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Pre-Stack Seismic Inversion With Deep LearningNormal access

Authors: Y. Zheng and Q. Zhang
Event name: First EAGE/PESGB Workshop Machine Learning
Session: Types of Machine Learning and Deep Learning
Publication date: 30 November 2018
DOI: 10.3997/2214-4609.201803008
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 1.78Mb )
Price: € 20

We present a study of seismic inversion using deep learning tools. The purpose is to investigate the feasibility of using neural networks to construct acoustic and elastic earth models directly from pre-stack seismic data. Training and testing of the neural networks are done using thousands of synthetic 1D earth models and seismic gathers. We use 2 different types of neural network architectures in our numerical experiments to investigate seismic inversion in different geological scenarios. In both cases, the quality of the prediction is comparable with that obtained from conventional model building processes as such as travel-time and waveform inversion methods. The predicted earth models contain abundant low and medium wave-number information. In terms of performance, training took only less than 30 minutes on 4 GPUs whilst prediction adds negligible cost.

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