Projects
01/2019 – Present, Postdoc, The Pennsylvania State University, University Park, PA, United States
Goals: Power of many – theory and development of domain independent deep learning solutions for Earth Science problems
Project-1: Develop deep learning solutions for volcanic surface movement monitoring (in Department of Geoscience, Penn State)
Developed a deep learning solution for automatically detecting volcanic surface deformation from Interferometric Synthetic Aperture Radar (InSAR) data;
Research attracted wide attentions and reported by media: Deep learning artificial intelligence keeps an eye on volcano movements
Achievements:
Presented at AGU-2019 annual meeting
Published a peer-reviewed article in JGR - Solid Earth: Sun et al., 2020,
Project-2: Deep learning applications for automatic target detection and hyperspectral imaging with shortwave remote sensing data (collaborating with Department of Geography, Penn State)
Developed a temporal-dependent deep learning solution for atmospheric correction and target detection using multi-scan hyperspectral data
The time-dependency of the proposed network ensures the precision of estimated reflectivity spectra for different materials, including vegetations, sea ice, and ocean
Discussed the uncertainty and performances with missing data
Achievements:
Submitted to ISPRS Journal of Photogrammetry and Remote Sensing for peer-reviewing (under review)
Presented at AGU-2020 annual meeting
Project-3: Deep learning for sea ice concentration prediction and discovering the connections between sea ice and atmospheric factors (collaborating with Department of Atmospheric and Meteorology Science, Penn State)
Developed a neural network architecture to analyze the spatiotemporal variations of different atmospheric variables
Successfully predicted sea ice concentration using the proposed CNN-LSTM
Investigated the impacts of different atmospheric variables on sea ice concentration using interpretable deep learning
Achievements:
In Preparation: Deep learning discovery of predicting sea ice concentration in Sea of Okhotsk
Project-4: Hybrid deep learning for subsurface imaging and inversion (collaborating with Department of Geosciences, University of Calgary)
Developed a hybrid deep learning framework for seismic inversion
Discussed the advantages of physics-guided and data-driven deep learning
Comprehensively analyzed the deep learning performances for seismic inversion
Investigated the uncertainty of deep learning predictions of subsurface parameters
Achievements:
Submitted to Geophysics for peer-review: Sun et al., 2020
05/2018 – 01/2019, Postdoc, University of Calgary, AB, Canada
Project: Theory-guided deep learning in seismic forward and inversion problems
Designed a physics-based recurrent neural network (RNN) to simulate wave propagation in a deep learning perspective
Theoretically analyzed the deep learning formulation of seismic inversion problems
Proven that automatic differential technique is equivalent to the gradient-based full waveform inversion using adjoint-state methods
Implemented the seismic waveform inversion using the proposed theory-guided RNN
Discussed the hyperparameter selections for different optimizers and compared performances between auto-differential based algorithms and adjoint-state methods
Achievements:
Presented at CSEG-2019 and SEG-2019 annual meetings
Published a peer-reviewed article in Geophysics: Sun et al., 2020, which is the most downloaded article of all time in Geophysics Top-20
09/2013 – 05/2018, Research Assistant, University of Calgary, AB, Canada
Project: Data-driven theory for multiple prediction and attenuation in novel domains
Developed the inverse scattering series (ISS) theory for internal multiple prediction in the (coupled) plane-wave domain
Implemented multi-dimensional internal multiple prediction in the coupled plane wave domain with distinct advantages, such as constant hyperparameter selection, sparse implementation, and less computational overburden
Developed the ISS-based approach for multi-dimensional and multi-component internal multiple prediction in the (coupled) plane-wave domain
First successfully implemented the elastic internal multiple prediction in the multi-component seismic data
Achievements:
Presented at CSEG-2015, CSEG-2016, CSEG-2017, CSEG-2018 annual meetings
Presented at SEG-2017, SEG-2018, EAGE-2018 annual meetings
Published a preprint article: Sun et al., 2019
Published an invited & reviewed article in CSEG Recorder, Sun et al., 2016
Published two peer-reviewed articles in Geophysics: Sun et al., 2018, Sun et al., 2019