PV
P. Varela Bernal
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Learning from Neighbouring Seismic Slices
Parameter-Efficient 2.5D Multi-Channel Adaptation of Visual Foundation Models for Seismic Denoising
Seismic reflection surveys image subsurface geological structures by recording waves reflected from interfaces between rock layers, which are then processed to form 3D seismic volumes. However, the acquired signals are often contaminated by noise that degrades interpretation quality. Existing denoising approaches adapt pretrained visual foundation models to seismic data but process each slice of the 3D seismic volume independently, discarding useful spatial context. To incorporate this context while retaining the efficiency of a 2D model, three input strategies are compared: 2D-1ch, in which a single slice is repeated across the input channels; 2.5D-3ch, which uses three consecutive slices from the same volume; and 2.5D-5ch, which uses five consecutive slices. DINOv3 is adapted with low-rank adaptation (LoRA), and a lightweight decoder is trained to predict the clean central slice. On 30 synthetic Image Impeccable volumes, mean test multi-scale structural similarity improves from $0.8624$ for 2D-1ch to $0.8947$ for 2.5D-3ch and $0.9039$ for 2.5D-5ch. 2.5D gains are largest on the slices with the most noise. Additional experiments show that these improvements arise primarily from the spatial context provided by neighbouring slices. On the real-field F3 dataset, 2.5D behaviour depends on slice orientation. In the horizontal time orientation the models were trained on, 2D-1ch leaves the least structured residual, while 2.5D-3ch and 2.5D-5ch over-smooth the output. However, in the inline/crossline F3 evaluation, 2.5D reduces the over-smoothing seen in 2D-1ch and retains more structure. Cross-backbone experiments with SFM-Base and SwinV2-T show that the 2D-to-2.5D trend is not specific to DINOv3, while full fine-tuning controls show that PEFT is sufficient to achieve the observed gains. These results support 2.5D input as an effective extension on synthetic data when neighbouring slices are aligned, while highlighting its sensitivity to field-data neighbour relationships.
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Seismic reflection surveys image subsurface geological structures by recording waves reflected from interfaces between rock layers, which are then processed to form 3D seismic volumes. However, the acquired signals are often contaminated by noise that degrades interpretation quality. Existing denoising approaches adapt pretrained visual foundation models to seismic data but process each slice of the 3D seismic volume independently, discarding useful spatial context. To incorporate this context while retaining the efficiency of a 2D model, three input strategies are compared: 2D-1ch, in which a single slice is repeated across the input channels; 2.5D-3ch, which uses three consecutive slices from the same volume; and 2.5D-5ch, which uses five consecutive slices. DINOv3 is adapted with low-rank adaptation (LoRA), and a lightweight decoder is trained to predict the clean central slice. On 30 synthetic Image Impeccable volumes, mean test multi-scale structural similarity improves from $0.8624$ for 2D-1ch to $0.8947$ for 2.5D-3ch and $0.9039$ for 2.5D-5ch. 2.5D gains are largest on the slices with the most noise. Additional experiments show that these improvements arise primarily from the spatial context provided by neighbouring slices. On the real-field F3 dataset, 2.5D behaviour depends on slice orientation. In the horizontal time orientation the models were trained on, 2D-1ch leaves the least structured residual, while 2.5D-3ch and 2.5D-5ch over-smooth the output. However, in the inline/crossline F3 evaluation, 2.5D reduces the over-smoothing seen in 2D-1ch and retains more structure. Cross-backbone experiments with SFM-Base and SwinV2-T show that the 2D-to-2.5D trend is not specific to DINOv3, while full fine-tuning controls show that PEFT is sufficient to achieve the observed gains. These results support 2.5D input as an effective extension on synthetic data when neighbouring slices are aligned, while highlighting its sensitivity to field-data neighbour relationships.