Surface-related multiple estimation and removal with focus on shallow water

Research output: ThesisDissertation (TU Delft)

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For exploration and development of the earth, seismic surveys are acquired to provide information about the subsurface, within specifications of accuracy set by geologists and engineers, and within business constraints on budgets and turn-around time for processing and interpretation of the data. The case of seismic surveys that are acquired, partly or entirely, in shallow water is relevant for the industry worldwide. However, the acquisition and processing for shallow water seismic surveys requires considerable modifications of standard procedures to meet the survey goals. In this work, the focus is on modifications in processing and in particular with respect to the handling of multiply scattered energy, assuming standard acquisition practices.

Multiple scattering is a significant wave phenomenon when seismic waves propagate through the earth. Its corresponding energy, i.e., seismic multiples, are usually unwanted due to the interference with primary reflections. The traditional seismic surface-related multiple estimation and removal method is limited by both the unrecorded data reconstruction (e.g., the missing near offsets and the data gap between the crosslines) and the subsequent multiple adaptive subtraction performance. These issues become even more severe for the shallow-water environment, which is typically defined as being around 50-200 m within the exploration seismic frequency range (i.e., 2-120 Hz) in this thesis. Shallow water creates highly curved seismic reflection events with strong lateral amplitude variations, and complex overlap between primaries and surface-related multiples. Conventional data reconstruction methods fail to tackle the missing data in shallow water, and are even more problematic in 3D. In addition, the dilemma between primary damage and surface multiple leakage during the adaptive subtraction is very much present for shallow-water data.

An integrated closed-loop surface-related multiple estimation (CL-SRME) and full-wavefield migration (FWM) framework for better primary and surface-related multiple estimation, which is able to support CL-SRME with good-quality near offsets in order to avoid primary estimation failure that typically occurs in shallow-water environments, is proposed to attack the unrecorded data reconstruction issue. We suggest to use multiples to provide information on the missing near-offset data by using FWM, where primaries and surface multiples together create an image of the shallow subsurface. Taking advantage of FWM - with its closed-loop simultaneous primaries and multiples imaging approach - as the data reconstruction method and feeding the reconstructed near offsets to CL-SRME are the most important components to tackle the shallow-water issues in a physically consistent manner. This new integrated framework will have its main impact on a full 3D implementation with coarse sampling. Therefore, a similar cascaded framework for 3D surface-related multiple estimation in shallow-water scenarios, which consists of a data reconstruction step via 3D FWM and a surface multiple estimation step via a 3D SRME-type method, is also introduced in the thesis. Improvements on estimating surface multiples and primaries, due to good data reconstruction via FWM, have been proved on both 2D and 3D synthetic data. Despite of lacking an accurate subsurface velocity model for 2D field data, the FWM reconstructed near-offset water-bottom reflection still improves the quality of the estimated surface multiples and primaries.

In order to mitigate the surface-related multiple adaptive subtraction dilemma, we have also introduced a two-step framework for surface multiple leakage extraction in this thesis, and thus extended our seismic multiple processing toolbox. The aforementioned two-step framework based on local primary-and-multiple orthogonalization (LPMO) is both versatile and efficient for leaked multiple extraction, therefore, primaries can be better preserved without leaving much multiple energy. The initial estimation step usually prefers SRME with a conservative adaptive subtraction or any conservative multiple estimation method, and LPMO is followed to compensate the initial estimated primaries and multiples. Promising multiple leakage extraction has been achieved on both synthetic and field data sets. Although effective compared to standard subtraction, LPMO is slow and computationally intensive. Therefore, a fast LPMO (FLPMO) using a scaled point-by-point division, rather than the time-consuming shaping regularization-based iterative inversion, is further introduced to accelerate the whole process. Results on two different field data sets display a very similar multiple leakage extraction performance compared to LPMO, while indicating that the scaled point-by-point division in FLPMO is approximately 40 times faster than the shaping regularization-based inversion in LPMO. Moreover, the complete FLPMO framework is approximately four times faster than the LPMO framework, and thereby is now equivalent to the industry-standard L2 adaptive subtraction.

With the advance of deep learning (DL) technology, the aforementioned two issues in shallow water can also be investigated via a U-Net based DL neural network (NN) framework. More specifically, a DL-based de-aliasing NN is introduced for the initial surface multiple estimation, where the strong data fitting power of DL can directly project the aliased multiples, due to coarse sampling, to its corresponding unaliased target multiples. Meanwhile, a DL-based adaptive subtraction NN is proposed with both total full wavefield and the predicted multiples as two input channels to overcome the adaptive subtraction dilemma. In this way, the robust physics, i.e., the estimated multiples, is used and the synthetic primary labels can be helpful to the framework. Note that the data distribution between training and test data plays a significant role on these U-Net based applications. Training on field data and test on nearby field data shows the best performance due to a similar data distribution.

Shallow water is very challenging for surface-related multiple estimation. Physics-based deterministic approaches, e.g., FWM-based data reconstruction and LPMO, can help geophysicists better understand and partially solve the essentials of the problem. For poorly described deterministic problems, e.g., adaptive subtraction and multiple de-aliasing, DL can find the underlying relationships that are not easily achievable by the deterministic methods. Combination of deterministic methods and DL will result in an optimal performance. This is where further research should concentrate on.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Delft University of Technology
  • Verschuur, D.J., Supervisor
  • de Jong, N., Supervisor
Thesis sponsors
Award date27 Oct 2022
Print ISBNs978-94-6366-614-5
Publication statusPublished - 2022


  • Surface-related multiple elimination
  • Seismic imaging
  • Seismic data processing
  • Deep Learning


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