Initial Velocity Model Research Articles

Studi Seismik Tomografi Waktu Tempuh Untuk Identifikasi Zona Lemah dengan Menggunakan Konsep Ray Tracing Berdasarkan Algoritma Dijkstra

Tomography is a popular method in describing the subsurface based on P and S waves parameter of the seismic wave. It can show the low velocity zone or the weak zone that usually associated with the structure or fluid evidence. However, it needs some informations about the hypocenter and stations location, and the initial velocity model as the geological model. Based on the information, then ray path on each grid/block in the research area is calculated along from the hypocenter to the each station. Ray path concept is based on the Snell Law, Fermat and Hyugens principle where the wave will be dispersioned when it faces to the different medium and it will choose the medium with the fastest travel time. Then by the inversion process, the tomogram can be resulted. Dijkstra Algorithm is choosen as the basic mathematical equation for the ray path calculation because it is easy and use in some digital platform. This algorithm is provened can result the good tomogram where it can be showed the structure of the geological model.

Potential of Phase-Amplitude-Based Multi-Scale Full Waveform Inversion with Total-Variation Regularization for Seismic Imaging of Deep-Seated Ores

As the demand for ore resources increases, the target for mineral exploration gradually shifts from shallow to deep parts of the Earth (>1 km). However, for the ore-hosting strata, it is difficult to obtain high-resolution images by using the electromagnetic method. Seismic full waveform inversion (FWI) is an optimization algorithm which aims at minimizing the prestack seismic data residual between synthetic and observed data. In this case, FWI provides an effective way to achieve high-resolution imaging of subsurface structures. However, acquired seismic data usually lack low frequencies, resulting in severe cycle skipping of FWI, when the initial velocity model is far away from the true one. Phase information in the seismic data provides the kinematic characteristics of waves and has a quasi-linearly relationship with subsurface structures. In this article, we propose to use a phase-amplitude-based full waveform inversion with total-variation regularization (TV-PAFWI) to invert the deep-seated ores. The ore-hosting velocity model test results demonstrate that the TV-PAFWI is suitable for high-resolution velocity model building, especially for deep-seated ores.

Physics-guided deep autoencoder to overcome the need for a starting model in full-waveform inversion

Full-waveform inversion (FWI) is a popular technique to obtain high-resolution estimates of earth model parameters using all information present in seismic data. Thus, it can provide important information about the subsurface. The FWI algorithm is formulated as a data-fitting minimization problem that iteratively updates an initial velocity model using the gradient of the misfit until an acceptable match is obtained between the real and synthetic data under a tolerance level based on noise in the data. The inversion is computationally expensive and can converge to a local minimum if the starting model used is not close enough to an optimal model. Here, we propose an alternative approach using a combination of machine learning and the physics of the forward model. Unlike conventional supervised machine learning, known answers are not required to train our network. The shot gathers are input to a convolutional neural network-based autoencoder, the output of which is used as the velocity model that is used to compute synthetic seismograms. The synthetic data are compared against observed input data, and the misfit is estimated. The gradient of the misfit with respect to the velocity model parameters is calculated using the adjoint state method. The adjoint state gradient is then used to update the network weights using the automatic differentiation technique. Once the misfit term converges, the neural network can generate velocity models consistent with the observed data. We observe that the neural network can capture spatial correlations at different scales and thus can introduce regularization in our inverse problem. Experiments with the Marmousi model and SEG Advanced Modeling Corporation Phase 1 salt model suggest that the proposed method can overcome local minima, requires no starting model, and produces robust results in the presence of noise and complex salt body structures.

Hypocenter Determination and Estimation 1-D Velocity Models Using Coupled Velocity-Hypocenter Method

Hypocenter relocation is performed to obtain a high-precision hypocenter location (accurate earthquake location). An accurate earthquake location is the key problem in seismology. Further information from an accurate hypocenter location can be used for seismicity analysis, velocity structure study, and earthquake prone mapping as one of the earthquake mitigation efforts. In this research, the method used to relocate the earthquake hypocenter was the Coupled Velocity-Hypocenter. Relocations were conducted in the Central Sulawesi region; we located 40 local earthquake events with a magnitude of ≥ 3.8 ML and a depth of ≤ 25 km. The selected P-wave traveltimes were inverted from 5 seismic stations. The variance of initial velocity models used the 1-D Primary wave velocity model of North Sulawesi, Jeffrey-Bullen and Central Sulawesi. The relocation results show that most of the hypocenters are concentrated precisely in minor faults present in the research area, and the hypocenter distribution of the events indicated as destructive shallow earthquakes occurs at depths of about 5-15 km. The residual distributions resulting from the relocation using the initial velocity model of the Central Sulawesi region indicates an improved quality if compared to Jeffrey-Bullen velocity model and the North Sulawesi velocity model, with RMS error value of 0.08 seconds. This research concluded that the 1-D velocity model in the regional (Central Sulawesi Region) reference was suitable for determining the high-precision hypocenter location.

A Numerical Experiment of Full Waveform Inversion of Complex Structures Concealed Around a Horizontal Hydraulic Fracturing Well Using Perforation Seismic Data

It is difficult to image the details of complex structures concealed around a horizontal hydraulic fracturing well using seismic data from the ground surface. In this paper, an approach is proposed to solve this problem by non-linear full waveform inversion (FWI) using perforation seismic data. The feasibility of the approach was investigated using numerical modeling based on an experimental model built from the well-known SEG/EAGE overthrust model, which contains complex geological structures with faults. First, seismic modeling was performed to produce experimental synthetic data, including three sets of perforation seismic data recorded by different acquisition systems deployed in observation wells and on the ground surface, and another set of conventional seismic reflection data with both sources and receivers deployed on the ground surface. Then, FWI was performed separately on each data set using an initial velocity model which was heavily smoothed to remove the target structures. The inversion results show that the concealed complex structures around the well were successfully recovered by FWI using perforation data, while the benchmark image from the FWI using conventional seismic data was poor. Particularly, the experiments also demonstrated that FWI using perforation seismic data can image the faults around a horizontal hydraulic fracturing well, while this is unable to achieve using conventional ground surface seismic data. This conclusion was also proved to be valid for noisy data deteriorated either by synthetic Gaussian or field noises. Further experiments demonstrated that FWI using perforation data recorded from wells outperformed that of surface data in terms of structure imaging accuracy characterized by quantitative errors.

Structured regularization\xa0based velocity structure estimation in local earthquake tomography for the adaptation to velocity discontinuities

We propose a local earthquake tomography method that applies a structured regularization technique to determine sharp changes in Earth’s seismic velocity structure using arrival time data of direct waves. Our approach focuses on the ability to better image two common features that are observed in Earth’s seismic velocity structure: sharp changes in velocities that correspond to material boundaries, such as the Conrad and Moho discontinuities; and gradual changes in velocity that are associated with pressure and temperature distributions in the crust and mantle. We employ different penalty terms in the vertical and horizontal directions to refine the earthquake tomography. We utilize a vertical-direction (depth) penalty that takes the form of the {l}_{1}-sum of the {l}_{2}-norms of the second-order differences of the horizontal units in the vertical direction. This penalty is intended to represent sharp velocity changes caused by discontinuities by creating a piecewise linear depth profile of seismic velocity. We set a horizontal-direction penalty term on the basis of the {l}_{2}-norm to express gradual velocity tendencies in the horizontal direction, which has been often used in conventional tomography methods. We use a synthetic data set to demonstrate that our method provides significant improvements over velocity structures estimated using conventional methods by obtaining stable estimates of both steep and gradual changes in velocity. We also demonstrate that our proposed method is robust to variations in the amplitude of the velocity jump, the initial velocity model, and the number of observed arrival times, compared with conventional approaches, and verify the adaptability of the proposed method to dipping discontinuities. Furthermore, we apply our proposed method to real seismic data in central Japan and present the potential of our method for detecting velocity discontinuities using the observed arrival times from a small number of local earthquakes.Graphical

Choosing optimal model parameterization for improving the accuracy of refraction seismic tomography

ABSTRACTSeismic ray tomography is a popular tool for reconstructing seismic velocity models from traveltime data. Here we study how the model parameterization affects the resolution and accuracy of the tomographic inversion for the near‐surface model building. In particular, we consider the weighting of the elements of the model perturbation vector based on the values of the initial velocity model. When the model parameters are defined in terms of velocities, then the tomographic‐inversion resolution is better for the shallow part but degrades for the deeper part of the model. The opposite is true when the model parameters are defined in terms of slowness values. This effect is associated with the method of forming the tomographic matrix. When linearizing the tomography problem for different model parameters, the matrix elements have different weight coefficients. This affects the inversion results and can lead to large errors. We suggest a new parameterization (in‐between the velocity and the slowness) that provides better quality of the tomographic inversion and balanced resolution between the shallow and deeper part of the model. The good performance of this new parameterization is confirmed by a series of synthetic tests and one real‐data example.

Wavefield Reconstruction Inversion via Physics-Informed Neural Networks

Wavefield reconstruction inversion (WRI) formulates a PDE-constrained optimization problem to reduce cycle skipping in full-waveform inversion (FWI). WRI is often implemented by solving for the frequency-domain representation of the wavefield using the finite-difference method. The approach requires matrix inversions and affords limited flexibility to accommodate irregular model geometries. On the other hand, the physics-informed neural network (PINN) uses the underlying physical laws as loss functions to train the neural network (NN) to provide flexible continuous functional approximations of the solutions without matrix inversions. By including a data-constrained term in the loss function, the trained NN can reconstruct a wavefield that simultaneously fits the recorded data and satisfies the Helmholtz equation for a given initial velocity model. Using the predicted wavefields, we rely on a small-size NN to predict the velocity using the reconstructed wavefield. In this velocity prediction NN, spatial coordinates are used as input data to the network, and the scattered Helmholtz equation is used to define the loss function. After we train this network, we are able to predict the velocity in the domain of interest. We develop this PINN-based WRI method and demonstrate its potential using a part of the Sigsbee2A model and a modified Marmousi model. The results show that the PINN-based WRI is able to invert for a reasonable velocity with very limited iterations and frequencies, which can be used in a subsequent FWI application.

Efficient Progressive Transfer Learning for Full-Waveform Inversion With Extrapolated Low-Frequency Reflection Seismic Data

The low-frequency seismic data provide crucial information for guiding the full-waveform inversion (FWI), especially when strong reflectors exist in the velocity model. However, hardware limitations make it difficult to acquire low-frequency data. To overcome the nonlinearity and ill-posedness caused by the absence of the low-frequency data, we develop an efficient progressive transfer learning algorithm for low-frequency extrapolation. The proposed method combines the FWI, the sparsity-promoted bandwidth-extension (BWE) algorithm, and the physics-guided data-driven deep learning approach. Compared with pure data-driven learning-based methods and the original progressive transfer learning method without BWE, our proposed algorithm shows better generalization ability. By integrating the physics constraints and the BWE algorithm, the performance of our method is less dependent on the quality of the initial training velocity model and the corresponding training set. We propose a logarithmic transformation to rebalance the loss function to overcome the challenge of predicting the weak reflection low-frequency data. To accelerate the algorithm, we propose a learning-based BWE method for initializing the training set and a truncated FWI method to reduce the iterative workflow’s computational cost. Experimental results show that our method achieves both high efficiency and high accuracy. The subsurface structures below the strong reflectors are successfully reconstructed with our extrapolated low-frequency data.

Interpretative Seismic Imaging With the Wavenumber-Structure Monitoring of Velocity Models

Despite a record of great success, seismic imaging is regarded to be far from solved because of a big challenge in velocity building where potential failures are particularly salient for salt-related structures and fault-karst belts that pose strong velocity contrasts and dipping angles. The challenge results from the traveltime equivalence of velocity distortion and structure anamorphose during the imaging. It is closely related to two different wavenumber contents implicit in velocity models and seismic structures, respectively. For salt-related structures, the initial velocity model obtained by current industry techniques is characteristic of a low-wavenumber (LW) distortion, which partially destroys the topological invariance of subsurface structures that are implicitly present in surface seismic data and yields erroneous high-wavenumber (HW) reflectivities correspondingly. For fault-karst belts, the estimated initial velocity is too low in resolution to satisfactorily image the high-frequency part in seismic data. The fundamental question regarding how to couple distorted LW and HW components in velocity building remains largely unaddressed. We propose a general scheme by integrating LW velocity models, HW seismic structures (achieved from automatic interpretation of migrated volumes), and logging salt velocities to achieve interpretative velocity building. For assistance monitoring analysis, we introduce a slowness accumulation method to measure the wavenumber content of velocity fields. It provides a quick and easy way to monitor the change of wavenumber contents during the velocity updating. We also address a tentative scheme to assess the complexity of seismic imaging by a statistical description of velocity contrasts and dipping angles that dominate the wavenumber content of velocity models. Numerical experiments and case studies with overthrust salt structures from western China and fault-karst belts from northwest China demonstrate the applicability of interpretative seismic imaging.

Multiscale Data-Driven Seismic Full-Waveform Inversion With Field Data Study

Seismic full-waveform inversion (FWI), which uses iterative methods to estimate high-resolution subsurface models from seismograms, is a powerful imaging technique in exploration geophysics. In recent years, the computational cost of FWI has grown exponentially due to the increasing size and resolution of seismic data. Moreover, it is a non-convex problem and can encounter local minima due to the limited accuracy of the initial velocity models or the absence of low frequencies in the measurements. To overcome these computational issues, we develop a multiscale data-driven FWI method based on fully convolutional networks (FCN). In preparing the training data, we first develop a real-time style transform method to create a large set of synthetic subsurface velocity models from natural images. We then develop two convolutional neural networks with encoder-decoder structure to reconstruct the low- and high-frequency components of the subsurface velocity models, separately. To validate the performance of our data-driven inversion method and the effectiveness of the synthesized training set, we compare it with conventional physics-based waveform inversion approaches using both synthetic and field data. These numerical results demonstrate that, once our model is fully trained, it can significantly reduce the computation time, and yield more accurate subsurface velocity models in comparison with conventional FWI.

Detection of karst voids at pile foundation by full-waveform inversion of single borehole sonic data

Drilled boreholes are commonly used to detect rock voids and fractures in underground construction projects such as pile foundations, tunnel borings, and other near surface human activities. However, voids can be missed by direct drilling due to their limited size. Borehole sonic sounding can be used to image voids in the vicinity of the borehole. We developed a new time domain full-waveform inversion method for borehole sounding data. The FWI method employ an amplitude envelope-based objective function which captures low-frequency components to provide the initial velocity model for further inversion. Then a global correlation-based objective function is applied for FWI based on the updated initial velocity model. Numerical studies show that modeled anomalies of a single void, multiple voids and fractures are successfully delineated by the inverted Vs velocity profile. The FWI method was applied to field data from a bridge foundation project in a karst area and compared to complementary cross-hole acoustic tomography borehole profiles and drilled corehole data. The single borehole FWI inverted velocity profiles identify voids in agreement with the cross hole tomography and corehole observations. The results demonstrate that FWI is capable of imaging karst voids near drilling boreholes using borehole sonic data.

Research advances in circumferential multiple linear explosively-formed projectile technology

The traditional fragmental warhead used in air defense and antimissile can not effectively destroy the incoming insensitive ammunition due to the weak fragmentations, which limits its development. After explosion of the circumferential multiple linear explosively-formed projectile (MLEFP) warhead, a number of folded linear explosively formed projectiles (LEFP) are produced in the circumferential direction with high speed, large mass and large length to diameter ratio. These projectiles can penetrate thick cases and ignite insensitive ammunitions, so the MLEFP warhead has a great application prospect in the medium-short range air defense and anti-missile system. Based on the development of linear projectile and the forming method of new LEFPs, the influences of charge and liner are focused on. Theories, merits and demerits of three initial velocity models are compared. The results of penetration tests using folded LEFPs in recent years are summarized. Finally, the future development direction of circumferential MLEFP warhead and linear projectile is analyzed.

Case study: Improving the quality of the seismic reflection image for a Fujian mineral exploration data set with offset-domain common-image gathers

Seismic reflection is a proven and effective method commonly used during exploration of deep mineral deposits in Fujian, China. In seismic data processing, rugged depth migration based on wave-equation migration can play a key role in handling surface fluctuations and complex underground structures. Because wave-equation migration in the shot domain cannot output offset-domain common-image gathers in a straightforward way, the use of traditional tools for updating the velocity model and improving image quality can be quite challenging. To overcome this problem, we have used the attribute migration method. This works by sorting the migrated stack results for every single-shot gather into the offset gathers. The value of the offset that corresponds to each image point is obtained from the ratio of the original migration results to the offset-modulated shot-data migration results. A Gaussian function is proposed to map every image point to a certain range of offsets. This helps improve the signal-to-noise ratio, which is especially important in handling low-quality seismic data obtained during mineral exploration. Residual velocity analysis is applied to these gathers to update the velocity model and improve the image quality. The offset-domain common-image gathers are also used directly for real mineral exploration seismic data with rugged depth migration. After several iterations of migration and updating the velocity, our procedure achieves an image quality better than the one obtained with the initial velocity model. The results can help with the interpretation of thrust faults and deep deposit exploration.

Multiparameter model building for the Qiuyue structure using 4C ocean-bottom seismometer data

Thin sand-mud-coal interbedded layers and multiples caused by shallow water pose great challenges to conventional 3D multichannel seismic techniques used to detect the deeply buried reservoirs in the Qiuyue field. In 2017, a dense ocean-bottom-seismometer (OBS) acquisition program acquired a 4C data set in the East China Sea. To delineate the deep reservoir structures in the Qiuyue field, we have applied a full-waveform inversion (FWI) workflow to this dense 4C OBS data set. After preprocessing, including receiver geometry correction, moveout correction, component rotation, and energy transformation from three dimensions to two dimensions, preconditioned first-arrival traveltime tomography based on an improved scattering-integral algorithm is applied to construct an initial P-wave velocity model. To eliminate the influence of the wavelet estimation process, a convolutional-wavefield-based objective function for the preprocessed hydrophone component is used during acoustic FWI. By inverting the waveforms associated with early arrivals, a relatively high-resolution underground P-wave velocity model is obtained, with updates at a depth of 2.0 and 4.7 km. The initial S-wave velocity and density models are then constructed based on their prior relationships to the P-wave velocity, accompanied by a reciprocal source-independent elastic FWI to refine both velocity models. Compared with a traditional workflow, guided by stacking velocity analysis or migration velocity analysis, and using only the pressure component or other single component, the workflow presented in this study represents a good approach for inverting the 4C OBS data set to characterize subseafloor velocity structures.

A reflection-based efficient wavefield inversion

Full-waveform inversion (FWI) is popularly used to obtain a high-resolution subsurface velocity model. However, it requires either a good initial velocity model or low-frequency data to mitigate the cycle-skipping issue. Reflection-waveform inversion (RWI) uses a migration/demigration process to retrieve a background model that can be used as a good initial velocity in FWI. The drawback of conventional RWI is that it requires the use of least-squares migration, which is often computationally expensive, and it is still prone to cycle skipping at far offsets. To improve the computational efficiency and overcome cycle skipping in the original RWI, we have incorporated it into a recently introduced method called efficient wavefield inversion (EWI) by inverting for the Born-scattered wavefield instead of the wavefield itself. In this case, we use perturbation-related secondary sources in the modified source function. Unlike conventional RWI, the perturbations are calculated naturally as part of the calculation of the scattered wavefield in an efficient way. Because the sources in the reflection-based EWI (REWI) are located in the subsurface, we are able to update the background model along the reflection wavepath. In the background velocity inversion, we calculate the background perturbation by a deconvolution process at each frequency. After obtaining the REWI inverted velocity model, a sequential FWI or EWI is needed to obtain a high-resolution model. We determine the validity of our approach using synthetic data generated from a section of the Sigsbee2A model. To further demonstrate the effectiveness of our approach, we test it on an ocean-bottom cable data set from the North Sea. We find that our methodology leads to improved velocity models as evidenced by flatter angle gathers.

Elastic reflection traveltime inversion with pure-wave propagators

Elastic full-waveform inversion (EFWI) updates high-resolution model parameters by minimizing the misfit function between the observed and modeled data. EFWI possesses strong nonlinearity and is likely to converge to a local minimum when the inversion begins with inaccurate initial models. Elastic reflection waveform inversion (ERWI) recovers the low-wavenumber components of P- and S-wave velocities along the “rabbit ear” wavepaths to provide initial velocity models for EFWI. However, every iteration of ERWI requires six times as many forward calculations with elastic-wave equations, which can be computationally expensive. Hence, we have developed a pure-wave reflection waveform inversion (PRWI) approach, which sequentially inverts low-wavenumber components of P- and S-wave velocity models. In our PRWI, we decompose elastic-wave operators into background and perturbed pure-wave parts and derive PRWI gradients using pure-wave operators. The background and perturbed wavefields in PRWI gradients are vector wavefields with a single wave mode. PRWI can remove the high-wavenumber noise caused by S-wave stress decomposition and reduce the computational cost of ERWI by almost 70%. Under the framework of PRWI, we have further developed the pure-wave reflection traveltime inversion (PRTI) approach to alleviate the issue of cycle skipping caused by waveform mismatch. To ensure the recovery of low-wavenumber components, we mute out the contribution of wavefields with small opening angles to PRTI gradients. Numerical examples have demonstrated that our PRTI method can efficiently provide good initial velocity models for EFWI.

Hypocenter Relocation, Determination of Velocity Model and Correction Station in Gede Volcano for December 2017 Data

The distribution of the volcanoes in indonesia gives positive impacts in the form of fertile land where many residents live to grow crops. Besides having positive impacts, there are also negative impacts in the form of natural disasters. Improvement in mitigation of natural disasters is needed to avoid any form of harm and undesired losses. Information on the accuracy of earthquake parameters is an important part of natural disaster mitigation efforts. Hypocenter relocation of earthquakes is a step to improve the accuracy of earthquake parameter information. The relocation of hypocenters earthquake is performed using the Coupled Velocity-Hypocenter method. The research area is Gede volcano which is included as type A active volcanoes. The data were processed using GAD and VELEST 3.3 software resulted in epicenters that were spread around Gede and Pangrango volcanoes. The hypocentre were distributed between -2.5 – 5.2 km depths based on GAD calculated. After relocation using VELEST 3.3, they were distributed between 1.0 – 10.5 km depth. The calculated. The calculated magnitudes of volcanic earthquakes occurred in December 2017 vary between 0.6 to 2.7. the difference between the initial and final velocity models is small. After station correction step, the velocity profiles beneath the most of station is indicated as sedimentary layers, and beneath of the peak station, it is indicated as instrusive rocks.

Rayleigh Wave Dispersion Curve Inversion with the Artificial Bee Colony Algorithm

Rayleigh wave dispersion curve inversion is a non-linear iterative optimization process with multi-parameter and multi-extrema. It is difficult to carry out inversion and reconstruction of stratigraphic parameters quickly and accurately with a single linear or non-linear inversion for the data processing of Rayleigh waves with complex seismic geological conditions. We proposed a new method that combines artificial bee colony algorithm (ABC) and damped least squares algorithm (DLS) to invert Rayleigh wave dispersion curve. First, food sources are initialized in a large scale of the model based on the prior geological information. Then, after three kinds of bee operators (employed bees, onlooker bees and scout bees) transform each other and perform search optimization with several iterations, the targets are converged near the optimal solution to obtain an initial S-wave velocity model. Finally, the final S-wave velocity model is obtained by local optimization of DLS inversion with fast convergence and strong stability. The correctness of the method has been verified by one high-velocity interlayer model, and it was further applied to a real Rayleigh wave dataset. The results show that our method not only absorbs the advantages of ABC global search optimization and strong adaptability, but also makes full use of the advantages of DLS inversion, such as high accuracy and fast convergence speed. The inversion strategy can effectively suppress the inversion falling into local extrema, get rid of the dependence on an initial model, enhance the inversion stability, further improve the convergence speed and inversion accuracy, while has good anti-noise ability.

Relocation of earthquakes in the southern and eastern Alps (Austria, Italy) recorded by the dense, temporary SWATH-D network using a Markov chain Monte Carlo inversion

Abstract. In this study, we analyzed a large seismological dataset from temporary and permanent networks in the southern and eastern Alps to establish high-precision hypocenters and 1-D VP and VP/VS models. The waveform data of a subset of local earthquakes with magnitudes in the range of 1–4.2 ML were recorded by the dense, temporary SWATH-D network and selected stations of the AlpArray network between September 2017 and the end of 2018. The first arrival times of P and S waves of earthquakes are determined by a semi-automatic procedure. We applied a Markov chain Monte Carlo inversion method to simultaneously calculate robust hypocenters, a 1-D velocity model, and station corrections without prior assumptions, such as initial velocity models or earthquake locations. A further advantage of this method is the derivation of the model parameter uncertainties and noise levels of the data. The precision estimates of the localization procedure is checked by inverting a synthetic travel time dataset from a complex 3-D velocity model and by using the real stations and earthquakes geometry. The location accuracy is further investigated by a quarry blast test. The average uncertainties of the locations of the earthquakes are below 500 m in their epicenter and ∼ 1.7 km in depth. The earthquake distribution reveals seismicity in the upper crust (0–20 km), which is characterized by pronounced clusters along the Alpine frontal thrust, e.g., the Friuli-Venetia (FV) region, the Giudicarie–Lessini (GL) and Schio-Vicenza domains, the Austroalpine nappes, and the Inntal area. Some seismicity also occurs along the Periadriatic Fault. The general pattern of seismicity reflects head-on convergence of the Adriatic indenter with the Alpine orogenic crust. The seismicity in the FV and GL regions is deeper than the modeled frontal thrusts, which we interpret as indication for southward propagation of the southern Alpine deformation front (blind thrusts).