Skip to Main Content
Skip Nav Destination
GEOREF RECORD

High-resolution acoustic-impedance inversion based on a deep-learning-aided representation model of nonstationary seismic data

Gao Zhaoqi, Yang Linsen, Yang Yang and Gao Jinghuai
High-resolution acoustic-impedance inversion based on a deep-learning-aided representation model of nonstationary seismic data
Geophysics (December 2024) 89 (6): R521-R539

Abstract

Building a high-resolution acoustic-impedance (AI) model based on nonstationary seismic data plays a key role in reservoir predictions. However, the common AI inversion methods are faced with two main shortcomings. First, because of the nonstationary feature of seismic data, a multiparameter inverse problem should be considered to not only estimate AI but also to estimate a time-varying wavelet or a quality factor (Q) model, leading the problem to be more ill posed. Second, the resolution of the estimated AI is limited due to the band-limited nature of nonstationary seismic data. To address these issues, we develop a new nonstationary seismic AI inversion method. Notably, we develop a deep-learning-aided representation model to replace the nonstationary convolution model to solve the forward problem in inversion. Benefiting from multisource information (seismic data and well-log data) and the powerful nonlinear function fitting ability of deep learning, this model can map high-resolution AI to band-limited nonstationary seismic data without requiring a time-varying wavelet or a Q model. A new deep-learning architecture is developed for processing the spatio-temporal seismic data with better accuracy. In addition, total-variation regularization is adopted to enforce a physically reasonable AI model. The results of our 3D synthetic and field data experiments clearly demonstrate that our method has significant advantages over other common methods in building a high-resolution AI model.


ISSN: 0016-8033
EISSN: 1942-2156
Coden: GPYSA7
Serial Title: Geophysics
Serial Volume: 89
Serial Issue: 6
Title: High-resolution acoustic-impedance inversion based on a deep-learning-aided representation model of nonstationary seismic data
Affiliation: Xi'an Jiaotong University, School of Information and Communications Engineering, Xi'an, China
Pages: R521-R539
Published: 202412
Text Language: English
Publisher: Society of Exploration Geophysicists, Tulsa, OK, United States
References: 41
Accession Number: 2024-086663
Categories: Applied geophysics
Document Type: Serial
Bibliographic Level: Analytic
Illustration Description: illus. incl. 4 tables, sects.
Country of Publication: United States
Secondary Affiliation: GeoRef, Copyright 2024, American Geosciences Institute. Reference includes data from GeoScienceWorld, Alexandria, VA, United States. Reference includes data supplied by Society of Exploration Geophysicists, Tulsa, OK, United States
Update Code: 2024
Close Modal

or Create an Account

Close Modal
Close Modal