Model-driven deep learning-based seismic super-resolution inversion method

US11226423B1 · kind B1 · utility

Assignee

• XI'AN JIAOTONG UNIVERSITY · CN

Inventors

• Jinghuai Gao · Tuji, CN
• Hongling CHEN · Nanhu, CN
• Zhaoqi Gao · Xi'an, CN
• Chuang Li · Chengdu, CN
• Lijun Mi · Xi'an, CN
• Jinmiao Zhang · Xi'an, CN
• Qingzhen Wang · Xi'an, CN

Key dates

• Filing date: Jul 8, 2021
• Grant date: Jan 18, 2022
• Expiry date: Jul 8, 2041

Classification

• Technology area (CPC G): Physics
• CPC primary: G01V2210/665
• WIPO field: Measurement
• WIPO sector: Instruments

Abstract

A model-driven deep learning-based seismic super-resolution inversion method includes the following steps: 1) mapping each iteration of a model-driven alternating direction method of multipliers (ADMM) into each layer of a deep network, and learning proximal operators by using a data-driven method to complete the construction of a deep network ADMM-SRINet; 2) obtaining label data used to train the deep network ADMM-SRINet; 3) training the deep network ADMM-SRINet by using the obtained label data; and 4) inverting test data by using the deep network ADMM-SRINet trained at step 3). The method combines the advantages of a model-driven optimization method and a data-driven deep learning method, and therefore the network has the interpretability; and meanwhile, due to the addition of physical knowledge, the iterative deep learning method lowers requirements for a training set, and therefore an inversion result is more reliable.