Advancing Seismic Interpretation through Spectral Decomposition: Techniques, Applications, and Innovative Visualization

Summary Spectral decomposition, or time-frequency analysis, is an advanced seismic attribute analysis technique that decomposes seismic data into its frequency components, providing different insights into subsurface geology. This study examines the principles, methodologies, and applications of spectral decomposition, including methods such as Short-Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT), and high-resolution techniques like Basis Pursuit (BP) and Empirical Mode Decomposition (EMD), to address varying resolution requirements in seismic data analysis. Emphasis is placed on detecting hydrocarbon reservoirs using amplitude variation with frequency (AVF) analysis and seismic attenuation phenomena, delineating thin beds, and enhancing seismic visualization. The AVF analysis emerges as a simple and handy tool for identifying hydrocarbon presence through frequency-dependent attenuation, even in the absence of pre-stack seismic data, with attenuation phenomena linked to lithology and fluid interactions offering valuable insights for reducing exploration risks. Through case studies, the spectral decomposition with AVF analysis is demonstrated, showcasing its effectiveness in delineating subsurface features, mitigating interpretation pitfalls, and accurately detecting hydrocarbons. This work highlights the transformative role of spectral decomposition in seismic interpretation, blending diverse perspectives, analytical precision, and innovative visualization methods to advance geological exploration.