Abstract Identifying gas hydrates and associated bottom simulating reflectors (BSRs) in seismic-reflection data requires the use of varied geophysical methods. Re-processed seismic data can primarily highlight seafloor features, but still show inherent signal limitations when resolving small-scale geological features. In contrast, seismic attributes applied to re-processed data have been successfully used in the recognition of hydrocarbon reservoirs. This work tests and applies an integrated seismic-attribute workflow for BSR mapping and qualitative interpretation of BSR-related anomalies, including those reflections commonly interpreted as representing free-gas beneath the BSR in the Makran Accretionary Wedge, offshore Pakistan. Seismic attributes were applied to the resolution-enhanced (re-processed) profile SO122-04a after a structure-oriented data conditioning that used dip-steered median filtering. They were followed by the extraction of amplitude attributes including Pseudo-Relief and Square-Root-of-Energy, and frequency decomposition including Fast Fourier Transform (FFT) and Continuous Wavelet Transform (CWT) evaluated at 20, 40, and 60 Hz. The results in this work show that Pseudo-Relief emphasizes the geometric expression of seismic anomalies and BSRs by illuminating their curvature and shape, while Square-Root-of-Energy highlights spatial variations in reflection energy across BSR intervals. FFTs and CWTs show comparatively stronger amplitude expression of BSRs and underlying reflections in the 40 and 60 Hz frequency bands. Finally, RGB color blending aids the visual identification of BSR-related anomaly zones (indicators of potential hydrate/free-gas systems), further supporting attribute-based mapping for the interpretation of BSR-related anomalies. Interpretations are qualitative in the absence of independent calibration data.
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Muhammad Kamal
Aamir Ali
Tiago M. Alves
Scientific Reports
Cardiff University
Quaid-i-Azam University
Saudi Aramco (Saudi Arabia)
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Kamal et al. (Tue,) studied this question.
www.synapsesocial.com/papers/69fc2c718b49bacb8b3480b4 — DOI: https://doi.org/10.1038/s41598-026-51110-z
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