Seismic First Break Picking in a Higher Dimension Using Deep Graph Learning

TL;DR

This paper introduces DGL-FB, a deep graph learning approach for seismic first break picking that effectively utilizes higher-dimensional data to improve accuracy and stability over traditional methods.

Contribution

It presents a novel deep graph learning framework that constructs large graphs from seismic data, efficiently extracts global features, and enhances first break detection performance.

Findings

DGL-FB outperforms 2D U-Net in accuracy and stability.

The subgraph sampling technique reduces computational complexity.

Global feature encoding improves seismic first break picking.

Abstract

Contemporary automatic first break (FB) picking methods typically analyze 1D signals, 2D source gathers, or 3D source-receiver gathers. Utilizing higher-dimensional data, such as 2D or 3D, incorporates global features, improving the stability of local picking. Despite the benefits, high-dimensional data requires structured input and increases computational demands. Addressing this, we propose a novel approach using deep graph learning called DGL-FB, constructing a large graph to efficiently extract information. In this graph, each seismic trace is represented as a node, connected by edges that reflect similarities. To manage the size of the graph, we develop a subgraph sampling technique to streamline model training and inference. Our proposed framework, DGL-FB, leverages deep graph learning for FB picking. It encodes subgraphs into global features using a deep graph encoder. Subsequently, the encoded global features are combined with local node signals and fed into a ResUNet-based 1D segmentation network for FB detection. Field survey evaluations of DGL-FB show superior accuracy and stability compared to a 2D U-Net-based benchmark method.