Closely Spaced Seismic Sensors for Accurate Epicenter Warnings
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Solution Overview
Problem
Existing Earthquake Early Warning Systems (EEWS) face challenges such as the need for sensors to be spaced far apart, leading to inaccurate epicenter and magnitude predictions, reliance on costly and unreliable network infrastructure, lack of situational awareness in warnings, and inefficient manual decision-making during earthquakes.
Innovation Solution
A system comprising closely located sensors with improved timing accuracy, autonomous decision-making, and local data processing, enabling precise epicenter determination and situational awareness, with local and remote communication capabilities for timely and efficient damage prevention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are placed far apart (tens to hundreds of kilometers), then the system can detect seismic events, but the epicenter and magnitude predictions become inaccurate
Solution Approach 1:
The system segments the seismic detection function across multiple closely-spaced sensors, with each sensor performing local data processing and autonomous decision-making. This segmentation allows accurate epicenter determination through triangulation of closely-spaced sensor locations while maintaining the ability to detect seismic events independently at each location.
Solution Approach 2:
The patent transitions from a single centralized decision-making point to multiple distributed decision-making nodes across different spatial dimensions. Each sensor location operates autonomously in its local dimension, then integrates findings across the network, enabling accurate localization without requiring vast sensor separations.
2Measurement precision
If sensors are placed close together, then accurate epicenter determination is possible, but the system requires improved timing accuracy and more complex data processing
Solution Approach 1:
Each sensor unit performs self-service through autonomous local data processing and independent decision-making capabilities. This eliminates the need for complex centralized processing and reduces timing synchronization requirements, as each node independently analyzes its own data and contributes findings to the network.
Solution Approach 2:
Data processing is performed preliminarily at each sensor location before network integration. This preliminary action at the source reduces the complexity of subsequent centralized processing and minimizes timing synchronization requirements, as the heavy computational burden is distributed across multiple nodes performing analysis locally.
3Device complexity
If centralized data processing is used, then the system architecture is simpler, but it relies on costly and unreliable wide-area network infrastructure
Solution Approach 1:
The centralized processing architecture is segmented into multiple distributed processing nodes. Each sensor location maintains local processing capabilities, eliminating dependence on continuous wide-area network connectivity for core analytical functions. This segmentation improves reliability by allowing autonomous operation even when network infrastructure fails.
Solution Approach 2:
The system implements local quality by enabling each sensor location to perform data processing autonomously at its local site. This local capability eliminates the need for reliable wide-area network infrastructure for critical processing functions, while still allowing network integration when available. Each location adapts its processing quality to local conditions and network availability.
4Productivity
If manual decision-making is used during earthquakes, then the system can respond to seismic events, but the response time is delayed and efficiency is reduced
Solution Approach 1:
The system implements self-service through autonomous decision-making algorithms at each sensor location. When seismic events are detected, local nodes automatically initiate appropriate emergency measures without waiting for manual human decision-making. This autonomous response dramatically reduces the time delay inherent in manual processes while maintaining appropriate judgment through programmed decision criteria.
Solution Approach 2:
Emergency measures are prepared and can be executed automatically based on pre-programmed decision criteria. The system performs preliminary configuration of response actions and thresholds, enabling rapid automatic execution when conditions are met, thereby eliminating the time delay of manual decision-making during critical earthquake response scenarios.
Data Source
AI summary
A seismic warning system comprises: a plurality of sensors, each sensor sensitive to a physical phenomenon associated with seismic events and operative to output an electronic signal representative of the sensed physical phenomenon; a data acquisition unit communicatively coupled to receive the electronic signal from each of the plurality of sensors, the data acquisition unit comprising a processor configured to estimate characteristics of a seismic event based on the electronic signal associated with a P-wave from each of the plurality of sensors; and a local device communicatively coupled to the data acquisition unit.