It is my understanding that you are trying to extract individual atmospheric cross-sections from a mixed dataset.
Clustering algorithms like 'K-Means' or 'DBSCAN' can be used to group similiar datapoints together. These methods help in identifying and categorizing the natural groupings in data, if the components are distinct enough.
An 'autoencoder' might be beneficial in dimensionality reduction or feature learning. Autoencoders can learn efficient representations of your data, which can be useful for tasks like anomaly detection or data compression. You can experiment with different neural network-based autoencoders architectures to see if they can separate the components.
Refer to the following MathWorks Documentations to know about these methods:
- Auto-encoder: https://www.mathworks.com/help/deeplearning/ref/autoencoder-class.html
- Train auto-encoder: https://www.mathworks.com/help/deeplearning/ref/trainautoencoder.html
- DBSCAN: https://www.mathworks.com/help/stats/dbscan.html
- K-Means: https://www.mathworks.com/help/stats/kmeans.html
- Cluster data: https://www.mathworks.com/help/stats/clusterdata.html
