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Keeping a Sentinel Eye on the Volcanoes – from Space!
- 1. Keeping a Sentinel Eye on theVolcanoes – From Space! Dr. HomaAnsari German Aerospace Center (DLR) Earth Observation Data Science Thanks to collaborators from German Aerospace Center (DLR) EO Data Science German Aerospace Center (DLR) SAR signal processing German Research Center for Geoscience (GFZ) Technical University of Munich (TUM)
- 2. Keeping the Sentinel Eye On the Ground Sentinel-1: ESA’s synthetic aperture radar (SAR) mission Opportunity : Global monitoring of geohazards with Interferometric SAR (InSAR) © ESA © DLR
- 3. Keeping the Sentinel Eye On the Ground Sentinel-1: ESA’s synthetic aperture radar (SAR) mission Opportunity : Global monitoring of geohazards with Interferometric SAR (InSAR) Challenge : Diving into Petabytes of Data for insight -> Efficient Big Data Processing and Mining
- 4. InSAR and Surface Deformation Mapping
- 5. Earth Surface Deformation from SARTime Series (TS) 𝑅1 𝑡1 𝑅1 Atmosphere at 𝑡1 Earth surface at 𝑡1 𝑡1 Acquisition at Single SAR image
- 6. Earth Surface Deformation from SARTime Series (TS) 𝑅1 𝑡1 𝑡2 Δ𝑅defo Δ𝑅 = Δ𝑟deformation + Δ𝑟atmosphere + Δ𝑟scattering + Δ𝑟system noise ∗ 𝑅2 = 𝑅1 + Δ𝑅 Atmosphere at 𝑡1 Atmosphere at 𝑡2 Earth surface at 𝑡1 Earth surface at 𝑡2 𝑡1 & Acquisition at 𝑡2 Interferogram Pairwise SAR Time Series
- 7. Big Data Challenge with SARTime Series Objectives: primarily : efficient Earth surface deformation retrieval from SARTime Series ultimately : efficient extraction of insight from the surface deformation data products Focus on the efficiency: designing innovative signal processing and deep learning solutions ⋯ 60 SAR Acquisitions 1770 Noisy Interferograms one-year Data Archive 4D Mapping of the Earth Surface TBs of data products
- 8. Efficient SARTime Series Processing
- 9. Sequential EMI: our efficient recipe for stream processing the data Generation of Artificial Interferograms Compression of Mini-stacks Division of Time Series to Mini-stacks 𝒕𝑵 𝒕𝟏 SAR Time Series Global Systematic Earth Observation Missions Open the Big Data challenge * Ansari, De Zan, Bamler. “Sequential Estimator: towards efficient InSAR time series analysis,” IEEE TGRS. 2017. ** Ansari, De Zan, Bamler. “Efficient Phase Estimation for Interferogram Stacks,” IEEE TGRS. 2018. *** Ansari. “Efficient High-Precision Time Series Analysis for SAR Interferometry,” Doctoral Dissertation, TUM+DLR, 2020
- 10. Deformation Velocity [mm/yr] Sentinel-1 A/B Sicily - Italy 10.2014 - 09.2018 905 Interferograms 1785 Interferograms 16836 Interferograms Impact of Big Data Processing Recipes on Accuracy Minimal Exploitation of TS Increased Exploitation of TS Our Efficient Recipe * Ansari, De Zan, Bamler. “Study of Systematic Bias in Measuring Surface Deformation With SAR Interferometry,” IEEE TGRS. 2021.
- 11. Efficient SARTime Series Processing in Action 4D mapping of the Earth surface SAR Image Time Series Processing Sentinel-1 data acquisition 5 Year SAR Data Sentinel-1 A/B 2014 – 2019 Central Volcanic Zone of the Andes TecVolSA: monitoringTectonics andVolcanoes of South America
- 12. TecVolSA: Earth Surface Deformation Products H. Volcanoes P. Volcanoes Ascending Geometry Descending Geometry Retrieved Data Products ❑ Surface Deformation Velocity Maps 5 Year SAR Data Sentinel-1 A/B 2014 – 2019 Central Volcanic Zone of the Andes Deformation Velocity [mm/yr] -5 5 © DLR – Earth Observation Center - InSAR Team
- 13. TecVolSA: Earth Surface Deformation Products Retrieved Data Products ❑ Surface Deformation Velocity Maps ❑ Point-wise Deformation Time Series temporal evolution of the Earth surface H. Volcanoes P. Volcanoes 5 Year SAR Data Sentinel-1 A/B 2014 – 2019 Central Volcanic Zone of the Andes
- 14. Diversity of Geophysical Signals Chile - Atacama Salt Pane Lithium mining activities Andes - Lastarria Volcano Volcanic deformation Hard carbonate crust uplifting Soft sulphate crust subsiding Argentina - Tebenquicho Solifluction signal Chile – Illapel 2015 8.3 Mw Earthquake and aftershocks Displacement Velocity [mm/yr] +5 -5 0 Coseismic interferogram Surface Displacement Time Series In-situ Measurement Campaign by GFZ Surface Displacement Map +20 -20 EQ event 16 Sept. 2015
- 15. Deep Learning for Exploring the Surface Deformation Products
- 16. TecVolSA: monitoringTectonics andVolcanoes of South America Binary classification o Volcanic activity o Non-volcanic activity Convolutional Neural Nets (CNN) 1- Surface DeformationVelocity Maps Supervised Learning Encoder Decoder Latent Space Deformation Signal Clustering Noise-aware Transformer Autoencoders 2- PointWise DeformationTime Series Unsupervised Learning
- 17. CNNs in Detection ofVolcanic Signals from DeformationVelocity Maps
- 18. The Rationale in Volcanic Deformation Detection Binary classification o Volcanic o Non-volcanic Convolutional Neural Nets Inference/Detection: Real Data o SAR-derived Deformation Velocity Maps Training the networks: Simulated Data o Volcanic deformation simulation: Geophysical models o Atmospheric simulations: Retrieved Statistics of atmospheric noise
- 19. Sentinel-1 A/B 2014 – 2019 CentralVolcanic Zone of theAndes Wide AreaVolcanic Activity Detection Exemplary patch-wise inference of CNNs over the volcanic belt Volcanic Activity Teo.Beker@dlr.de
- 20. Sentinel-1 A/B 2014 – 2019 CentralVolcanic Zone of theAndes TP: DetectedVolcanic Activities FN: MissedVolcanic Activities FP: False Alarm Wide AreaVolcanic Activity Detection Teo.Beker@dlr.de
- 21. Sequence Models in Mining DeformationTime Series
- 22. The problem at hand Deformation Time Series Products: ▪ Point-wise time series ▪ millions to billions of time series The challenge: ▪ Efficient exploration of the big data ▪ Low SNR due to the atmospheric errors ▪ Arbitrary complex non-linear deformation signals Our approach: ▪ Unsupervised machine learning (ML) Point-Wise Deformation Time Series 4D Mapping – Multi-temporal SAR • H. Ansari • Oct. 2021 DLR.de • Chart 22
- 23. The Rationale in Time Series Mining Input Deformation Time Series 1. Various Preprocessing of the time series 2. Learning Latent Features 3. Clustering based on the Latent Features Output Clusters of Similar TS * Ansari, Rußwurm, Ali, Montazeri, Parizzi, Zhu. “InSAR Displacement Time Series Mining: A Machine Learning Approach,” IGARSS 2021. • H. Ansari • Oct. 2021 DLR.de • Chart 23
- 24. Various Data Compressions in the Pipeline 1. Various Preprocessing of the time series 3. Clustering based on the Latent Features ▪ Compressing the time series (𝑇) to latent features (𝑚) ▪ Reducing millions of points (𝑛) to hundreds of clusters (𝑘) Output Clusters of Similar TS 𝑛 × 𝑡 𝑛: point-wise measurements 𝑡: size of time series 𝑡 → 𝑚 𝑛 × 𝑚 𝑚: Latent Features 𝑛 → 𝑘 𝑘 × 𝑡 𝑘: Clusters 2. Learning Latent Features Input Deformation Time Series • H. Ansari • Oct. 2021 DLR.de • Chart 24
- 25. Deep Latent Feature Learning Sequence Model: ▪ Transformer Encoder 𝑚 Latent Features Encoder Decoder Noise-aware Transformer Autoencoder Latent Space Input 𝑋 ∈ 𝑅1×𝑡 Noisy TS Output 𝑋 ∈ 𝑅1×𝑡 Deformation TS Output 𝐿 ∈ 𝑅1×𝑚 Latent Features Linear Layer Feature Repeater Sequence Model: ▪ Transformer Encoder Noise Power: Γ Encoder: Z = 𝑓Θ(𝑋) Decoder: 𝑋 = 𝑓Θ′ ′ 𝑍 Noise-Aware Loss function: ℒ = 1 2𝑡 𝑋 − 𝑋 Γ 2 + 1 2𝑡 𝑖=1 𝑡 log(Γ𝑖𝑖) Optimization: 𝑓, 𝑓′ = argminΘ,Θ′,Γ ℒ Outputs: o Latent deformation learning 𝐿 o Time Series Denoising 𝑋 o Modeling noise power per epoch Γ𝑖𝑖 • H. Ansari • Oct. 2021 DLR.de • Chart 25
- 26. Demonstration: Lazufre Volcanic Complex Volcanos in Lazufre Complex: Cordon, Bayo, Lastaria, Escorial Deformation Velocity [mm/yr] -5 0 5 Deformation Velocity Map Sentinel-1 A/B Dec. 2014 – Jul. 2019 Chile-Argentina ~2500 km² • H. Ansari • Oct. 2021 DLR.de • Chart 26
- 27. Latent Deformation Features versus Linear Deformation from signal processing using spatial information • H. Ansari • Oct. 2021 DLR.de • Chart 27 Learned Latent Features (𝑚 = 5) from the Noise-Aware Autoencoders spatial information not revealed to the Autoencoders
- 28. Time Series Denoising and Clustering InSAR velocity map Clustered points shown in black dots Clustered InSAR Time Series (Based on the latent deformation sources) Denoised InSAR Time Series (Reconstructed by Noise-Aware Autoencoder) • H. Ansari • Oct. 2021 DLR.de • Chart 28
- 29. In short o Innovative algorithms are a necessity for exploiting the mission capabilities to the fullest o Focus must be on the efficiency of algorithms but not at the cost of accuracy o We resorted to a combination of resources to realize this objective: o Data and dimensionality reduction o Statistical signal processing o Supervised learning with convolutional neural networks o Unsupervised learning with combination of sequence models • H. Ansari • Oct. 2021 DLR.de • Chart 29
- 30. @HomaAnsari_ • H. Ansari • Oct. 2021 DLR.de • Chart 30
- 31. Next in my journey … homa.ansari@datarobot.com