SESolid EarthSESolid Earth1869-9529Copernicus PublicationsGöttingen, Germany10.5194/se-7-425-2016POLENET/LAPNET teleseismic P wave travel time tomography model of the upper mantle beneath northern FennoscandiaSilvennoinenHannaKozlovskaya ElenaKisslingEduardSodankylä Geophysical Observatory, University of Oulu, P.O. Box
3000, 90014 Oulu, FinlandOulu Mining School, University of Oulu,
P.O. Box 3000, 90014 Oulu, FinlandInstitute of Geophysics, ETH
Zürich, Sonneggstrasse 5, 8092 Zürich, SwitzerlandH. Silvennoinen (hanna.silvennoinen@oulu.fi)24March20167242543922August201510September201526February201629February2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://se.copernicus.org/articles/7/425/2016/se-7-425-2016.htmlThe full text article is available as a PDF file from https://se.copernicus.org/articles/7/425/2016/se-7-425-2016.pdf
The POLENET/LAPNET (Polar Earth Observing Network) broadband seismic network was deployed in northern
Fennoscandia (Finland, Sweden, Norway, and Russia) during the third
International Polar Year 2007–2009. The array consisted of roughly 60
seismic stations. In our study, we estimate the 3-D architecture of the upper
mantle beneath the northern Fennoscandian Shield using high-resolution
teleseismic P wave tomography. The P wave tomography method can
complement previous studies in the area by efficiently mapping lateral
velocity variations in the mantle. For this purpose 111 clearly recorded
teleseismic events were selected and the data from the stations hand-picked
and analysed. Our study reveals a highly heterogeneous lithospheric mantle
beneath the northern Fennoscandian Shield though without any large high P
wave velocity area that may indicate the presence of thick depleted
lithospheric “keel”. The most significant feature seen in the velocity
model is a large elongated negative velocity anomaly (up to -3.5 %) in
depth range 100–150 km in the central part of our study area that can be
followed down to a depth of 200 km in some local areas. This low-velocity
area separates three high-velocity regions corresponding to the cratonic
units forming the area.
Introduction
Recently, dense two-dimensional (2-D) networks of broadband seismic
instruments have proved to be a most effective means to study the
three-dimensional (3-D) structure of the lithosphere .
One such network was the POLENET/LAPNET broadband seismic network
(http://www.oulu.fi/sgo-oty/lapnet) deployed in northern Fennoscandia
(Finland, Sweden, Norway, and Russia) during the third International Polar
Year 2007–2009. The project was a part of the POLENET (Polar Earth Observing
Network, http://www.polenet.org) consortium. The network consisted of
37 temporary and 21 permanent seismic stations (Fig. 1). All the stations,
except two temporary stations, were broadband with flat frequency response at
least between 0.01 and 25 Hz. The network registered waveforms from
teleseismic, regional, and local events during May 2007–September 2009. The
average distance between the stations was 70 km.
The map of the study area. (a) Simplified map of orogenies
based on in northern Fennoscandia. The Fennoscandian
crustal block is outlined by a black dotted line and our study area by a
rectangle. (b) The simplified geological map is based on the
1 : 2 000 000 geological map of Fennoscandia . The
seismic stations are shown with black stars, triangles, and dots, and main
geological provinces are named.
One of the main targets of POLENET/LAPNET was to obtain a 3-D seismic model
of the upper mantle in the northern Fennoscandian Shield, in particular,
beneath its Archaean domain (Fig. 1), as the area has not been studied
previously by dense broadband seismic networks. Since 1980–1990, after the
discovery of two large diamond deposits in its north-eastern margin (close to
the city Arkhangelsk in Russia), the area has been considered to be
worth further investigating for diamondiferous kimberlitic rocks. This supposition is based on three
empirically established factors necessary for diamond preservation: Archaean
bedrock, a low geothermal gradient, and a thick lithosphere
.
As shown by , recordings of teleseismic events can be used
to explore the mantle lithosphere to depths of several hundred kilometres,
and through geological interpretations, to assess its potential as a diamond
reservoir. Generally, such modelling of the upper mantle needs to include P
and S wave velocity models estimated by teleseismic body wave tomography,
the position of major boundaries in the crust and upper mantle estimated by
controlled source seismics (CSS), and/or receiver function techniques and
strength and orientation of seismic anisotropy that can be assessed by
shear-wave splitting, receiver function analysis, or ambient noise
tomography. Such a combined seismic model makes it possible to evaluate the
thickness and composition of the mantle lithosphere beneath the study area.
It may further be used for 3-D mapping of lithospheric architecture and
structures responsible for the formation, accumulation, and preservation of
economically significant mineral deposits .
In our study, we explore the 3-D P wave architecture of the upper mantle
beneath the northern Fennoscandian Shield using high-resolution teleseismic
tomography. This technique was used by , who obtained a
3-D P wave velocity model of the upper mantle beneath the south-eastern
Fennoscandian Shield, documenting a deep lithospheric “keel” structure
beneath that region. A similar technique was also used by
and , in order to study the upper mantle structure in
the transition zone between the Precambrian Fennoscandian Shield and the
Palaeozoic lithosphere of Western Europe. used teleseismic
tomography with data of the Swedish National Seismic Network in order to
estimate the upper mantle structure beneath Sweden. The data of the
POLENET/LAPNET experiment were used by and
, who estimated seismic anisotropy in the upper mantle
beneath the POLENET/LAPNET study area. report on variations
of S and P wave velocities and Vp/Vs ratio beneath the POLENET/LAPNET
network using joint inversion of P and S receiver functions. Our study
thus complements the previous studies of the upper mantle in the Fennoscandia
by the body-wave tomography technique. It is also a valuable contribution to the
combined seismic model of the upper mantle beneath the northern Fennoscandian
Shield.
Tectonic setting
Our study area is located in the Fennoscandian Shield in the northern part of
the East European Craton. The area consists of the Archaean Karelian craton
in the eastern part of the study area, subdivided into Karelian and Kola
provinces and the Belomorian Mobile Belt in between, the Svecofennian Norrbotten
craton in the western part, and the Caledonides in the north-western corner
(Fig. 1b).
The Karelian craton started rifting in the Palaeoproterozoic era some 2.5–2.1 Ga
ago. Rifting began in the north-east and led to a separation of cratonic
components by oceans around 2.1 Ga . The rifting event was
followed by two orogenies: the Lapland–Kola orogeny
1.94–1.86 Ga, and the northern part of the Svecofennian
orogeny 1.92–1.89 Ga, (Fig. 1a).
The Lapland-Kola orogeny was preceded by subduction of the new oceanic crust
and by island arc accretion at 1.95–1.91 Ga. The orogeny was a
transpressional continent-continent collision between Kola province and
Karelian province and produced only a minimal amount of juvenile crust
. The juvenile material seems to be dominated by Archaean
crustal origin though mantle contribution cannot be ruled out
. Although in general, the Svecofennian orogeny formed a large
unit of new Palaeoproterozoic crust, similarly to the Lapland–Kola orogeny,
its northern part is mainly comprised of reworked Archaean material. The orogeny
began from the north, where the Karelian craton and Norrbotten Craton
collided at ca. 1.92 Ga .
After these two orogenies, there have been no major tectonic events in our
study area, but there is still some smaller volume magmatism with ages from
Palaeoproterozoic to Devonian . These rocks are often from
relatively deep sources and, though quite diverse in rock types, are some of
the most important carriers of mantle xenoliths .
Data set
The main data set used in this study is the data of the POLENET/LAPNET network
that was recorded during May 2007–September 2009 . For
this study, we selected 96 teleseismic events that were located at epicentral
distances between 30∘ and 90∘ from our station network and
were clearly recorded by most of the stations. In addition to the new data set
from the POLENET/LAPNET network, we included previous data from
the northern part of the SVEKALAPKO network located south
of the POLENET/LAPNET network but still within our study area in our study. These data consist
of 15 events.
Most of the selected events have magnitudes larger than 6.0, but events with
magnitudes larger than 5.5 were also considered in order to improve the
azimuthal coverage. In this way, we obtained a good coverage over all
back azimuths (see Fig. 2); the largest azimuthal gap in events recorded by
POLENET/LAPNET network is smaller than 15∘. The first arrivals of P
waves were picked manually using Seismic Handler software
. We used the Worldwide Standard Seismological Network (WWSSN) short period simulation filter with
maximum displacement magnification at approximately 1 s for easier
comparison between waveforms recorded by different types of sensors. The
travel time residuals were calculated using the iasp91 reference model
. While picking the arrivals, the uncertainty of each
arrival time was also estimated. The 3167 picked arrival times were then
divided into three quality classes based on individual uncertainty estimates.
Table 1 shows the error estimates for each class and the number of arrival
times attributed to different classes. Example waveforms with arrival time
picks of different quality are shown in Fig. 3.
Location of the 111 teleseismic events used as sources for the
tomography study. The centre of our array is shown with the blue star, while
events recorded by the POLENET/LAPNET array are shown with red stars (96 events)
and those recorded by the SVEKALAPKO project with yellow stars (15 events).
Example travel time picks of different quality from the same event
observed at three different stations. The recorded waveforms have been
filtered with the WWSSN short period simulation filter. The travel time
observation from the station OUL was attributed a quality class 1, the one
from station LP62 a quality class 2, and from station SGF a quality class 3.
For OUL, the absolute travel time (Pabs) was picked as well as the
relative travel time (P). For each pick, the timing uncertainty estimate of
the quality class is shown with a grey rectangle.
The travel time data quality classes. The table shows the data
quality classes, the corresponding error estimates, and the number of travel
time residual in the quality class for POLENET/LAPNET data, SVEKALAPKO data,
and the total. The bottom row shows the total number of travel time
residuals over all quality classes for both projects, and finally the total
number of travel time residuals in our database.
The absolute residuals calculated as differences between observed and
theoretical arrival times, using the iasp91 velocity model and ISC catalogue
hypocentre parameters, exhibit large amplitudes while showing only small
azimuthal variations between stations (see Fig. 4a). This is the combined
effect of deep mantle velocity variations located outside our study volume
and of near-source structure or teleseismic hypocentre parameter
uncertainties. To separate these effects from our observations, the average
travel time residual over all stations was calculated for each event and
subtracted from all corresponding residuals. The resulting relative residuals
contain the effects of the velocity variations within our study volume with
reference to the iasp91 model. Figure 4b shows the azimuthal distributions of
the residuals for three selected stations representing different regions
within our study area.
Distribution of travel time residuals relative to standard Earth
model iasp91 at three example stations selected from
different parts of the POLENET/LAPNET network. The observed absolute residuals
are on the left-hand side and the relative residuals after the average over
all stations for each event has been removed are on the right-hand side.
The additional data recorded by SVEKALAPKO network included 15 events
recorded at 31 stations yielding 360 P wave residual times
. To ascertain the compatibility of the two data sets, we
compared the residuals recorded at four seismic stations operational during
both SVEKALAPKO and POLENET/LAPNET projects and found no significant
difference in residual amplitudes or distribution. The comparison figures are
available in the Supplement. The quality class distribution of the SVEKALAPKO
data is shown in Table 1. All seismic stations included in this study are
shown in Fig. 1b and the event distribution is shown in Fig. 2.
The inversion method
In seismic tomography, the basic system of equations relating velocity
perturbations inside the study volume to travel time residuals is
d=Gm,
where d is a data vector composed of travel time residuals, m
is a model parameter vector composed of velocity perturbations in each cell
of the velocity model, and G is a matrix of partial derivatives
defining the coupling between data and model parameters e.g.
. The method searches the velocity perturbations inside a
defined 3-D model in order to explain the observed travel time residuals.
Velocities outside and initial velocities inside the study volumes are
approximated using a reference model. The reference model used in this study
is iasp91 .
For the inversion of the P wave residual data, we used TELINV, a non-linear
tomographic inversion program, originally developed by , who
applied the ACH tomographic method by to lithosphere
investigations at regional scale, and later modified by several authors
e.g.
to include 3-D ray tracing and iterative non-linear inversions.
The initial 3-D model is defined as an orthogonal grid of nodes approximating
the study volume, where the velocity is defined at the node points. The
travel time calculation is based on the 3-D simplex ray-tracing technique
. The inversion problem is formulated as a weighted damped least
square problem and solved using a singular value decomposition technique.
The overall non-linear inversion scheme iteratively inverts the travel time
residuals for velocity changes relative to the 3-D velocity model of the
previous iteration, beginning with the initial reference 3-D model. Each
iteration involves a complete one-step inversion, including both ray tracing
(forward problem) and inversion for an update of the velocity distribution in
the model.
The basic inversion equation for the TELINV code can be written as
mest=(GTWDG+ε2WM)-1GTWDd,
where mest are estimated model parameters,
WD is the weighting matrix of the data, ε2 is
a damping factor, and WM is the smoothing matrix of the
model .
The capabilities of the ray geometry and model parameter grid to resolve the
velocity perturbations can be formally estimated by the resolution matrix
:
R=(GTWDG+ε2WM)-1GTWDG.
The best results for resolution estimates, however, are obtained by synthetic
data testing in combination with hit count, derivative weighted sums, and
resolution matrix .
Model parameterization and regularization
The POLENET/LAPNET study area is approximately 550 km in the east–west
direction and 600 km in the north–south direction. For this study area, we
selected an 80 km × 80 km × 60 km inversion grid. The
horizontal size of the cells is slightly larger than the average distance
between stations to guarantee at least one station for each surface cell
within the network space. Thus, the minimum dimension of a resolvable
anomalous body beneath the POLENET/LAPNET network is approximately 100 km. The
uppermost 60 km is mostly made of crust and is not included in the
high-resolution teleseismic inversion e.g..
In the case of real data, a priori crustal corrections are applied to
travel time residuals (see Sect. 4.2). For the inversion of synthetic data
(see Sect. 4.3) no crustal correction was applied as the crustal layers were
assumed to have velocities according to the iasp91 reference model. The first
inverted layer, below the 60 km crustal layer, is 40 km thick, and all
deeper layers are 60 km thick. For better performance of the ray tracer, the
study volume was surrounded on all sides and at the bottom with large cells
of respective iasp91 velocities that were kept fixed during inversion. The
P wave velocities of the initial model correspond to the iasp91 reference
model.
Because of the uneven ray geometry the kernel matrix (G in Eq. 1)
can be singular and needs to be regularized. The regularization can be done
by selecting an appropriate damping value for the data set (ε2 in
Eq. 2). We analysed the effect of the damping values by performing multiple
inversions with different damping values both with synthetic data and real
data to find the best damping value for our data set. The final damping value
of 100 was selected by investigating the trade-off curve between model and data
variance (see Fig. 5 for the trade-off curve of the real data and Fig. 6 for
examples of the crustal corrected residuals). At the same time, the smallest
singular value to be used (minSV) was estimated using a similar method that
was used with damping. Finally, a value of 70 was selected.
A comparison between data and model variance with different damping
values. The selected damping value was first found using one inversion round
only and the results are shown with diamonds with the damping value used
shown on the right side of the symbols. The number of iterations was also
optimized (dots with the number of iterations marked to their left side) for
the selected damping value of 100. The final number of iterations, 4, is marked
with a red circle. The grey line denotes the overall data uncertainty,
0.017 s2, calculated as the average uncertainty of all observations.
Crustal correction map. Panel (a) shows crustal correction values
for all of the stations. The values are based on a travel time of a vertical
seismic ray travelling through the crustal correction model described in
Sect. 4.2. Panel (b) shows the comparison of the travel times through a
vertical section of the pseudo-3-D crustal correction model used in this study
(black line) and the southern part of the POLAR wide-angle reflection and refraction
profile red line, . The location of the comparison is
shown by a black line in (a).
Crustal correction model
Due to steep incidence angles and subsequent near-lack of cross firing,
teleseismic rays are largely incapable of resolving upper lithosphere
structure such as Moho topography and 3-D crustal velocity variations.
Lateral variation of crustal structure, however, may significantly affect
travel times of teleseismic rays . Hence, when illuminating the structures at upper mantle
depths with high-resolution teleseismic tomography, it is necessary to correct the data a priori for the effect of
crustal structures as documented for southern Finland by
. A new map of the crustal thickness was established by
based on previous and new controlled source seismic
and receiver function results in our study area. This map in combination with
an averaged 1-D velocity–depth function is used in this study to construct a
3-D crustal model for the purpose of correcting travel times for crustal
effects.
The 3-D crustal model used in this study was established by using Bloxer
software (https://wiki.oulu.fi/display/~mpi/Block+model+maintenance).
Bloxer is a piece of software designed to build 3-D block models with different
parameters (density, seismic velocity, magnetization etc.). The Moho depth
map by was imported to Bloxer. All blocks below the
Moho to a maximum model depth of 60 km were given a P wave velocity value
8.05 km s-1 that is the P wave velocity of the uppermost mantle of
the iasp91 reference model.
As our study area does not have enough P wave velocity information available to
estimate a true 3-D distribution of seismic velocities in the crust between
CSS profiles, we used an average P wave velocity for crustal depths defined
from major refraction seismic profiles, namely FENNOLORA
in Sweden, POLAR
in Finland, and PECHENGA-KOSTOMUKSHA
in Russia. A map of crustal correction
times for vertical incident rays arriving at each station is shown in
Fig. 6a. The crustal effect established by Sandoval et al. (2003) for
southern and central Finland has an almost perfect fit with the crustal
effect established in this study for the overlapping southern part of our
study area (south of 65.5∘ N).
To evaluate the travel time through this pseudo-3-D model with averaged
velocity–depth function and locally variable Moho depths in comparison to the
travel time through a local 2-D velocity distribution, derived from a wide-angle
reflection and refraction profile, we calculated the travel times trough our
model to the depth of 70 km along the POLAR profile . This
profile is located near the centre of our study area. From the comparison of
travel times through our crustal correction model and the 2-D model by
, we found the effect of local crustal velocity–depth
variations to be minor compared to the effect of the variation in Moho depth
(see Fig. 6b).
Figure 7 shows an example of crustal corrected travel time residuals for the
same stations that were used in Fig. 4. The crustal corrected residuals for
station LP62 generally exhibit slightly late arrivals for almost all azimuths, suggesting generally lower velocities beneath the station. This contrasts
with the azimuthally pronounced variation of residuals for stations KIF and
MSF that again corresponds with the station location above the boundaries
between high-velocity and low-velocity regions in several layers.
Distribution of crustal corrected travel time residuals at a few
example stations from different parts of the POLENET/LAPNET array.
Two examples of data used to evaluate the lateral resolution of the
inversion results with our data set at depths of (a) 120 km and
(b) 360 km. The evaluation was based on diagonal elements of the
resolution matrix (RDE) in the upper left corner of both subplots but also on
ray coverage, shown in the upper right corner of both subplots, and the three
synthetic test ST1, ST2, and ST3. Red dots and triangles in RDE plots show the
locations of the POLENET/LAPNET and SVEKALAPKO stations, respectively, and the
yellow line shows the boundary of the fairly well-resolved area. The
boundary mostly follows a 0.3 RDE line but has occasionally been adjusted based
on ray coverage and synthetic test results. In the right side of the figure,
west–east vertical sections through the three synthetic models are shown, clarifying
the location of the anomalous bodies in the vertical direction. The resolution
analysis figures at all depths can be found in the Supplement.
Resolution and the synthetic tests
To evaluate the reliability of tomography inversion results, it is necessary
to identify which parts of the resulting model are resolved well and which
parts may contain artefacts caused by the method. In our study we analysed
the resolution by scaling the diagonal elements of the resolution matrix
(RDE) using layer-wise cross firing of rays and a series of synthetic tests,
to find the optimal RDE limit to correspond to fairly well-resolved part of
our model. The resolution assessment scheme is described in detail by
for local earthquake tomography. Instead of the RDE,
used the whole resolution matrix but this is not
necessary for teleseismic tomography since the bias due to predominantly
near-vertical rays and lack of horizontal rays is well known.
The resolution matrix can be used to evaluate the capabilities of the ray
geometry and model parameter grid to resolve the velocity perturbations by
inversion of the travel time data set. The diagonal elements of the resolution
matrix correspond to a posteriori variances of the model parameters
. Therefore, the closer the diagonal element is to a value
of 1.0 and the smaller all off-diagonal elements are, the better the
correspondent model parameter is resolved. Based on this criterion, the
resolution is fair to reasonably good below the station network at all
depths. Figure 8 shows the diagonal elements of the resolution matrix mapped
at two horizontal sections through our study volume. The best resolution is
below the centre of the network in the depth range of less than 360 km (see
Fig. 8). The area with the best resolution moves eastward at larger depths,
corresponding with the region of most cross firing from events in the NE, E, and
SE (see Fig. 8b). This is understandable as the stations in the western part
of the network belong to the permanent Swedish National Seismic Network (SNSN) in Sweden, which
was being updated during the POLENET/LAPNET data acquisition period.
Additionally, the most common recorded back azimuth direction pointed towards
east. As a consequence, the number of rays crossing the cells in the western
part of the study area, especially from south to north, was smaller and the
ray coverage sparser.
Chequerboard test results. Two horizontal sections are shown at the
depths of 120 (a) and 360 km (b) as well as two vertical
sections, one in SN direction (c) and one in EW direction
(d). For each section, both the model used to compute the synthetic
data set and the result after inversion are shown. The model plot of subplot
(a) shows the grid used and the locations of the vertical and
horizontal sections with thicker lines, respectively. The locations of the
anomalies are bordered with rectangles. The areas that were not inverted are
marked with grey blocks.
The sensitivity of our data set to velocity heterogeneities in the upper
mantle was tested with the “chequerboard” test. We constructed a model
consisting of alternating positive and negative anomalies placed regularly
through the study area in both vertical and horizontal directions. The
magnitudes of the P wave velocity anomalies were ±2 % compared to
the iasp91 reference model and the anomaly size was
160 km × 160 km × 120 km or 2×2×2 cells in
the inversion grid. Between the anomalies, we left layers with no velocity
perturbations as suggested by . Examples of the model at
two depths (120 and 360 km) are shown in Fig. 9.
Horizontal sections through our final inversion result. The depth of
each section is marked above each plot. The grey corners show areas that
were crossed by no rays and consecutively were excluded from inversion. The
Roman numbers in (b) are referred to in the text when discussing the
anomalies.
A synthetic data set was calculated using this model and the ray parameters of
the real data set, and the resulting synthetic data set was inverted back to a
velocity model. In the horizontal direction, the anomalies were generally
well recovered below the station network at all depths. Also, in the vertical
direction the recovery was good in the central and eastern parts of the study
area. In the western part, however, there was some smearing especially in the
north–south direction. Figure 9 compares the model used for computing the
synthetic data set and the results after inversion with our selected damping
value at selected depths of 120 and 360 km as well as through two vertical
sections.
Additionally, the resolution was evaluated using synthetic tests with
different structures simulating large-scale anomalies in the upper mantle. We
speculated that there could be such structure in the upper mantle roughly
below the Belomorian Mobile Belt (see Fig. 1). Hence, all three tests have an
anomalous body there and an additional body crossing it diagonally to help us
visualize how anomalies can affect each other. The synthetic test models and
results are shown in Fig. 8. From the results, we can see that while the
anomalies are recovered quite well, there is some leakage both up- and
downward. Similarly to chequerboard tests, we see that the resolution in the
western part of the study area is not as good as in the eastern part. In the
areas with fairly good resolution, leakage may generally extend into the next
layer up or down from the anomalous body (i.e. up to 60 km), while in areas
with poor resolution, the leakage can extend to two layers (up to 120 km). From
the test (Fig. 8) we can also see that we obtain slight positive anomalies
around the negative anomalous body and vice versa, marking an “overswinging”
effect .
The fairly well-resolved regions for each layer of our study area are denoted
in Fig. 8. The regions are mainly based on an RDE value of 0.3, which was selected
as the optimal RDE limit of our model based on ray coverage and synthetic
test results. The regions may have slight deviations from the RDE based on
contracting information from ray coverage and synthetic tests. Similar
analysis for the rest of the depth layers can be found in the Supplement.
Synthetic tests were used to derive the damping parameter and singular value
cut-off appropriate for data error and model parameterization. Results of
those tests yielded a damping parameter of 100 and a minSV of 70 as the best
choice for our data set.
Results
The main results of inversion with the real data are shown in Figs. 10 and
11. Our study revealed a highly heterogeneous lithospheric mantle beneath the
northern Fennoscandian Shield, without any large high P wave velocity area
that might indicate the presence of thick depleted lithospheric keel
revealed beneath the southern part of the shield in Sweden
and beneath the SVEKALAPKO study area , as well as in some
other shield areas e.g.
. The anomalies in the
well-resolved part of our study area are concentrated in the upper part of
the model volume, especially in the layer at 120 km depth (see Fig. 10).
Vertical sections
through our final inversion result. The locations of the sections are shown
as black lines on the map also showing the horizontal grid. The depth grid is
shown in plot of A-A′. The areas where no inversion was done are shown in
grey.
Comparison to previous lithosphere thickness results. The figure has
been modified from and it shows the S wave velocity
structure obtained in that study in comparison to the global S wave velocity
model by . The area outlined with the yellow line shows the
well-resolved part of our model at the depth of the low-velocity anomaly
found in the upper mantle.
While teleseismic tomography is not effective in resolving vertical
variations of seismic velocities, the recent result of joint analysis of P
and S wave receiver functions by revealed velocities
close to those in the iasp91 model at upper mantle depths. Additionally,
their results show that absolute average values of seismic velocities in the
lithospheric mantle beneath southern Finland are generally higher than those
beneath the northern Finland.
In our model, we can recognize several anomalies with higher velocities in
the upper part of the lithospheric mantle (down to depths of about
120–200 km) that spatially correlate with the Karelian, Kola, and Norrbotten
cratons (anomalies I, II, and III, respectively, in Fig. 10b). While only
anomaly I is clearly located within the fairly well-resolved part of the
study area, the high velocities of anomalies II and III are stable results of
inversions obtained with different inversion parameters. We interpret these
higher velocity anomalies as non-reworked fragments of cratonic lithosphere
preserved since the Archaean aeon.
The non-reworked part of the Karelian craton (anomaly I in Fig. 11b) can be
recognized as a higher velocity anomaly (+3 %) from the Moho down to a
depth of about 160 km. Below this depth we observed velocities that are low
compared to the overall velocity at the depth. At the margin of the Kola
craton, we also see a positive (+3.5 %) anomaly roughly down to
200 km. Below the anomaly, velocity values are close to the average
(Fig. 10d). The Norrbotten craton is also seen as a high-velocity anomaly
(+1.5 %), starting from the Moho down to a depth of about 160 km
(Fig. 10).
The most significant feature seen in the velocity model is a large negative
velocity anomaly (up to -3.5 %, anomaly IV in Fig. 10b) in the central
part of our study area that can be followed down to a depth of 160–200 km.
In the upper part of the model this low-velocity area separates the three
high-velocity regions corresponding to the cratons and it extends to the
greater depth below the Karelian craton (Fig. 10b, c, d).
The previous teleseismic tomography results close to the study area are based
on SVEKALAPKO network data in southern and central Finland
and Swedish National Seismic Network (SNSN) data in
Sweden south and southwest of our study area,
respectively. While the comparison of the three models is not entirely
straightforward, taking into account the relative nature of the teleseismic
tomography method, particularly the anomalies obtained by
fit our results very well in the overlapping part of the
study areas. There is some similarity between anomalies obtained in our study
and those revealed by SNSN data , but their comparison is
less reliable due to the smaller overlapping area of both studies and lower
resolution in that part of our study area.
Discussion
As is shown in several previous studies e.g.
, the lithospheric thickness in
the POLENET/LAPNET study area in northern Fennoscandia must be at least roughly
150 km, which makes it unlikely that the upper boundary of the low-velocity
anomaly IV (Fig. 10b) seen in our model at depths shallower than 100 km is
the lithosphere–asthenosphere boundary. In Fig. 12 we show a north–south-directed cross section through a model, based on surface waves by
, together with the location of the low-velocity zone
found in our study.
Our results at the depth of 120 km together with cratonic units in
our study area and locations of the Kola alkaline province, an upper mantle
low-velocity anomaly found in SVEKALAPKO data by and
, and Baltic-Bothnia megashear.
The relative sizes of the anomalies recovered in the upper mantle of the
POLENET/LAPNET study area are relatively large for a shield area; at 120 km
depth, the negative anomaly IV is -3.5 %, while positive anomalies I and
II are +3.5 %, resulting in a total of 7 % maximum velocity
perturbation over the layer. Generally, the velocity variation in shield
areas fits within 4 % variation e.g.
. On the other hand,
large variations in P wave velocity in cratonic areas have been found
recently close to our study area in the upper mantle beneath Sweden by
.
These upper mantle heterogeneities can generally be explained by three major
factors, namely temperature variations, compositional variations, or seismic
anisotropy. In addition, a water content in the mantle can enhance seismic
attenuation and decrease seismic velocities . Seismic
anisotropy beneath the POLENET/LAPNET area was studied independently by
and .
analysed the relative P wave travel time deviations
of selected teleseismic events and lateral variations of shear-wave
splitting. Their results demonstrate that the lithosphere of the study area
consists of distinctive domains of anisotropic structures. One of their
findings was that the patterns related to the Proterozoic–Archaean
transition zone in central Finland continued to the POLENET/LAPNET study
area. The Archaean mantle block interpreted by is
collocated with the western part of the upper mantle high-velocity anomaly I
of our model, with both northern and western boundaries fitting well together.
North of the Archaean mantle block they found an area with fast S wave
direction roughly towards west. This area is roughly collocated with our
low-velocity anomaly IV though the northern boundaries of the domain by
and our anomaly IV does not exhibit as good a fit as the
southern ones do.
analysed the southern half of the POLENET/LAPNET study
area using joint inversion of P wave receiver functions and SKS recordings.
In contrast with the work by they obtained a laterally
averaged depth distribution of the magnitude and azimuthal direction of the
anisotropy in the area but not lateral variations. They found an anisotropic
layer with approximate S wave anisotropy of 2.5 % from below Moho to
approximately 110 km depth with fast direction of 40–60∘, as well as
another, slightly less anisotropic layer with the same azimuthal direction
starting from approximately 220 km depth. Above this lower layer, they
modelled another anisotropic layer with a contrasting azimuthal direction of
110∘.
While we do have reasonably good azimuthal coverage in our data set with
seismic energy arriving from all directions, the events from a roughly
east–west direction have on average slightly larger distances, and hence
smaller incidence angles, than events from a south–north direction. This could
cause the anisotropy to affect the results of the tomography that could
explain some portion of the lateral velocity variations in our model.
shows that these effects are likely to concentrate on less
well-resolved areas at depths larger than 200 km but in our case they might
have some effect in the uppermost mantle depths, too, as
estimated the anisotropy beneath our uppermost mantle
anomaly IV to have a contrasting fast direction of 55∘ from north when
compared with the roughly 0∘ of the units both north and south of it.
In summary, we may conclude that at least in some regions our – for the mantle
lithosphere – significantly strong lateral velocity anomalies could in part
be attributed to seismic anisotropy.
As shown by , , and ,
compositional variations between mantle peridotites that are depleted in Fe and more fertile can explain up to 1–2 % velocity anomalies of P wave
velocities. In order to explain larger anomalies, one would need the combined
effect of major element chemistry and temperature as shown by
. That is why the lowered seismic velocities in the
lithospheric mantle of the central part of our study area (anomaly IV
compared to anomalies I, II and III) are probably most due to the combined
effect of anisotropy, a more fertile composition, and a generally higher
temperature.
The low-velocity zone spatially overlaps in the east with the Kola
alkaline province (see Fig. 13), in which alkaline magmas have intruded the
crust during several metasomatic events.
proposed that at least one of them was ancient while the
latest occurred in the Devonian. The same events would also result in extensive
reworking and refertilization of the originally depleted Archaean mantle keel
while the latest Devonian magmatism would also explain higher mantle
temperatures.
On the other hand, the area of low seismic velocities in the central part of
the network separates Norrbotten, Kola and Karelian cratons from each other
and is spatially correlating with the northern part of the 1.9–1.8 Ga N–S
trending Baltic-Bothnia Megashear (BBMS) stretching below the Baltic Sea and
the Gulf of Bothnia and continuing to the Caledonides in Norway see
Fig. 13. The northern part of the BBMS was later named
Pajala shear zone by . This shear zone is about 40 km wide,
represented by a complex set of N–S striking shear and thrust zones.
proposed that the Pajala shear zone originated as a
divergent plate boundary due to the collision of two Archaean continental
units (Norrbotten and Karelian), and it was multiply reactivated after the
continental collision with both lateral and vertical movements before
1.83 Ga. As the horizontal sections in the upper mantle obtained in our
modelling suggest a depth distribution of the velocity perturbations beneath
the megashear similar to that revealed by and
(see Fig. 13), we suggest that the Pajala shear zone may
continue to the south beneath the Gulf of Bothnia as was originally proposed
by .
However, the metasomatic processes and refertilization of the upper mantle in
the Palaeoproterozoic alone would not produce such low seismic velocities as
we observed in our model and combined effect of temperature and composition
would be necessary . Thus, we may speculate that since
the Palaeoproterozoic the whole BBMS was reactivated by a later
tectonothermal event (or multiple events), during which the cratonic
lithosphere was partly destroyed. The time of those events is not clear, but
they could have occurred the same time with the Paleozoic post-collisional alkaline
magmatism in the Kola alkaline province caused by plume activity
.
The Supplement related to this article is available online at doi:10.5194/se-7-425-2016-supplement.
Acknowledgements
The authors would like to thank the staff of the Sodankylä geophysical
observatory, Institute of Geophysics of ETH Zürich, and the Institute of
Geophysics of the Academy of Sciences of the Czech Republic for all technical
help during this study. Special thanks to Helena Munzarova, who provided us
the ray distribution plots of Fig. 8. Hanna Silvennoinen would also like to
thank the Finnish Academy of Science and Letters and Apteekin rahasto for funding
her during her part of this study. The reviews of J. R. R. Ritter and
an anonymous referee led to substantial improvements in this manuscript. The
POLENET/LAPNET project is part of the International Polar Year 2007–2009 and
a part of the POLENET consortium. Equipment for the temporary deployment was
provided by RESIF-SISMOB, FOSFORE, EOST-IPG Strasbourg Equipe seismologie
(France), Seismic pool (MOBNET) of the Geophysical Institute of the Czech
Academy of Sciences (Czech Republic), the Sodankylä Geophysical Observatory
(Finland), the Institute of Geosphere Dynamics of RAS (Russia), the Institute
of Geophysics ETH Zürich (Switzerland), the Institute of Geodesy and
Geophysics, the Vienna University of Technology (Austria), and the University
of Leeds (UK). The study was financed by the Academy of Finland (grant
no. 122762), University of Oulu (Finland), FBEGDY program of the Agence
Nationale de la Recherche, Institut Paul Emil Victor (France), ILP
(International Lithosphere Program) task force VIII, grant no. IAA300120709
of the Grant Agency of the Czech Academy of Sciences, and the Russian Academy
of Sciences (programs no. 5 and no. 9). The POLENET/LAPNET working group
members are Elena Kozlovskaya, Helle Pedersen, Jaroslava Plomerová,
Ulrich Achauer, Eduard Kissling, Irina Sanina, Teppo Jämsén,
Hanna Silvennoinen, Catherine Pequegnat, Riitta Hurskainen, Robert Guiguet,
Helmut Hausmann, Petr Jedlicka, Igor Aleshin, Ekaterina Bourova,
Reynir Bodvarsson, Evald Brückl, Tuna Eken, Pekka Heikkinen,
Gregory Houseman, Helge Johnsen, Elena Kremenetskaya, Kari Komminaho,
Helena Munzarova, Roland Roberts, Bohuslav Ruzek, Hossein Shomali,
Johannes Schweitzer, Artem Shaumyan, Ludek Vecsey, and Sergei Volosov.
Edited by: J. Plomerova
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