| dc.description.abstract | Distributed acoustic sensing (DAS) uses fibre optic cables to measure or monitor
strain in the subsurface. With DAS being a relatively new technique it gained
recently more interest for geophysical studies. The main advantages of DAS are;
its high spatial resolution which is between 1-10 m, its successful use in high-
temperature conditions and the low costs compared to the traditional geophones
used. Eventually DAS can be used for monitoring geothermal projects and CO2-
sequestration projects (Wang et al., 2018).
With distributed acoustic sensing the strain or strain rate is measured, based
on the Rayleigh scattering principle. Pulses of laser signals are sent through
the fibre and the interrogator measures the back-scattered light of these pulses.
The time of the laser signal and it's back-scattered component are measured
and converted to strain, using the speed of light and the refractive index of the
fibre (Jousset et al., 2018).
One of the challenges that DAS faces is measuring the P-waves that enter the
fibre with a large angle, this is called limited broadside sensitivity. When the
fibre is orientated parallel to the surface, the fibre can be insensitive to P-waves
travelling vertically. Kuvshinov (2016) suggest a correction factor for this lim-
ited broadside sensitivity, which will be tested in this thesis.
With the advantages and challenges of DAS, the focus of this thesis will be
on limited broad side sensitivity and the main question of this master thesis
is as follows: Can reflection information from Distributed Acoustic sensing be
improved for broadside P-waves, in a surface seismics configuration?
The approach for this new technique, is to first model DAS data with the spec-
tral element solver SPECFEM. SPECFEM is an open-source spectral elemental
modelling program, which can be used to model wave propagation (particle
velocity). The aim is to translate the synthetic particle velocity data to DAS
(strain) data, in order to be able to understand DAS data completely and be
able to compare it to real DAS data. From the synthetic particle velocity it will
be researched if broadside waves can be captured. Which is one step towards
achieving synthetic DAS data.
In order to research the limited broadside sensitivity issue, the steps that need
to be taken are: filtering out the surface waves, selecting the peak amplitude of
the P-wave reflection and applying the correction factor for broadside sensitivity
cos^2(θ)/sin(θ) (θ being the angle between the incoming and the horizontal fi-
bre.), obtained from two point ray tracing. By applying this factor the synthetic
DAS data is simulated. Then it can be tested if the reversed correction factor
sin(θ)/cos2(θ) can correct the simulated DAS data for the limited broadside
sensitivity, so that it can be interpreted.
Based on the results it can be concluded that SPECFEM can be used success-
fully for modelling DAS configurations. The CREWES toolbox from MATLAB
is a sufficient tool box for ray tracing and achieving travel time and horizontal
slowness. By applying a slope filter, to extract the surface waves, it became
clear that a receiver spacing of 0.5 m is preferred for the model considered here.
Furthermore with the use of the correction factor for broadside sensitivity, DAS
data can successfully be simulated and the reversed correction factor can be
used to correct for limited broadside sensitivity in DAS data.
The limited broadside sensitivity is only one step in modelling DAS data and
achieving synthetic DAS data. For further research it is desirable to improve
the isolation of the P-wave reflections from the subsurface interface of inter-
est, by designing a more advanced amplitude picker. To get a more realistic
model, noise should be added to the reflection data followed by stacking the
reflection data. SPECFEM3D instead of SPECFEM2D, is recommended to be
used for further modelling , because of a more realistic 3D geometrical spread-
ing of the source. For modelling DAS data the next step would be to convert
the particle velocity to strain and research other parameters to eventually be
able to compare it to real DAS data. But it is shown that DAS looks like a
promising technique, where the outcomes of the considered model shows the
correction factor for broadside sensitivity can improve the data for relatively
small source-receiver offsets (x=-250,+250 m). | |
