Optimizing Subsurface Characterization Through Target-Specific Peg-Leg Multiple Attenuation, Lower Indus Basin, Pakistan

Abstract

Seismic Data Processing (SDP) is a fundamental and transformative step in exploration geophysics, playing a pivotal role in enhancing the interpretability and resolution of seismic signals recorded from the subsurface. It involves a systematic application of algorithms, workflows, and parameter tuning aimed at maximizing the signal-to-noise ratio, thereby enabling more accurate imaging of geological structures. This study focuses on the re-processing of legacy 2D seismic data acquired in 1987-88 along a regional profile in the structurally complex area of the Lower Indus Basin, Pakistan. The LIB is characterized by intense deformation, including well-developed thrust faults and folded anticlines that form significant hydrocarbon traps, primarily within the Cretaceous-age reservoir formations. A crucial aspect of seismic data processing in this research is the identification and removal of possible multiples, particularly peg-leg and interbed multiples, which often mimic primary reflections and can mislead interpreters during structural and stratigraphic analysis. If not adequately addressed, multiples can obscure true geological features and create false anomalies, severely compromising exploration outcomes. In this study, careful demultiple analysis was conducted as part of the re-processing workflow. Specialized algorithms, such as Surface-Related Multiple Elimination (SRME) (Bisley, 2005) and Radon transform-based filtering, (Trad. D, 2003) were evaluated and applied where necessary to suppress these coherent noise patterns. These processes were supported by proper velocity discrimination and muting strategies to isolate primaries from multiple-contaminated wavefields. The primary objective of this study is to demonstrate how advanced reprocessing techniques, when guided by appropriate geophysical parameters and software-specific modules, can significantly enhance subsurface imaging. The same dataset, previously processed at a different processing center, yielded limited interpretational value due to outdated algorithms, lack of adequate multiple suppression, and suboptimal parameterization. In contrast, this study applies to a tailored workflow using modern processing modules that led to markedly improved imaging results. Key processes such as noise attenuation (using Time Variant Filtering, F-K filters, and Bandpass filters), True Amplitude Recovery (TAR) for compensating inelastic losses, and predictive deconvolution for vertical resolution enhancement were meticulously calibrated. Furthermore, precise velocity analysis and application of PreStack Kirchhoff Time Migration (PSTM) successfully repositioned dipping reflectors and clarified fault geometries critical for trap delineation. The comparative analysis between the newly processed data and the legacy version illustrates how seismic processing is not merely a mechanical task but a highly interpretative and iterative one. The correct choice of algorithms, sequence, and parameter settings, often software-dependent can redefine exploration outcomes. This case study underscores the importance of seismic data re-processing, particularly multiple elimination, as a powerful tool to unlock overlooked subsurface features, ultimately influencing exploration decisions and hydrocarbon prospectivity assessments.