Removal Of Internal Multiples Research Articles

Unsupervised Learning for Seismic Internal Multiple Suppression Based on Adaptive Virtual Events

Seismic internal multiples are the key factors affecting the accuracy and reliability of velocity analysis and migration. The removal of internal multiples is a challenging direction. To effectively remove the internal multiples from the seismic data, we propose the unsupervised deep neural network (DNN) combined with the adaptive virtual events (AVEs) method. First, we use the AVE method to get the predicted internal multiples, which can calibrate the true internal multiples in the original data, also called the full wavefield data. Second, the unsupervised learning with the DNN is used as a nonlinear operator to minimize the difference between the estimated internal multiples and original data. The trained DNN can obtain the estimated internal multiples through the predicted internal multiples, thereby completing the suppression of the internal multiples. Since our proposed unsupervised learning is essentially an optimization process, it does not require true primaries as the label data to participate in the training process for the DNN. Therefore, our proposed method can deal with the problem of lack of training set and would have some good practical application value with low computational cost. The effectiveness and efficiency of our proposed method are verified through two sets of synthetic data and one land field data examples.

Removal of internal multiples by iterative construction of virtual primaries

Removal of internal multiples by iterative construction of virtual primaries

True-azimuth 3D inverse scattering series method for internal multiple attenuation

Removal of internal multiples is a long-standing problem and is still very challenging for the industry. The inverse scattering series (ISS) method is one of the advanced approaches addressing this...

Exemplifying the specific properties of the inverse scattering series internal-multiple attenuation method that reside behind its capability for complex onshore and marine multiples

The world of petroleum exploration constantly demands higher efficacy at every link in the data processing chain, from preconditioning to imaging and inversion. Within that chain, the removal of internal multiples constitutes a particularly resilient problem, whose resolution has only partially benefited from the advent of the data-driven technologies which have transformed the practice of free-surface multiple elimination.

Clarifying the underlying and fundamental meaning of the approximate linear inversion of seismic data

Linear inversion is defined as the linear approximation of a direct-inverse solution. This definition leads to data requirements and specific direct-inverse algorithms, which differ with all current linear and nonlinear approaches, and is immediately relevant for target identification and inversion in an elastic earth. Common practice typically starts with a direct forward or modeling expression and seeks to solve a forward equation in an inverse sense. Attempting to solve a direct forward problem in an inverse sense is not the same as solving an inverse problem directly. Distinctions include differences in algorithms, in the need for a priori information, and in data requirements. The simplest and most accessible examples are the direct-inversion tasks, derived from the inverse scattering series (ISS), for the removal of free-surface and internal multiples. The ISS multiple-removal algorithms require no subsurface information, and they are independent of earth model type. A direct forward method solved in an inverse sense, for modeling and subtracting multiples, would require accurate knowledge of every detail of the subsurface the multiple has experienced. In addition, it requires a different modeling and subtraction algorithm for each different earth-model type. The ISS methods for direct removal of multiples are not a forward problem solved in an inverse sense. Similarly, the direct elastic inversion provided by the ISS is not a modeling formula for PP data solved in an inverse sense. Direct elastic inversion calls for [Formula: see text], [Formula: see text], [Formula: see text], … data, for direct linear and nonlinear estimates of changes in mechanical properties. In practice, a judicious combination of direct and indirect methods are called upon for effective field data application.

Removal of internal multiples with the common-focus-point (CFP) approach: Part 1 — Explanation of the theory

Removal of surface and internal multiples can be formulated by removing the influence of downward-scattering boundaries and downward-scattering layers. The involved algorithms can be applied in a model-driven or a data-driven way. A unified description is proposed that relates both types of algorithms based on wave theory. The algorithm for the removal of surface multiples shows that muted shot records play the role of multichannel prediction filters. The algorithm for the removal of internal multiples shows that muted CFP gathers play the role of multichannel prediction filters. The internal multiple removal algorithm is illustrated with numerical examples. The conclusion is that the layer-related version of the algorithm has significant practical advantages.

Removal of internal multiples with the common-focus-point (CFP) approach: Part 2 — Application strategies and data examples

In the past, the surface-multiple-removal method based on the feedback model has been successfully applied to many different field data sets. The extension of surface to internal multiples can be made by replacing shot records with common-focus-point (CFP) gathers, a CFP gather representing focused data with one source in the subsurface and all receivers at the surface (or vice versa for a receiver gather). The internal-multiple-removal algorithm can be formulated in terms of boundary-related and layer-related versions. In the boundary-related version, the internal multiples are removed for one downward-scattering reflector at a time. In the layer-related version, the internal multiples are removed for a sequence of downward-scattering reflectors at a time. An exact velocity model is not required, but proper muting is critical; muting becomes straightforward in the CFP domain. The strategy for applying the two versions of the multiple-removal algorithm is demonstrated on physical-model and field data. One can conclude that the layer-related version is the most appropriate in most situations because it requires less user action and does not need exact knowledge of the multiple-generating boundary.

Estimation of multiple scattering by iterative inversion, Part I: Theoretical considerations

A review has been given of the surface‐related multiple problem by making use of the so‐called feedback model. From the resulting equations it has been concluded that the proposed solution does not require any properties of the subsurface. However, source‐detector and reflectivity properties of the surface need be specified. Those properties have been quantified in a surface operator and this operator is estimated as part of the multiple removal problem. The surface‐related multiple removal algorithm has been formulated in terms of a Neumann series and in terms of an iterative equation. The Neumann formulation requires a nonlinear optimization process for the surface operator; while the iterative formulation needs a number of linear optimizations. The iterative formulation also has the advantage that it can be integrated easily with another multiple removal method. An algorithm for the removal of internal multiples has been proposed as well. This algorithm is an extension of the surface‐related method. Removal of internal multiples requires knowledge of the macro velocity model between the surface and the upper boundary of the multiple generating layer. In part II (also published in this issue) the success of the proposed algorithms has been demonstrated on numerical experiments and field data examples.