A Deep CNN Model for Ringing Effect Attenuation of Vibroseis Data

TL;DR

This paper introduces a deep CNN model that effectively reduces ringing effects in vibroseis seismic data, enhancing data quality and first-break picking accuracy in exploration geophysics.

Contribution

A novel end-to-end deep CNN approach with skip connections for deringing vibroseis data, trained on synthesized and real data, improving bandwidth and data analysis.

Findings

Deep CNN effectively attenuates ringing effects.

Model expands vibroseis data bandwidth.

Improves first-break picking accuracy.

Abstract

In the field of exploration geophysics, seismic vibrator is one of the widely used seismic sources to acquire seismic data, which is usually named vibroseis. "Ringing effect" is a common problem in vibroseis data processing due to the limited frequency bandwidth of the vibrator, which degrades the performance of first-break picking. In this paper, we proposed a novel deringing model for vibroseis data using deep convolutional neural network (CNN). In this model we use end-to-end training strategy to obtain the deringed data directly, and skip connections to improve model training process and preserve the details of vibroseis data. For real vibroseis deringing task we synthesize training data and corresponding labels from real vibroseis data and utilize them to train the deep CNN model. Experiments are conducted both on synthetic data and real vibroseis data. The experiment results show that deep CNN model can attenuate the ringing effect effectively and expand the bandwidth of vibroseis data. The STA/LTA ratio method for first-break picking also shows improvement on deringed vibroseis data using deep CNN model.