Wind turbine induced seismic signals: the large‐scale SMARTIE1 experiment and a concept to define protection radii for recording stations
Sign Up to like & getrecommendations! Published in 2020 at "Near Surface Geophysics"
DOI: 10.1002/nsg.12109
Abstract: ABSTRACT Wind turbines produce mechanical energy that can propagate to the ground and disturb sensitive measurements such as seismic recordings. The aim of the large‐scale experiment Seismic Monitoring And Research of wind Turbine Induced Emissions… read more here.
Keywords: turbine; seismic signals; turbine induced; induced seismic ... See more keywords
Analysis of Regolith Properties Using Seismic Signals Generated by InSight’s HP3 Penetrator
Sign Up to like & getrecommendations! Published in 2017 at "Space Science Reviews"
DOI: 10.1007/s11214-017-0391-3
Abstract: InSight’s Seismic Experiment for Interior Structure (SEIS) provides a unique and unprecedented opportunity to conduct the first geotechnical survey of the Martian soil by taking advantage of the repeated seismic signals that will be generated… read more here.
Keywords: seismic signals; resolution; insight hp3; analysis ... See more keywords
STanford EArthquake Dataset (STEAD): A Global Data Set of Seismic Signals for AI
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2947848
Abstract: Seismology is a data rich and data-driven science. Application of machine learning for gaining new insights from seismic data is a rapidly evolving sub-field of seismology. The availability of a large amount of seismic data… read more here.
Keywords: earthquake; seismic signals; seismology; global data ... See more keywords
Unsupervised Clustering of Seismic Signals Using Deep Convolutional Autoencoders
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2019.2909218
Abstract: In this letter, we use deep neural networks for unsupervised clustering of seismic data. We perform the clustering in a feature space that is simultaneously optimized with the clustering assignment, resulting in learned feature representations… read more here.
Keywords: seismic signals; clustering seismic; deep convolutional; unsupervised clustering ... See more keywords
Time Window Selection of Seismic Signals for Waveform Inversion Based on Deep Learning
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2021.3076531
Abstract: Seismic full-waveform inversion (FWI) is a powerful tool to image the high-resolution subsurface properties by fitting synthetic seismogram to observed data. To reduce the nonlinearity of FWI, a critical step is to exclude the noises… read more here.
Keywords: time; time window; inversion; waveform inversion ... See more keywords
A Method for Denoising Seismic Signals With a CNN Based on an Attention Mechanism
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2022.3215819
Abstract: Suppressing random noise in seismic data is a significant problem in seismic data processing. Often, there is serious aliasing between the effective signal and random noise, affecting the identification of weak signals, and even resulting… read more here.
Keywords: noise; method denoising; data processing; seismic signals ... See more keywords
DenoiseNet: Deep Generator and Discriminator Learning Network With Self-Attention Applied to Ocean Data
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2022.3217402
Abstract: Well-organized seismic signals play a significant role in the subsequent seismic data processing. The multiscale learning of characteristic signals of complex structures by deep convolutional neural networks has obvious benefits in reducing random noise in… read more here.
Keywords: network; network self; self attention; seismic signals ... See more keywords