Imaging structure and geometry of slabs in the greater Alpine area – a P-wave travel-time tomography using AlpArray Seismic Network data

We perform a teleseismic P-wave travel-time tomography to examine the geometry and structure of subducted lithosphere in the upper mantle beneath the Alpine orogen. The tomography is based on waveforms recorded at over 600 temporary and permanent broadband stations of the dense AlpArray Seismic Network deployed by 24 different European institutions in the greater Alpine region, reaching from the Massif Central to the Pannonian Basin and from the Po Plain to the river Main. Teleseismic travel times and travel-time residuals of direct teleseismic P waves from 331 teleseismic events of magnitude 5.5 and higher recorded between 2015 and 2019 by the AlpArray Seismic Network are extracted from the recorded waveforms using a combination of automatic picking, beamforming and cross-correlation. The resulting database contains over 162 000 highly accurate absolute P-wave travel times and travel-time residuals. For tomographic inversion, we define a model domain encompassing the entire Alpine region down to a depth of 600 km. Predictions of travel times are computed in a hybrid way applying a fast TauP method outside the model domain and continuing the wave fronts into the model domain using a fast marching method. We iteratively invert demeaned travel-time residuals for P-wave velocities in the model domain using a regular discretization with an average lateral spacing of about 25 km and a vertical spacing of 15 km. The inversion is regularized towards an initial model constructed from a 3D a priori model of the crust and uppermost mantle and a 1D standard earth model beneath. The resulting model provides a detailed image of slab configuration beneath the Alpine and Apenninic orogens. Major features are a partly overturned Adriatic slab beneath the Apennines reaching down to 400 km depth still attached in its northern part to the crust but exhibiting detachment towards the southeast. A fast anomaly beneath the western Alps indicates a short western Alpine slab whose easternmost end is located at about 100 km depth beneath the Penninic front. Further to the east and following the arcuate shape of the western Periadriatic Fault System, a deep-reaching coherent fast anomaly with complex internal structure generally dipping to the SE down to about 400 km suggests a slab of European origin limited to the east by the Giudicarie fault in the upper 200 km but extending beyond this fault at greater depths. In its eastern part it is detached from overlying lithosphere. Further to the east, well-separated in the upper 200 km from the slab beneath the central Alps but merging with it below, another deep-reaching, nearly vertically dipping high-velocity anomaly suggests the existence of a slab beneath the eastern Alps of presumably the same origin which is completely detached from the orogenic root. Our image of this slab does not require a polarity switch because of its nearly vertical dip and full detachment from the overlying lithosphere. Fast anomalies beneath the Dinarides are weak and concentrated to the northernmost part and shallow depths. Low-velocity regions surrounding the fast anomalies beneath the Alps to the west and northwest follow the same dipping trend as the overlying fast ones, indicating a kinematically coherent thick subducting lithosphere in this region. Alternatively, these regions may signify the presence of seismic anisotropy with a horizontal fast axis parallel to the Alpine belt due to asthenospheric flow around the Alpine slabs. In contrast, low-velocity anomalies to the east suggest asthenospheric upwelling presumably driven by retreat of the Carpathian slab and extrusion of eastern Alpine lithosphere towards the east while low velocities to the south are presumably evidence of asthenospheric upwelling and mantle hydration due to their position above the European slab.

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

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.

Microseismic P-Wave Travel Time Computation and 3D Localization Based on a 3D High-Order Fast Marching Method

The travel time computation of microseismic waves in different directions (particularly, the diagonal direction) in three-dimensional space has been found to be inaccurate, which seriously affects the localization accuracy of three-dimensional microseismic sources. In order to solve this problem, this research study developed a method of calculating the P-wave travel time based on a 3D high-order fast marching method (3D_H_FMM). This study focused on designing a high-order finite-difference operator in order to realize the accurate calculation of the P-wave travel time in three-dimensional space. The method was validated using homogeneous velocity models and inhomogeneous layered media velocity models of different scales. The results showed that the overall mean absolute error (MAE) of the two homogenous models using 3D_H_FMM had been reduced by 88.335%, and 90.593% compared with the traditional 3D_FMM. On that basis, the three-dimensional localization of microseismic sources was carried out using a particle swarm optimization algorithm. The developed 3D_H_FMM was used to calculate the travel time, then to conduct the localization of the microseismic source in inhomogeneous models. The mean error of the localization results of the different positions in the three-dimensional space was determined to be 1.901 m, and the localization accuracy was found to be superior to that of the traditional 3D_FMM method (mean absolute localization error: 3.447 m) with the small-scaled inhomogeneous model.

The Community Velocity Model V.1.0 of Southwest China, Constructed from Joint Body- and Surface-Wave Travel-Time Tomography

Southwest China, located at the southeastern margin of the Tibetan plateau, plays an important role for the plateau growth and its material extrusion. It has complicated tectonic environment and strong seismic activities including the 2008 Wenchuan great earthquake. Numerous geophysical studies have been conducted in southwest China. However, a community velocity model (CVM) in this region is still not available, which makes it difficult to have a consistent catalog of earthquake locations and focal mechanisms and a consistent velocity model for simulating strong ground motions and evaluating earthquake hazards. In this study, we aim at building a high-resolution CVM (both VP and VS) of the crust and uppermost mantle in southwest China along with earthquake locations by joint inversion of body- and surface-wave travel-time data. In total, we have assembled 386,958 P- and 372,662 S-wave first arrival times and nearly 8100 Rayleigh-wave dispersion curves in the period band of 5–50 s. A multigrid strategy is adopted in the joint inversion. A coarser horizontal grid interval of 0.5° is first used and then a finer grid interval of 0.25° is used with initial models interpolated from the coarser-grid inverted velocity models. The spatial resolution of both VP and VS models can reach up to 0.5° horizontally and 10 km vertically according to the checkerboard tests. The comparisons of our inverted VP and VS models with those from other studies show general consistency in large-scale features. The inverted models are further validated by P-wave arrival times from active sources and Rayleigh-wave data. In general, our velocity models show two low-velocity zones in the middle-lower crust and a prominent high-velocity region in between them. Our new models have been served as the first version of the CVM in southwest China (SWChinaCVM-1.0) for future studies.

Artificial Intelligence Method for Shear Wave Travel Time Prediction considering Reservoir Geological Continuity

The existing artificial intelligence model uses single-point logging data as the eigenvalue to predict shear wave travel times (DTS), which does not consider the longitudinal continuity of logging data along the reservoir and lacks the multiwell data processing method. Low prediction accuracy of shear wave travel time affects the accuracy of elastic parameters and results in inaccurate sand production prediction. This paper establishes the shear wave prediction model based on the standardization, normalization, and depth correction of conventional logging data with five artificial intelligence methods (linear regression, random forest, support vector regression, XGBoost, and ANN). The adjacent data points in depth are used as machine learning eigenvalues to improve the practicability of interwell and the accuracy of single-well prediction. The results show that the model built with XGBoost using five points outperforms other models in predicting. The R2 of 0.994 and 0.964 are obtained for the training set and testing set, respectively. Every model considering reservoir vertical geological continuity predicts test set DTS with higher accuracy than single-point prediction. The developed model provides a tool to determine geomechanical parameters and give a preliminary suggestion on the possibility of sand production where shear wave travel times are not available. The implementation of the model provides an economic and reliable alternative for the oil and gas industry.

Teleseismic P-wave travel time tomography of the Alpine upper mantle using AlpArray seismic network data

<p>We perform a teleseismic P-wave travel time tomography to examine geometry and slab structure of the upper mantle beneath the Alpine orogen. Vertical component data of the extraordinary dense seismic network AlpArray are used which were recorded at over 600 temporary and permanent broadband stations deployed by 24 different European institutions in the greater Alpine region, reaching from the Massif Central to the Pannonian Basin and from the Po plain to the river Main. Mantle phases of 370 teleseismic events between 2015 and 2019 of magnitude 5.5 and higher are evaluated automatically for direct and core diffracted P arrivals using a combination of higher-order statistics picking algorithms and signal cross correlation. The resulting database contains over 170.000 highly accurate absolute P picks that were manually revised for each event. The travel time residuals exhibit very consistent and reproducible spatial patterns, already pointing at high velocity slabs in the mantle.</p><p>For predicting P-wave travel times we consider a large computational box encompassing the Alpine region up to a depth of 600 km within which we allow 3D-variations of P-wave velocity. To account for influences of the strongly heterogeneous crust that cannot be resolved with teleseismic data, we integrate a complex three-dimensional crustal model directly into our model. Outside the box we assume a spherically symmetric earth and apply the Tau-P method to calculate travel times and ray paths. These are injected at the boundaries of the regional box and continued using the fast marching method (Rawlinson et al. 2005). We invert differences between observed and predicted traveltimes for P-wave velocities inside the box. Velocity is discretized on a regular grid with a spacing of about 25x25x15 km. The misfit reduction reaches values of over 80% depending on damping and smoothing parameters.</p><p>The resulting model shows several steeply dipping high velocity anomalies following the Alpine arc. The most prominent structure stretches from the western Alps into the Apennines mountain range reaching depths of over 500 km. Two further anomalies of high complexity extending down to a depth of 300 km are located below the central and eastern Alps, both being detached from the lithosphere and separated by a clear gap below the western part of the Tauern window. The central anomaly shows mainly southwards dipping, whereas the eastern anomaly is mainly dipping to the northeast. We compare our results to former studies, confirming lateral positions of the anomalies. However, the new results can benefit from the superior resolution capabilities of the dense AlpArray seismic network, providing more accurate insights into depth extent, dip angle and directions. We perform various general, as well as purpose-built resolution tests, to verify the capabilities of our setup to resolve slab gaps as well as different possible slab dipping directions.</p>

The effects of seismic anisotropy on S-wave travel time-tomography: the problem of apparent anomalies and possible solutions

<p>Teleseismic travel-time tomography remains one of the most popular methods for obtaining images of Earth's upper mantle. While teleseismic shear phases, most notably SKS, are commonly used to infer the anisotropic properties of the upper mantle, anisotropic structure is often ignored in the construction of body wave shear velocity models. Numerous researchers have demonstrated that neglecting anisotropy in P-wave tomography can introduce significant imaging artefacts that could lead to spurious interpretations. Less attention has been given to the effect of anisotropy on S-wave tomography partly because, unlike P-waves, there is not a ray-based methodology for modelling S-wave travel-times through anisotropic media. Here we evaluate the effect that the isotropic approximation has on tomographic images of the subsurface when shear waves are affected by realistic mantle anisotropy patterns. We use SPECFEM to model the teleseismic shear wavefield through a geodynamic model of subduction that includes elastic anisotropy predicted from micromechanical models of polymineralic aggregates advected through the simulated flow field. We explore how the chosen coordinates system in which S-wave arrival times are measured (e.g., radial versus transverse) affects the imaging results. In all cases, the isotropic imaging assumption leads to numerous artefacts in the recovered velocity models that could result in misguided inferences regarding mantle dynamics. We find that when S-wave travel-times are measured in the direction of polarisation, the apparent anisotropic shear velocity can be approximated using sinusoidal functions of period pi and two-pi. This observation allows us to use ray-based methods to predict S-wave travel-times through anisotropic models. We show that this parameterisation can be used to invert S-wave travel-times for the orientation and strength of anisotropy in a manner similar to anisotropic P-wave travel-time tomography. In doing so, the magnitude of imaging artefacts in the shear velocity models is greatly reduced.</p>