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Data Preparation:
- Collect and preprocess a dataset of seismic images containing salt domes.
- Annotate the images with salt body masks, focusing on samples where the salt occupies between 10% and 90% of the total area.
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Training the Variational Autoencoder (VAE):
- Encoder Structure: Build the encoder using a simple feedforward neural network with four stacked dense layers.
- Latent Space: Encode the seismic image data into a latent space by learning the distribution of key features (e.g., salt boundaries).
- Decoder Structure: Construct the decoder with five stacked dense layers to decode latent variables into salt body masks.
- Training Process: Train the VAE using only the annotated salt masks, focusing on learning the boundaries and shapes of salt bodies.
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Generating New Salt Body Masks:
- Latent Sampling: During inference, sample latent variables ( z ) from the learned prior distribution ( \pi(z) ).
- Mask Generation: Decode the sampled ( z ) using the VAE’s decoder to generate new salt body masks that align with the distribution of the training data.
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Contextual Data Augmentation:
- Use the generated salt body masks as context orientation for augmenting the seismic data.
- Identify the boundaries between the salt body and surrounding rock. Everything within the boundary is treated as salt.
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Texture Synthesis:
- Apply a non-parametric texture synthesis algorithm to generate new seismic image samples.
- Ensure the synthesized image preserves the distinct characteristics of seismic images with salt domes, maintaining realistic texture and context zones.
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Final Output:
- Produce new seismic images that combine the generated salt body masks with synthesized textures, enhancing the dataset for further training and analysis.