SESolid EarthSESolid Earth1869-9529Copernicus PublicationsGöttingen, Germany10.5194/se-8-671-2017Electric resistivity and seismic refraction tomography: a challenging joint underwater survey at Äspö Hard Rock LaboratoryRonczkaMathiasmathias.ronczka@tg.lth.seHellmanKristoferGüntherThomashttps://orcid.org/0000-0001-5409-0273WisénRogerDahlinTorleifEngineering Geology, Lund University, Lund, SwedenLeibniz Institute for Applied Geophysics, Hanover, GermanyMathias Ronczka (mathias.ronczka@tg.lth.se)13June20178367168214November201622November201613April20175May2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://se.copernicus.org/articles/8/671/2017/se-8-671-2017.htmlThe full text article is available as a PDF file from https://se.copernicus.org/articles/8/671/2017/se-8-671-2017.pdf
Tunnelling below water passages is a challenging task in terms of planning,
pre-investigation and construction. Fracture zones in the underlying bedrock
lead to low rock quality and thus reduced stability. For natural reasons, they
tend to be more frequent at water passages. Ground investigations that
provide information on the subsurface are necessary prior to the construction
phase, but these can be logistically difficult. Geophysics can help close the gaps
between local point information by producing subsurface images. An approach
that combines seismic refraction tomography and electrical resistivity
tomography has been tested at the Äspö Hard Rock Laboratory (HRL).
The aim was to detect fracture zones in a well-known but logistically challenging area
from a measuring perspective.
The presented surveys cover a water passage along part of a tunnel that
connects surface facilities with an underground test laboratory. The tunnel
is approximately 100 m below and 20 m east of the survey line and gives
evidence for one major and several minor fracture zones. The geological and
general test site conditions, e.g. with strong power line noise from the
nearby nuclear power plant, are challenging for geophysical measurements.
Co-located positions for seismic and ERT sensors and source positions are
used on the 450 m underwater section of the 700 m profile.
Because of a large transition zone that appeared in the ERT result and the
missing coverage of the seismic data, fracture zones at the southern and
northern parts of the underwater passage cannot be detected by separated
inversion. Synthetic studies show that significant three-dimensional (3-D)
artefacts occur in the ERT model that even exceed the positioning errors of
underwater electrodes. The model coverage is closely connected to the
resolution and can be used to display the model uncertainty by introducing
thresholds to fade-out regions of medium and low resolution. A structural
coupling cooperative inversion approach is able to image the northern
fracture zone successfully. In addition, previously unknown sedimentary
deposits with a significantly large thickness are detected in the otherwise
unusually well-documented geological environment. The results significantly
improve the imaging of some geologic features, which would have been undetected
or misinterpreted otherwise, and combines the images by means of cluster
analysis into a conceptual subsurface model.
Introduction
Underground structures have become an increasingly important part of modern
infrastructure, and the possibilities to improve construction approaches have
attracted much attention. With constantly reduced space for new structures on
the surface, underground space is attractive for use in the transportation
sector to challenge the increase in traffic in and around cities or as
underground storage facilities. Geological uncertainties increase the risk of
delays and thus the costs of underground construction. A detailed subsurface
model is essential for reducing risks and for a successful project. In order to ensure a smooth construction
phase, a
critical point is to locate
weak zones, especially those that can generate a large inflow of
water, causing problems and slowing down the construction progress. Except for
southwestern Scania and the islands Gotland and Öland, crystalline
bedrock is the dominant material for underground infrastructure
construction in Sweden. For these geologic conditions, weakness zones that
are important for the underground design are normally indicated by dry,
water-bearing or sediment-filled fractures.
Two methods for site investigation in crystalline bedrock are drilling and
surface-based or borehole geophysics. Drilling is often the first choice
since it provides high resolution and accuracy at any given depth. Nevertheless,
drilling is expensive and delivers only point information. Therefore,
surface-based geophysical methods have gained more attention, since they
provide
continuous models that reveal the extreme points and an opportunity for
extrapolation into 2-D or 3-D space. The usage of geophysics has
increased lately to obtain more continuous and comprehensive subsurface
models. Recently, the Swedish transportation authority has provided funding
for research in an increasing number of projects with the aim of developing site
investigations based on additional geophysical measurements for mapping
the structure and quality of the rock mass.
report a case in which electrical resistivity tomography (ERT)
has been used successfully to map weak and permeable rock in an onshore railway tunnel project in Sweden. used different
geoelectrical applications to detect weak zones of approx. 40 m × 40 m
during underground construction. In the Norwegian
R & D project “Tunnels for the citizens”, which was funded by the road
administration, several publications
report
that elaborate site investigations are important in a controlled tunnelling
process, but also that further studies are needed.
assessed ERT, refraction seismics, very low frequency (VLF) electromagnetics
and the AMAGER method (aeromagnetic and geomorphological relations). They
concluded that these are all able to locate fracture zones and state that
ERT is able to give more hints to the fracture width, dip and depth extent
compared to the other methods used. They also suggest a quantitative rock
quality measure on the basis of resistivity values. Refraction seismics has
long been an established method for information on fracture width
and seismic p-wave velocity; the latter has an obvious coupling to the
hardness of the rock and hence to rock quality . During
an investigation for a road tunnel in Norway, found
that 2-D resistivity and refraction seismics are the most suitable geophysical
methods, while ERT gave detailed results at lower costs compared to seismics.
successfully conducted seismic refraction and ERT surveys
and associated resistivity and velocity changes with the main and secondary
structures of a major fault zone. The final velocity and resistivity models were
also consistent with deformed sedimentary units. Another multidisciplinary
geophysical approach for mapping a fault zone is given by
. used seismic and electric
tomography to assess the rock quality on a hard rock slope in Norway.
Several methods are generally combined to overcome the limits of the
natural resolution and corresponding ambiguity in inversion and
interpretation. One example for synthetic and field data is given by
. Seismic data and ERT were used to reduce model
ambiguities to improve the estimation of the geophysical parameters. With the
joint inversion approach, data sets from different methods are used to constrain
each other. The general assumption is that subsurface structures lead to
parameter changes for the different methods, i.e. imaging the same underlying
geologic conditions. Smoothness constraints often prevent the correct mapping
of sharp interfaces. Different joint inversion approaches
exist. The cross-gradient method for ERT and seismic data is explained in
. Although a significant improvement in the results
compared to separated 2-D inversions was observed, it can only be applied on
regular grids, which makes an accurate incorporation of topography difficult.
present a joint inversion algorithm using seismic
refraction and ERT data, assuming a fixed number of layers. The derived
subsurface models agreed well with independent in situ tests, but the authors also stated
that their approach would lead to misinterpretations in environments with
smooth subsurface variations.
This paper describes a representative case study for the combination of
geoelectric and refraction seismics in typical Scandinavian geologic
conditions at a coastal region. The survey was conducted at Äspö
Hard Rock laboratory (HRL) and designed to perform a joint inversion on the
data. The main objective was the localisation and characterisation of
fracture zones under challenging conditions, which are the extreme variation
in electrode coupling, possible 3-D effects on ERT data and high acoustic
damping due to gas-bearing sediments. and
showed that underwater field surveys are possible and
quite promising. demonstrated that the water layer has a
large effect on apparent resistivities, but subsurface resistivity can be
recovered if water resistivity and seabed topography are properly
incorporated in the finite-element meshes. The methodical approach of a
structurally coupled joint inversion presented in this study shows how
results can be improved such that an easier and more unique interpretation of
the underground models is possible. In order to increase the reliability of
the results, a combined inversion and interpretation was investigated. This
was done by joint inversion followed by a cluster analysis as an additional
integrated interpretation approach. After describing the site conditions and
the numerical background, we show a synthetic study on the 3-D effects and the
influence of the seabed topography on ERT data before the analysis and
interpretation of the field data is presented.
Site description
The Swedish Nuclear Fuel and Waste Management Company (Svensk
Kärnbränslehantering AB; SKB) started to design a solution for the deep final
disposal of
nuclear fuel. Äspö HRL is SKB's underground facility for
research that tests concepts for the final disposal of nuclear waste material
in hard rock . The laboratory has provided a full-scale test environment
for different technological solutions. It has now mainly fulfilled
its purpose so that the laboratory has also become available for other
branches of research. The facility provides a research opportunity in a
well-documented and relatively undisturbed geological environment that is
representative of many Swedish metropolitan areas.
The Äspö Hard Rock Laboratory is located on the east coast of the
Baltic Sea, about 400 km south of Stockholm (see Fig. ). From 1990
to 1995, the excavation of a 3600 m tunnel that connects the nuclear
power plant with the disposal at approximately 450 m of depth was conducted.
During the construction phase, a detailed site characterisation was carried out that
included geological, hydrogeological and geochemical investigations.
The Äspö bedrock is part of the Transscandinavian Igneous Belt (TIB)
that extends from southern Sweden toward the north and northwest. Generally,
granitoids and volcanic rocks can be found in the TIB. Four rock types are
dominant: the Äspö diorites, Ävrö granite, greenstone and
fine-grained granite. found that continuous magma
mixing processes supported the development of dikes and mafic inclusions,
which form an inhomogeneous rock mass. The crystalline bedrock exhibits
porosities of 0.4–0.45 % for the Äspö diorite and 0.23–0.27 % for
the fine-grained granite . During the pre-investigation
of Äspö HRL, fracture zones were divided into major (width > 5 m)
and minor (width < 5 m) categories. The majority of the fractures are oriented
northwest–southeast . All fracture zones that are
important for this field survey are depicted as black lines in Fig. .
The location, major fracture zones (black lines) after
, the scheduled ERT profile (solid red line) and the
seismic (dashed green) line at Äspö Hard Rock
Laboratory.
The filling material of the fractures was extracted from drill cores and
analysed. Missing unconsolidated material that might have been additionally
filling the fractures was probably washed away and thus not taken into
account in these analyses. The crystallised calcite in the fractures was possibly
formed by hydrothermal processes and can be used as an indicator for water
paths in the rock . This indicated that fractures in the N–S
and E–W directions most likely conduct or formerly conducted water. According
to all fracture zones are at least partly water bearing.
They also gave a judgement of the fracture zones according to
. Based on that, the most critical fracture zone along
the measured profile is NE-1, which is judged as “certain”. EW-3 is
also judged “certain”, but hydraulically of minor importance. NE-3
and NE-4 are judged as “certain” as well. Both consist of several subzones that
are one to a few metres wide, some of which are open fractures that are
hydraulically highly conductive. In general, the fracture zones NE-3, NE-4
and EW-7 are judged to be “probable” in a hydraulic sense
. The authors also stated that the Quaternary sediments on top of the
bedrock were supposed to be scarce at the Äspö test site. Due to the
deep target of the Äspö HRL within the bedrock, no detailed
investigation of the Quaternary sediments was carried out.
stated that the unconsolidated overburden should rarely exceed 5 m in thickness
and consists mainly of clay, sand and gravel.
Electrical resistivity tomography
ERT measurements were carried out along a profile in the N–S direction
simultaneously with the seismic survey on 20–24 April 2015. The profile
lies between Hålö and Äspö (see Fig. ) to the west
of the tunnel line, about 10 m away from a small island. Electrodes were
placed onshore and underwater with a 5 m electrode spacing along a 780 m
profile. Data were recorded using the multichannel instrument ABEM
Terrameter LS (Guideline Geo, Sundbyberg, Sweden). A multiple-gradient array was employed
to ensure fast measuring progress as it can fully exploit the recording
channels. The resistivity of the water was measured with a micro
Wenner alpha array at different depths with the ABEM Terrameter LS. The
collected ERT data were first published in . A model based
on accurate bathymetry measurements was used to determine the heights of the
sensor positions at the seabed. A nearby power plant caused a high noise
level in the ERT data. Large variations in the contact impedance between the
water and the rock outcrops created a technically difficult measuring
situation. Contact resistances, including cable resistance, started from
100 Ω for electrodes in brackish water and exceeded 100 kΩ on
rock outcrops. An over-amplification of the signals was avoided due to an
automated gain control of the instrument. Furthermore, the input channels are
galvanically separated; i.e. one channel can have a high gain and the next
channel a low gain, avoiding any problems. The full wave form of the transmitted
and received signals was recorded in order to recover possibly valuable IP
signals from the data. However, the signal-to-noise ratio was sufficiently
good for recovering DC resistivity but not IP data. About 6700 data points
were gathered during the ERT survey. While the raw data were processed,
combinations with uncoupled electrodes were identified and all combinations
containing these electrodes were deleted. To account for the variable data
quality of the individual data, usually a data error is estimated by a fixed
percentage and a voltage error. They can be retrieved by analysing reciprocal
measurements , which were not available
here. Therefore, we used the default values of 3 % noise and a voltage error
of 0.1 mV.
Seismic refraction tomography
The green dashed line in Fig. marks the profile for the seismic
refraction. Hydrophone streamers were laid out with 91 hydrophones in total
and a 5 m spacing along a 450 m profile line. For data acquisition, the
instruments ABEM Terraloc (Guideline Geo) and Geometrics Stratavisor (San Jose, CA, USA) were used, both with 48
channels and a 5-channel overlap of the two streamers. Hydrophone
positions were determined by a differential GNSS, while the topography of the
seabed was mapped with a multibeam echo sounder (MBES). For all underwater
sensors (electrodes and hydrophones), a very accurate DTM (digital terrain
model) from the MBES survey was used for the heights. The positions of the sensors (E/S)
are coincident and measured with sufficient accuracy. For the
excitation of seismic p-waves, small explosives were placed approximately
0.5 m above the seabed. Shots were performed every 20 m. Data were first
processed and published in . Due to time constraints,
not all planned shots were fired, and hence there are two small gaps in the
data coverage in the northern part of the data set. Raw data processing
revealed that the seismic signal quality was significantly reduced in the
southern part of the profile, which made it difficult to pick first arrivals.
However, no additional filters were used during the raw data processing.
About 650 first-arrival times were semi-automatically picked and manually
checked using the software package Rayfract (http://www.rayfract.com).
Numerical modelling and inversion
We used the open-source ERT software package BERT (Boundless Electrical
Resistivity Tomography) for ERT inversion using
irregular triangle meshes to accurately take into account both the surface and submarine
topography . Furthermore, we used the
underlying framework pyGIMLi (Python Geophysical Inversion and Modelling
Library; http://www.pygimli.org) for the refraction tomography and the
implementation of the coupled inversion.
Inversion
Geophysical inversion describes the process of estimating a model with a
forward response that fits the observed data. The linearised problem for ERT is
given in Eq. () and for seismic in Eq. (). Here,
the model parameters are either the logarithmic resistivities or the
velocity/slowness held in the model vector m:
JΔm=d-f(m),AΔm=Δt.
The Jacobian matrices J and A contain the partial derivatives
∂ρa,i/∂mj (ERT) or ∂ti/∂mj.
Apparent resistivities (ρa) are held in d and
travel times in t. The inversion of ERT and SRT (seismic
refraction tomography) was done by a smoothness-constrained minimisation
using the cost function
Φ=Φd+λΦm,=∑i=1Ndi-fi(m)ϵi2+λ∥Cm∥22,
containing an error-weighted data misfit Φd and a model
roughness Φm weighted by the regularisation parameter λ. As the
travel time t between source and receiver along a ray path is given by
t=∑i=1nli/vi, it is a linear combination of the path
length li and the slowness 1/vi for a segment i. The difference between
the individual data points di and the corresponding forward responses fi(m),
both as logarithmic apparent resistivities or travel times,
is weighted by their individual errors ϵi.
The roughness (second term in Eq. ) consists of the derivative
matrix C applied to the model m,
whereas each row in C is associated with a boundary. Additional model
constraints can be incorporated in the object function by extending Φm
from Eq. () with a weighted model functional Wc, resulting in
Φm=∥WcCm∥22.
The weighting matrix Wc is diagonal and contains the elements
wi, representing penalty factors for the different model cell boundaries
. Very small values can lead to sharper boundaries. The
limited amount and quality of recorded data leads to a non-unique inversion
result. Due to the model smoothing needed for mixed determined problems, it
is possible that sharp boundaries appear as transition zones that lead to
misinterpretations. A structurally coupled joint inversion finds common
structures and allows the models to emphasise these and reduce smoothing
effects . Here, the roughness vector r=CWmm
is used to calculate the mutual penalty factors wi using Eq. () after
:
wi=a|ri|+a+bc.
The parameters a, b and c are used to adjust the coupling strength and
the influence of the gradients. Differently from the latter
approach, we multiply the wi of the different methods and calculate one
weighting matrix for both methods.
A certain number of separated iterations is done before the coupling starts
so that each method can first independently develop structures before their
similarity is promoted. A schematic sketch of the structurally coupled joint
inversion is shown in Fig. .
Scheme of the coupled inversion approach, where the
roughness C of one inversion is influenced by the other
.
Forward modelling and inversion are done accurately on unstructured finite-element (FE)
meshes that allow for the incorporation of both the surface and the underwater
topography. The finite-element mesh used for the joint inversion
of seismic and ERT data is shown in Fig. .
The shown mesh consists of three regions that present the background (red),
the water (blue) and the parameter domain (green) on which the data
inversion is conducted. The original mesh extension is 1250 m in the x and
approximately 420 m in the z direction and is clipped for display reasons.
In situ water conductivity measurements showed resistivity values of about
1.4 Ωm and negligible variation with position or depth. As the
seismic velocity of water is constant (about 1400 m s-1), the water
region can be assumed as homogeneous and is incorporated as a single region with
a fixed resistivity or velocity so that the correct values are used for the
forward calculation but are not subject to inversion. The parameter domain is
extended to approximately 790 m in the x and 190 m in the z direction.
Additionally, an outer background region is needed for accurate forward
calculation using approximate boundary conditions .
Although the seismic line is shorter than the ERT, the shown mesh was used
for both data sets in the joint inversion. The parameter domain consists of
about 3500 cells, which is the number of model parameters. More details on
region-based inversion can be found in .
Cropped mesh used for the structurally coupled inversion of ERT and
seismic data. Three regions are used: (i) the background (in red; much bigger)
to prevent influences of the boundaries, (ii) the parameter domain (green) on
which the inversion is done and (iii) the water region (blue), which was
fixed.
Model appraisal and display
All shown inversion results are faded out using the coverage to point out the
contribution of the model parts to the data. The calculation of the coverage is
based on the sensitivity, which is the partial derivative Si,j(mn)=∂fi(mn)∂mj.
Whereas m=logρ are the model
parameters and f=logρa is the forward response (both logarithmically
transformed) for ERT, the seismic model parameters are m= 1/v (slowness) with the corresponding forward response f=t (travel times). The
summation of all sensitivities for each model parameter gives the coverage
for the model cell assigned with this parameter. Unlike ERT, a normalised
coverage is calculated for seismics, which is either 1 or 0 depending on whether
a ray crosses a cell or not. Additionally, resolution radii after
were calculated for the purpose of a comparison with
the coverage. For that the model resolution matrix RM is required,
which can be calculated by a singular value decomposition (SVD) of the
Jacobian matrix of the final model, i.e. the resistivity or velocity/slowness
distribution. The radius rj=Acell/(Rj,jMπ) is
calculated for each model cell j and is an equivalent of the cell
area Acell to a perfectly resolved sphere. The coverage and resolution radii
distributions are shown in Fig. for ERT.
Calculated model resolution radii (a) and
coverage (b) for ERT using the Jacobian matrix of the final
model.
In general, low-resolution radii correspond with a high coverage and vice
versa. The water and the low-resistive sediments between x= 180 m and
x= 550 m lead to a reduced investigation depth with high-resolution radii starting at
approx. 50 m of depth. Compared to that, the onshore model parts at x< 180 m
and x> 550 m show a medium reliability up to 70 m of depth with resolution
radii < 50 m. Figure clearly shows the existing relationship
between the coverage and the model resolution radii. Thus, the coverage can
also be used as a resolution measure to display well-, medium- and poorly resolved
model regions. In the following, two coverage thresholds were used to define
regions of high, medium and low certainty. The low-certain region is
completely blanked out and considered as untrustworthy, while the region of
high certainty is imaged without shading and can thus be judged as trustworthy.
Synthetic study on 3-D effects and seabed topography
We follow a strict two-dimensional (2-D) scheme, i.e. assuming constant values perpendicular to
the profile. For the given test site, it can be assumed that the seismic
refraction data are not or only minimal corrupted by 3-D effects. By picking
first arrivals, only signals that took the shortest way or
travelled in the fastest medium are taken into account. The small island next to the profile
consists of the same bedrock as that directly in the profile line. Assuming the same
velocity, the recorded first arrival is still from the signal travelling in
the profile line, because it is the shortest way. The small bay (water body)
north of the profile would be a low-velocity anomaly because the velocity in
water is lower than in bedrock. Therefore, the bay can be ignored because a
refraction only appears for an increasing velocity. Three-dimensional (3-D)
effects occur if significant resistivity changes perpendicular to a 2-D
profile are present. According to the test site map in Fig. ,
severe 3-D effects can be expected near the small island in the middle of the
profile and in the northern part, where the water continues just a few metres
next to the profile. The latter is not expected to have a significant effect
on the first-arrival times, since these are related to the smallest distance
to the layers. It will, however, have an effect on the measured apparent
resistivity by all materials present within the measured volume. In order to
appraise the expected shapes and magnitudes of 3-D effects, we generated a
simplified model based on the Äspö geometry. The underlying model
used for generating synthetic data is shown in Fig. . The water
body is simulated by a cube with an extension of 450 m in the x direction
starting at x= 100 m, being 10 m in depth and infinite in the y direction. A
large cube simulating the bedrock (brown) surrounds the water cube, with an
infinite extension in the x, y and z direction. The water (blue) is assigned
with a resistivity of 3 Ωm, while the bedrock is assigned with
3000 Ωm. Two anomalies are inserted representing the island in the
middle and the small bay at the northern end of the ERT profile. The island
(red) is a 10 m thick cube, with an extension of 90 m in the x and 70 m in
the
y direction, placed between x= 370 m and x= 460 m with a distance of 10 m to the
ERT profile. The small bay at the northern part (green) is incorporated by a
rectangular cube with an edge length of 100 m (x, y direction) and 3 m
of
depth. It starts directly after the water cube at x= 550 m with a distance of
5 m to the profile. The ERT line consists of 153 electrodes, starts at x= 15 m
and y= 0 m and is aligned along the x direction. The simulated survey
is identical to the field measurements except that the electrodes are assumed
to be at the surface and topography is neglected. The ERT profile is marked
with red spheres in Fig. .
Sketch of the synthetic model used to generate synthetic data. It
reflects a simplified version of the Äspö test site conditions. The
red spheres mark electrode positions, the blue coloured areas simulate a
low-resistive body, like sea water, and the brown parts mark highly resistive
bodies, like bedrock.
For reference, we additionally calculated data from a 2-D model, where the
island is assigned with a water resistivity of 3 Ωm and the bay
with 3000 Ωm (bedrock), i.e. with no 3-D effects. Both data sets were
corrupted with Gaussian noise with an error level consisting
of 3 % plus a voltage error of 100 µV. A smoothness-constrained
inversion was performed to estimate resistivity models from the two synthetic
data sets. Figure a and b show the
inversion results from the data set with and without 3-D effects. The ratio
between those two is shown in Fig. c.
Inversion results of the synthetic case with (a) a pure 2-D
model, (b) the incorporated island and the small bay causing 3-D effects
and (c) the ratio between (a)
and (b).
Figure a shows the expected smooth resistivity distribution with
a horizontal interface between the simulated bedrock and water. When the
island and the small bay are included in the underlying model, serious 3-D effects
occur. These lead to higher resistivities in the middle of the profile where
the island was included with additional low-resistive compensation artefacts
next to it. The small water-filled bay at the end of the profile leads to a
characteristic low-resistive feature at intermediate depths. Both anomalies,
including the possible compensation artefacts, are more visible in the ratio
plot given in Fig. c.
The second synthetic study investigates the effect of the seabed topography
on ERT data. Three different geometries were used for generating synthetic
data. Based on the Äspö case, a water-filled valley with a depth of
10 m and a length of 550 m was used. The reference model contains a flat
seabed, whereas cases one and two contain a depth variation of
±0.30 m. For the first case, the depth of the seabed was set to -10.3 m
between x= 230 m and x= 300 m and to -9.7 m between x= 380 m and x= 450 m. The
depth varies, alternating from -10.3 to 9.7 m for
250 m ≤x≤ 395 m for the second case. While the data were generated,
2 % Gaussian noise was added. Afterwards, geometric factors for the first and
second case were replaced with the reference data in order to simulate a flat
seabed. One mesh for the data inversion was used with a
flat seabed. The ratios between the two cases and the reference data set are
shown as pseudo-sections of the simulated data in Fig.
Pseudo-section of the synthetic data for the gradient array. The
ratios between case one and the reference model (a) and
between case two and the reference model (b).
The deviation due to the changed seabed topography is in the range of
±20 %. Figure a shows a clear pattern due to the changed model,
which is a lower apparent resistivity for a slight downwards shift of the
seabed and an increased apparent resistivity for an upwards shift. Compared
to that, an alternately varying seabed leads to a rather random pattern
(Fig. b). This simple synthetic study confirms that the ERT data
gathered at the Äspö test site are contaminated or distorted by 3-D
effects that have to be taken into account when interpreting the results.
Results
A smoothness-constrained inversion was done with the abort criterion
χ2=Φd/N= 1; i.e. the data are fitted within their errors. A visual
inspection of the data misfit ensured that there was no more unresolved
structure. The L1 norm data (robust) inversion was used to account for
remaining outliers in the ERT data set that lead to poor data fits.
Nevertheless, the apparent resistivities cover several orders of magnitude
(3–47 000 Ωm) and extraordinarily high resistivity variations occur,
which is challenging for ERT inversion. The ERT inversion result is shown in
Fig. a using the coverage (sum of the absolute Jacobian values over
all data for each model) for alpha shading. In the middle of the profile, the
penetration depth is limited due to the low-resistive water body and the anomalies below.
Outcrops of the bedrock lead to high resistivities of about 35 000 Ωm
at the northern and southern ends of the profile. A low-resistive zone appears
at x= 200–600 m directly below the sea. The depth varies between
approximately 80 m at x= 270 m and 30 m at x= 450–600 m. As such
a deep weathering zone seems implausible and the resistivity is too low for
usual weathering, we interpret this structure as a deep valley filled with
sediments. This has not been documented by previous investigations conducted
in the construction phase of the test nuclear waste disposal. The
low-resistive zone is extended diagonally downwards towards the north for
x> 600 m at a depth range of 50–100 m. Although the coverage is
low for this part, it is still possible that this feature indicates fractured
water-bearing bedrock.
Resistivities of about 500 Ωm at x= 100–200 m and a depth of
100 m indicate a larger transition zone that continues below the sediment
body. This could possibly lead to an incorrect depth of the sediment-filled
valley and thus the bedrock interface. It also prevents any further
interpretation regarding possible fracture zones.
The inversion result of the refraction seismics shown in Fig. b
images the interface to the bedrock more accurately. However, the poor signal
quality in the southern part results in a lower coverage and thus larger
uncertainty. To display the inversion result, a standardised coverage was
calculated, which is either 0 or 1 depending on whether any ray travels
through a model cell or not.
Separated inversion results of the ERT data set (a) and the
refraction seismic data (b). The shading is based on the coverage.
According to Fig. , the crystalline bedrock appears as a high-velocity zone of about 5600 m s-1, which agrees with the velocity for intact
crystalline rock at Äspö HRL given by .
recently showed that the velocity decreases from
> 5000 m s-1 for intact rock down to approx. 4200–4700 m s-1 for fracture
zones. Towards the northern part, the velocity of the bedrock decreases down
to 5000 m s-1. At the southern part between x= 200 m and x= 300 m, the result
shows a low-velocity zone down to 60 m of depth, which is extended towards the
north for shallow parts of the model above 20 m of depth. This finding
coincides with the low-resistive part in the ERT result. The sediments exhibit a
minimum velocity of about 1000 m s-1, which is below the velocity of water
(1400 m s-1). A reason could be gas contained in the sediments, which
reduces
the acoustic velocity for frequencies below 1 kHz . This
is supported by the presence of gas bubbles rising to the water surface
during the blasting. Gas-bearing sediments were also reported by
near Stockholm, which has a similar geologic history. It is
assumed that the gas-bearing sediments lead to the poor data quality in the
southern part by damping the seismic signals. No further low-velocity zones
appear at larger depths.
To summarise, a (possibly gas-bearing) sediment body could be identified,
which appears as a zone of low resistivities and velocities. Furthermore, the
interface towards the bedrock could be found by the joint interpretation of
the separated inversion results. However, the bedrock appears with a low
resistivity due to the large transition zone. Fracture zones are not visible
in the separated inversion results (Fig. ) because of a low
coverage in the refraction model and a large transition zone in the resistivity model.
In order to improve the results and enable further interpretation, a
structurally coupled joint inversion of the ERT and seismic data was
performed. To ensure that common structures are present in the models, the
first four iterations were done separately. A robust data fit,
i.e. L1 norm, was used for ERT data inversion, while the first arrivals were
fitted using the L2 norm (least squares). Both data sets were fitted
within their errors, i.e. with χ2= 1.1 for ERT and χ2= 1.3 for
refraction data. In this case, the RMSE (root mean square error) for the first-arrival fit was about 2.4 ms. The result is shown in Fig. .
Joint inversion result with resistivity (top panel) and velocity
(bottom panel) distribution. The shading is based on the coverage of each
model cell.
Both models show significant changes compared to the separated inversions and
allow for further interpretations. Generally, most changes occur in the
resistivity model, while the velocity model shows only small improvements.
The low-resistive zone, which corresponds to the sediment-filled valley,
appears thinner followed by a much smaller transition zone. This reduces the
ambiguity in estimating the bedrock interface. The bedrock is also assigned
with a higher resistivity, which is more realistic as it agrees with the
resistivity of near-surface rock outcrops at the northern and southern ends of the profile.
Additional structural constraints that moved from the velocity to the
resistivity model pointed out the diagonal low-resistive zone in the northern
part in more detail. This anomaly matches very well with the water-bearing
fracture zone NE-1 in the northern part of the profile. The southern fracture
zones NE-3, NE-4 and EW-1 cannot be identified directly. Possible
explanations could be that these are (i) too small to be detected from the
surface or (ii) filled with a material so that no parameter contrast appears.
According to the synthetic study, the low-resistive feature directly at the
surface at x= 610 m and, in part, the diagonal low-resistive zone at
x= 600 m are most likely caused by 3-D effects and should not be interpreted
any further. Following , a post-processing of the two
inversion models was done using a cluster analysis to obtain a simplified
result (Fig. ). For clustering the resistivity and velocity model,
a modified mean-shift algorithm approach was used, which is described in
. The input for this algorithm is a feature space that
consists in this case of resistivities and velocities. In order to analyse
the feature space, a window or bandwidth is needed. The bandwidth can be
determined by an estimator that uses a selected quantile as input, whereas
the quantile is defined between 0 and 1. In general, a low quantile will
produce a larger number of clusters than a high quantile. In contrast to
cluster number-driven algorithms such as the K-means algorithm (see
), the input is data and a window to the data. Therefore,
the selection of clusters is driven only by data and not by an arbitrary
number of clusters.
As data input for the clustering, we only used model parameters included by
the coverage of the seismic result (displayed cells in Fig. b)
because the seismically covered volume is also covered by ERT.
Cluster analysis of the joint inversion result using tree clusters.
The upper picture shows the spatial distribution of the clusters and the
lower one shows the parameter distribution within each cluster.
The data-driven cluster algorithm divided the model parameters into three
clusters that represent sedimentary deposits, the bedrock and the transition
zone between those two. It can most likely be assumed that the interface
between the sediments and the bedrock is within the third cluster.
Conceptual model based on the geophysical results and known geologic
interpretations of the test site at Äspö. The hash signature at the
bedrock interface indicates a higher uncertainty.
As a final interpretation of the presented ERT and seismic results, a
conceptual model was developed (Fig. ). The primary origin of the
deep sedimentary deposits can be explained by glacial erosion. The small
valley was formed between the fracture zones NE-3 and NE-4. It might have
been easier to erode the bedrock along zones with an already low rock
quality. Two possible explanations can be given for the remaining transition
zone at the bottom of the sedimentary valley. The first is that the
bedrock–sediment interface is (i) fractured or weathered to a certain
extent, and the second is
(ii) that coarse sediments could have been deposited before fine-grained
marine material was sedimented above. The latter possibility is visualised by
the dark yellow and orange parts at the bottom of the valley in
Fig. . As the medium velocities north of the sedimentary valley
appear slightly thicker, the most probable explanation could be weathered
bedrock. During an earlier investigation, it was found that the NE-1 fracture
zone in the northern part of the model is water bearing at its boundaries and
dry in its core due to clay deposits. Thus, it appears as a zone of lower
resistivities and velocities. Only the NE-1 fracture zone could be identified
by this survey, although the fracture zones NE-3, NE-4 and EW-3 are also
partly water bearing according to . As shown in
Fig. , NE-4 and EW-7 are close to each other at the profile line,
which means that they most likely cannot be imaged separately by ERT
measurements. In addition, the low-resistive sediments are a complicating
factor that may mask the fracture zones by reducing the model resolution such
that it is not sufficient to resolve the fracture zones NE-3, NE-4 and EW-7.
Conclusions and outlook
A combination of refraction seismics and ERT data has been tested on an
underwater profile crossing a water passage along part of the access
tunnel that connects surface facilities with an underground test laboratory
at the Äspö Hard Rock Laboratory. The aim was to detect fracture
zones in a well-known but logistically challenging area. Co-located sensor
positions for ERT and seismics were used on a 450 m underwater section
of the 700 m ERT profile.
A synthetic study inspired by the geologic conditions of the Äspö
test site showed that significant 3-D effects are expected that
contaminate the ERT data and thus influence the obtained inversion result.
This was taken into account to prevent the misinterpretation of the final
inversion results. The results of the separated inversions showed a previously
unknown sediment-filled valley that appeared as a zone with low resistivities
and low velocities, even in an unusually well-documented geological
environment. The poor coverage of the seismic model in the northern and
southern parts of the profile in conjunction with the large transition zone of
the ERT result prevent further detailed interpretations. However, the water-bearing fracture zone NE-1 could be identified by the results of the
structurally coupled joint inversion. The evaluation shows that the
joint inversion approach combining ERT and seismics has very promising
results for three reasons: (i) the decreased extent of the transition
zone, (ii) the more reliable interpretation of two independent parameters and
(iii) their combination by a clustering approach. Although the refraction
seismic does not cover the fraction zone NE-1, the additional constraints by
the joint inversion helped to determine the fracture zone with ERT. The
southern fracture zones NE-3, NE-4 and EW-1 could not be detected due to the
missing parameter contrast and/or the model resolution. The latter is considered
to be the main reason, which was shown by the distribution of the model
resolution radii and coverage. The reduced investigation depth of ERT is due
to the fact that the current preferably flows through low-resistive bodies
(water or sediments) and is the major disadvantage of this method.
The comparison of the joint inversion with the separated inversion result
shows significant improvements. Therefore, the combination of geoelectric and
seismic refraction is recommended as the standard tool for site
investigations under geologic conditions similar to those presented.
Two geophysical data sets were used for this study, refraction
seismic and ERT data . Both were used to generate Figs. 8–10.
Synthetic ERT data were calculated to conduct two synthetic studies. The underlying
models and all data sets used are available upon request by contacting the corresponding
author at mathias.ronczka@tg.lth.se. Data sets and codes for the joint inversion
are available under the citation above.
The authors declare that they have no conflict of interest.
Acknowledgements
Thanks to all who participated in the field survey and made the measurements
possible. Per-Ivar Olsson, Erik Fennvik and Nayeli Lasheras Maas were also
instrumental in the field data acquisition. Special thanks go to Marcus
Wennermark, who planned the survey and was responsible for seismic
instrumentation and data collection in the field campaign. We are grateful to
SKB (Swedish Nuclear Fuel and Waste Management Company) for logistic support
during the field campaign. The funding which made this work possible was provided
by Nova FoU, BeFo (Swedish Rock Engineering Research Foundation; refs. 314
and 331), SBUF (the Development Fund of the Swedish Construction Industry;
refs. 12718 and 12719) and Formas (the Swedish Research Council for
Environment, Agricultural Sciences and Spatial Planning; ref. 2012-1931) as
part of the Geoinfra-TRUST framework (http://www.trust-geoinfra.se/).
Edited by: U. Werban
Reviewed by: two anonymous referees
References
Bäckblom, G., Gustafsson, G., Stanfors, R., and Wikberg, P.: A synopsis of
predictions before the construction of the Äspö Hard Rock Laboratory
and the process of their validation, Tech. rep., SKB, Stockholm, 1990.
Berglund, J., Curtis, P., Eliasson, T., Olsson, T., Starzec, P., and Tullborg,
E.: Äspö Hard Rock Laboratory, Update of the geological model 2002,
Tech. rep., SKB (Swedish Nuclear Fuel and Waste Management Company), Stockholm, 2003.Bergman, B., Tryggvason, A., and Juhlin, C.: Seismic tomography studies of
cover thickness and near-surface bedrock velocities, Geophysics, 71, U77–U84,
10.1190/1.2345191, 2006.
Brodic, B., Malehmir, A., and Juhlin, C.: Fracture System Characterization
Using Wave-mode Conversions and Tunnel-surface Seismics, in: EAGE Near
Surface Geophusics, EAGE, Barcelona, Spain, 2016.
Comaniciu, D. and Meer, P.: Mean Shift : A Robust Approach Toward Feature
Space Analysis, IEEE T. Pattern Anal. Mach. Intell., 24, 603–619, 2002.
Dahlin, T. and Wisén, R.: Underwater ERT Surveys for Urban Underground
Infrastructure Site Investigation in Central Stockholm, in: 17th Nordic
Geotechnical Meeting, 25–28 May 2016, Reykjavik, 2016.
Dahlin, T. and Zhou, B.: Multiple-gradient array measurements for multichannel
2D resistivity imaging, Near. Surf. Geophys., 4, 113–123, 2006.
Dahlin, T., Bjelm, L., and Svensson, C.: Use of electrical imaging in site
investigations for a railway tunnel through the Hallandså s Horst,
Sweden, Q. J. Eng. Geol., 32, 163–173, 1999.
Dahlin, T., Loke, M. H., Siikanen, J., and Höök, M.: Underwater ERT
Survey for Site Investigation for a New Line for Stockholm Metro, Near
Surface Geoscience, in: 20th European meeting of Environmental and Egineering
Geophysics, 14–18 September 2014, Athens, Greece, 2014.Diaz, D., Maksymowicz, A., Vargas, G., Vera, E., Contreras-Reyes, E., and
Rebolledo, S.: Exploring the shallow structure of the San Ramón thrust
fault in Santiago, Chile (∼ 33.5∘ S), using active seismic and
electric methods, Solid Earth, 5, 837–849, 10.5194/se-5-837-2014, 2014.
Fennvik, E.: Resistivitets- och IP-mätningar vid Äspö Hard Rock
Laboratory, bSc thesis 438, Dept. of Geology, Lund University, Lund, 2015.Friedel, S.: Resolution, stability and efficiency of resistivity tomography
estimated from a generalized inverse approach, Geophys. J. Int., 153, 305–316,
10.1046/j.1365-246X.2003.01890.x, 2003.Gallardo, L. A. and Meju, M. A.: Joint two-dimensional DC Resistivity and
Seismic travel time inversion with cross-gradients constraints, J. Geophys. Res.,
109, B03311, 10.1029/2003JB002716, 2004.Ganerød, G., Rønning, J. S., Dalsegg, E., Elvebakk, H., Holmøy, K.,
Nilsen, B., and Braathen, A.: Comparison of geophysical methods for
sub-surface mapping of fault and fracture zones in a section of the Viggja
road tunnel, Norway, Bull. Eng. Geol. Environ., 65, 231–243, 10.1007/s10064-006-0041-6, 2006.Garofalo, F., Sauvin, G., Socco, L. V., and Lecomte, I.: Joint inversion of
seismic and electric data applied to 2D media, Geophysics, 80, 93–104,
10.1190/GEO2014-0313.1, 2015.
Günther, T. and Südekum, W.: New Approach for Routine Investigation of
the Shallow Sea Floor with Bottom-towed DC Resistivity Measurements, in:
14th European Meeting of Environmental and Egineering Geophysics,
15–17 September 2007, Kraków, 2007.
Günther, T., Bentley, L. R., and Hirsch, M.: A new joint inversion
algorithm applied to the interpretation of DC resistivity and refraction
data, in: XVI International Conference on Computational Methods in Water
Recources, 19–22 Jun 2006, Copenhagen, Denmark, 2006a.Günther, T., Rücker, C., and Spitzer, K.: Three-dimensional modeling
and inversion of dc resistivity data incorporating topography – Part II:
Inversion, Geophys. J. Int., 166, 506–517, 10.1111/j.1365-246X.2006.03011.x, 2006b.Günther, T., Dlugosch, R., Holland, R., and Yaramanci, U.: Aquifer
Characterization using coupled inversion of DC/IP and MRS data on a
hydrogeophysical test-site, in: SAGEEP, Symposium on the Application of Geophysics
to Engineering and Environmental Problems 2010, 302–307, 10.4133/1.3445447, 2010.
Ha, H. S. K., D. S., and Park, I. J.: Application of electrical resistivity
techniques to detect weak and fracture zones during underground construction,
Environ. Earth Sci., 60, 723–731, 2010.Heincke, B., Günther, T., Dahlsegg, E., Rønning, J. S., Ganerød, G.,
and Elvebakk, H.: Combined three-dimensional electric and seismic
tomography study on the Åknes rockslide in western Norway, J.
Appl. Geophys., 70, 292–306, 10.1016/j.jappgeo.2009.12.004, 2010.Joydeep, G. and Alexander, L.: The Top Ten Algorithms in Data Mining, in:
chap 2: K-Means by Joydeep and Alexander, edited by: Wu, X. and Kumar, V.,
Chapman and Hall/CRC, 21–35, 10.1201/9781420089653.ch2, 2009.
Juhojuntii, N. and Kamm, J.: Joint inversion of seismic refraction and
resistivity data using layered models – Applications to groundwater
investigation, Geophysics, 80, EN43–EN55, 2015.
Karlsrud, K., Erikstad, L., and Snilsberg, P.: Tunnels for the citizens – investigations
of and restrictions on water leakage to maintain the environment, Tech. Rep. 103,
Public Road Administration, Oslo, 2003.
Lasheras Maas, N.: Site characterisation at the Äspö Hard Rock
Laboratory through seismic refraction, Civil Engineering, bSc thesis, Lund
University, Lund, 2015.
Lindstrøm, M. and Kveen, A.: Tunnels for the citizens – final report,
Tech. Rep. 105, Public Road Administration, Oslo, 2004.
Loke, M. H. and Lane, J. W. J.: Inversion of data from electrical resistivity
imaging surveys in water-covered areas, Explor. Geophys., 35, 266–271, 2004.Malehmir, A., Andersson, M., Mehta, S., Brodic, B., Munier, R., Place, J.,
Maries, G., Smith, C., Kamm, J., Bastani, M., Mikko, H., and Lund, B.:
Post-glacial reactivation of the Bollnäs fault, central Sweden – a
multidisciplinary geophysical investigation, Solid Earth, 7, 509–527, 10.5194/se-7-509-2016, 2016.
Palmstrøm, A., Nilsen, B., Pedersen, K. B., and Grundt, L.: Correct extent
of site investigations for underground facilities, Tech. rep., Directorate of
Public Roads, Oslo, 2003.
Rhén, I., Rhen, I. G., Gustafsson, G., Stanfors, R., and Wikberg, P.:
Äspö HRL – Geoscientific evaluation 1997/2, Results from
pre-investigation and detailed site characterisation, Summary report, Tech. rep.,
SKB (Swedish Nuclear Fuel and Waste Management Company), Stockholm, 1997.Ronczka, M., Hellmann, K., Guenther, T., Dahlin, T., and Wisen, R.: Data and
codes for Äspö field case, 10.5281/zenodo.806753, June 2017.
Rønning, J. S., Ganerød, G., Dalsegg, E., and Reiser, F.: Resistivity
mapping as a tool for identification and characterisation of weakness zones
in crystalline bedrock: definition and testing of an interpretational model,
Bull. Eng. Geol. Environ., 73, 1225–1244, 2013.Rücker, C.: Advanced Electrical Resistivity Modelling and Inversion using
Unstructured Discretization, PhD thesis, University of Leipzig, Leipzig,
http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-69066 (last access:
1 May 2017), 2011.Rücker, C., Günther, T., and Spitzer, K.: 3-d modeling and inversion of
DC resistivity data incorporating topography – Part I: Modeling, Geophys. J.
Int., 166, 495–505, 10.1111/j.1365-246X.2006.03010.x, 2006.Stanfors, R., Rhen, I., Tullborg, E. L., and Wikberg, P.: Overview of
geological and hydrogeological conditions of the Äspö hard rock
Laboratory site, Appl. Geochem., 14, 819–834, 1999.
Udphuay, S., Günther, T., Everett, M., Warden, R., and Briaud, J.-L.:
Three-dimensional resistivity tomography in extreme coastal terrain amidst
dense cultural signals: application to cliff stability assessment at the
historic D-Day site, Geophys. J. Int., 185, 201–220, 2011.
Vidstrand, P.: Äspö Hard Rock Laboratory, Update of the
hydrogeological model 2002, Tech. rep., SKB (Swedish Nuclear Fuel and Waste
Management Company), Stockholm, 2003.
Wikberg, P., Gustafsson, G., Rhén, I., and Stanfors, R.: Äspö hard
rock laboratory, Evaluation and conceptual modelling based on the
pre-investigation 1986–1990, Tech. rep., SKB (Swedish Nuclear Fuel and Waste
Management Company), Stockholm, 1991.Wilkens, R. H. and Richardson, M. D.: The influence of gas bubbles on sediment
acoustic properties: in situ, laboratory, and theoretical results from
Eckernförde Bay, Baltic sea, Cont. Shelf Res., 18, 1859–1892, 10.1016/S0278-4343(98)00061-2, 1998.
Wisén, R., Mykland, J., Rønning, J. S., Elvebakk, H., and Olsen, F.:
Experience from Multidisciplinary Geophysical Survey for Tunneling
In Norway – Refraction Seismic, Resistivity Profiling and Borehole Logging,
in: 16th Nordic Geotechnical Meeting, 9–12 May 2012, Copenhagen, 2012.