AVA analysis of very high resolution seismic data for the study of sediments in

Lake Geneva (Switzerland).

D. Hammami(1), A. Egreteau(2), F. Marillier(1) and P. Thierry(2)

(1)
Institute of Geophysics, University of Lausanne, Switzerland.
(2)
École des Mines de Paris, Fontainebleau, France

Summary
To better understand the physical characteristics of sedimentary deposits in Lake Geneva, we
acquired and processed very high resolution seismic reflection data. Accurate amplitude
measurements were obtained after correction for the frequency response of the hydrophones
which were individually calibrated. In a first part, we present the acquisition and the pre-
processing done on these data to correct the amplitude. In a second part, we migrate the data
using a preserved amplitude pre-stack depth migration, and then apply a post-migration
processing in order to correct for residual move-out. Finally, AVA analysis enables to
differentiate sedimentary unit boundaries.

Introduction
A system to record very high resolution (VHR) seismic data on lakes in 2D and 3D was
developed at the Institute of Geophysics, University of Lausanne (Scheidhauer et al., 2005).
Several seismic surveys carried out on Lake Geneva helped us to better understand the
geology of the area and to identify sequences of Quaternary deposits (mainly lacustrine,
glaciolacustrine and glacial sediments) as well as Tertiary (molasse) units.
However, more sophisticated analysis of the data such as the AVA method provides means of
deciphering the detailed structure of the complex Quaternary sedimentary fill of the Lake
Geneva trough.
In comparison with classical AVA sequences done on pre-stack data (amplitude correction
followed by “kinematics” processing such as NMO, DMO, pre-stack time or depth
migration) , AVA results can be greatly improved by a pre-stack depth migration (Ray +
Kirchhoff migration) , preserving the amplitude variations in 3D heterogeneous media (Baina
et al., 2002). Thus, migration artefacts, stretching effects and residual move-out coming from
any inaccuracy in the velocity field, can be present on common image gathers (CiGs)
depending on data quality and pre-processing parameters.
In this paper, we suppose that the CiGs are only contaminated by thin bed tuning and residual
move-out. The computation of the move-out is achieved by predicting and detecting the
reflectivity variations along the CiG events using the technique of trajectory move-out
(Egreteau et al., 2003). The tuning effects (Egreteau et al., 2005) are also considered during
the computation of the residual move-out correction.

Data acquisition system

2D seismic data were acquired with a streamer providing offsets of up to 185 m. A home-
developed software that uses differential GPS with a radio link to a shore station controlle d
ship navigation and distance shot-triggering. One or more GPS antennas attached to the
streamer enabled us to calculate individual hydrophone positions with an accuracy of 20 cm
after post-processing of the navigation data. Shots triggered at 5 m spacing provided 18 fold
data. The streamer included 72 hydrophones at 2.5 m interval and one channel per
hydrophone, the first one being located at a distance of 5 m from the source. The Mini GI air
gun from Sodera with a 15 x 2 in3 double -chamber bubble -cancelling was operated at 80 bars
at a depth of 1 m. The dominant frequency of the source signal was 330 Hz, thus providing a
theoretical vertical resolution of 1.1 m.

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EAGE 68 Conference & Exhibition — Vienna, Austria, 12 - 15 June 2006
Sensor calibration and data pre-processing
Because AVA analysis requires accurate amplitude measurements, the sensor’s amplitude
frequency response must be known. We carried out a calibration of our streamer in an
anechoic room using a loudspeaker source.
Our streamer is actually made of three sections, one being of different type from the two
others. They are all built with solid state technology. Streamer I has a sensitivity of -192 dB re
1v/µPa +/- 1.5 dB (22 volts/bar) with a nominal 20 dB amplifier for each hydrophone.
Streamer II has a sensitivity of -193 dB re 1v/µPa +/- 1 dB (22 volts/bar) with nominal 5 dB
amplifiers. The hydrophone bandwidth is supposed to be constant from 4 Hz to 4 kHz.
During calibration great care was taken to decouple the source from the streamer so that only
sound waves propagating through air would reach the hydrophones. A series of “white noise”
centered on different frequencies was acquired in order to cover a frequency range from 100
to 700 Hz. The amplitude frequency response of the hydrophones was compared with the
response an accurately calibrated hydrophone (Fig. 1). We found a maximum amplitude
variation of 15 dB between the various hydrophone responses. A frequency dependent
correction for each hydrophone was computed so that it would correspond to the response of
the reference hydrophone. Figure 2 shows an example of amplitude correction on a shot
gather.

Figure 1: Spectrum of thedifference between a reference Figure 2: Example of a shot gather

hydrophone and 7 hydrophones of the streamer. before (left) and after (right) frequency
dependent amplitude correction. The
amplitude maximum is the same for
both gathers.

Amplitude corrections were followed by preserved amplitude processing. Traces containing

noisy or anomalously high amplitudes (spikes) were removed with a despiking processing. In
addition, we corrected for source strength variations due to small variations in the
compressed-air pressure.

Geological target and post-migration processing for AVA inversion

In Lake Geneva, a typical sedimentary succession shows, from top to bottom, Quaternary
lacustrine sediments, followed by glacigenic sediments (front or bottom moraine, glacio-
lacustrine sediments) and finally Tertiary Molasse (Fig. 3). Near river inlets, however, there is
a generally thick cover of deltaic sediments. The seismic energy in these areas often poorly
penetrates the sediments, probably because of the presence of gas.
Figure 3: Intercept (vertical incidence) seismic section in Lake Geneva. The boundaries between
lacustrine sediments and moraine as well as between moraine and molasse are emphasized.

Move-out compensation is achieved by predicting and detecting reflectivity variations along

the CiG events using the technique of trajectory move-out (Egreteau et al., 2003). Starting
from initial intercept and slope sections, and using Shuey’s approximation (Shuey, 1985), we
then model and detect reflectivity variations without considering any incidence angle limit.
Following the amplitude variation along CiG events directly gives the so-called trajectories.
The AVA inversion, done along these trajectories, exhibits several advantages. First, the
number of inverted or cross-plotted values tremendously decreases. Secondly, we
automatically obtain a geological skeleton in the depth domain by simply considering the first
common offset or angle section.
We migrated the data using a velocity model with a water velocity of 1450 m/s and a velocity
gradient of 0.75 s-1 below lake Bottom. The depth sampling interval was set to 50 centimeters
and the horizontal sampling to 2.5 meters. We computed intercept (Fig. 3) and gradient
sections using the two-term approximation of Shuey (1985). Between a horizontal distance of
1000 and 1500 meters, we observe an amplitude attenuation on the intercept section, probably
because of the presence of gas.

(a) (b) (c)

Figure 4: CiG in angle (a) and its trajectories (b). Skeleton section (c). After detection of trajectories on the
CiGs, we automatically obtain a depth interpretation of the structure when looking at the first common angle
section.

We computed the trajectories of reflections and calculate AVA attributes along the
trajectories (Figure 4a and 4b). We obtained a geological skeleton (that includes multiples) by
considering the first common angle section (Figure 4c).

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EAGE 68 Conference & Exhibition — Vienna, Austria, 12 - 15 June 2006
 (a) (b)
Figure 5: AVA cross plot for initial AVA attributes (a) and for AVA attributes computed along the
trajectories (b).

Values corresponding to the interface between moraine and molasse and to the interface
between lacustrine sediments and moraine exhibit a positive intercept and a negative slope. Its
seems that values corresponding to the moraine/molasses interface follow the fluid line, but
not the values corresponding to the lacustrine sediments/moraine (Fig. 5a). Computed AVA
attributes along trajectories show that the position of the sample point (intercept, slope) on an
AVA cross-plot (Fig. 5b) for the lacustrine sediments/moraine interface is similar to the
position of the points computed at constant depth (Fig. 5a). This is caused by a more
important move out on the CiGs for the lacustrinesediments/moraine event than the
moraine/molasses event. For AVA attributes computed along trajectories, the two events can
be differentiated from the fluid line.

Conclusion
The acquisition of very high resolution seismic data together with accurate calibration of the
instruments and amplitude preserving processing enabled us to carry out a first AVA analysis
in Lake Geneva. Major discontinuities within the sedimentary fill of the lake basin display
signatures in the gradient/intercept cross-plot based on Shuey’s approximation.

Acknowledgments:
This work has been partly funded by the French FSH (Fonds de soutien aux hydrocarbures),
Swiss National Science Foundation (Nr. 200020-101937) and the École des Mines de Paris.

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