Seismic data interpolation through convolutional autoencoder
- Sara Mandelli (Politecnico di Milano, Italy) | Federico Borra (Politecnico di Milano, Italy) | Vincenzo Lipari (Politecnico di Milano, Italy) | Paolo Bestagini (Politecnico di Milano, Italy) | Augusto Sarti (Politecnico di Milano, Italy) | Stefano Tubaro (Politecnico di Milano, Italy)
- Document ID
- Society of Exploration Geophysicists
- 2018 SEG International Exposition and Annual Meeting, 14-19 October, Anaheim, California, USA
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Exploration Geophysicists
- Processing, Neural networks, Data reconstruction, Machine learning, Interpolation
- 0 in the last 30 days
- 15 since 2007
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A common issue of seismic data analysis consists in the lack of regular and densely sampled seismic traces. This problem is commonly tackled by rank optimization or statistical features learning algorithms, which allow interpolation and denoising of corrupted data. In this paper, we propose a completely novel approach for reconstructing missing traces of pre-stack seismic data, taking inspiration from computer vision and image processing latest developments. More specifically, we exploit a specific kind of convolutional neural networks known as convolutional autoencoder. We illustrate the advantages of using deep learning strategies with respect to state-of-the-art by comparing the achieved results over a well-known seismic dataset.
Presentation Date: Wednesday, October 17, 2018
Start Time: 1:50:00 PM
Location: 204C (Anaheim Convention Center)
Presentation Type: Oral
|File Size||2 MB||Number of Pages||5|
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