  Seismic data often contain gaps due to various obstacles in the investigated
area and recording instrument failures. Deep learning techniques offer
promising solutions for reconstructing missing data parts by leveraging
existing information. However, self-supervised methods frequently struggle with
capturing under-represented features such as weaker events, crossing dips, and
higher frequencies. To address these challenges, we propose a novel ensemble
deep model along with a tailored self-supervised training approach for
reconstructing seismic data with consecutive missing traces. Our model
comprises two branches of U-nets, each fed from distinct data transformation
modules aimed at amplifying under-represented features and promoting diversity
among learners. Our loss function minimizes relative errors at the outputs of
individual branches and the entire model, ensuring accurate reconstruction of
various features while maintaining overall data integrity. Additionally, we
employ masking while training to enhance sample diversity and memory
efficiency. Application on two benchmark synthetic datasets and two real
datasets demonstrates improved accuracy compared to a conventional U-net,
successfully reconstructing weak events, diffractions, higher frequencies, and
reflections obscured by groundroll. However, our method requires a threefold of
training time compared to a simple U-net. An implementation of our method with
TensorFlow is also made available.
