Please add further explanations on the limitation of existing CNN-based reconstruction methods for seismic data to highlight the motivation of the work.

Existing CNN-based reconstruction methods for seismic data have some limitations that hinder their effectiveness in producing high-quality images. These limitations include:

1. Limited data availability: CNN-based reconstruction methods require large amounts of training data to learn the underlying patterns in the data. However, seismic data is often limited in availability due to high acquisition costs and limited access to seismic stations. This makes it difficult to train CNN models, resulting in limited accuracy and reliability.

2. Complexity of the data: Seismic data is highly complex and contains a range of frequencies and seismic structures. This complexity makes it difficult to accurately reconstruct seismic images using CNN-based methods, as the models struggle to capture the nuances of the data.

3. Inability to handle missing data: CNN-based reconstruction methods often struggle to handle missing or incomplete data, which is common in seismic imaging due to missing sensors or data gaps. This can result in inaccurate or incomplete reconstructions, limiting the usefulness of the resulting images.

4. Overfitting: CNN models are susceptible to overfitting, where the model becomes too specialized to the training data and fails to generalize to new data. This can result in inaccurate and unreliable reconstructions, especially when the training data is limited.

These limitations highlight the need for improved methods for reconstructing seismic data using CNN models. By addressing these challenges, the resulting reconstructions could lead to more accurate and reliable seismic images, enabling better insights into subsurface structures and improving seismic exploration and monitoring.