Towards a quantification of ocean wave heights off the west coast of Ireland using
land based seismic data
Sarah Donne ([email protected])(1), Christopher Bean (1), Ivan Lokmer (1)
School of Geological Sciences Seismology and Computational Rock Physics Laboratory, Geophysics Group, University College Dublin, Dublin 4, Ireland.
Funded under a SFI PI award
Project Outline and Aims
WaveObs Network
• Quantify the forcing of the ocean on the seafloor using land
based seismic data.
• Develop a method to determine characteristic features of ocean
waves using terrestrial seismic network.
• Track the temporal and spatial evolution of the largest waves in
the North West Atlantic.
• Improve spatial and temporal resolution of ocean wave
measurements.
Introduction
K4
M4
K6
M6
M1
K2
M3
M2
Fig 3: Yellow
circles - Single
stations UCD
WaveObs, Blue
square –Global
Seismic
Network (GSN),
White -Met
Eireann and
Marine Institute
Buoys, Green UK Met Office
Buoys
M5
• Microseisms are composed mainly of surface Rayleigh waves.
Fig. 1: Spectral plot of
microseisms recorded at
WaveObs station UEASK.
Secondary
Primary
• Secondary
microseisms
dominate over
primary
microseisms.
Current models for primary and secondary microseism generation:
Primary
Secondary
•
Occur at the frequency of the causative
ocean wave.
•
Occur at double the frequency/half the period
of the causative wave.
•
Have periods of 8-20s.
•
Have periods of 3-10s.
•
Non-linear interactions of the ocean wave
pressure signal on a sloping seafloor in
shallow water by wave breaking near the
shoreline.
•
Opposing wave trains with the same
frequency and wavenumber interact and
produce a standing wave in the ocean.
• These are the current models of microseism generation.
• Observations in Ireland do not match these models and require further investigation.
Storm Diameter
D
S1
A
Class I
Swell
from
S1
B Class II
Land
Fig 2: Generation mechanisms for
secondary microseisms
(modified from Ardhuin et al. (2012)).
Class I, At point A, in the storm S1, the
local wind-sea spectrum is broad
enough by itself to contain energy in
opposite wave trains.
Class II, At point B wind waves from S1
interact with their reflection from the
shore.
Class III, Interaction of swell generated
by storms S1 and S2.
• Amplitudes of secondary
microseisms are
C
Class III
proportional to the square
S2
of the standing wave
height.
• Secondary Microseisms are sensitive to larger waves/swell and are
the main focus of this study.
Fig 10:
Estimated SWH
at M4 for Nov 931 2011 using
ANN trained
using UACH
amplitude and
component
ratios.
Inset: Estimated
SWH vs True
SWH at M4 as
per network.
Mean Square (MS)
Amplitude of microseisms
calculated for comparison
with ocean wave heights.
Fig 6: Comparison between MS
Amplitude at WaveObs stations and
SWH at buoy M4. (Bottom) focuses
on the first 10 days of the month
when the seismics correlate best
with the ocean wave heights
• Fig 9 shows that a correctly trained Neural Network can successfully
reconstruct SWH at a particular ocean buoy, M4 in this case.
• Initial results indicate that discrete locations dominate in winter and
in spring and summer these locations are more diffuse.
• Networks may require seasonal training to account for this.
Fig 7: Scatter plots for different
times of the month. The red
dots which identify the last 11
days where the correlation is
not as good as at the beginning
of the month show the
deviation from the main scatter
population.
• Ocean gravity waves generate pressure changes at the sea floor.
(Longuet-Higgins, 1954).
• These generate continuous background seismic noise called
‘microseisms’ (Bromirski, 2009).
Results
Microseism Amplitude compared with
Significant Wave Height
K1
• Ocean Buoys record Significant Ocean Wave Height (SWH) and
Dominant Wave Period (DPD), over last 17.5 mins of each hour.
• SWH = 4 x RMS of wave heights.
• DPD is 1/fp where fp is the peak frequency of the ocean waves.
Microseismic amplitude reflects ocean wave
activity
Fig 4: The top plot in this figure shows
seismic amplitude for one day at
UEASK.(See Fig 3 for location) Data is not
corrected for instrument response,
100sps, filtered from 3-8sec. The bottom
plot shows Significant Ocean Wave Height
for the same day recorded at buoy M4
(See Fig 3 for location).
Considerations
• Information on source location necessary for inversion of
microseisms for ocean wave characteristics.
Artificial Neural Networks (ANNs)
Input (Seismics)
UACH:MS Amplitude,
Z-N ratio, Z-E ratio
Dominant Ocean Wave Period vs Dominant
Microseism Period
Fig 5:
Panel 1: Dominant
Ocean wave Period
(DPDocean).
Panel 2: DPD of
secondary microseisms
(DPDsecondary).
Panel 3: the ratio of
DPDocean:DPDmicroseism
and the mean ratio in
pink.
• We do not see a 2:1 ratio between ocean wave period and
microseism period.
• Fig 5,Panel 3 shows that the mean ratio is much less than 2 at 1.36.
Trained Neural Network
Output (Buoy)
Significant Wave
Height: M4
Fig 8: For an Artificial
Neural Network to
perform at its best, the
testing period must lie
within the same
probability distribution
(PD) as the training set.
Panel 1 shows the
training set of this
network ranging from
0.8-14.7m.
Panel 2 shows the
testing period with a
range from 1.56-9.22m.
• Fig 4 shows that there is a
realtionship between
microseism amplitude and
ocean wave activity.
• Theory states that Secondary microseisms should have half the
period of the causative ocean waves (Longuet-Higgins, (1954)).
Fig 11: Scatter
plot of SWH at
M6 and Mean
Square
Amplitude at
UACH for 1
year.
• Unlike Fig 9, Panel 3, no clear relationship can be seen when
attempting to train a network over 1 year.
• Fig 11 shows that sources are spatially and temporally variable
throughout the year and source separation and location are needed
to correctly train a network for wave height reconstruction.
• Source locations vary in time throughout the year and Neural
Networks must be trained when microseismic sources and ocean
buoys are spatially ‘close’.
Conclusion
• Test set lies within the same PD as the training set, NN should not
have a difficulty reconstructing all wave heights.
Fig 9: Panel 1: SWH at
M4. Panel 2, Mean
Square Amplitude at
UACH (see Fig 3 for
locations). Panel 3
shows the scatter plot
of M4 against UACH
during the training
period of this network.
A relationship between
SWH and microseism
amplitude can be
seen, as ocean wave
heights increase, so
too do microseism
amplitudes.
• Positive result, without any relationship the Neural Network cannot
work.
1. At specific time periods, ocean wave heights and microseismic
amplitudes correlate very well.
2. At some time periods the correlation is poor.
3. Expected relationships between seismic periods and ocean wave
periods not seen in these data.
4. Initial ANN results show promise in constructing wave
characteristics from seismic data.
5. Other inversion methods and constrained microseismic source
locations will be used in next steps.
References
•
•
•
•
B.H. Demuth, M.Beale, M.T. Hagan, (1997(, Neural network design, PWS Publishing Co, Boston, MA, USA.
M.S.Longuet-Higgins (1950), A theory of the origin of microseisms, Phil. Trans. Roy. Soc London, Ser. A 243, 1-35.
Bromirski, P.D. (2009), Earth ibrations, Science, 324, 1026, doi:10.1126/science.1171839
Bromirski, P. D., R. E. Flick, and N. Graham (1999), Ocean wave height determined from inland seismometer data: Implications for investigating wave climate changes
in the NE Pacific, J. Geophys. Res., 104(C9), 20,753–20,766, doi:10.1029/1999JC900156.
• Ardhuin, F., A. Balanche, E. Stutzmann, and M. Obrebski (2012), From seismic noise to ocean wave parameters: General methods and validation, J. Geophys.
Res., 117, C05002, doi:10.1029/2011JC007449.
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