Seismic reflection imaging aims to generate a representation of the subsurface of the earth using acoustic or elastic waves recorded in the form of seismic data. During the processing of the data for imaging, we pick a part of the data as signal and discard the rest as noise. Since so-called primary wavefields carry the single-scattering reflection response of the subsurface, they are assumed to be sufficient to carry out reflection imaging. This also means that significant effort is devoted towards removing not only the noise but also the multiple reflection events to avoid imaging artefacts, also known as cross-talk. Surface-related multiples are the multiples generated during the marine acquisition by at least one downward reflection at the water-air boundary, and tend to be the strongest in amplitude compared to the other multiples. Over the past several decades, many novel techniques have been developed to remove the surface-related multiples effectively. While we are getting better at primary-multiple separation, it is still a very challenging and expensive problem. In recent years, multiples have gained recognition as valuable signals, not just noise. Multiples also contain the reflection responses of the subsurface and since they travel different paths they often contain additional information about the subsurface compared to the primaries-only wavefields. Imaging with primaries and multiples without separation is the way forward as it avoids the expensive multiple removal steps along with providing (potentially) additional illumination from the multiples...@en

To get the best result for seismic imaging using primary reflections, data with densely-spaced sources and receivers are ideally preferred. However, dense acquisition can sometimes be hindered by various obstacles, like platforms or complex topography. Such areas with large data gaps may deter exploration or monitoring, as conventional imaging strategies would either provide poor seismic images or turn out to be very expensive. Surface-related multiples travel along different paths compared to primaries, illuminating a wider subsurface area and hence making them valuable in case of data with large gaps. We propose different strategies of using surface-related multiples to get around the problem of imaging in the case of a large data gap. Conventional least-squares imaging methods that incorporate surface-related multiples do so by re-injecting the measured wavefield in the forward-modelling process, which makes it still sensitive to missing data. We introduce a 'non-linear' inversion approach in which the surface multiples are modelled from the original source field. This makes the method less dependent on the receiver geometry, therefore, effectively exploiting the information from surface multiples in cases of limited illumination. However, such an approach is sensitive to the knowledge of the source properties. Therefore, we propose a 'hybrid' method that combines the non-linear imaging method with the conventional 'linear' multiple imaging method, which further improves our imaging result. We test the methods on numerical as well as field data. The results indicate substantial removal of artefacts in the image derived from linear imaging methods due to incomplete data, by exploiting the surface multiples to a maximum extent.

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Although considered as noise in the past, multiples are being increasingly seen as a tool for better illumination of the subsurface. We explore the different strategies of exploiting the surface-related multiples specifically in the case of large acquisition gaps. Surface-related multiples travel via different propagation paths compared to the primaries and hence, illuminate a wider area. This property makes them valuable in cases of limited illumination. Existing migration methods incorporate surface-related multiples in imaging by re-injecting the total measured data as a downgoing wavefield; this makes the method dependent on a dense receiver configuration and, therefore, sensitive to missing data. Using 3D synthetic examples we will illustrate a `non-linear' inversion approach in which all multiples are modeled from the original source field. This makes the method less dependent on the receiver geometry, therefore, helping us in case of limited illumination. We also discuss a `hybrid' method on the same 3D model that utilises the benefits of both `linear' and `non-linear' methods. The results indicate substantial reduction of migration holes compared to the `linear' inversion methods, which is demonstrated on a 3D synthetic data with an obstruction zone from the platform.

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Multiples have now become a useful commodity in seismicimaging as they travel different paths compared to primary waves, thereby extending the illumination of the subsurface. With the popularity of acquisition using ocean-bottom nodes (OBN), it becomes necessary to explore the different strategies of imaging with multiples rather than removing them. We discuss few such methods of using surface-related multiples to get around the problem of imaging with a large acquisition gap, e.g., due to a platform. We test the ‘linear’ least-squares imaging method that incorporates surface-related multiples via re-injection of measured data on numerically generated OBN data with a large acquisition gap. To reduce the dependency of such methods on dense surface acquisition, we will propose a ‘non-linear’ inversion approach in which the surface multiples are modeled from the original source field. Further improving the imaging result with such acquisition constraint, we build a ‘hybrid’ method that combines the ‘non-linear’ imaging method with the conventional ‘linear’ multiple imaging method. We demonstrate the methods on 2-D numerical data. Despite large acquisition gaps, the results indicate mitigation of effects on the image previously caused by incomplete data.@en

While surface-related multiples have long been considered as noise in seismic data, recently the consensus is changing to accept multiples as a tool for better illumination of the subsurface. In this paper we explore the different strategies of exploiting the surface-related multiples specifically in case of large acquisition gaps. Since surfacerelated multiples travel different propagation paths compared to the primaries, they illuminate a wider area making them vital in such cases of limited illumination. Existing imaging methods include surface-related multiples by reinjecting the total measured data as a downgoing wavefield. This however, makes the method dependent on a dense receiver configuration and, therefore, sensitive to missing data. Addressing a way around this problem, we will illustrate a `non-linear' inversion approach in which all multiples are modeled from the original source field. Such modeling makes the method less dependent on the receiver geometry, therefore, helping us in case of limited illumination. We also demonstrate a `hybrid' method on both synthetic and field data to overcome the effects of incomplete data by utilising the benefits of both `linear' and `non-linear' method. The results indicate substantial mitigation of effects on the image previously caused due to incomplete data and better images compared to the `linear' inversion methods. @en

Seismic acquisition in an area can often get hindered by reasons such as complex topography, infrastructure (e.g. platforms) or lack of access due to legal and environmental reasons. Such areas with possibilities of large data gaps may deter exploration or monitoring, as the conventional imaging strategies would either provide bad seismic images or turn out to be very expensive. Surface-related multiples travel different paths compared to primaries, illuminating a wider subsurface area. This property makes the surface-related multiples particularly important in case of data with large gaps. In this paper we show different strategies of using surface-related multiples to get around the problem of imaging with a large data gap. Conventional least-squares imaging methods that incorporate surface-related multiples do so by re-injecting the measured wavefield, which makes it sensitive to missing data. Therefore, we …@en

Often seismic surveys are restricted due to unavoidable circumstances such as presence of platforms, busy sea routes and bad traces. These lead to missing data, which affects the migrated image severely. Since re-shooting the data to fill in these gaps is extremely expensive, migration strategies utilising the available data need to be developed in order to overcome this problem. Surface-related multiples illuminate a wider area, as they travel different propagation paths compared to primaries, becoming indispensable in such cases of limited illumination. However, existing migration methods that include surface-related multiples for imaging do so by re-injecting the total data as a downgoing wavefield; this makes the method dependent on dense receiver configuration and, therefore, inoperative in case of missing data. In this paper we demonstrate a ‘non-linear’ inversion approach on real data to overcome the effects of incomplete data. The results indicate substantial mitigation of effects on the image due to incomplete data and better images compared to the ‘linear’ inversion methods. Since in this method all the surface multiples are incrementally built from the original source field, it becomes dependent on the accuracy of the forward physical model and on the knowledge of the source wavelet. Therefore, we finally demonstrate that a hybrid approach combining both the ‘linear’ and ‘non-linear’ approaches provides the best results.@en

Seismic surveys are often restricted due to physical constraints resulting in missing traces, affecting the migrated image severely. To add to the plight, in traditional processing multiples are removed as noise, further reducing
the information contained in the signal. However, it has been established that multiples carry extra information that can become crucial, especially in cases of finite illumination. Therefore, it has become necessary to find the best strategy of using multiples for imaging. In this paper we discuss using surface-related multiples particularly in the case of incomplete seismic data. Current imaging methods incorporating surface-related multiples via reinjection of the measured wavefields bank heavily on dense receiver sampling, hence, falling short in case of limited receiver sampling. To tackle this issue, we introduce a migration method that incorporates a non-linear inversion approach, modelling all surface multiples from the original source field. This method gives better
imaging result and mitigates the effect of incomplete data substantially. Although both linear and on-linear approach have their pros and cons, via an example we demonstrate that a hybrid approach of both methodologies provides the best results.@en

As multiples are becoming indispensable in imaging methods, it has become crucial to find the best way of exploiting information from them. In this chapter we discuss the different strategies involving imaging using multiples. Usually, surface multiples are imaged by re-injecting the total data as a downgoing wavefield such that the last bounce of each multiple is imaged. We then suggest an addition to the current migration methods by involving a non-linear inversion approach where all surface multiples are built from the original source field. The preliminary results indicate improvement in the resolution of reflectivity imaging.@en

Seismic surveys are often restricted due to finite number of receivers as well as missing near and far offsets, thus affecting the illumination of subsurface. Surface-related multiples are known to carry valuable information, making them indispensable for dealing with these cases of finite illumination. As the use of surface-related multiples is gaining prominence in imaging methods, it has become crucial to find the best way of exploiting information from them. Usually, these imaging methods image surface-related multiples by re-injecting the total data as a downgoing wavefield, hence banking heavily on a dense receiver configuration making it sensitive to missing data. In this paper we discuss different imaging strategies using surface-related multiples in case of missing offset data. To overcome the effect of incomplete receiver data, we also suggest an addition to the current migration methods that involves a non-linear inversion approach, where all surface multiples are built from the original source field. This method gives better results compared to the linear inversion methods and mitigate the effect of incomplete data substantially.

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