Summary of High-resolution Detection Of Earth Structural Heterogeneities From Seismic Amplitudes Using Convolutional Neural Networks with Attention Layers, by Luiz Schirmer et al.
High-Resolution Detection of Earth Structural Heterogeneities from Seismic Amplitudes using Convolutional Neural Networks with Attention layers
by Luiz Schirmer, Guilherme Schardong, Vinícius da Silva, Rogério Santos, Hélio Lopes
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper proposes a novel approach to automatically detect detailed structural heterogeneities in the earth using machine learning techniques like deep neural networks. The authors recognize that traditional methods are limited by the availability of training data and aim to develop an assisted interpretation tool that can leverage modern computing power. By leveraging earth structural heterogeneities, the proposed system has significant implications for both exploration and production projects in the petroleum industry. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to find hidden patterns in the Earth’s structure. This information is really important for finding oil and gas, which powers our cars and homes. Right now, it’s hard to find these patterns because we need a lot of data to train special computer programs called deep neural networks. The authors want to make it easier by developing a new tool that can help us understand the Earth better. |
Keywords
» Artificial intelligence » Machine learning
