We present an extensive dataset of highly accurate absolute travel times and travel-time residuals of teleseismic P waves recorded by the AlpArray Seismic Network and complementary field experiments in the years from 2015 to 2019. The dataset is intended to serve as the basis for teleseismic travel-time tomography of the upper mantle below the greater Alpine region. In addition, the data may be used as constraints in full-waveform inversion of AlpArray recordings. The dataset comprises about 170 000 onsets derived from records filtered to an upper-corner frequency of 0.5 Hz and 214 000 onsets from records filtered to an upper-corner frequency of 0.1 Hz. The high accuracy of absolute and residual travel times was obtained by applying a specially designed combination of automatic picking, waveform cross-correlation and beamforming. Taking travel-time data for individual events, we are able to visualise in detail the wave fronts of teleseismic P waves as they propagate across AlpArray. Variations of distances between isochrons indicate structural perturbations in the mantle below. Travel-time residuals for individual events exhibit spatially coherent patterns that prove to be stable if events of similar epicentral distance and azimuth are considered. When residuals for all available events are stacked, conspicuous areas of negative residuals emerge that indicate the lateral location of subducting slabs beneath the Apennines and the western, central and eastern Alps. Stacking residuals for events from 90

The recently acquired AlpArray dataset provides a fascinating opportunity to extend our knowledge on the structure of the upper mantle below the greater Alpine area, and in particular to answer long-standing questions regarding the orientation and penetration of lithospheric slabs, their connection to the well-studied crustal structure, and their influence on surface processes. AlpArray

Tectonic map of the Alpine chains compiled by

To tackle the challenging research opportunities offered by the AlpArray data with regard to Alpine mantle structure, travel-time tomography of teleseismic body waves certainly belongs to the methods of choice

A method which reaches beyond teleseismic tomography is full waveform inversion (FWI), where entire or partial waveforms are inverted for velocity and also density perturbations

One basic preparatory step for both methods is the determination of travel times. While the need for travel times is obvious for travel-time tomography, teleseismic full waveform inversion can also benefit from travel times in two different ways. First of all, FWI requires a good (ideally 3-D) starting model to ensure that the inversion converges to the global minimum. This model can be obtained from a travel-time tomography. Secondly, since the waveforms are typically band-passed to some (narrow) frequency range, they become monochromatic and waveform matching may suffer from cycle skipping. In such a situation, absolute travel times as additional constraints can help to make waveform matching less ambiguous. Traditionally, arrival times were determined by manual reading of onset times from seismic records, but it is well-known that even manual readings are affected by different reading styles of analysts

One of the first automatic picking procedures that is still used as a fast signal detection method was introduced by

These approaches work well in the context of local to regional scales and have been used for earthquake location and local earthquake tomography methods. In case of similar waveforms, e.g. from earthquake clusters or teleseismic waves, one can improve travel-time measurements by cross-correlation of waveforms

In this paper, we confirm that even advanced techniques of automatic reading of arrival times do not reach the accuracy required by teleseismic travel-time tomography on dense arrays. Using AlpArray data, we demonstrate that an appropriate combination of automatic picking, correlation measurements and beamforming can attain the required accuracy and provide both reliable travel-time residuals and absolute travel times. Applying this technique, we are able to map the propagation of P wave fronts across the AlpArray network and to obtain sufficiently accurate travel-time residuals at all stations of the network. By analysing records of hundreds of teleseismic earthquakes, we can show the coherency and reproducibility of the residuals and study their dependency on event azimuth and frequency. Stacking of event-specific travel-time residuals yields very stable patterns that already indicate the approximate location of high- and low-velocity anomalies in the upper mantle prior to any tomographic inversion. We shall use these time measurements in a later study for performing a teleseismic tomography and full waveform inversion.

Deployment of temporary stations of AlpArray backbone network Z3 was started in 2015 (Fig.

From the available data described above, we assembled records suitable for teleseismic tomography from 974 teleseismic earthquakes with origin times between January 2015 and July 2019 and moment magnitude 5.5 or higher. They encompass waveforms of all stations available in a 5

We produced a high-frequency dataset (

Event distribution of the high-frequency dataset

The distribution of earthquakes of both datasets relative to the Alps strongly varies with azimuth and epicentral distance. Figure

In the following part, we will examine the capability of characteristic functions to resolve travel-time residuals with an accuracy required for high-resolution travel-time tomography. We will summarise the most prominent difficulties and demonstrate how we can benefit from a combination of the AIC algorithm, beamforming and cross-correlation. The resulting multi-stage algorithm combines theoretical onset calculation for spherically symmetric earth models, characteristic functions and various steps of signal cross-correlation and beamforming to obtain absolute as well as relative onsets with an uncertainty of fractions of a second. We also present an empiric way of automatic evaluation of uncertainties which has proven to be extremely robust.

In the following, we will use the quantities absolute travel time at some station,

Highly accurate travel times and travel-time residuals are obtained as follows: first a very low-noise beam trace associated with some selected reference station is constructed by stacking appropriately shifted waveforms of all or selected stations on top of the reference station trace. Then, cross-correlation of the beam trace with all other traces is performed to determine highly accurate time lags relative to the beam. Finally, a travel time is read from the low-noise beam trace itself using automatic picking. Let us denote the beam travel time by

Workflow of the correlation picking algorithm. Solid lines show exemplary waveforms on three different stations. Black solid line shows reference station trace. Dashed lines of waveforms indicate that a waveform has been cut and shifted onto the reference (or beam) trace. Red vertical lines show reference AIC onset times, blue solid lines show corrected onsets.

To obtain the beam itself, we first select a reference station and consider travel-time differences to all other stations,

To get initial P wave onsets as reference times for cross-correlation in records of teleseismic earthquakes we use the HOS/AIC algorithm by

We choose kurtosis, the central moment of order 4, as the characteristic function, which is calculated on a mean removed seismogram in a moving window of

As initial guess, we use theoretical onsets of the phase estimated for a spherically symmetric earth model and calculate characteristic functions in a properly chosen time window around those onsets. The most probable pick (mpp) is defined as the minimum of the AIC of the phase in this window. We select the moving time window a full order of magnitude larger than those typically used for local earthquake onset determination and calculate the most probable onset. Subsequently, an automatic quality is assigned to the onset based on the signal-to-noise ratio and the difference between the latest and earliest possible pick

By definition, using a maximum frequency of 0.5 Hz, we obtain a minimum uncertainty from the earliest possible pick of a full second. Assuming

By visual inspection of selected examples, we validated that the large uncertainties result from difficulties of the characteristic function algorithm to find that part of the first P wave onset which is similar in all traces. The reason for this is the relatively low amplitude of the P onset which is often hidden in site-specific noise. The resulting most probable onsets therefore strongly scatter, confirming estimated uncertainties of about one half of the signal period. Another limitation of the characteristic function approach is the false picking of either later-arriving phases due to the first motion being completely masked by noise or of other signals produced in the vicinity of the station leading to a severe number of outliers and to a time-intensive manual postprocessing.

Although too uncertain to be used for tomographic inversion, the AIC onsets turned out to be more precise than onsets predicted with standard 1D earth models and are therefore better suited as reference times for signal cross-correlation. We found that theoretical phase onsets can differ from actual arrivals by up to some tens of seconds, most probably owing to differences of the true physical properties in the global earth from those of the spherically symmetric earth model, uncertainties in origin time, dispersion processes along the travel path and the use of centroid times of the gCMT catalogue as earthquake origin times (which we want to use for the FWI). The resulting need for large cross-correlation shifts to catch all overlapping phases would involve a high risk of cycle skipping.

An analysis of the necessary shift in travel times predicted by the standard earth model AK135

Especially for lower-magnitude events and high-noise OBS records it may happen for some stations that useful automatic picks are not available. Provided that there are sufficient records left with a reliable automatic pick, we go back to theoretical travel times as correlation reference times which have been corrected by the median time difference between the available automatic picks and the corresponding theoretical travel times. In this way, we still obtain good time references for cross-correlation and avoid omitting all records with unreliable automatic picks. This approach can greatly increase the number of picks obtained with the cross-correlation technique.

Applying a cross-correlation method to improve first arrivals on a large regional array like the AlpArray seismic network is based on the hypothesis of a high similarity of the waveforms of the selected phase across the array. We found this requirement to be satisfied especially well for teleseismic P waves travelling through the mantle but not for PKP phases that penetrate the core. In contrast to mantle P phases, PKP phases are composed of several arrivals which modify the shape of the waveform across the array owing to the different epicentral distances, making signal correlation challenging.

We start by searching for a reference station which represents the waveforms of the entire array best for each single event. The most important criterion for such a station is a continuous operation with high data quality. Therefore, we only consider permanent stations with low noise that were ideally running for the entire time span of events in our database. Also, we want this station to be in a central position in the Alps within the shortest possible distance to all other stations to minimise possible changes in waveform related to large-scale heterogeneities in the global earth (see Sect.

After correlating all stations with the reference station, we align the waveforms according to the time of the maximum of the cross-correlation function. For each event we then form a beam representing the onset of the first P wave phase by stacking the vertical component traces onto the reference station if the maximum cross-correlation coefficient is

Stacking example for

Waveform fit for P arrivals of a

The resulting beam (Fig.

The different role of theoretical, AIC and correlation-corrected travel time is illustrated in Fig.

Estimating an error for automatically determined as well as for manually assigned travel times is a difficult task and can be rather subjective. The concept of earliest and latest possible pick for error estimation uses information of a single trace only and is not suited for travel-time residuals determined by cross-correlation, as the credibility of a time difference to a reference trace associated with a high cross-correlation coefficient is by far higher. This argument also applies for uncertainties of the absolute onsets, if the reference trace is a low-noise beam where the concept of estimating the earliest possible pick as half the signal wavelength is questionable, as the first onset may be clearly identifiable without any risk of missing the first oscillation.

As the beam represents the waveform of the majority of stations, we consider the maximum cross-correlation between station and reference trace as the most important indicator for the relative accuracy of a travel-time difference. However, this assumption only holds if the stations forming the beam trace are evenly distributed in the array and not just representing a part of the array (for example stations close to the reference station). This is vital for the consistency of the full dataset.

Moreover, using the cross-correlation coefficient as a measure of accuracy might lead to a down-weighting of traces of stations influenced by strong local heterogeneities whose waveform does not fit the shape of the reference trace. Fortunately, this matter can be easily identified by looking at spatial distributions of maximum correlation. Affected stations should stand out in comparison to adjacent stations when looking at correlation coefficients averaged over many events (Sect.

A second criterion for a good match of station and reference trace is the shape of the cross-correlation function itself. Hence, we also evaluate the full width at half-maximum (FWHM) of the cross-correlation function. If the FWHM increases, the cross-correlation maximum loses sharpness and the accuracy of a travel-time difference decreases. This approach implies a frequency dependency of travel-time uncertainty, leading to a higher uncertainty for longer periods (and hence wavelengths).

For a parabola fitted to the maximum of the cross-correlation function of the form

The influence of a bad fit owing to signal coda on the cross-correlation coefficient and hence travel-time residual uncertainty is illustrated in Fig.

We categorise travel-time uncertainties into five different classes in steps of 0.1 s ranging from class 0 (best), below 0.1 s, to class 4 (worst), over 0.4 s. Although there is only a lower bound of the uncertainty for class 4, each onset in this class still has a well defined uncertainty and could in principle be used for tomography. Comprising over 170 000 onsets, the travel-time uncertainty distribution of

Pick uncertainty distribution of

The low-frequency dataset

An evaluation of the regional distribution of the median of travel-time uncertainty per station in the

Maps of the median uncertainty of all picks for the high-frequency

The travel-time uncertainty distribution pattern of the lower-frequency dataset

The total number of picks per station is highest on permanent station networks which are distributed most densely in the central Alps and Apennines. Temporal coverage decreases slightly in the western part of the Array due to a delayed start of deployment of temporary stations in this area.

In the following, we examine the variation of travel times and travel-time residuals across the array, study their dependence on event azimuth and in particular delve into the reproducibility and consistency of the travel-time residuals. In particular, the latter is a crucial prerequisite for a successful tomographic inversion.

We start with teleseismic P wave fronts constructed as isolines from the estimated travel times. To further demonstrate the improvement of correlation-corrected travel times over AIC travel times, we show interpolated P wave fronts constructed from both kinds of travel times. As an example, we take the

Travel-time fields of the Eastern Xizang–India Border Region event on 17 November 2017. Onsets from

Wave fronts and travel-time residual patterns of different earthquakes.

To illustrate the varying shapes of the wave fronts crossing the AlpArray network from different azimuths and epicentral distances, we have selected four different earthquakes as representative examples: two with nearly equal back-azimuth (75

Comparing Fig.

A closer examination of travel-time residuals shown in Fig.

A comparable behaviour is observed for events arriving from other back-azimuths. Isoline spacing is again much larger for the more distant event whose waves arrive from a WNW direction. In Fig.

Although travel-time residuals differ with epicentral distance and event back-azimuth as waves move through high- or low-velocity zones from different angles before reaching the surface, there are certain features which tend to occur for a large number of events. The most prominent ones are the negative residuals along the Apenninic and Alpine chain. We stacked residuals for all analysed events to identify regions of stable negative or positive travel-time residuals. It is important to understand that after stacking of the demeaned travel-time residuals, the resulting residuals are relative to an unknown one-dimensional earth model defined by all events used for stacking and not to the standard earth model used to calculate travel-time differences in the first place

Stacked travel-time differences for 370 events of the high-frequency dataset

As the azimuthal distribution of the events in our database is strongly uneven (Sect.

We refrained from binning according to epicentral distances because an examination of residuals of different individual events (Fig.

For an interpretation of mantle features in the residual pattern, we chose to correct the stacked residual patterns for influences of the strongly heterogeneous Alpine crust. We assembled a crustal model from different studies in the greater Alpine region, which we will show in more detail in the upcoming travel-time tomography. To create the model, we start with the generic crustal background model for Europe EuCrust-07

The most striking features of the stacked travel-time residuals after crustal correction are the negative residuals following the Alpine arc from 45

We already showed travel-time residuals for individual wave fields.
To give a more stable impression of the azimuthal variation of the residuals, we stacked three neighbouring 30

Travel-time residuals stacked for 90

This fact is easily demonstrated for the four anomalies defined in the previous section. For example, for waves incident from the northeast (Fig.

Shifting and change of appearance is also observed for the anomalies C and E located between 12 and 15

The western Alpine anomaly (W) shows negative residuals for illumination from the SE that are shifted to the NW (Fig.

Owing to the high noise on the OBS records in the higher-frequency band, we assembled a low-frequency dataset with a maximum frequency of 0.1 Hz. As for the 0.5 Hz dataset, we determined absolute travel times and travel-time residuals using the same procedures as for the high-frequency data (including azimuthal binning, crustal corrections, etc.). We find that the obtained maps of travel-time residuals differ systematically between the considered frequencies (Fig.

To illustrate the differences between both frequency bands, we directly show travel-time residuals determined for the 0.1 Hz dataset (Fig.

There are no striking differences between residuals of

There is a massive increase in the total number of picks for the ocean bottom seismometers reflecting the increase in onsets and onset quality described in Sect.

With large and dense arrays like AlpArray the amount of available records for travel-time measurements may readily accumulate to hundreds of thousands depending on the duration of the deployment. Hence, automatic procedures for determining travel times become mandatory. Moreover, with higher resolution capabilities of such arrays, demands on the accuracy of travel times have also increased, in particular if we want to resolve the correspondingly small travel-time differences between nearby stations. Sophisticated, automatic single-channel picking procedures apparently do not achieve the targeted accuracy for teleseismic travel times. To overcome this problem, measurements of relative time shifts between two traces by cross-correlation are used. They can be automated and are particularly well suited for dense arrays which provide a wealth of similar waveforms. However, they do not provide absolute travel times. For this reason, stacking or beamforming to obtain stable low-noise reference traces is an essential further element in travel-time determination

The uncertainty of a cross-correlation time delay measurement is evidently related to the width of the maximum of the cross-correlation function where the time delay is read off. We measure the full width at half-maximum (FHWM) which is, however, a too conservative estimate of the real error. For this reason, we include the maximum normalised correlation

We do not perform a tomographic inversion of the dataset here (which will be presented in a follow-up paper), but the maps of residual travel times and in particular their azimuthal variations already allow some inferences on the underlying mantle heterogeneities. We focus here on the maps of stacked residuals in Figs.

Teleseismic waves can be considered as planar waves propagating through the subsurface and accumulating travel-time residuals on their way to the surface. Lags or advances of travel time due to velocity perturbations in the mantle are transported by the ray to the surface where they finally appear as travel-time residuals. The lateral shift between the location of the velocity perturbation and its associated travel-time residual at the surface depends on the incidence angle of the ray. This angle is not constant but, owing to the increase in seismic velocity with depth in the earth, decreases successively as the waves approach the surface. Thus, for velocity perturbations at shallow mantle depths and hence subvertical rays we expect small lateral shifts while we expect large lateral shifts for deep seated perturbations. On top of that, variations of the location of travel-time residuals with azimuth allow some inferences on the dip of a velocity perturbation. For example, a dipping slab will produce a maximum travel-time residual for teleseismic waves entering it along the updip direction.

Based on these considerations, we conclude that travel-time residuals that stack coherently over all azimuths must be caused by velocity perturbations located in the shallow mantle. Thus, the anomalies W, C, E and A appearing in the overall stack in Fig.

Besides the areas of negative travel-time residuals, we find large regions of positive residuals in SE France and in the northeastern corner of AlpArray. These anomalies appear in the overall stack of the residuals (Fig.

The fact that the negative anomalies along the Alpine chain in Fig.

Another interesting aspect of our travel-time measurements is their frequency dependence, in particular the differences between the travel-time residuals derived from the 0.5 and the 0.1 Hz dataset. Physical reasons for a frequency dependence of travel-time residuals estimated by cross-correlation can be dispersion due to attenuation

We argue here that the frequency dependence is due to the finite-frequency effect because dispersion due to attenuation predicts disparity patterns which are inconsistent with the observations. The negative differences between the travel-time residuals of the 0.5 and the 0.1 Hz dataset in the region of anomalies C and E as well as the positive ones in the area of the Po plain (Fig.

The dense AlpArray Seismic Network and its complementary deployments offer the unique opportunity to infer mantle structure beneath the greater Alpine region with an unprecedented resolution. However, to benefit fully from the array, absolute travel times and travel-time residuals of high accuracy and consistency are required. We have shown that even very sophisticated automatic picking algorithms based on higher-order statistics and the Akaike information criterion is unable to fulfill this requirement. We demonstrate that, instead, a hybrid approach combining characteristic function picking, waveform cross-correlation and beamforming techniques that takes advantage of the dense array is indeed capable of achieving the required accuracy. Since this hybrid approach is also fully automated, human effort is drastically reduced and the consistency of the generated dataset is ensured by the reproducibility of the automatically determined onsets. Beamforming requires similar waveforms posing demands on array density depending on frequency range. The AlpArray seismic network proved to be sufficiently dense to obtain high waveform correlation at the chosen low-pass-filter frequencies (0.5 and 0.1 Hz). Admitting higher frequencies may require smaller interstation distances to preserve waveform coherency.

The accuracy of travel times and residuals is validated by the fact that they allow a reliable and flawless construction of teleseismic wave fronts in terms of travel-time isochrons. These exhibit small undulations indicating the presence of mantle heterogeneities. The travel-time residuals for individual events show very coherent and reproducible spatial patterns that perfectly fit to these undulations and, although masked by their dependence on illumination incidence and azimuth, already give a glimpse into mantle velocity anomalies, in particular conspicuous slab-like high-velocity structures along the Alpine arc and the Apennines. Studying the azimuthal variations of the residuals provides the first hints of the dip of these anomalies. Even stacks of residuals maps from hundreds of events show distinct, spatially coherent areas of positive and negative residuals and, in particular, reproduce the conspicuous negative residuals. These results indicate the stable presence of mantle heterogeneities in each map of travel-time residuals and allow us to make assertions about the geometry and position of the high- and low-velocity objects below the Alps even before performing a full teleseismic tomography.

Maps of travel-time residuals derived from data filtered to different maximum frequencies show similar patterns but are different with respect to amplitude and sharpness of the anomalies confirming that the sensitivity of waves to heterogeneities depends on wavelength. Hence, datasets of travel time and residuals obtained from differently filtered waveforms cannot be used together in a classical travel-time tomography.

Data are not publicly available. For further information, please contact the corresponding author.

The AlpArray Seismic Network team: György Hetényi, Rafael Abreu, Ivo Allegretti, Maria-Theresia Apoloner, Coralie Aubert, Simon Besançon, Maxime Bès de Berc, Götz Bokelmann, Didier Brunel, Marco Capello, Martina Čarman, Adriano Cavaliere, Jérôme Chèze, Claudio Chiarabba, John Clinton, Glenn Cougoulat, Wayne C. Crawford, Luigia Cristiano, Tibor Czifra, Ezio d'Alema, Stefania Danesi, Romuald Daniel, Anke Dannowski, Iva Dasović, Anne Deschamps, Jean-Xavier Dessa, Cécile Doubre, Sven Egdorf, Ethz-Sed Electronics Lab, Tomislav Fiket, Kasper Fischer, Florian Fuchs, Sigward Funke, Domenico Giardini, Aladino Govoni, Zoltán Gráczer, Gidera Gröschl, Stefan Heimers, Ben Heit, Davorka Herak, Marijan Herak, Johann Huber, Dejan Jarić, Petr Jedlička, Yan Jia, Hélène Jund, Edi Kissling, Stefan Klingen, Bernhard Klotz, Petr Kolínský, Heidrun Kopp, Michael Korn, Josef Kotek, Lothar Kühne, Krešo Kuk, Dietrich Lange, Jürgen Loos, Sara Lovati, Deny Malengros, Lucia Margheriti, Christophe Maron, Xavier Martin, Marco Massa, Francesco Mazzarini, Thomas Meier, Laurent Métral, Irene Molinari, Milena Moretti, Anna Nardi, Jurij Pahor, Anne Paul, Catherine Péquegnat, Daniel Petersen, Damiano Pesaresi, Davide Piccinini, Claudia Piromallo, Thomas Plenefisch, Jaroslava Plomerová, Silvia Pondrelli, Snježan Prevolnik, Roman Racine, Marc Régnier, Miriam Reiss, Joachim Ritter, Georg Rümpker, Simone Salimbeni, Marco Santulin, Werner Scherer, Sven Schippkus, Detlef Schulte-Kortnack, Vesna Šipka, Stefano Solarino, Daniele Spallarossa, Kathrin Spieker, Josip Stipčević, Angelo Strollo, Bálint Süle, Gyöngyvér Szanyi, Eszter Szűcs, Christine Thomas, Martin Thorwart, Frederik Tilmann, Stefan Ueding, Massimiliano Vallocchia, Luděk Vecsey, René Voigt, Joachim Wassermann, Zoltán Wéber, Christian Weidle, Viktor Wesztergom, Gauthier Weyland, Stefan Wiemer, Felix Wolf, David Wolyniec, Thomas Zieke, Mladen Živčić, Helena Žlebčíková. The AlpArray SWATH-D field team: Luigia Cristiano (Freie Universität Berlin, Helmholtz-Zentrum Potsdam Deutsches GeoForschungsZentrum (GFZ), Peter Pilz, Camilla Cattania, Francesco Maccaferri, Angelo Strollo, Günter Asch, Peter Wigger, James Mechie, Karl Otto, Patricia Ritter, Djamil Al-Halbouni, Alexandra Mauerberger, Ariane Siebert, Leonard Grabow, Susanne Hemmleb, Xiaohui Yuan, Thomas Zieke, Martin Haxter, Karl-Heinz Jaeckel, Christoph Sens-Schonfelder (GFZ), Michael Weber, Ludwig Kuhn, Florian Dorgerloh, Stefan Mauerberger, Jan Seidemann (Universität Potsdam), Frederik Tilmann, Rens Hofman (Freie Universität Berlin), Yan Jia, Nikolaus Horn, Helmut Hausmann, Stefan Weginger, Anton Vogelmann (Austria: Zentralanstalt für Meteorologie und Geodynamik (ZAMG)), Claudio Carraro, Corrado Morelli (Südtirol/Bozen: Amt für Geologie und Baustoffprüfung), Günther Walcher, Martin Pernter, Markus Rauch (Civil Protection Bozen), Damiano Pesaresi, Giorgio Duri, Michele Bertoni, Paolo Fabris (Istituto Nazionale di Oceanografia e di Geofisica Sperimentale OGS (CRS Udine)), Andrea Franceschini, Mauro Zambotto, Luca Froner, Marco Garbin (also OGS) (Ufficio Studi Sismici e Geotecnici-Trento).

WF developed the initial idea of the project. MP developed the code and ran the calculations. MP prepared the article with contributions from WF. The AlpArray and AlpArray-Swath D working groups provided the data.

The authors declare that they have no conflict of interest.

Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the special issue “New insights into the tectonic evolution of the Alps and the adjacent orogens”. It is not associated with a conference.

We greatly acknowledge the contributions of the AlpArray temporary network Z3

We want to acknowledge all permanent and other temporary seismic networks used in this study:

The authors would also like to thank all members of the AlpArray Seismic Network team and the AlpArray SWATH-D field team, mentioned in the team list above, as well as the members of the EASI field team: Jaroslava Plomerová, Helena Munzarová, Ludek Vecsey, Petr Jedlicka, Josef Kotek, Irene Bianchi, Maria-Theresia Apoloner, Florian Fuchs, Patrick Ott, Ehsan Qorbani, Katalin Gribovszki, Peter Kolinsky, Peter Jordakiev, Hans Huber, Stefano Solarino, Aladino Govoni, Simone Salimbeni, Lucia Margheriti, Adriano Cavaliere, John Clinton, Roman Racine, Sacha Barman, Robert Tanner, Pascal Graf, Laura Ermert, Anne Obermann, Stefan Hiemer, Meysam Rezaeifar, Edith Korger, Ludwig Auer, Korbinian Sager, György Hetényi, Irene Molinari, Marcus Herrmann, Saulé Zukauskaité, Paula Koelemeijer, Sascha Winterberg. For more information on the team visit

A special thanks to the authors of Matplotlib

Last but not least we want to thank the seismology group of the Ruhr-Universität Bochum, which helped to improve the quality of this work by numerous discussions and contributions.

This research has been supported by the Deutsche Forschungsgemeinschaft (grant no. FR 1146/12-1).

This paper was edited by CharLotte Krawczyk and reviewed by two anonymous referees.