Deep Learning-based Seismic Reflectivity Estimation by Pre-training on Labeled Synthetic Data and Physics-guided Fine-tuning in Field Data

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ID: 324344
2026
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Abstract
Summary Reflectivity estimation aims to enhance the resolution of seismic data, providing crucial support for the detailed inversion of reservoir parameters. We propose a method that combines supervised pre-training with synthetic data and physics-guided fine-tuning in field data to estimate reasonable reflectivity from seismic data. Initially, a U-shaped network is pre-trained by supervised learning on a large amount of synthetic seismic data. Subsequently, multiple geophysically meaningful constraints including structure-oriented smoothness, reflectivity sparsity, and data reconstruction, are introduced to formulate a self-supervised or unsupervised learning mechanism to fine-tune the pre-trained model so that it is better adapted to field data for obtaining more reasonable reflectivity. The pre-trained model provides an initial reflectivity that aligns with fundamental structural features. Based on the initial estimate, the model is further optimized through physics-guided fine-tuning to obtain a more reasonable reflectivity estimation that better reflects the characteristics of the field data and geophysical priors. Furthermore, the well-log-based correlation evaluation metric is applied to automatically and adaptively determine the early-stopping point of the fine-tuning process where favorable results are achieved. Experiments on synthetic and field seismic data confirm that the proposed method yields reasonable and high-resolution reflectivity estimation.
Reference Key
openalex_W7201960120 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yuting Wang, Jintao Li, Xiaoming Sun, Xinming Wu
Journal geophysical journal international
Year 2026
DOI
10.1093/gji/ggag317
URL
https://doi.org/10.1093/gji/ggag317
Keywords Keywords not found

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