{
  "$type": "site.standard.document",
  "bskyPostRef": {
    "cid": "bafyreifulozlqpppkydy47sqxvklcfr2hkzuzm2ttffinx2yurxuxhpwz4",
    "uri": "at://did:plc:3smsdppscbbmgh33ttbch2od/app.bsky.feed.post/3mgregylbmw32"
  },
  "coverImage": {
    "$type": "blob",
    "ref": {
      "$link": "bafkreihpa5m5kseozp2s3blc63sa7isrg5w6u2jcgllksxqxev2njpq3bq"
    },
    "mimeType": "image/png",
    "size": 150844
  },
  "path": "/editor-highlights/collinearity-is-not-always-a-problem-in-machine-learning",
  "publishedAt": "2026-03-10T12:00:00.000Z",
  "site": "https://eos.org",
  "tags": [
    "Editors' Highlights",
    "Journal of Geophysical Research: Machine Learning and Computation",
    "machine learning & AI",
    "seismology",
    "Xu et al. [2026]"
  ],
  "textContent": "The workflow used in the paper is illustrated here, showing how synthetic geological models were constructed and how their geophysical properties were derived via forward simulation. Then, different statistical techniques were applied to the data, including the training of a neural network to recognize lithology (the “ground truth”), an assessment of collinearity of features, and a projection of the data using principal components (effectively removing collinearity). Self-Organizing maps are then tested on different subsets of this data, and the paper demonstrates that the algorithm is relatively stable even when colinear features are included. Credit: Xu et al. [2026], Figure 3",
  "title": "Collinearity is Not Always a Problem in Machine Learning"
}