The model is trained by minimizing a composite loss function consisting of: • Reconstruction loss, computed using binary cross-entropy between the input and reconstructed output • Kullback–Leibler (KL) divergence, which regularizes the latent space toward a standard normal distribution The total loss is defined as: = reconstruction + KL where β = regularization coefficient. 4.4 Training Procedure The model was trained for 25 epochs using a batch size of 512, the ADAM optimizer, a latent dimension of 3, and a β value of 1. This configuration was selected to ensure stable convergence while maintaining computational efficiency. 4.5 Model Evaluation Metrics During training, the total loss decreased from approximately 3959 in the first epoch to 2806 in the final epoch. The reconstruction loss showed a similar decreasing trend, indicating progressive improvement in the model’s ability to reconstruct the input samples. The KL divergence decreased sharply in the first epochs and then increased gradually, remaining at a low but non-zero value toward the end of training. The loss curves are presented in Figure 5. 5. LATENT SPACE PROSPECTIVITY MAPPING 5.1 Extraction of Porphyry Signature To define a representative signature of porphyry systems, latent vectors corresponding to subcrops centered on known western deposits (n = 25) were extracted. A mean latent vector was then computed: Figure 17 – Evolution of total loss, reconstruction loss, and KL divergence during training of the VAE-CNN model, illustrating convergence behavior across epochs.
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