Edge-aware filtering with Siamese neural networks
| Autor: | Michael P. Matheney, Joe Molyneux, Mehdi Aharchaou, Erik Neumann |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Process (engineering) Computer science Interpretation (philosophy) Geology 02 engineering and technology 010502 geochemistry & geophysics computer.software_genre 01 natural sciences Seismic exploration Geophysics Investment decisions 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Enhanced Data Rates for GSM Evolution Data mining Stratigraphy (archaeology) computer 0105 earth and related environmental sciences |
| Zdroj: | The Leading Edge. 39:711-717 |
| ISSN: | 1938-3789 1070-485X |
| DOI: | 10.1190/tle39100711.1 |
| Popis: | Recent demands to reduce turnaround times and expedite investment decisions in seismic exploration have invited new ways to process and interpret seismic data. Among these ways is a more integrated collaboration between seismic processors and geologist interpreters aiming to build preliminary geologic models for early business impact. A key aspect has been quick and streamlined delivery of clean high-fidelity 3D seismic images via postmigration filtering capabilities. We present a machine learning-based example of such a capability built on recent advances in deep learning systems. In particular, we leverage the power of Siamese neural networks, a new class of neural networks that is powerful at learning discriminative features. Our novel adaptation, edge-aware filtering, employs a deep Siamese network that ranks similarity between seismic image patches. Once the network is trained, we capitalize on the learned features and self-similarity property of seismic images to achieve within-image stacking power endowed with edge awareness. The method generalizes well to new data sets due to the few-shot learning ability of Siamese networks. Furthermore, the learning-based framework can be extended to a variety of noise types in 3D seismic data. Using a convolutional architecture, we demonstrate on three field data sets that the learned representations lead to superior filtering performance compared to structure-oriented filtering. We examine both filtering quality and ease of application in our analysis. Then, we discuss the potential of edge-aware filtering as a data conditioning tool for rapid structural interpretation. |
| Databáze: | OpenAIRE |
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