Ensemble Deep Learning for enhanced seismic data reconstruction

TL;DR

This paper introduces an ensemble deep learning model with a self-supervised training approach to improve seismic data reconstruction, especially for weak signals and complex features, outperforming traditional methods.

Contribution

The authors propose a novel ensemble U-net architecture with tailored self-supervised training to enhance seismic data reconstruction of under-represented features.

Findings

Improved reconstruction accuracy over conventional U-net.

Effective recovery of weak events, diffractions, and high-frequency signals.

Demonstrated on synthetic and real datasets.

Abstract

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.