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Related Experiment Video

Updated: Jun 10, 2026

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

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Self-Supervised Three-Dimensional Ocean Bottom Node Seismic Data Shear Wave Leakage Suppression Based on a Dual

Zhaolin Zhu¹, Zhihao Chen¹, Bangyu Wu²

¹Hainan Institute, Zhejiang University, Sanya 572025, China.

Sensors (Basel, Switzerland)

|February 13, 2025

Summary

A novel self-supervised neural network effectively suppresses shear wave noise in 3D Ocean Bottom Node (OBN) seismic data. This method uses horizontal components to predict and remove noise from the vertical component without requiring clean data.

Keywords:

OBN U-Net deep learning noise suppression shear wave

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Area of Science:

Geophysics
Seismic Data Acquisition and Processing

Background:

Ocean Bottom Node (OBN) seismic acquisition uses hydrophones and geophones.
Shear wave noise in the vertical component degrades dual-sensor seismic data processing.

Purpose of the Study:

Introduce a self-supervised neural network for shear wave noise suppression in 3D OBN data.
Replace traditional adaptive matching subtraction with a more efficient neural network approach.

Main Methods:

A neural network takes horizontal seismic components as input to predict shear wave noise.
A loss function, incorporating cross-correlation regularization, trains the network using the noisy vertical component.
The method is self-supervised, eliminating the need for clean reference data.

Main Results:

The proposed neural network effectively suppresses shear wave noise in 3D OBN seismic data.
Experiments on synthetic and field data validate the method's performance.
The approach balances signal preservation and noise reduction.

Conclusions:

The self-supervised neural network offers an effective and efficient solution for shear wave noise suppression in 3D OBN seismic data.
This method simplifies data processing by removing the need for manual noise identification or clean data.