Summary of Seismic First Break Picking in a Higher Dimension Using Deep Graph Learning, by Hongtao Wang et al.
Seismic First Break Picking in a Higher Dimension Using Deep Graph Learning
by Hongtao Wang, Li Long, Jiangshe Zhang, Xiaoli Wei, Chunxia Zhang, Zhenbo Guo
First submitted to arxiv on: 12 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Signal Processing (eess.SP); Geophysics (physics.geo-ph)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper proposes a novel approach called DGL-FB that uses deep graph learning to improve automatic first break (FB) picking in seismic data analysis. The current methods typically analyze 1D signals, 2D source gathers, or 3D source-receiver gathers, but using higher-dimensional data can incorporate global features and improve stability. However, high-dimensional data requires structured input and increases computational demands. To address this, DGL-FB constructs a large graph where each seismic trace is represented as a node, connected by edges that reflect similarities. A subgraph sampling technique is developed to streamline model training and inference. The framework uses deep graph learning to encode global features from the graph and combine them with local node signals using a ResUNet-based 1D segmentation network for FB detection. Field survey evaluations show superior accuracy and stability compared to a 2D U-Net-based benchmark method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers pick out important parts in old seismic data, like finding the start of an earthquake signal. They’re trying new ways to do this by using special computer programs that look at more information all at once. This makes it easier to get good results and can help us understand the Earth better. |
Keywords
* Artificial intelligence * Inference
