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Abstract

Summary

We investigate a simple Deep Learning Neural Network architecture to invert High Resolution seismic data for reflectivity and acoustic impedance. We generate synthetic reflectivity model and corresponding seismic traces by a simple convolution with a wavelet for training the machine learning network. Synthetic reflectivity models are correctly recovered when the reflectors separation is in the range of the train set. Noise in the data is correctly handled if the network is trained with adding noise on the data. Finally, we investigate the prediction of the acoustic impedance using the Marmousi model.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.202020169
2020-12-07
2024-04-16

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References

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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202020169
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Acoustic Impedance Inversion of High Resolution Marine Seismic Data with Deep Neural Network
Conference Proceedings 2020, 1 (2020); https://doi.org/10.3997/2214-4609.202020169
/content/papers/10.3997/2214-4609.202020169
/content/papers/10.3997/2214-4609.202020169
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