Akademik Veri Yönetim Sistemi
Earthquake Research Advances, 2026 (Scopus)
Seismic tomography has revolutionized Earth sciences by enabling three-dimensional imaging of the planet's interior, thus providing critical insights into geological structures and processes. This paper reviews the evolution of seismic tomography from its inception in the 1970s to present-day advancements, examines the persistent limitations of traditional approaches, and explores the emerging role of artificial intelligence (AI) in this field. We outline how early developments in seismic tomography laid the groundwork for imaging Earth's subsurface, and how technological progress – including improved algorithms and computational power – has enhanced resolution and model accuracy. Key limitations, such as the non-uniqueness of solutions, limited resolution due to data coverage and frequency content, and high computational costs, are discussed in detail. We then highlight recent efforts to integrate AI and machine learning techniques into seismic tomography workflows, aiming to accelerate data processing, improve inversion techniques, and potentially overcome some longstanding challenges. Findings indicate that while classical seismic tomography has dramatically advanced our understanding of Earth's interior, it faces inherent constraints that novel AI-driven methods are beginning to address. The significance of this review lies in synthesizing historical progress with modern innovations, providing a comprehensive perspective on how AI can complement geophysical expertise to further refine seismic imaging in Earth sciences.