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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Seismic Full-Waveform Inversion Using Deep Learning Tools and Techniques · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1801.07232/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1801.07232/feed.xml" rel="self" type="application/rss+xml" /><item><title>Seismic Full-Waveform Inversion Using Deep Learning Tools and Techniques — mentioned by emmynoether</title><link>https://news.ycombinator.com/item?id=39564414</link><guid isPermaLink="false">urn:hackerpapers:mention:1801.07232:39564414</guid><pubDate>Fri, 01 Mar 2024 17:46:52 GMT</pubDate><description>&lt;p&gt;It has been shown that a finite difference implementation of wave propagation can be expressed as a deep neural network (e.g., [1]). These networks can have thousands of layers and yet I don&amp;#x27;t think they suffer from the exploding/vanishing gradient problem, which I imagine is because in the physical system they model there are conservation laws such as conservation of energy. [1] https://arxiv.org/abs/1801.07232&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1801.07232/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>physics.geo-ph</category></item></channel></rss>
