ABSTRACT

Seismic traces are missing due to limitations in acquisition conditions, bad sectors, etc., which seriously affects the quality of seismic dataset. Seismic data interpolation technology is an effective way to reconstruct missing seismic traces and improve the quality of seismic dataset. In this paper, we propose a method for seismic data interpolation by using the conditional generative adversarial network in time and frequency domain (TF-CGAN). This network consists of two parts, a generation network and a discrimination network. Seismic data and the FFT-transformed data are used for training of the network model to realize dual-domain feature learning. Experimental results show that the TF-CGAN can simultaneously discriminate the mathematical distribution of the interpolated seismic traces in the time and frequency domains, which makes the interpolated seismic traces have the same characteristics with the complete seismic dataset in time and frequency domain.

Presentation Date: Wednesday, September 18, 2019

Session Start Time: 1:50 PM

Presentation Time: 1:50 PM

Location: Poster Station 2

Presentation Type: Poster

Keywords:
seg international exposition, reservoir characterization, neural network, frequency domain, seismic trace, dataset, machine learning, seismic dataset, conditional generative adversarial network, generative adversarial network
Subjects:
Reservoir Characterization, Information Management and Systems, Seismic processing and interpretation
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2019. Society of Exploration Geophysicists
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