Signal Reconstruction of Arbitrarily Lack of Frequency Bands from Seismic Wavefields Based on Deep Learning

Author:

Li Xin1,Zhang Fengjiao1ORCID,Han Liguo1

Affiliation:

1. College of Geoexploration Science and Technology, Jilin University, Xi Min Zhu Street No. 938, Changchun 130026, China

Abstract

Due to the limitations of seismic exploration instruments and the impact of the high frequencies absorption by the earth layers during subsurface propagation of seismic waves, recorded seismic data usually lack high and low frequency information that is needed to accurately image geological structures. Traditional methods face challenges such as limitations of model assumptions and poor adaptability to complex geological conditions. Therefore, this paper proposes a deep learning method that introduces the attention mechanism and Bi-directional gated recurrent unit (BiGRU) into the Transformer neural network. This approach can simultaneously capture both global and local characteristics of time series data, establish mappings between different frequency bands, and achieve information compensation and frequency extension. The results show that the BiGRU-Extended Transformer network is capable of compensating and extending the synthetic seismic data sets with the limited frequency band. It has certain generalization capabilities and stability and can effectively handle various problems in the data reconstruction process, which is better than traditional methods.

Funder

National Natural Science Foundation of China

Publisher

MDPI AG

Reference33 articles.

1. Zhang, P. (2018). The Study on Full Waveform Inversion Based on Low-Frequency Seismic Wavefield Reconstruction. [Ph.D. Thesis, Jilin University].

2. Fang, S. (2022). Bandwidth Extension Method of Seismic Data Based on Full-band Extension Filter. [Master’s Thesis, China University of Petroleum].

3. Joint FWI of Active Source Data and Passive Virtual Source Data Recon-structed Using an Improved Multidimensional Deconvolution;Shang;IEEE Trans. Geosci. Remote Sens.,2023

4. Chen, Z. (2022). Seismic Data Frequency Extension Based on Deep Learning. [Master’s Thesis, China University of Petroleum].

5. Application of seismic frequency extension technology in dynamic analysis of thin layer reservoir development;Sun;Oil Geophys. Prospect.,2010

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