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Attenuation of seismic migration smile artifacts with deep learning

Attenuation of migration artifacts on Kirchhoff migrated seismic data can be challenging due to the relatively low amplitude of migration artifacts compared to reflections as well as the overlap in the kinematics of reflection and migration smiles. Several ‘conventional’ filtering methods exist and recently deep learning based workflows have been proposed. A deep learning workflow can be a simple and fast alternative to existing methods. In case of supervised training of a deep neural network using training data made by physics-based modelling or actual migrations is expensive and lacks diversity in terms of noise, amplitude, frequency content and wavelet. This can result in poor generalization beyond the training data without re-training and transfer learning. In this paper we demonstrate successful applications of migration smile separation using a conventional U-net architecture. The novelty in our approach is that we do not use synthetic data created from physics-based modelling, but instead use only synthetic data build form basic geometric shapes. Our domain of application is the migrated common offset domain, or simply the stack of the pre-stack migrated data, where reflections resemble local geology and migration smiles are upward convex hyperbolic patterns. Both patterns were randomly perturbed in many ways while maintaining their intrinsic features. This approach is inspired by the common practice of data augmentation in deep learning for machine vision applications. Since many of the standard data augmentation techniques lack a geophysical motivation, we have instead perturbed our synthetic training data in ways to make more sense for a signal processing perspective or given our ‘domain knowledge’ of the problem at hand. We did not have to retrain the network to produce good results on the field dataset. The large variety and diversity in examples enabled the trained neural network to show encouraging results on synthetic and field datasets that were not used in training.

Ketersediaan
278551Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
Artificial Intelligence in Geosciences
No. Panggil
551
Penerbit
Beijing : KeAi Communications Co. Ltd..,
Deskripsi Fisik
9 hlm PDF, 12.044 KB
Bahasa
Inggris
ISBN/ISSN
2666-5441
Klasifikasi
551
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.3, December 2022
Subjek
Deep learning
U-net
Seismic data processing
Noise attenuation
Migration
Info Detail Spesifik
-
Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • Attenuation of seismic migration smile artifacts with deep learning
    Attenuation of migration artifacts on Kirchhoff migrated seismic data can be challenging due to the relatively low amplitude of migration artifacts compared to reflections as well as the overlap in the kinematics of reflection and migration smiles. Several ‘conventional’ filtering methods exist and recently deep learning based workflows have been proposed. A deep learning workflow can be a simple and fast alternative to existing methods. In case of supervised training of a deep neural network using training data made by physics-based modelling or actual migrations is expensive and lacks diversity in terms of noise, amplitude, frequency content and wavelet. This can result in poor generalization beyond the training data without re-training and transfer learning. In this paper we demonstrate successful applications of migration smile separation using a conventional U-net architecture. The novelty in our approach is that we do not use synthetic data created from physics-based modelling, but instead use only synthetic data build form basic geometric shapes. Our domain of application is the migrated common offset domain, or simply the stack of the pre-stack migrated data, where reflections resemble local geology and migration smiles are upward convex hyperbolic patterns. Both patterns were randomly perturbed in many ways while maintaining their intrinsic features. This approach is inspired by the common practice of data augmentation in deep learning for machine vision applications. Since many of the standard data augmentation techniques lack a geophysical motivation, we have instead perturbed our synthetic training data in ways to make more sense for a signal processing perspective or given our ‘domain knowledge’ of the problem at hand. We did not have to retrain the network to produce good results on the field dataset. The large variety and diversity in examples enabled the trained neural network to show encouraging results on synthetic and field datasets that were not used in training.
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