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Velocity Model Building from Raw Shot Gathers Using Machine LearningNormal access

Authors: O.K. Øye and E.K. Dahl
Event name: 81st EAGE Conference and Exhibition 2019
Session: Velocity Model Estimation I
Publication date: 03 June 2019
DOI: 10.3997/2214-4609.201901522
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 730.17Kb )
Price: € 20

We present a machine learning setup that can estimate a velocity model from raw seismic shot gathers without the need for an initial velocity model. Our setup is based on a convolutional neural network (CNN) trained on pairs of random generated synthetic velocity models and corresponding forward modelled synthetic shot gathers. The network is trained to predict the correct velocity model for a given input shot gather. We evaluate the performance of the trained network on both synthetic and real seismic data and observe that the system is able to estimate background velocity trends directly from the raw shot gathers without any need for preprocessing or preconditioning. Once trained, the network is very fast to run, and can deliver a velocity model in seconds running on a single GPU. The precision and resolution of the estimated velocity models is not on par with state of the art velocity model building techniques such as FWI and reflection tomography but shows that machine learning can extract meaningful velocity information from real shot gathers, and that there is potential in using such methods for velocity model building.

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