Robust Concurrent Detection of Salt Domes and Faults in Seismic Surveys Using an Improved UNet Architecture

Abstract

Interpretation of seismic structural traps for accurate hydrocarbon reservoirs characterization is a challenging task. Seismic interpreters learn to accurately delineate subsurface structures after going through a lengthy process of training and expertise-acquiring that is challenging and time-consuming. In this paper, we propose a novel semantic segmentation model for salt domes and faults identification in a real concurrent scenario using an improved encoder-decoder deep neural network that achieves high detection accuracy for both salt domes and faults. We also introduce transfer learning to alleviate the everlasting scarcity issue of labeled seismic data and develop a robust model whose performance is not affected by event similarities among various discontinuities in seismic data. In addition, we use residual blocks in our deep neural network to make it even more robust. To demonstrate the effectiveness of our model, extensive experiments were conducted through validation and testing on real-world seismic data from the publicly available Netherlands offshore F3 block, the LANDMASS, and the TGS datasets. Both qualitative and quantitative evaluations are provided to confirm the superior performance achieved by our deep learning based workflow under the challenging scenario of multiple events detection in subsurface surveys.

Keywords

• Seismic interpretation
• seismic salt domes
• seismic faults
• handcrafted features
• deep learning (DL)
• transfer learning