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SUMMARY:From Mars to Earth: Deep Learning Denoising for Seismic Monitoring
DTSTART:20260512T120000Z
DTEND:20260512T140000Z
DTSTAMP:20260502T173500Z
UID:indico-event-13@solarsystem.events
DESCRIPTION:Speakers: Nikolaj Dahmen (ETH Zürich)\n\nA fundamental challe
 nge in seismic data analysis is the presence of noise that corrupts wavefo
 rms and masks weak event signals\, limiting the completeness and quality o
 f seismicity catalogs and with that the analysis of seismic processes. Thi
 s challenge is particularly acute for NASA's InSight mission on Mars\, whe
 re a single-station setup\, low magnitudes\, and large epicentral distance
 s combined with highly variable background noise lead to low signal-to-noi
 se ratios that limit event detection and characterization. Deep learning m
 odels have emerged as a game changer in seismic data processing\, and here
  I show how a deep learning-based denoising approach that separates event 
 and noise energy improves upon conventional processing across a wide range
  of signal-to-noise conditions\, substantially advancing the analysis of m
 arsquake signals\, from signal detection\, phase picking\, and manual and 
 automated waveform analysis. Transferring a similar framework to earthquak
 e data\, specifically the Swiss seismic network\, I demonstrate its broad 
 applicability and efficiency in processing large data volumes\, significan
 tly improving earthquake catalog completeness at the low-magnitude end and
  enhancing operational monitoring settings - in both cases leading to a mo
 re detailed picture of seismicity and ultimately advancing the observation
  and characterization of seismic processes.\nhttps://eaps.ethz.ch/en/peopl
 e/profile.nikolaj-dahmen.html\n\nhttps://solarsystem.events/event/13/
LOCATION:Lamarck 522 (IPGP - Campus des Grands Moulins)
URL:https://solarsystem.events/event/13/
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