Results 11 to 20 of about 22,545,449 (395)
Thickness and structure of the martian crust from InSight seismic data
Science, 2021 Single seismometer structure Because of the lack of direct seismic observations, the interior structure of Mars has been a mystery. Khan et al., Knapmeyer-Endrun et al., and Stähler et al.
B. Knapmeyer‐Endrun +38 more
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Deep-seismic-prior-based reconstruction of seismic data using convolutional neural networks [PDF]
Geophysics, 2019 The reconstruction of seismic data with missing traces has been a long-standing issue in seismic data processing. Deep learning (DL) has emerged as a popular tool for seismic interpolation; it learns priors from training data sets of incomplete/complete ...
Qun Liu, Lihua Fu, Meng Zhang
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Deep Prior-Based Unsupervised Reconstruction of Irregularly Sampled Seismic Data
IEEE Geoscience and Remote Sensing Letters, 2022 Irregularity and coarse spatial sampling of seismic data strongly affect the performances of processing and imaging algorithms. Therefore, interpolation is a usual preprocessing step in most of the processing workflows. In this work, we propose a seismic
F. Kong +5 more
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Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning. [PDF]
Nat Commun, 2020 The continuously growing amount of seismic data collected worldwide is outpacing our abilities for analysis, since to date, such datasets have been analyzed in a human-expert-intensive, supervised fashion.
Seydoux L +5 more
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Upper mantle structure of Mars from InSight seismic data
Science, 2021 Single seismometer structure Because of the lack of direct seismic observations, the interior structure of Mars has been a mystery. Khan et al., Knapmeyer-Endrun et al., and Stähler et al.
Amir Khan +29 more
semanticscholar +1 more source
Seismic Data Reconstruction Using Deep Bidirectional Long Short-Term Memory With Skip Connections
IEEE Geoscience and Remote Sensing Letters, 2021 Due to environmental and economic constraints on their acquisition, seismic data are always irregularly sampled and include bad or missing traces, which can cause problems for seismic data processing.
D. Yoon, Z. Yeeh, J. Byun
semanticscholar +1 more source
The Denoising of Desert Seismic Data Based on Cycle-GAN With Unpaired Data Training
IEEE Geoscience and Remote Sensing Letters, 2021 The seismic data with high quality are the essential foundation of imaging and interpretation. However, the real seismic data are inevitably contaminated by noise, which affects the subsequent processing and interpretation of seismic data.
Yue Li, Hongzhou Wang, Xintong Dong
semanticscholar +1 more source
Geosciences, 2020 The presented study is devoted to the subsurface Upper Jurassic carbonate buildups and surrounding stratified inter-buildup deposits in the hitherto less recognized area, in comparison with other parts of the northern Tethyan shelf in Poland and Europe ...
Łukasz Słonka, Piotr Krzywiec
doaj +1 more source
A convolutional neural network approach to deblending seismic data [PDF]
Geophysics, 2020 For economic and efficiency reasons, blended acquisition of seismic data is becoming increasingly commonplace. Seismic deblending methods are computationally demanding and normally consist of multiple processing steps.
Jing Sun +5 more
semanticscholar +1 more source
Characterising Seismic Data [PDF]
Proceedings of the 2014 SIAM International Conference on Data Mining, 2014 When a seismologist analyses a new seismogram it is oftenuseful to have access to a set of similar seismograms.For example if she tries to determine the event, if any,that caused the particular readings on her seismogram.So, the question is: when are two seismograms similar?To dene such a notion of similarity, we rst preprocessthe seismogram by a ...
Bertens, R., Siebes, A.P.J.M.
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