82nd EAGE Conference & Exhibition 2020

8-11 December 2020, Amsterdam, The Netherlands

We_Dome1_14

Model Building in Complex Geological Situations Using Low-Frequency Data from an Optimised Airgun Technology Based Source J. Brittan1*, Y. Cobo1, P. Farmer1, C. Wang1, D. Brookes1 1 ION

Summary The emergence of seismic inversion techniques such as full-waveform inversion (FWI) has provided a significant increase in the resolution that may be achieved in models of physical earth properties. It has become clear that achieving optimal results i.e. a broad band of model wavenumbers implies data with good azimuthal coverage, long offsets, full frequency coverage and, ideally, inversion of multiscattered energy. Modern ocean-bottom node surveys commonly provide good azimuthal and offset coverage, however such surveys often still do not provide good quality data at low frequencies. This is due to deficiencies in both low frequency energy generation by the source and low frequency energy detection at the receiver. Consequently, there has been considerable recent interest in improving the low-frequency performance of seismic sources. In this paper we discuss a survey recently conducted in the Western Gulf of Mexico in which a new seismic source based on airgun technology was utilized with an array of sparse ocean-bottom nodes. We show that the increase in signal-to-noise at low frequencies (1.5-4Hz) that is achieved by the new source (relative to a conventional multi-airgun array) leads to an improvement in the resulting velocity model derived using FWI.

82nd EAGE Conference & Exhibition 2020

8-11 December 2020, Amsterdam, The Netherlands

Introduction

The emergence of seismic inversion techniques such as full-waveform inversion (FWI) has provided a

significant increase in the resolution that may be achieved in models of physical earth properties. With

the widespread use of these techniques, it has become clear that achieving optimal results with wave-

equation based model-building methods implies that the acquired data should have certain

characteristics. As discussed by Alkhalifah et al. (2018) the seismic data required for successful

recovery of a broad band of model wavenumbers implies, in 3-D, good azimuthal coverage, long offsets,

full frequency coverage and, ideally, inversion of multiscattered energy. Leaving aside the difficulties

of inverting for high-order scattered energy, modern ocean-bottom node surveys commonly provide

good azimuthal and offset coverage, however such surveys often still do not provide good quality data

at low frequencies. This is due to deficiencies in both low frequency energy generation by the source

and low frequency energy detection at the receiver.

Consequently, there has been considerable recent interest in improving the low-frequency performance

of seismic sources (e.g. Brenders et al., 2018). We recently conducted a survey in the Western Gulf of

Mexico in which a new seismic source based on airgun technology was utilized with an array of sparse

ocean-bottom nodes (Brittan et al., 2019). In this paper we show that the increase in signal-to-noise at

low frequencies (1.5-4Hz) that is achieved by the new source (relative to a conventional multi-airgun

array) leads to an improvement in the resulting velocity model derived using FWI.

Why do we think low frequencies matter?

In Figure 1 we show a synthetic experiment that illustrates the contribution of data with low temporal

frequencies to the model building process. The synthetic model (Figure 1(a)) has characteristics typical

of salt regimes worldwide in that the sedimentary section above the salt is relatively simple, the salt

body is complex and there are low-velocity structures sub-salt.

Figure 1. (a) Section of the EAGE 2004 workshop synthetic model (Billette and Brandsberg-Dahl, 2005)

showing a complex salt body and low-velocity sub-salt structure. (b) The result of using a standard

multiscale, least-squares FWI algorithm (with offsets up to 30km) to recover the velocity structure if the

minimum frequency used in the inversion is 3 Hz. (c) The result of using the same standard, multiscale,

least-squares FWI algorithm to recover the velocity structure while only using frequencies between 0.5

and 2 Hz.

a b

c

82nd EAGE Conference & Exhibition 2020

8-11 December 2020, Amsterdam, The Netherlands

We choose to start, in a somewhat unrealistic but usefully indicative manner, with an initial model that

does not contain any salt or sub-salt structure. If we use a standard, multiscale FWI algorithm with a

least-squares objective function (Brittan and Jones, 2019 and references therein) and choose a starting

frequency that is typical of the limit of acceptable S/N in modern OBS with conventional sources (3 Hz)

we find that the algorithm recovers the shallow sediments and top-salt well but fails to insert the bulk

of the salt body and any accurate sub-salt structure (Figure 1(b)). However, if the data contains very

low-frequency energy (between 0.5 and 2Hz) – the same FWI algorithm and approach can recover both

the salt body and the sub-salt structure (Figure 1(c)). While 0.5Hz is very difficult in practice (due to

both the increase in ambient noise at low frequencies in all environments and the practical, mechanical

difficulties in generating large amounts of low-frequency energy) – this synthetic experiment illustrates

the value of low-frequency data to the inversion process – in both information content and resistance to

cycle-skipping.

A new seismic source

We have developed a new seismic source based on airgun technology that improves the signal-to-noise

in the low-frequency part of the seismic spectrum (typically showing an improvement in the 1.5-5 Hz

range relative to a conventional multi-gun seismic array (Brittan et al., 2019). The improvements are

achieved by careful control of the oscillations of the bubble created by the source. Key factors that

control both the creation and oscillations of this bubble are the final pressure of the source chamber, the

volume of the source chamber, the depth of tow and the way the air is released from the chamber. In

Figure 2 we compare examples from the input data for the FWI algorithm suite used in the modelling.

These data were collected in a survey in the Western Gulf of Mexico in which the both the new source

(volume 6000 cubic inch) and a conventional 5110 cubic inch multi-gun source array were recorded on

the same ocean-bottom node instruments. It should be noted that the OBN used in this survey were

commercial, industry standard units with 3 Hz roll-off hydrophones and 10 Hz roll-off geophones. The

data from the new source (Figure 2 right) shows considerable improvement in first arrival continuity at

low frequencies (<5Hz) than that of the conventional multi-gun seismic array.

Figure 2. Comparison of data from (left) the conventional 5110 cubic inch multi-gun array and (right)

the new source used in the survey. The data shown are receiver gathers from the same ocean-bottom

node. The data have been low-pass filtered in the frequency band to show the 4Hz, are shown with

offsets up to 45km and have been equalised based on the first arrival amplitude. Note the considerable

difference in S/N between data from the two sources. The time-axis length is 20 seconds.

82nd EAGE Conference & Exhibition 2020

8-11 December 2020, Amsterdam, The Netherlands

Comparison of model building results

The data from both the new source and the conventional source array were used in a model building

exercise with a suite of FWI algorithms. In a manner analogous to the synthetic experiment described

above, the starting model used was deliberately chosen to be as simple as possible – a velocity gradient

hung from the water bottom. It was known from previous surveys in the area, and obvious from

inspection of the raw data, that the area contains significant high-impedance contrast salt bodies, many

of which are close to the seabed. Thus, starting with such a simplistic velocity model offers a marked

challenge to the inversion algorithm.

In this exercise, offsets up to 30km were utilised and the data was restricted to the first arrivals. Thus,

the FWI algorithms were using only the refracted arrivals to derive the velocity model. Figure 3 (left)

shows a comparison of the data from the new source (in black/white) with the modelled data from the

starting model overlain in green (Figure 4(top)). It is clear that the starting model does not fit the real

data at all – in fact the differences are so large that any FWI algorithm using a least-squares objective

function will suffer from cycle-skipping at all offsets.

Figure 3. Comparison of the fit of the modelled data (derived using FWI) to the data from the new

source. (Left) The modelled data (in green) from the initial starting model (a velocity gradient hung

from the picked water bottom). (Right) The modelled data (in green) from the model derived using both

TT-FWI and LS-FWI.

Therefore, we started the model building exercise using a traveltime based FWI approach (using a

similar objective function to that described in Wang et al., 2018). The approach was run in a multiscale

approach with a starting frequency of 1Hz. Even after an inversion of data with a maximum frequency

of 1.5 Hz the FWI process could be seen to matching the first arrival data well. Figure 3 (right) shows

a match between the modelled data and field data after the FWI has been run with frequencies from 1-

4.5 Hz. The majority of the iterations used the traveltime objective function, however a number of final

iterations with a least-squares objective function were undertaken once it was clear from QC that cycle-

skipping in the fitting had been mostly eliminated. The same FWI process was replicated for both the

data from the new source and the data from the conventional source array.

In Figure 4, we compare the starting model (top) with the model derived using data from the new source

(middle) and the model derived using data from the conventional multi-airgun source array. It can be

seen that in both cases the FWI process has inserted high-velocity (4500 km/s and higher) salt bodies

throughout the section. However, the model derived using the new source data suffers from much less

non-geological variation than that derived using the conventional data. In particular, it can be noted that

in a small section of the model where a (human) interpreted salt body from an independent legacy project

was available, the FWI derived model from the new source has a salt body that matches well the top

and flank of the interpreted body. This is not the case for the model derived from the conventional,

multi-airgun source array.

82nd EAGE Conference & Exhibition 2020

8-11 December 2020, Amsterdam, The Netherlands

Figure 4. Comparison of (top) the initial starting model (a velocity gradient hung from the picked water

bottom); (middle) the model derived using both TT-FWI and LS-FWI and data from the new source

(bottom) the model derived using both TT-FWI and LS-FWI and data from the conventional 5110 cubic

inch airgun array. Marked in black is an interpretation of the top-salt boundary from one of the diapirs

derived during a previous model-building exercise.

Conclusions

We have developed a new source, based on airgun technology, that has been designed to improve the

signal-to-noise ratio in the low frequency range (1.5-5Hz) that is crucial to obtaining well-resolved earth

property models with FWI. In this paper we show that the velocity models derived using data from this

new source show clear improvements over those derived in a similar manner from data acquired with a

conventional source array.

Acknowledgements

The authors wish to thank BHP for permission to publish the data and ION for permission to publish

this paper. We also thank Ian Jones for his useful comments.

References

Alkhalifah, T., Sun, B.B. and Wu, Z., 2018. Full model wavenumber inversion: Identifying sources of information for the

elusive middle model wavenumbers. Geophysics, 83, R597-R610.

Billette. F.J. and Brandsberg-Dahl, S., 2005. The 2004 BP velocity benchmark. 67th Conference and Exhibition, EAGE,

Extended Abstracts, B035.

Brenders, A., Dellinger, J., Kanu, C., Li, Q. and Michell, S., 2018. The Wolfspar® field trial: Results from a low-frequency

seismic survey designed for FWI. SEG Technical Program Expanded Abstracts.

Brittan, J., Farmer, P., Brookes, D., Bernitsas, N. and Dudley, T., 2019. Enhanced low frequency signal to noise

characteristics of an airgun technology bases source. SEG Annual Meeting Workshop – New Technologies in Marine

Acquisition.

Brittan, J. and Jones, I.F., 2019. FWI evolution – from a monolith to a toolkit. The Leading Edge, 38, 179-184.

Wang, C., Farmer, P., Yingst, D., Jones, I., Martin, G. and Leveille, J., 2018. Traveltime based reflection full waveform

inversion. 80th Conference and Exhibition, EAGE Extended Abstracts.