A physics-informed deep learning approach for 3D acoustic impedance estimation from seismic data: application to an offshore field in the Southwest Iran

Abstract This study presents a hybrid seismic inversion framework to estimate 3D acoustic impedance volumes in a geologically complex and data-limited environment. The approach integrates physics-informed pseudo-well generation, based on calibrated rock physics modeling and variogram statistics, wit...

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主要な著者:	Arash Ghiasvand, Abdolrahim Javaherian, Maryam Amirmazlaghani, Mohammad Reza Saberi, Hadi Mahdavi Basir
フォーマット:	Artigo
言語:	Inglês
出版事項:	Nature Portfolio 2025-11-01
シリーズ:	Scientific Reports
主題:	Seismic inversion Acoustic impedance Pseudo-well generation DFNN Rock physics modeling
オンライン･アクセス:	https://doi.org/10.1038/s41598-025-23611-w
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