DUG’s deep learning research delivers step change in seismic swell noise attenuation.

Advanced machine-learning (ML) methods have been gaining traction in the data-processing community due to their impressive performance when tackling complex tasks that would otherwise consume much more resources.

Many researchers in geophysics are now also enjoying the age of big data and deep learning—a subset of ML that trains machines via neural networks designed after the structure of the biological brain—producing many efficient algorithms to address various seismic-processing challenges.

Our research team has previously proposed a novel deep-learning approach for matching data sets as applied to adaptive subtraction of multiple models.

Now, the team has delivered a breakthrough in deep-learning technology to estimate swell noise more effectively.

The research produced a novel deep-learning framework enabled by marrying a conditional generative adversarial network (cGAN) with a residual network (ResNet) architecture to estimate and subtract swell noise from recorded data.

The examples shown below are field data examples demonstrating the proposed framework can rapidly estimate swell noise to a high degree of accuracy, without going through the extensive parameter selection of a conventional workflow.

We get superior results, faster.

Excitingly, our research team has found that this approach is similarly effective for other types of noise such as ground roll.

 

A second set of shot records before (left), after (centre), and difference (right) using DUG’s advanced deep-learning swell noise attenuation process. The algorithm is able to accurately determine swell noise from useful signal even in extremely noisy environments.

 

Stack sections before (left) and after (centre) DUG’s advanced deep-learning swell noise attenuation process. The difference (right) further demonstrates the power of this approach to isolate the swell noise without attenuating useful signal.

 

This technique is part of DUG Insight—available as software to run on your own hardware or on our high-performance computing. You can opt to have it delivered as a traditional service project too!

Interested? We’re more than excited to let you know more about our latest technologies and how they can help turbocharge your workflows. Drop an email to info@dug.com for more info!

Our compute. Your success.

 

Main picture: Shot records before (left) and after (centre) DUG’s advanced deep-learning swell noise attenuation process. The difference (right) demonstrates the power of this approach to isolate the swell noise without attenuating useful signal.

By Team DUG

You know the saying "It takes a village...."?  Well sometimes it takes the whole team to write a blog post. We're a team of science-loving computer nerds, geo junkies and tech heads. Wanna hang for a while?

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