Hybrid Science Data System for Rapid Geodetic Imaging
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Solution Overview
Problem
Current earth science systems lack the necessary computing resources and infrastructure to efficiently process the rapidly increasing volumes of geodetic data from various sources, such as satellite and GPS data, in a timely and efficient manner, hindering the derivation of useful observations for scientists and non-scientists.
Innovation Solution
A hybrid science data system (HySDS) that combines on-premise and cloud-based infrastructure to monitor and process geodetic data, dynamically allocating additional computing resources from the cloud when needed to generate high-level data products like interferograms, time series velocity maps, and damage proxy maps, especially during triggering events like natural disasters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional on-premise processing infrastructure is used, then system complexity is reduced, but processing capacity and speed are insufficient for large volumes of geodetic data
Solution Approach 1:
The patent combines on-premise processing infrastructure with cloud-based computing resources into a hybrid system. The on-premise component handles data ingestion and initial processing, while the cloud component provides scalable computing power for intensive data processing tasks, achieving both high productivity and manageable complexity.
Solution Approach 2:
The cloud-based infrastructure provides universal computing resources that can dynamically scale to handle varying data volumes from multiple geodetic sensors. This multi-functional approach allows the same infrastructure to process different types of geodetic data (InSAR, GPS, seismic) with varying computational requirements.
2Productivity
If computing resources are increased to process large volumes of geodetic data, then processing capacity improves, but resource allocation efficiency deteriorates
Solution Approach 1:
The system dynamically allocates computing resources based on real-time data processing needs. The cloud-based infrastructure can scale resources up during high-volume data processing events (such as earthquakes) and scale down during normal operations, optimizing resource utilization and avoiding waste.
Solution Approach 2:
The hybrid system incorporates feedback mechanisms that monitor data processing demands and automatically adjust resource allocation. When processing queues indicate high demand, additional cloud resources are provisioned; when demand decreases, resources are released, ensuring efficient resource utilization.
3Loss of time
If real-time processing of geodetic data is implemented, then response time to triggering events improves, but processing accuracy may deteriorate due to insufficient computational resources
Solution Approach 1:
The processing pipeline is segmented into multiple stages: initial data processing and triggering event detection occur on-premise with low latency, while more computationally intensive processing steps (such as interferogram generation and damage assessment) are distributed to cloud resources. This segmentation enables real-time response while maintaining processing accuracy.
Data Source
AI summary
Many embodiments provide a hybrid data processing system (HySDS) of an end-to-end geodetic imaging data system enabling near-real-time science, assessment, response, and rapid recovery. The HySDS may be an operation data processing system that integrates data from many different geodetic data sources and/or sensors, including interferometric synthetic aperture radar (InSAR), GPS, pixel tracking, seismology, and/or modeling, and processes the data to generate actionable high quality science data products. The HySDS may provide for an automated imaging and analysis capabilities that is able to handle the imminent increases in raw data from new and existing geodetic monitoring sensor systems.