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0 Replies and 567 Views
SEG Anaheim 2018: A Machine Learning Haven 567 0
Started by Andrew Munoz
The Society of Exploration Geophysicists (SEG) annual meeting is one of the largest gathering of geophysicists in the world. The most important and relevant topics affecting geophysics are always discussed in many different forms. This year, by far the most discussed topic was machine learning. From the plenary session that featured a very well attended talk from Darryl Willis of Google, to the many panels, oral sessions, posters, and workshop talks addressed how machine learning is impacting th...
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05 Nov 2018 03:59 AM |
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1 Replies and 503 Views
Salt Identification Challenge on Kaggle 503 1
Started by Patrick Ng
Consider the rate of external innovation in machine learning algorithms exceeds that of internal organization-specific development. 'To create the most accurate seismic images and 3D renderings, TGS is hoping Kaggle’s machine learning community will be able to build an algorithm that automatically and accurately identifies if a subsurface target is salt or not.' Check out the Kaggle Salt Identification Challenge - click on this link https://www.kaggle.com/c/tgs-salt-identification-challe...
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1 Replies and 446 Views
Common Framework - Machine Learning and Geoscience 446 1
Started by Patrick Ng
During recent events, machine-learning focused Hackathon and Workshop, on July 19 and 26 respectively, it becomes apparent a common framework making ML relevant to geoscience will be very useful. Here a first-iteration tabulated template* is intended for further discussion. 1) Data Prep and Processing - AVO - say a simple two-term reflection amplitudes, R(𝜭) = P G * sin2(𝜭), AVZ(𝜙) = AVO vs azimuth, where 𝜭 denotes angle of reflection (in sub...
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0 Replies and 498 Views
Reinforcement Learning 498 0
Started by Patrick Ng
Recently I was asked 'is there benefit of reprocessing data and machine learning together' Yes. It has been standard business practice that every few years, with improved algorithm, we reprocess data, get higher resolution and a more detailed look. Like going from 4K to 8K HDTV, instead of 80 to 100 feet resolution in seismic, we may get that down to 40 ft. With higher resolution data, we’d retrain machine learning and get better results. Both go hand in hand. That brings up a good point....
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27 Sep 2018 11:53 AM |
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0 Replies and 479 Views
Neural Network to predict aftershocks in earthquakes 479 0
Started by Patrick Ng
The physics is relating transfer of stresses via elasticity (physical rock property). Instead of treating neural network output weights out of a 'black box', according to the authors, 'We could look at what was coming out of this network and actually make sense of it, and it’s actually pointed us to some possibly different physical theories of what causes earthquake triggering, and so it’s leading us in a new direction, which is exciting for us.” For the full story, click on link below. Li...
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30 Aug 2018 01:10 PM |
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