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Despite the explosion of new, innovative technologies in the area of multiple identification and subsequent attenuation, their applicability is mostly limited to marine environments especially in deep water. In land seismic data sets however, the application of such multiple-elimination methodologies is not always straightforward and in many cases poor results are obtained. The unique characteristics of land seismic data (i.e., noise, statics and coupling) are major obstacles in multiple estimation and subsequent elimination. The well-defined surface multiples present in marine data are rarely identifiable in land data. Particularly in desert terrains with a complex near surface and low-relief structures, surface multiples hardly exist. In most cases, we are dealing with so called “near-surface-related multiples.” These are primarily internal multiples generated within the complex near surface.

Multiple removal is a longstanding problem in exploration seismology. Although methods for removing multiples have advanced and have become more effective, the concomitant industry trend toward more complex exploration areas and difficult plays has often outpaced advances in multiple-attenuation technology. The topic of multiples, and developing ever more effective methods for their removal, remains high in terms of industry interest, priority and research investment. The question as to whether today, in 2011, multiples or multiple removal is winning is a way of describing what we are about to discuss. This paper focuses on recent advances, progress and strengths and limitations of current capability and a prioritized list of open issues that need to be addressed.

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.

The estimation and subsequent elimination of internal multiples in land seismic data is one of the most challenging steps in data processing. Although marine multiple elimination techniques, such as the SRME technology, are well established, in land their implementation is not straightforward and in many cases poor results are obtained. In this paper we use theoretical concepts from the Inverse Scattering Series (ISS) formulation and develop computer algorithms for land internal multiple elimination. The key characteristic of the ISS-based methods is that they do not require any information about the subsurface, i.e., they are fully data driven. Internal multiples from all possible generators are computed and adaptively subtracted from the input data. These methodologies can be applied preand post-stack and their performance is demonstrated using realistic synthetic and field datasets from the Arabian Peninsula. These are the first published results of the application of the ISS internal multiple attenuation method to the daunting challenge of land internal multiples.

Removal of multiples from seismic reflection data is an important preprocessing step before conventional seismic imaging and inversion in most onshore and offshore environments. While many methods have been developed and successfully used to remove free-surface multiples, internal multiple attenuation remains a challenge when working in land and complex marine environments. The inverse scattering series (ISS) internal multiple algorithm is a data-driven tool to predict all orders of internal multiples for all horizons at once, without requiring subsurface information. However, use of the multidimensional version of this algorithm has been limited due to high computational cost, which increases with the maximum output frequency in the prediction. Even with the recent advances in computer hardware, the cost of the multidimensional algorithm remains expensive. To overcome this problem, we use the quasi-Monte Carlo integration technique that can significantly improve the computational efficiency of the multidimensional ISS internal multiple algorithm. The efficiency is improved by reducing the number of samples being evaluated and combining multiple integrals into a single summation.