Applications of Convolutional Neural Networks (CNN) to Seismic Interpretation

Applications of Convolutional Neural Networks (CNN) to Seismic Interpretation

As part of our quarterly series on machine learning, we were delighted to have had Dr. Tao Zhao present applications of Convolutional Neural Networks (CNN) in a worldwide webinar on 20 March 2019 that was attended by participants on every continent.  Dr. Zhao highlighted applications in seismic facies classification, fault detection, and extracting large scale channels using CNN technology.  If you missed the webinar, no problem!  A video of the webinar can be streamed via the video player below.  Please provide your name and business email address so that we may invite you to future webinars and other events.  The abstract for Dr. Zhao’s talk follows:

We welcome your comments and questions and look forward to discussions on this timely topic.

Abstract:  Leveraging Deep Learning in Extracting Features of Interest from Seismic Data

Mapping and extracting features of interest is one of the most important objectives in seismic data interpretation. Due to the complexity of seismic data, geologic features identified by interpreters on seismic data using visualization techniques are often challenging to extract. With the rapid development in GPU computing power and the success obtained in computer vision, deep learning techniques, represented by convolutional neural networks (CNN), start to entice seismic interpreters in various applications. The main advantages of CNN over other supervised machine learning methods are its spatial awareness and automatic attribute extraction. The high flexibility in CNN architecture enables researchers to design different CNN models to identify different features of interest. In this webinar, using several seismic surveys acquired from different regions, I will discuss three CNN applications in seismic interpretation: seismic facies classification, fault detection, and channel extraction. Seismic facies classification aims at classifying seismic data into several user-defined, distinct facies of interest. Conventional machine learning methods often produce a highly fragmented facies classification result, which requires a considerable amount of post-editing before it can be used as geobodies. In the first application, I will demonstrate that a properly built CNN model can generate seismic facies with higher purity and continuity. In the second application, compared with traditional seismic attributes, I deploy a CNN model built for fault detection which provides smooth fault images and robust noise degradation. The third application demonstrates the effectiveness of extracting large scale channels using CNN. These examples demonstrate that CNN models are capable of capturing the complex reflection patterns in seismic data, providing clean images of geologic features of interest, while also carrying a low computational cost.

 

Webinar: Leveraging Deep Learning in Extracting Features of Interest from Seismic Data

Webinar: Leveraging Deep Learning in Extracting Features of Interest from Seismic Data

20 March 2019

We are delighted to kick off our quarterly machine learning webinar series this year with a presentation on applications of Convolutional Neural Networks (CNN) to seismic interpretation by Dr. Tao Zhao, Sr. Research Geophysicists with Geophysical Insights in Houston, Texas. Enabled by the development of GPU computing CNN, a form of deep learning, is proving to be an effective tool for many pattern recognition problems. Dr. Zhao will focus on three important applications of CNN that can have an immediate impact on interpretation workflows:

  • Seismic facies classification
  • Channel extraction
  • Fault detection

The webinar is free and runs an hour, including time for questions and answers at the end of the presentation.

Register today to attend the free webinar on Applications of Convolutional Neural Networks to Seismic Interpretation by Dr. Tao Zhao via the link below. You will receive a confirmation of your registration and details about how to log into the event upon registration. See below for the time of the webinar in your region.

Tao Zhao

Research Geophysicist | Geophysical Insights

TAO ZHAO joined Geophysical Insights in 2017. As a Research Geophysicist, Dr. Zhao develops and applies shallow and deep machine learning techniques on seismic and well log data, and advances multiattribute seismic interpretation workflows. He received a B.S. in Exploration Geophysics from the China University of Petroleum in 2011, an M.S. in Geophysics from the University of Tulsa in 2013, and a Ph.D. in geophysics from the University of Oklahoma in 2017. During his Ph.D. work at the University of Oklahoma, Dr. Zhao was an active member of the Attribute-Assisted Seismic Processing and Interpretation (AASPI) Consortium developing pattern recognition and seismic attribute algorithms.

Abstract:

Mapping and extracting features of interest is one of the most important objectives in seismic data interpretation. Due to the complexity of seismic data, geologic features identified by interpreters on seismic data using visualization techniques are often challenging to extract. With the rapid development in GPU computing power and the success obtained in computer vision, deep learning techniques, represented by convolutional neural networks (CNN), start to entice seismic interpreters in various applications. The main advantages of CNN over other supervised machine learning methods are its spatial awareness and automatic attribute extraction. The high flexibility in CNN architecture enables researchers to design different CNN models to identify different features of interest.

In this webinar, using several seismic surveys acquired from different regions, I will discuss three CNN applications in seismic interpretation: seismic facies classification, fault detection, and channel extraction. Seismic facies classification aims at classifying seismic data into several user-defined, distinct facies of interest. Conventional machine learning methods often produce a highly fragmented facies classification result, which requires a considerable amount of post-editing before it can be used as geobodies. In the first application, I will demonstrate that a properly built CNN model can generate seismic facies with higher purity and continuity. In the second application, compared with traditional seismic attributes, I deploy a CNN model built for fault detection which provides smooth fault images and robust noise degradation. The third application demonstrates the effectiveness of extracting large scale channels using CNN. These examples demonstrate that CNN models are capable of capturing the complex reflection patterns in seismic data, providing clean images of geologic features of interest, while also carrying a low computational cost.

What is Machine Learning?

What is Machine Learning?

If you’re new to Machine Learning, let’s start at the top. The whole field of artificial intelligence is broken up into two categories – Strong AI and Narrow AI.

Strong AI is coming up with a robot that looks and behaves like a person. Narrow AI, or “neural networks” attempt to duplicate the brain’s neurological processes that have been perfected over millions of years of biological development.

Machine Learning is a subset of Narrow AI that does pattern classification. It’s an engine – an algorithm that learns without explicit programming. It learns from the data. What does that mean? Given one set of data, it’s going to come up with an answer. But given a different set of data, it will come up with something different.

A Self-Organizing Map is a type of neural network that adjusts to training data. However, it makes no assumptions about the characteristics of the data. So, if you look at the whole field of artificial intelligence, and then we look at machine learning as a subset of that, there’s two parts: supervised neural networks and unsupervised neural networks. Unsupervised is where you feed it the data and say “you go figure it out.” In supervised neural networks, you give it both the data and the right answer. Some examples of supervised neural networks would be convolutional neural networks and deep learning algorithms. Convolutional is a more classical type of a supervised neural network, where for every data sample, we know the answer.

Here’s a classical example of a supervised neural network: Your uncle just passed away and gave you the canning operations in Cordova, Alaska. You go there and observe the employees taking the fish off the conveyor and manually sorting them by type – buckets for eels and buckets for flounder and so forth. Can you use AI (machine learning) to do something more efficient? Perhaps have those employees do something more productive? Absolutely! As the eels come along, you weigh them, you take a picture of them, you see what the scales are, general texture, you get some idea about the general shape of them. There’s three properties already. You continue running eels through and maybe get up to four or five properties, including measurements, etc. The neural network is then trained on eels. Then, you do the same thing with all the flounder. There are going to be variations, of course, but in attribute space, of those four or five properties that we made for each one, they’re going to wind up in a different cluster in attribute space. And that’s how we tell the difference between eels and flounder. Everything else that you can’t classify very well, you don’t know. All of that goes into the algorithm. That’s the difference between supervised neural networks and unsupervised neural networks.

At Geophysical Insights, we believe we should be able to query our seismic data for information with learning machines just as effortlessly and with as much reliability as we query the web for the nearest gas station.