ALBERT

Description: In probabilistic Bayesian inversions, data uncertainty is a crucial parameter for quantifying the uncertainties and correlations of the resulting model parameters or, in transdimensional approaches, even the complexity of the model. However, in many geophysical inference problems it is poorly known. Therefore, it is common practice to allow the data uncertainty itself to be a parameter to be determined. Although in principle any arbitrary uncertainty distribution can be assumed, Gaussian distributions whose standard deviation is then the unknown parameter to be estimated are the usual choice. In this special case, the paper demonstrates that a simple analytical integration is sufficient to marginalise out this uncertainty parameter, reducing the complexity of the model space without compromising the accuracy of the posterior model probability distribution. However, it is well known that the distribution of geophysical measurement errors, although superficially similar to a Gaussian distribution, typically contains more frequent samples along the tail of the distribution, so-called outliers. In linearized inversions these are often removed in subsequent iterations based on some threshold criterion, but in Markov chain Monte Carlo (McMC) inversions this approach is not possible as they rely on the likelihood ratios, which cannot be formed if the number of data points varies between the steps of the Markov chain. The flexibility to define the data error probability distribution in McMC can be exploited in order to account for this pattern of uncertainties in a natural way, without having to make arbitrary choices regarding residual thresholds. In particular, we can regard the data uncertainty distribution as a mixture between a Gaussian distribution, which represent valid measurements with some measurement error, and a uniform distribution, which represents invalid measurements. The relative balance between them is an unknown parameter to be estimated alongside the standard deviation of the Gauss distribution. For each data point, the algorithm can then assign a probability to be an outlier, and the influence of each data point will be effectively downgraded according to its probability to be an outlier. Furthermore, this assignment can change as the McMC search is exploring different parts of the model space. The approach is demonstrated with both synthetic and real tomography examples. In a synthetic test, the proposed mixed measurement error distribution allows recovery of the underlying model even in the presence of 6 per cent outliers, which completely destroy the ability of a regular McMC or linear search to provide a meaningful image. Applied to an actual ambient noise tomography study based on automatically picked dispersion curves, the resulting model is shown to be much more consistent for different data sets, which differ in the applied quality criteria, while retaining the ability to recover strong anomalies in selected parts of the model.

Description: LITHOS-CAPP is the German contribution to the international ScanArray experiment. ScanArray is an array of broadband seismometers with which we aim to study the lithosphere and upper mantle beneath the Scandinavian Mountains and the Baltic Shield. LITHOS-CAPP contributed 20 broadband recording stations from September 2014 to October 2016, 10 in Sweden and 10 in Finland, continuously recordings at 100 samples per second. The stations were deployed by the KIT Geophysical Institute and GFZ section 2.4 (seismology). They form part of the temporary network ScanArrayCore (FDSN network code 1G 2012-2017). This data publication contains the original log-files of the recorders.

Description: LITHOS-CAPP is the German contribution to the international ScanArray experiment. ScanArray is an array of broadband seismometers with which we aim to study the lithosphere and upper mantle beneath the Scandinavian Mountains and the Baltic Shield. LITHOS-CAPP contributed 20 broadband recording stations from September 2014 to October 2016, 10 in Sweden and 10 in Finland, continuously recordings at 100 samples per second. The stations were deployed by the KIT Geophysical Institute and GFZ section 2.4 (seismology). They form part of the temporary network ScanArrayCore (FDSN network code 1G 2012-2017)

Description: The ScanArray international collaborative program acquired broadband seismological data at 192 locations in the Baltic Shield during the period between 2012 and 2017. The main objective of the program is to provide seismological constraints on the structure of the lithospheric crust and mantle as well as the sublithospheric upper mantle. The new information will be applied to studies of how the lithospheric and deep structure affect observed fast topographic change and geological‐tectonic evolution of the region. The program also provides new information on local seismicity, focal mechanisms, and seismic noise. The recordings are generally of very high quality and are used for analysis by various seismological methods, including P‐ and S‐wave receiver functions for the crust and upper mantle, surface wave and ambient noise inversion for seismic velocity, body‐wave P‐ and S‐wave tomography for upper mantle velocity structure using ray and finite frequency methods, and shear‐wave splitting measurements for obtaining bulk anisotropy of the upper and lowermost mantle. Here, we provide a short overview of the data acquisition and initial analysis of the new data, together with an example of integrated seismological results obtained by the project group along a representative ∼1800‐km‐long profile across most of the tectonic provinces in the Baltic Shield between Denmark and the North Cape. The first models support a subdivision of the Paleoproterozoic Svecofennian province into three domains, where the highest topography of the Scandes mountain range in Norway along the Atlantic Coast has developed solely in the southern and northern domains, whereas the topography is more subdued in the central domain.

Description: The Scandes mountain range along the western rim of the Archean Baltic craton with elevation up to 2500 m forms an exceptional setting as the orogeny terminated 420 Ma ago and the Caledonides were deeply eroded afterwards. Since this region lacks recent compressional tectonic forces, a comprehensive explanation for the topography, which shows north-south and lateral variations along the Scandes, is missing. In my dissertation, I use earthquake surface waves and ambient noise to image the crustal and mantle structure aiming to provide new clues about the topography’s origin. The focus is also on exploring structural differences between the various tectonic domains. Here, I benefit from the seismic recordings by the ScanArray network supplemented by permanent and previous projects, distributed over entire Scandinavia. First, I performed a beamforming of Rayleigh surface waves which yielded average phase velocities for the study region and several of its sub-regions. An unusual 360° or sin(1θ) phase velocity variation with propagation azimuth is observed in northern Scandinavia and southern Norway/Sweden but not in the central area. For periods 〉35 s, a 5% variation between the maximum and minimum velocities was measured for opposite backazimuths of 120° and 300°, respectively. Such a variation is incompatible with the intrinsic azimuthal anisotropy and the path average approximation made in tomography. I assumed an eastward dipping lithosphere-asthenosphere boundary (LAB) to be the causing structure, inspired by some preliminary velocity models and observations made in previous studies. To test this hypothesis, I carried out 2D full-waveform modeling of the Rayleigh wave propagation. The models include a steep gradient at the LAB in combination with a pronounced reduction in the shear velocity below the LAB. The synthetic results are consistent with the observations: Faster phase velocities are obtained for propagation towards the thinning lithosphere, and lower ones for propagation in the direction of deepening LAB. The interference of reflected surface wave energy at the steep LAB with the forward propagating fundamental mode probably causes this peculiar effect. Second, the joint inversion of Rayleigh surface waves and ambient noise provides structural imaging down to 250 km depth. Resultant from my velocity model, I derive a new crustal model from which maps of the Moho depth as well as of the high-density lower crustal layer (LCL) are obtained. I observe crustal thickening from west to east below the Precambrian low-topography terranes, which is mainly a consequence of eastward thickening of the LCL. In contrast to the southern Scandes, with the overall highest topography (2,500 m), a crustal root below the northern Scandes (max. 2,100 m) is seen which diminish towards the central Scandes (max. 1,000 m). The LAB below the Scandes is deepening from west to east. The sharp steps in the LAB and strong velocity reductions both in the south (90–120 km LAB depth with 5.5% Vsv contrast) and the north (150 km LAB depth with 9% Vsv contrast) surprisingly correlate with the Caledonian mountain front. Whereas smoother laterally varying structures (150–170 km LAB depth with 4% Vsv contrast) are found below the central Scandes. The correlation of the lithosphere thickening with the Caledonian front might be related to metasomatism as result of the orogeny and/or the passive margin rifting. In Precambrian Scandinavia, low-velocity areas below 150 km depth are observed beneath the Archean Karelia craton in northern Finland. At mantle depth, the Paleoproterozoic Norrbotten craton can be separated from the Karelia craton, Caledonides and Paleoproterozic Svecofennian likely due to different degrees of metasomatism. Based on the structural differences, I conclude that different mechanisms are responsible for the compensation of the topography. The northern Scandes are likely compensated by a combined Airy-Pratt isostasy as implied by low-density rocks in the shallow crust (〈15 km depth), a high-density layer in the deep crust (〉10 km LCL thickness) and the mountain root. The strongly reduced velocities at sub-lithospheric depth additionally suggest an uplift contribution from the upper mantle. Since the southern Scandes lacks these crustal attributes, they experience mainly mantle-driven buoyancy. In both cases, however, I assume the influence of small-scale edge-driven convections (EDC) that can arise at sharp LAB gradients. EDC emplaces thereby low-density material at sub-lithospheric depths by the upwelling of hot asthenosphere which implies additional buoyancy of the lithosphere. Moreover, the lateral topography differences along the Scandes can be explained by varying EDC cell dimensions. Primarily, Pratt isostasy compensates the low topography central Scandes, but a contribution from dynamic support could act as well. Ultimately, I see the strong gradients at the LAB below the southern and northern Scandes as the cause of the observed 1θ phase velocity variation. While the smoother velocity structure in the central study area explains the absence of the 1θ effect.

Description: The data set consists of dispersion curves and the corresponding 2D phase velocity maps based on earthquake generated Rayleigh surface waves and ambient noise, as well as the resultant shear-wave velocity model for entire Scandinavia (Norway, Sweden and Finland). We resolved the crust and mantle to 250 km depth to provide new insight into the maintenance of the Paleozoic Scandes mountain range and the lithospheric architecture of the Precambrian Baltic Shield (Mauerberger et al., in review). For this study, we use the virtual ScanArray network which consists of more than 220 seismic stations of the following contributing networks: The ScanArray Core (1G network, Thybo et al., 2012) consists of 72 broadband instruments which were operated by the ScanArray consortium (Thybo et al., 2021) between 2013-2017. We also used 28 stations from the NEONOR2 (2D network), 20 stations from the SCANLIPS3D (ZR network; England et al., 2015), 72 permanent stations from the Swedish National Seismic Network (SNSN; UP network; SNSN 1904) as well as further 35 permanent stations from the Finnish (HE and FN networks), Danish (DK network), Norwegian (NO network (NORSAR, 1971); NS (University of Bergen, 1982)) and international IU network (ALS/USGS, 1988). Since the exact operation times of the different temporary networks differ, we analyse data between 2014 and 2016, when most of the stations were operational. The pre-processing of the data involved the removal of a linear trend, application of a band-pass filter between 0.5 s and 200 s, downsampling to 5 Hz and deconvolution of the instrument response to obtain velocity seismograms. We also corrected for the misorientations stated in Grund et al., 2017.

Description: In probabilistic Bayesian inversions, data uncertainty is a crucial parameter for quantifying the uncertainties and correlations of the resulting model parameters or, in transdimensional approaches, even the complexity of the model. However, in many geophysical inference problems it is poorly known. Therefore, it is common practice to allow the data uncertainty itself to be a parameter to be determined. Although in principle any arbitrary uncertainty distribution can be assumed, Gaussian distributions whose standard deviation is then the unknown parameter to be estimated are the usual choice. In this special case, the paper demonstrates that a simple analytical integration is sufficient to marginalise out this uncertainty parameter, reducing the complexity of the model space without compromising the accuracy of the posterior model probability distribution. However, it is well known that the distribution of geophysical measurement errors, although superficially similar to a Gaussian distribution, typically contains more frequent samples along the tail of the distribution, so-called outliers. In linearized inversions these are often removed in subsequent iterations based on some threshold criterion, but in Markov chain Monte Carlo (McMC) inversions this approach is not possible as they rely on the likelihood ratios, which cannot be formed if the number of data points varies between the steps of the Markov chain. The flexibility to define the data error probability distribution in McMC can be exploited in order to account for this pattern of uncertainties in a natural way, without having to make arbitrary choices regarding residual thresholds. In particular, we can regard the data uncertainty distribution as a mixture between a Gaussian distribution, which represent valid measurements with some measurement error, and a uniform distribution, which represents invalid measurements. The relative balance between them is an unknown parameter to be estimated alongside the standard deviation of the Gauss distribution. For each data point, the algorithm can then assign a probability to be an outlier, and the influence of each data point will be effectively downgraded according to its probability to be an outlier. Furthermore, this assignment can change as the McMC search is exploring different parts of the model space. The approach is demonstrated with both synthetic and real tomography examples. In a synthetic test, the proposed mixed measurement error distribution allows recovery of the underlying model even in the presence of 6 per cent outliers, which completely destroy the ability of a regular McMC or linear search to provide a meaningful image. Applied to an actual ambient noise tomography study based on automatically picked dispersion curves, the resulting model is shown to be much more consistent for different data sets, which differ in the applied quality criteria, while retaining the ability to recover strong anomalies in selected parts of the model.

Description: We use the recently deployed ScanArray network of broad-band stations covering most of Norway and Sweden as well as parts of Finland to analyse the propagation of Rayleigh waves in Scandinavia. Applying an array beamforming technique to teleseismic records from ScanArray and permanent stations in the study region, in total 159 stations with a typical station distance of about 70 km, we obtain phase velocities for three subregions, which collectively cover most of Scandinavia (excluding southern Norway). The average phase dispersion curves are similar for all three subregions. They resemble the dispersion previously observed for the South Baltic craton and are about 1 per cent slower than the North Baltic shield phase velocities for periods between 40 and 80 s. However, a remarkable sin(1θ) phase velocity variation with azimuth is observed for periods 〉35 s with a 5 per cent deviation between the maximum and minimum velocities, more than the overall lateral variation in average velocity. Such a variation, which is incompatible with seismic anisotropy, occurs in northern Scandinavia and southern Norway/Sweden but not in the central study area. The maximum and minimum velocities were measured for backazimuths of 120° and 300°, respectively. These directions are perpendicular to a step in the lithosphere–asthenosphere boundary (LAB) inferred by previous studies in southern Norway/Sweden, suggesting a relation to large lithospheric heterogeneity. In order to test this hypothesis, we carried out 2-D full-waveform modeling of Rayleigh wave propagation in synthetic models which incorporate a steep gradient in the LAB in combination with a pronounced reduction in the shear velocity below the LAB. This setup reproduces the observations qualitatively, and results in higher phase velocities for propagation in the direction of shallowing LAB, and lower ones for propagation in the direction of deepening LAB, probably due to the interference of forward scattered and reflected surface wave energy with the fundamental mode. Therefore, the reduction in lithospheric thickness towards southern Norway in the south, and towards the Atlantic ocean in the north provide a plausible explanation for the observed azimuthal variations.

Description: We present a new 3D shear-wave velocity model and Moho map of Scandinavia, which is based on the inversion of the merged phase dispersion curves from ambient noise and earthquake-generated Rayleigh waves. A classic two step inversion scheme is used where first maps of phase velocities at different periods are derived, and then a 1D transdimensional Bayesian method is applied to determine the VSV-depth structure. We assess the question of what compensates for the unusual high Scandes mountains and aim to identify the different tectonic domains of the adjacent continental lithosphere (Baltic Shield). While the southern Scandes lacks a pronounced crustal root, we observe a crustal root below the northern Scandes that is decreasing towards the central Scandes. A ∼10 km thick high-density lower crustal layer is present below the northern Scandes and generally thickening to the east below the Baltic Shield. The lithosphere-asthenosphere boundary (LAB) below the Scandes is deepening as well from west to east with a sharp step and a strong VSV decrease with depth of 9% in the north and of 5.5% in the south. The LAB of the thinner lithosphere is at 150 km depth in the north and varies from 90 to 120 km depth in the south. Both LAB steps coincide with the mountain front. The central area shows rather smoothly varying structures (170 km LAB depth, −4% VSV with depth) towards the east and no clear spatial match with the front. We infer therefore distinct uplift mechanisms along the Scandes. The southern Scandes might sustain their topography due to dynamic support from the mantle, while the northern Scandes experience both crustal and mantle lithosphere isostasy. In both cases, we suspect a dynamic support from small-scale edge-driven convection that developed at the sharp lithospheric steps. Beneath the Archean Karelia craton in northern Finland, we find low-velocity areas below 150 km depth while a 250 km deep lithospheric keel is imaged below the Paleoproterozic southern Finland. The Norrbotten craton in northern Sweden can be identified at mantle depths as a unit different from the Karelia craton, Scandes and Paleoproterozic central Sweden.

Description: Radial anisotropy (RA) in the upper mantle of the Fennoscandian Shield is analyzed by joint inversion of Love and Rayleigh wave phase velocities measured from recordings of teleseismic events at the ScanArray network. The phase velocities are measured by beamforming using three geographical subsets of the network as well as the full network. We analyze how different procedures for determining the phase velocities influence the final result and uncertainty. Joint inversion of the phase velocities in the period range 22–100 s reveals the presence of similar RA in the three subregions, with an average ξ value of about 1.05 in the subcrustal lithosphere down to at least 200 km depth. This corresponds to SH waves faster than SV by 2%–3%, a value very similar to those found in other continental regions. Considering this anisotropy together with other observables pertaining to seismic anisotropy in the area, we cannot propose a unique model satisfying all data. We can show, however, in which conditions different types of olivine crystallographic preferred orientations (CPOs) commonly observed in natural samples are compatible with the observations. CPO types associated with the preferred orientation of the a-axis, in particular the common A-type CPO, require a-axes dipping not more than 25° from the horizontal plane to explain our observations. AG-type CPO, characterized by preferred orientation of the b-axis and occurring in particular in compressional settings, can be considered as an interesting alternative interpretation of continental lithospheric anisotropy, provided the olivine b-axis is dipping by at least 60°.

Description: Radial anisotropy (RA) in the upper mantle of the Fennoscandian Shield is analyzed by joint inversion of Love and Rayleigh wave phase velocities measured from recordings of teleseismic events at the ScanArray network. The phase velocities are measured by beamforming using three geographical subsets of the network as well as the full network. We analyze how different procedures for determining the phase velocities influence the final result and uncertainty. Joint inversion of the phase velocities in the period range 22–100 s reveals the presence of similar RA in the three subregions, with an average ξ value of about 1.05 in the subcrustal lithosphere down to at least 200 km depth. This corresponds to SH waves faster than SV by 2%–3%, a value very similar to those found in other continental regions. Considering this anisotropy together with other observables pertaining to seismic anisotropy in the area, we cannot propose a unique model satisfying all data. We can show, however, in which conditions different types of olivine crystallographic preferred orientations (CPOs) commonly observed in natural samples are compatible with the observations. CPO types associated with the preferred orientation of the a-axis, in particular the common A-type CPO, require a-axes dipping not more than 25° from the horizontal plane to explain our observations. AG-type CPO, characterized by preferred orientation of the b-axis and occurring in particular in compressional settings, can be considered as an interesting alternative interpretation of continental lithospheric anisotropy, provided the olivine b-axis is dipping by at least 60°.