Diffraction wave separation and imaging by encoder-decoder network embedded Transformer

Ming Ma

doi:10.1016/j.jappgeo.2025.106023

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Diffraction wave separation and imaging by encoder-decoder network embedded Transformer

2025-11-01

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Ming Ma *

Rui Zhang

Bao, Qianzong

DOI：10.1016/j.jappgeo.2025.106023

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Abstract

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High-precision detection of geological anomalies can be achieved through diffractions generated when seismic or electromagnetic waves propagate in subsurface discontinuities, e.g., caves and fractures. However, capturing the diffracted portions of the full wavefield from acquired seismic or ground penetrating radar (GPR) data is challenging due to the strong interference and waveform blending. Furthermore, compared with reflections, diffractions possess low magnitudes and complex shapes. The aforementioned factors hinder difficulty in deploying robust diffraction extraction and imaging across various data domains. To enhance the accuracy of diffraction imaging and simplify the processing steps, we have built a new intricate mapping from full wavefield in dip-angle domain common image gather (Dip-ADCIG) to unique migrated diffractions with deep learning (DL) technique. By virtue of the encoder-decoder framework, characteristics of diffracted waves can be depicted, which are applied to classify disordered waveforms with improved efficiency. Self-attention computation in the improved backbone Swin Transformer V2 ensures the coincident fidelity between input and prediction result. Apart from the utilization of optimally configured encoder panel, mode of feature maps concatenating is modified in decoder module so as to obtain the diffraction imaging of small-scale heterogeneities. Through a stable training with a flood of data for the diverse designed geological models, the new workflow can provide a high-resolution depth-domain imaging of diffractions even with poor quality input gathers. Numerical and field data tests verify the high performance and validity of our proposed method.

Keywords:

Diffraction imaging

Deep learning

Dip-ADCIG

Self-attention computation