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Introduction to Machine Learning techniques for Geoscience

Introduction to Machine Learning techniques for Geoscience

Jesper Dramsch

October 14, 2020
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  1. Where are we going today? • My Background • What’s

    Machine Learning? • Geo to ML?! • What’s important in ML?
  2. Real Data (CC-BY A. Çuğun) 30 How do we generate

    data like this with a Neural Network?
  3. Latent Space (CC-BY Hudson) Generator (CC-BY-SA T. Vaughn) Real Data

    (CC-BY A. Çuğun) Network Data Legend 33 Feed it to a Generator
  4. Latent Space (CC-BY Hudson) Generator (CC-BY-SA T. Vaughn) Fake Data

    (CC-BY Tony A.) Real Data (CC-BY A. Çuğun) Network Data Legend 34 That generates some Fake Data
  5. Latent Space (CC-BY Hudson) Generator (CC-BY-SA T. Vaughn) Fake Data

    (CC-BY Tony A.) Real Data (CC-BY A. Çuğun) Discriminator(CC-BY Brickset) Network Data Legend 35 Discriminator judges if sample is real
  6. Latent Space (CC-BY Hudson) Generator (CC-BY-SA T. Vaughn) Fake Data

    (CC-BY Tony A.) Real Data (CC-BY A. Çuğun) Loss (CC-BY S. MacEntee) Discriminator(CC-BY Brickset) Network Data Legend (CC-BY Dramsch) 36 Both networks learn regardless if D is correct
  7. • Generating Synthetic Data • Earthquake localisation • Drill Core

    Processing • Clustering of Well Data • Prediction of Volcanic activity • Satellite Data What else is possible?
  8. Start Projects Application as important as Theory • Participate in

    Hackathons • Build Apps • Start Small • Compete on Kaggle • Get familiar with the Math
  9. Available Data For Projects and Experiments • Open Data: https://wiki.seg.org/wiki/Open_d

    ata • Full Reservoir with Wells: https://wiki.seg.org/wiki/The_No rth_Sea_Volve_Data_Village • Seismic Interpretation: https://github.com/yalaudah/faci es_classification_benchmark • Salt Identification in Seismic: http://bit.ly/kaggle-salt