Free-surface Multiple Attenuation Research Articles

Free-surface multiple attenuation and seismic deghosting for blended data using convolutional neural networks

Simultaneous source acquisition has become common over the past few decades for marine seismic surveys because of the increased efficiency of seismic acquisition by limiting the time, reducing the cost, and having less environmental impact than conventional single-source (or unblended) acquisition surveys. For simultaneous source acquisition, seismic sources at different locations are fired with time delays, and the recorded data are referred to as the blended data. The air-water interface (or free surface) creates strong multiples and ghost reflections for blended seismic data. The multiples and/or ghost reflections caused by a source in the blended data overlap with the primary reflections of another source, thus creating a strong interference between the primary and multiple events of different sources. We develop a convolutional neural network (CNN) method to attenuate free-surface multiples and remove ghost reflections simultaneously from the blended seismic data. The CNN-based solution that we develop operates on single traces and is not sensitive to the missing near-offset traces, missing traces, and irregular/sparse acquisition parameters (e.g., for ocean-bottom node acquisition and time-lapse monitoring studies). We illustrate the efficacy of our free-surface multiple attenuation and seismic deghosting method by presenting synthetic and field data applications. The numerical experiments demonstrate that our CNN-based approach for simultaneously attenuating free-surface multiples and removing ghost reflections can be applied to the blended data without the deblending step. Although the interference of primaries and multiples from different shots in the blended data makes free-surface multiple attenuation harder than the unblended data, we determine that our CNN-based method effectively attenuates free-surface multiples in the blended synthetic and field data even when the delay time for the blending is different in the training data than in the data to which the CNN is applied.

Multiple attenuation methods in short-offset 2D marine seismic data: a case study in Cendrawasih Bay

Real marine seismic data are typically embedded with free-surface multiples energy, which are troublesome in imaging an accurate seismic cross-section. In addition, more challenging situation is to bring optimum result with a short-offset streamer due to the coherent nature of multiples. In this study, we present a comparison of three methods for attenuating free-surface multiples energy in short-offset 2D seismic data from Cendrawasih Bay. Multiple attenuation methods include F-K filter, Radon transform, and Surface Related Multiple Elimination (SRME) are processed until the final Pre-Stack Time Migration (PSTM) results. Predictive deconvolution is applied in order to suppress short period multiples prior to free-surface multiple attenuation method. Predictive deconvolution successfully identifies and removes the predictable wavelet of short period multiples. Radon transform shows poor result in short-offset seismic data even if it is combined with F-K filter method. Combination of both F-K filter and SRME are successfully attenuate free-surface multiples and should be considered as proper solution to increase signal to noise ratio.

Free-surface multiple attenuation for blended data

Blended data sets are now being acquired because of improved efficiency and reduction in cost compared with conventional seismic data acquisition. We have developed two methods for blended data free-surface multiple attenuation. The first method is based on an extension of surface-related multiple elimination (SRME) theory, in which free-surface multiples of the blended data can be predicted by a multidimensional convolution of the seismic data with the inverse of the blending operator. A least-squares inversion method is used, which indicates that crosstalk noise existed in the prediction result due to the approximate inversion. An adaptive subtraction procedure similar to that used in conventional SRME is then applied to obtain the blended primary — this can damage the energy of primaries. The second method is based on inverse data processing (IDP) theory adapted to blended data. We derived a formula similar to that used in conventional IDP, and we attenuated free-surface multiples by simple muting of the focused points in the inverse data space (IDS). The location of the focused points in the IDS for blended data, which can be calculated, is also related to the blending operator. We chose a singular value decomposition-based inversion algorithm to stabilize the inversion in the IDP method. The advantage of IDP compared with SRME is that, it does not have crosstalk noise and is able to better preserve the primary energy. The outputs of our methods are all blended primaries, and they can be further processed using blended data-based algorithms. Synthetic data examples show that the SRME and IDP algorithms for blended data are successful in attenuating free-surface multiples.

Data matching for free-surface multiple attenuation by multidimensional deconvolution

A common strategy for surface-related multiple elimination of seismic data is to predict multiples by a convolutional model and subtract these adaptively from the input gathers. Problems can be posed by interfering multiples and primaries. Removing multiples by multidimensional deconvolution (MDD) (inversion) does not suffer from these problems. However, this approach requires data to be consistent, which is often not the case, especially not at interpolated near-offsets. A novel method is proposed to improve data consistency prior to inversion. This is done by backpropagating first-order multiples with a time-gated reference primary event and matching these with early primaries in the input gather. After data matching, multiple elimination by MDD can be applied with a deterministic inversion scheme.

Free-surface multiple attenuation using inverse data processing in the coupled plane-wave domain

Free-surface multiples contain a large amount of energy in the seismic data because of large reflectivity of the free surface. We propose a method for free-surface multiple attenuation by a simple muting in the inverse coupled plane-wave domain. Our method is based on inverse data processing and the well-known 2D invariant embedding technique. If the lateral variation in subsurface structure is smooth, the data are well compressed in the 2D coupled plane-wave domain, reducing computation costs and stabilizing the inversion procedure. Surface multiples and primaries are well separated in the inverse coupled plane-wave domain, and multiples can be eliminated by simple muting, which does not damage the primary energy. To reduce artifacts, wraparound, and noise introduced by the frequency-domain data matrix inversion, horizontal and vertical tapers are applied. A least-squares matrix inversion method is chosen to stabilize the inversion. Synthetic data examples show that plane-wave inverse data processing is stable and successful in attenuating free-surface multiples.

An analysis of 2D and 3D multiple attenuation for a canonical example

Three-dimensional formulations of free-surface multiple attenuation for multioffset seismic data are well known. They are not yet used in practice because they require very dense source-receiver coverage, which is still out of reach with existing seismic-acquisition systems. The development of alternative solutions based on 2D algorithms depends on our understanding of the relationship between 2D and 3D free-surface multiple-attenuation methods. This paper attempts to enhance this understanding by establishing the relationship between 2D and 3D inverse scattering free-surface multiple attenuation. A 3D model consisting of three scattering points (one scattered point located in the vertical plane containing the shooting line and the other two points outside this plane) in a homogeneous medium (for which the exact pressure field is analytically known) is used to show that the 2D inverse scattering multiple-attenuation algorithm predicts all free-surface multiples as does its 3D counterpart but with some traveltime and amplitude errors. One implication of this result is that the current 2D inverse scattering multiple-attenuation algorithm, with an appropriate 2D-to-3D correction, can be used to predict the free-surface multiples for data containing out-of-plane scattering.

Separation of upgoing and downgoing wavefields using the boundary element method

Summary The boundary element method has been applied for decomposing seafloor measurements into upgoing and downgoing waves. Two complementary solutions in the forms of a linear matrix system and a multiple multipole expansion are obtained without any assumptions about the subsurface. If the elastic parameters below the seafloor are known, these solutions are explicit. They do not require any knowledge of the source signature. Basic components—such as Green’s function computation—can be replaced by alternative acoustic or elastic solutions. Results are useful for deghosting, free-surface multiple attenuation and P/S splitting of ocean-bottom cable data.