integrate public geoscience datasets using a discrete global grid to system to model the mineral potential for MVT and CD deposits across Canada, the United States of America, and Australia. Statistical analysis of the model results demonstrates that surface–wave tomography and derivative products from satellite gravity datasets can be used to map the most favourable paleo–tectonic settings of MVT and CD deposits inboard of orogenic belts and at the rifted edges of cratonic lithosphere, respectively. Basin development at pre–existing crustal boundaries was likely important for maintaining the low geothermal–gradients that are favourable for metal transport and generating the crustal fluid pathways that were reactivated during ore–formation, as suggested by the statistical association of both sediment–hosted mineral deposit types with the edges of upward–continued gravity and long–wavelength magnetic anomalies. Multivariate statistical analysis demonstrates that the most prospective combination of these geophysical datasets varies for each geological region and deposit type. We further demonstrate that maximum and minimum geological ages, coupled with Phanerozoic paleogeographic reconstructions, represent mappable proxies for the availability of oxidized, brine–generating regions that are the most likely source of ore–forming fluids (e.g., low– to mid–latitude carbonate platforms and evaporites). Ore deposition was likely controlled by interaction between oxidized, low–temperature brines and sulfidic and/or carbonaceous rocks, which, in some cases, can be mapped at the exposed surface or identified using the available rock descriptions. Baseline weights–of–evidence models are based on regional geophysics and are the least impacted by missing surface information but yield relatively poor results, as demonstrated by the low area–under–the–curve (AUC) for the spatially independent test set on the success–rate plot (AUC = 0.787 for MVT and AUC = 0.870 for CD). Model performance can be improved by: (1) using advanced methods that were trained and validated during a series of semi–automated machine learning competitions; and/or (2) incorporating geological and geophysical datasets that are proxies for each component of the mineral system. The best–performing gradient boosting machine models yield higher AUC for the test set (AUC = 0.983 for MVT and AUC = 0.991 for CD) and reduce the search space by >94%. The model results highlight the potential benefits of mapping sediment–hosted mineral systems at continental scale to improve mineral exploration targeting for critical raw materials.

Application of an Automatic Noise or Signal Removal Algorithm Based on Synchrosqueezed Continuous Wavelet Transform of Passive Surface Wave Imaging: A Case Study in Sichuan, China

Passive surface wave imaging based on noise cross-correlation has been a research hotspot in recent years. However, because randomness of noise is difficult to achieve in reality, prominent noise sources will inevitably affect the dispersion measurement. Additionally, in order to recover high-fidelity surface waves, the time series input during cross-correlation calculation is usually very long, which greatly limits the efficiency of passive surface wave imaging. With an automatic noise or signal removal algorithm based on synchrosqueezed continuous wavelet transform (SS-CWT), these problems can be alleviated. We applied this method to 1-h passive datasets acquired in Sichuan province, China; separated the prominent noise events in the raw field data, and enhanced the cross-correlation reconstructed surface waves, effectively improving the accuracy of the dispersion measurement. Then, using the conventional surface wave inversion method, the shear wave velocity profile of the underground structure in this area was obtained.

On the Potential of 3D Transdimensional Surface Wave Tomography for Geothermal Prospecting of the Reykjanes Peninsula

Seismic travel time tomography using surface waves is an effective tool for three-dimensional crustal imaging. Historically, these surface waves are the result of active seismic sources or earthquakes. More recently, however, surface waves retrieved through the application of seismic interferometry have also been exploited. Conventionally, two-step inversion algorithms are employed to solve the tomographic inverse problem. That is, a first inversion results in frequency-dependent, two-dimensional maps of phase velocity, which then serve as input for a series of independent, one-dimensional frequency-to-depth inversions. As such, a set of localized depth-dependent velocity profiles are obtained at the surface points. Stitching these separate profiles together subsequently yields a three-dimensional velocity model. Relatively recently, a one-step three-dimensional non-linear tomographic algorithm has been proposed. The algorithm is rooted in a Bayesian framework using Markov chains with reversible jumps, and is referred to as transdimensional tomography. Specifically, the three-dimensional velocity field is parameterized by means of a polyhedral Voronoi tessellation. In this study, we investigate the potential of this algorithm for the purpose of recovering the three-dimensional surface-wave-velocity structure from ambient noise recorded on and around the Reykjanes Peninsula, southwest Iceland. To that end, we design a number of synthetic tests that take into account the station configuration of the Reykjanes seismic network. We find that the algorithm is able to recover the 3D velocity structure at various scales in areas where station density is high. In addition, we find that the standard deviation of the recovered velocities is low in those regions. At the same time, the velocity structure is less well recovered in parts of the peninsula sampled by fewer stations. This implies that the algorithm successfully adapts model resolution to the density of rays. It also adapts model resolution to the amount of noise in the travel times. Because the algorithm is computationally demanding, we modify the algorithm such that computational costs are reduced while sufficiently preserving non-linearity. We conclude that the algorithm can now be applied adequately to travel times extracted from station–station cross correlations by the Reykjanes seismic network.

Surface Wave Tomography of Southeast Asia

Surface Wave Tomography of Southeast Asia

Lithospheric structure of the eastern Mediterranean Sea: Inferences from surface wave tomography and stochastic inversions constrained by wide-angle refraction measurements

The tectonic plate under the eastern Mediterranean Sea shows a remarkable variability as it comprises Earth's oldest oceanic lithosphere as well as the transition towards continental lithosphere beneath the Levant Basin. Its thickness and other properties offer essential information on the lithospheric evolution but have been difficult to determine seismically due to the high heterogeneity of the region and its complex crustal structure. Here, we combine a large, new surface wave dataset with published wide-angle data in order to determine lithospheric properties in the eastern Mediterranean. Our stochastic inversions of broad-band, phase-velocity dispersion measurements resolve the crust-mantle structural trade-offs and yield robust, 1-D shear-wave velocity models down to 300 km depth beneath the Ionian and Levant Basins. The thickness of the crust beneath the two locations is 16.4 ± 3 km and 22.3 ± 2 km, respectively. The Poisson's ratio (σ) of 0.32 and Vp/Vs of 1.93 in the crystalline crust confirm the presence of serpentinized oceanic crust beneath the Ionian Basin. Beneath the Levant Basin, low crustal Vp/Vs (∼ 1.7) and Poisson's (∼ 0.24) ratios indicate continental crust. Beneath the Ionian Basin, the lithosphere is about 180 km thick. By contrast, thin, 75 km thick lithosphere is found beneath the Levant Basin. S-velocity tomography based on surface wave data also shows thick, spatially variable oceanic mantle lithosphere beneath the eastern Mediterranean. Thickness of the oceanic lithosphere increases eastwards from the Triassic Ionian towards the Permo-Carboniferous lithosphere in the Central Eastern Mediterranean. These results demonstrate that oceanic lithosphere can thicken by cooling substantially beyond the limits suggested by the plate cooling model. Beneath the eastern Herodotus oceanic Basin, lithospheric thickness is decreasing to about 180 km. Thin continental lithosphere and shallow asthenosphere are present beneath the Dead Sea Fault, demonstrating that the localization of the lithospheric deformation and crustal seismicity along the fault correlates spatially with the thinning of the underlying continental lithosphere.

Convolutional Neural Network, Res‐Unet++, ‐Based Dispersion Curve Picking From Noise Cross‐Correlations

AbstractAmbient seismic noise cross‐correlation has been widely applied in surface wave tomography at regional to global scales, including for seismic exploration of near‐surface structures. Reliable seismic imaging requires the accurate selection of dispersion curves. However, manual picking has become cumbersome work with the increase in available correlation traces; it is even more difficult when the number of dispersion curves increases by using frequency‐Bessel (F‐J) transform. Here, we show that the neural network Res‐Unet++ can automatically and accurately extract both fundamental dispersion curves and overtones from the F‐J dispersion spectra after training the network. Results show that selected dispersion curves had high accuracies in the synthetic data (greater than 95%). The network could effectively extract both the fundamental and higher modes in real data, and transfer learning improved the adaptability of neural networks for different geological areas. The obtained dispersion curves from the real data agreed well with those acquired manually and were advantageous for generating more effective dispersion points.

Tectonic Inheritance During Plate Boundary Evolution in Southern California Constrained From Seismic Anisotropy

AbstractThe style of convective force transmission to plates and strain‐localization within and underneath plate boundaries remain debated. To address some of the related issues, we analyze a range of deformation indicators in southern California from the surface to the asthenosphere. Present‐day surface strain rates can be inferred from geodesy. At seismogenic crustal depths, stress can be inferred from focal mechanisms and splitting of shear waves from local earthquakes via crack‐dependent seismic velocities. At greater depths, constraints on rock fabrics are obtained from receiver function anisotropy, Pn and P tomography, surface wave tomography, and splitting of SKS and other teleseismic core phases. We construct a synthesis of deformation‐related observations focusing on quantitative comparisons of deformation style. We find consistency with roughly N‐S compression and E‐W extension near the surface and in the asthenospheric mantle. However, all lithospheric anisotropy indicators show deviations from this pattern. Pn fast axes and dipping foliations from receiver functions are fault‐parallel with no localization to fault traces and match post‐Farallon block rotations in the Western Transverse Ranges. Local shear wave splitting orientations deviate from the stress orientations inferred from focal mechanisms in significant portions of the area. We interpret these observations as an indication that lithospheric fabric, developed during Farallon subduction and subsequent extension, has not been completely reset by present‐day transform motion and may influence the current deformation behavior. This provides a new perspective on the timescales of deformation memory and lithosphere‐asthenosphere interactions.

Vestiges of underplating and assembly in the central North China Craton based on S-wave velocities

The destruction of the North China Craton (NCC) is a controversial topic among researchers. In particular, the crustal structure associated with the craton’s destruction remains unclear, even though a large number of seismic studies have been carried out in this area. To investigate the crustal structure and its dynamic implications, we perform noise tomography in the central part of the NCC. In this study, continuous vertical-component waveforms spanning one year from 112 broadband seismic stations are used to obtain the group velocity dispersion curves of Rayleigh waves at different periods, and surface wave tomography is employed to extract the Rayleigh wave group velocity distributions at 9–40 s. Finally, the S-wave velocity structure at depths of 0–60 km is determined by the inversion of pure-path dispersion data. The results show obvious differences in the crustal structure among the Western Block (WB), the Trans-North China Orogen (TNCO) and the Eastern Block (EB). The lower crust of the northern part of the EB exhibits a high-velocity S-wave anomaly, which may be related to magmatic underplating in the lower crust induced by an upwelling mantle plume. The S-wave velocity of the WB is lower than that of the TNCO in the upper and middle crust and is lower than that of both the TNCO and the EB in the lower crust. The crust of the TNCO shows higher S-wave velocities than the WB and EB in the upper and middle crust, and its overall S-wave velocity structure is clearly different from those of the WB and EB, implying that the crustal structure of the TNCO may contain vestiges of the Paleoproterozoic collision between the WB and EB and their subsequent assembly. This study marks the first time these findings are identified for the NCC.

Constraints on Crustal P Wave Structure With Leaking Mode Dispersion Curves

AbstractClassic surface wave imaging relies mainly on the dispersion of Rayleigh waves or Love waves. In addition to these types of waves, waveforms that arrive prior to the S wave also exhibit dispersion. These early dispersed waves are controlled mainly by the leaking modes and are rarely used for imaging. We applied the frequency‐Bessel transform method to waveforms that arrive before the S wave and successfully extracted multiple leaking mode dispersion curves. The extracted “0th” mode shows continuous sensitivity to S wave velocity (Vs) and mass density structure whereas the “higher” modes behave more like surface waves that are sensitive to P wave velocity (Vp) structure in the crust. Thus, the “higher” modes can be used to invert the Vp structure, which can compensate for the fact that conventional surface wave imaging methods are mainly sensitive to Vs structure.

USTClitho2.0: Updated Unified Seismic Tomography Models for Continental China Lithosphere from Joint Inversion of Body-Wave Arrival Times and Surface-Wave Dispersion Data

Abstract Xin et al. (2019) presented 3D seismic velocity models (VP and VS) of crust and uppermost mantle of continental China using seismic body-wave travel-time tomography, which are referred to as Unified Seismic Tomography Models for Continental China Lithosphere 1.0 (USTClitho1.0). Compared with previous models of continental China, the VP and VS models of USTClitho1.0 have the highest spatial resolution of 0.5°–1.0° in the horizontal direction and are useful for better understanding the complex tectonics of continental China. Although USTClitho1.0 is implicitly constrained by surface-wave data by using the VS model from surface-wave tomography and the converted VP model as initial models for body-wave travel-time tomography, the predicted surface-wave dispersion curves from USTClitho1.0 do not fit the observed data well. Here, we present updated 3D VP and VS models of the continental China lithosphere (USTClitho2.0) by joint inversion of body-wave arrival times and surface-wave dispersion data. Compared with the previous joint inversion scheme of Zhang et al. (2014), similar to Fang et al. (2016), it is further improved by including the sensitivity of surface-wave dispersion data to VP in the new joint inversion system. As a result, the shallow VP structure is also better imaged. In addition, the new joint inversion scheme considers the large topography variations between the eastern and western parts of China. Thus, USTClitho2.0 better resolves the upper-crustal structure of the Tibetan plateau. Compared with USTClitho1.0, USTClitho2.0 fits both body-wave arrival times and surface-wave dispersion data. Thus, the new velocity models are more accurate and can serve as a better reference model for regional-scale tomography and geodynamic studies in continental China.

Selection of noise sources and short-time passive surface wave imaging——A case study on fault investigation

As a comprehensive resource, geothermal energy plays an important role in local economic development. Faults that control the distribution of geothermal resources are often delineated by geophysical methods. Aiming at a geophysical task of detecting local faults, we used the multichannel analysis of passive surface-wave method and deployed three survey lines with 178 three-component seismic nodal stations in Qingtian town, Zhejiang Province, China, to record ambient noise for imaging local shear (S)-wave velocity structures. Results of time-frequency analysis and beamforming determined the periodic distribution of noise sources. Artificial noise generated around array stations during the daytime greatly reduced the quality of imaging results, while useful ambient noise for imaging came from the flowing river out of the array area at nighttime. We performed cross-coherence operation on the data recorded during nighttime. The signal of surface waves appeared clearly in the anti-causal part of virtual shot gathers, which was chosen for dispersion measurements. After inversion, totally 32 1-D S-wave velocity profiles were combined and interpolated to reveal the 2-D subsurface structure down to a depth of 1.5 km. Two main faults are observed and correspond well with the previous geological maps. Even with two-hour-long noise recordings during nighttime, the similar results are achievable. Our results demonstrated the accuracy and efficiency of passive surface wave methods in delineating local faults. In addition, the imaging test suggests that the stable and reliable result can be obtained using short-time noise recordings by choosing proper distribution of sources.

A new levee control system based on geotechnical and geophysical surveys including active thermal sensing: A case study from Poland

Specific features of levees, particularly their large lengths, cause significant problems in the early and precise detection of leaks as well as erosion zones in both the levee body and subsoil. These processes are the primary threats to levees safety. Fast and relatively cheap methods for the internal investigation of levees, i.e., non-invasive geophysical methods, are used only during non-flooding periods and do not have equivalent accuracy to sensors installed in the levee. Additionally, there is no optimal monitoring method for levee leak detection during a flood. Such a method, which use a fibre-optic thermal linear sensor is very effective in the case of earth dams, but it has significant limitations when to applied to existing levees.In consequence, the primary goals of our research were to improve the thermal method for levee investigation and to propose a coherent levee investigation methodology that considers the entirety of the currently used methods.We therefore developed a new type of sensor: the Multi Points Thermal Sensor (MPointS). In our solution, thermal sensors are mounted next to one another inside the levee or/and in its subsoil by hammering them from the surface. This procedure creates a quasi-continuous line of sensors along the levee. This technology's key feature is that each sensor has an integrated micro-heater that uses an active (heating) thermal method for leak detection and in situ measurements of the seepage velocity.A pilot section of the Vistula River levee in Poland was chosen to verify the MPointS solution in addition to a geophysical, geological, and geotechnical survey. Some leakage zones on this levee were previously identified during the flood in 2010. We next used a wide variety of methods to investigate the levee during a period of non-flooding. Dynamic penetration light (DPL) soundings and geological drillings were applied in addition to geophysical methods such as ground-penetrating radar (GPR), electrical resistivity tomography (ERT), electromagnetic profiling (EMP), capacitively-coupled resistivity (CCR), and high-resolution reflection seismic imaging and multichannel analysis of surface waves (MASW).Loose zones of higher porosity in the levee and its subsoil were identified using geophysical methods. These zones also included the local leakage zones detected during the 2010 flood. However, the leakage zones themselves were not clearly and unambiguously detected by these geophysical methods. The thermal monitoring system using MPointS was next installed in accordance with the locations of the loose zones indicated by the geophysical methods. During the flood in 2019, MPointS allowed us to detect precise leakage zones and monitor whether the seepage velocity exceeded the critical values that may lead to the development of internal erosion.The results showed the importance of using a comprehensive and optimised methodology for levee investigation. MPointS proved to be an effective solution for levee leakage monitoring; however, it was found to be primarily applicable to the most vulnerable levee sections. Overall, levee investigations must be performed with cheaper and faster geophysical methods and supplemented by geotechnical surveys, which are also able to choose MPointS system locations.

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.

New Insights Into Potassic Intraplate Volcanism in Northeast China From Joint Tomography of Ambient Noise and Teleseismic Surface Waves

AbstractWudalianchi volcano is one of the youngest volcanic groups among the Cenozoic intraplate volcanoes in Northeast China and is notable for its potassium‐rich basalts. With newly deployed dense broadband seismic stations, we construct a high‐resolution 3‐D S‐wave velocity model of Wudalianchi volcano and its adjacent region, including the Keluo and Erkeshan volcanoes, using ambient noise and teleseismic surface wave tomography. We find a prominent channel‐like low‐velocity body that extends from the Moho to a depth of 100 km directly beneath the Wudalianchi, Erkeshan, and Keluo (WEK) volcanoes. This low‐velocity anomaly suggests that these three volcanoes may be derived from the same mantle magmatic source. Our model also shows that the prominent low‐velocity anomaly is associated with the pervasive low‐velocity anomalies at a depth of 100–200 km, which is interpreted as the asthenosphere. We further find that the lithosphere beneath the WEK volcanoes thickens abruptly from ∼80 to ∼110 km. Combining our results with those of other geophysical studies, we propose a geodynamic scenario for WEK intraplate volcanism. When the background asthenospheric flow in Northeast China encounters sharp changes in lithosphere thickness beneath the WEK volcanoes, it produces locally turbulent flow, promotes asthenospheric upwelling, and finally forms volcanoes.

Joint tomographic inversion of crustal structure beneath the eastern Tibetan Plateau with ambient noise and gravity data

SUMMARY We have developed a joint tomographic inversion method with seismic surface wave dispersion and gravity data for obtaining more reliable crustal 3-D shear wave structures. We take the eikonal-based direct surface wave tomographic method and adaptive gravity modelling method in spherical coordinates in the inverse problem. Based on the empirical relations between seismic velocity and density parameters, our method combines surface wave dispersion curves (i.e. surface wave traveltimes at different periods) and Bouguer gravity anomaly data together to invert for 3-D shear wave velocity structures. In our method, off-great-circle propagation of the surface wave and the earth's curvature is considered in the forward modelling. Synthetic tests suggest that the joint tomographic method could improve the reliability and obtain more convincing results than individual seismic surface wave tomography. The gravity data can provide more constraints into the model resolution and help restore the crustal anomalies better. The inversion results in the eastern Tibetan Plateau and Sichuan basin indicate complex distributions of low-velocity zone in the mid-crust of the eastern Tibetan Plateau and a craton-like basement of the Sichuan basin, which supports the crust channel flow model. Although both the 3-D shear wave velocity model from joint inversion and the individual seismic surface wave inversion can fit the surface wave data almost equally well, the joint inversion model can better match the gravity data We also found that the 3-D model from joint inversion in this study shows similar structural characteristics with the surface wave tomographic model, which indicates the icing on the cake effects of gravity data.

Measurement of Surface-Wave Phase-Velocity Dispersion on Mixed Inertial Seismometer – Distributed Acoustic Sensing Seismic Noise Cross-Correlations

ABSTRACTThe application of ambient seismic noise cross-correlation to distributed acoustic sensing (DAS) data recorded by subsurface fiber-optic cables has revolutionized our ability to obtain high-resolution seismic images of the shallow subsurface. However, passive surface-wave imaging using DAS arrays is often restricted to Rayleigh-wave imaging and 2D imaging along straight segments of DAS arrays due to the intrinsic sensitivity of DAS being limited to axial strain along the cable for the most common type of fiber. We develop the concept of estimating empirical surface waves from mixed-sensor cross-correlation of velocity noise recorded by three-component seismometers and strain-rate noise recorded by DAS arrays. Using conceptual arguments and synthetic tests, we demonstrate that these cross-correlations converge to empirical surface-wave axial strain response at the DAS arrays for virtual single step forces applied at the seismometers. Rotating the three orthogonal components of the seismometer to a tangential–radial–vertical reference frame with respect to each DAS channel permits separate analysis of Rayleigh waves and Love waves for a medium that is sufficiently close to 1D and isotropic. We also develop and validate expressions that facilitate the measurement of surface-wave phase velocity on these noise cross-correlations at far-field distances using frequency–time analysis. These expressions can also be used for DAS surface-wave records of active sources at local distances. We demonstrate the recovery of both Rayleigh waves and Love waves in noise cross-correlations derived from a dark fiber DAS array in the Sacramento basin, northern California, and nearby permanent seismic stations at frequencies ∼0.1–0.2 Hz, up to distances of ∼80 km. The phase-velocity dispersion measured on these noise cross-correlations are consistent with those measured on traditional noise cross-correlations for seismometer pairs. Our results extend the application of DAS to 3D ambient noise Rayleigh-wave and Love-wave tomography using seismometers surrounding a DAS array.