1887
Volume 73, Issue 9
  • E-ISSN: 1365-2478
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Abstract

ABSTRACT

Missing seismic traces from data acquisition limits often significantly degrade data quality. This study presents an unsupervised method using implicit neural representation (INR), specifically sinusoidal representation network (SIREN), to enhance seismic data quality from a single shot gather. Notably, the unsupervised framework trains the SIREN by optimizing it on the observed traces in the single‐gather data. The network learns a continuous function, enabling the reconstruction of missing data at any spatio‐temporal coordinate. This algorithm directly addresses both missing trace interpolation and the enhancement of sparsely sampled data resolution. Key network design choices, such as exponential frequency scaling and dense skip connections, are shown to enhance reconstruction accuracy by mitigating spectral bias and incorporating multi‐scale features. Furthermore, our analysis of different coordinate handling strategies identifies a key trade‐off on the geometry setting. Reframing interpolation as a super‐resolution task enables the successful reconstruction of up to 75% regularly missing traces and can maintain continuity across large gaps of up to 10 traces. However, this method proves geometrically inaccurate for irregular missing data, as it discards true physical coordinates, leading to incorrect solutions. In contrast, strategies that maintain physical coordinates show significantly degraded performance when faced with such large‐scale data gap. The proposed framework successfully interpolated multichannel seismic data and enhanced sparse ocean bottom cable (OBC) data resolution. Although challenges remain for large irregular gaps and computational efficiency, this work establishes SIREN as a promising unsupervised tool for single‐gather seismic interpolation and sparse data resolution enhancement without requiring external training data.

© 2025 European Association of Geoscientists & Engineers. © 2025 The Author(s). Geophysical Prospecting published by John Wiley & Sons Ltd on behalf of European Association of Geoscientists & Engineers.
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2025-12-01
2026-01-18

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Implicit Neural Representations for Unsupervised Seismic Data Interpolation From Single Gather
Geophysical Prospecting 73, e70110 (2025); https://doi.org/10.1111/1365-2478.70110
/content/journals/10.1111/1365-2478.70110
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  • Article Type: Research Article
Keyword(s): implicit neural representation (INR); seismic data interpolation; single gather; sinusoidal representation network (SIREN); unsupervised learning

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