Detect GNSS Spoofing Signals Using a Machine Learning Method

Xu, Patrick; Hay, Curtis; Kumar, Rakesh; Surti, Iqbal; Tjhai, Chandra

doi:10.33012/2022.18256

Cited by 2 publications

(1 citation statement)

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“…An approach similar to the one developed here can enhance regional low‐resolution deformation observations with higher‐resolution local results. Furthermore, the low temporal resolution of InSAR data can be improved by high temporal resolution GPS data regarding the timeline as an extra dimension (e.g., Xu et al., 2022). The interpretability of the dictionary learning method can facilitate discovering spatial and temporal correlations between different physical quantities, such as geological features, seismic velocity structure, and surface deformation.…”

Section: Discussionmentioning

confidence: 99%

Enhancing Regional Seismic Velocity Models With Higher‐Resolution Local Results Using Sparse Dictionary Learning

Zhang,

Ben‐Zion

2024

JGR Solid Earth

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We use sparse dictionary learning to develop transformations between seismic velocity models of different resolution and spatial extent. Starting with data in the common region of both models, the method can enhance a regional lower‐resolution model to match the style and resolution of local higher‐resolution results while preserving its regional coverage. The method is demonstrated by applying it to two‐dimensional VS and three‐dimensional VP and VS regional and local velocity models in southern California. The enhanced reconstructed regional results exhibit clear visual improvements, especially in the reconstructed VP/VS ratios, and better correlations with geological features. Moreover, the reconstructed regional VP, VS models outperform the original ones in comparison of simulated earthquake waveforms to observations. The improved fitting to observed waveforms extends beyond the domain of the overlapping region. The developed dictionary learning approach provides physically interpretable results and offers a powerful tool for additional applications of data enhancement in earth sciences.

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Section: Discussionmentioning

confidence: 99%