Distributed Optical Fiber Rainfall Sensing With Deep Frequency Filtering
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Solution Overview
Problem
Current methods for rainfall and rainfall intensity detection, such as land-based weather stations and Earth-orbiting satellites, are limited by availability and accessibility, and there is an urgent need for improved, real-time, long-range, and wide-coverage sensors due to increasing frequency and intensity of precipitation events.
Innovation Solution
A Deep Phase-Magnitude Network (DFMN) is introduced for DFOS systems, dividing raw data into phase and magnitude components, using a Phase Frequency Learnable Filter (PFLF) for phase component filtering and standard convolution layers for magnitude, leveraging optical fiber sensing properties to enhance rainfall sensing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If land-based weather stations and Earth-orbiting satellites are used for rainfall detection, then rainfall and rainfall intensity can be detected, but availability and accessibility are limited
Solution Approach 1:
The patent replaces traditional mechanical weather stations and satellite-based detection systems with distributed acoustic sensing technology using optical fibers. The optical fiber sensing system detects rainfall through acoustic vibrations and phase changes in the fiber, eliminating the need for physical weather station infrastructure and providing widespread accessibility without requiring direct contact with precipitation events.
Solution Approach 2:
The patent introduces optical fibers as an intermediary medium between the rainfall event and the detection system. The optical fiber acts as a distributed sensor that converts mechanical vibrations from raindrops into optical phase changes, which can be measured remotely and transmitted through existing communication networks, thereby improving both availability and accessibility of rainfall detection data.
2Measurement precision
If traditional rainfall detection methods are used, then rainfall data can be obtained, but real-time capability and coverage range are insufficient
Solution Approach 1:
The patent segments the optical fiber into multiple sensing points along its length, creating a distributed array of measurement locations. Each segment of the fiber can independently detect acoustic vibrations and phase changes, enabling simultaneous real-time monitoring across extensive geographic areas. This segmentation transforms a single-point measurement system into a distributed sensing network with both real-time capability and wide coverage.
Solution Approach 2:
The patent transitions from traditional point-based or area-based rainfall measurement to a distributed linear sensing dimension. By deploying optical fibers along roads, power lines, or other infrastructure, the system creates a continuous one-dimensional sensing array that can monitor rainfall intensity at multiple locations simultaneously, effectively adding spatial dimensionality to real-time detection capability.
3Area of stationary object
If distributed optical fiber sensing is used, then long-range and wide-coverage detection is achieved, but data processing complexity increases
Solution Approach 1:
The patent extracts and isolates the phase component from the complex optical signal measurements, recognizing that phase changes contain the primary information about rainfall intensity and acoustic vibrations. By focusing analysis on the phase component rather than processing the entire complex signal, the system reduces computational complexity while maintaining the ability to detect rainfall events across long ranges and wide areas.
Solution Approach 2:
The patent applies different processing strategies to different components of the optical signal. Specifically, it processes the phase component separately from the amplitude component, using phase-based methods that are more suitable for detecting acoustic vibrations from rain. This localized processing approach allows the system to handle the complexity of distributed sensing data more efficiently by targeting computational resources at specific informative features.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The proposed method achieves superior performance in rain intensity monitoring compared to state-of-the-art approaches, demonstrating effective and efficient real-time, wide-coverage rainfall detection.
Implementation Method 1
Distributed Acoustic Sensing (DAS) is a DFOS technology that uses fiber optic cables to detect acoustic vibrations
Implementation Method 2
dividing raw DFOS sensing data into phase and magnitude components
Data Source
AI summary
Disclosed are integrated DFOS/DAS systems, methods, and structures that advantageously enhances rainfall sensing by employing a Deep Phase-Magnitude Network (DFMN), dividing raw DFOS sensing data into phase and magnitude components, and performing targeted feature learning on each component independently. The disclosed systems, methods, and structures employ a Phase Frequency learnable filter (PFLF) for phase component filtering and utilize standard convolution layers on the magnitude component, advantageously leveraging inherent physical properties of optical fiber sensing. Finally, a phase-magnitude channel is formulated in a parallel network and subsequently fuses the features for a comprehensive analysis. Experimental results on collected fiber sensing data show that our systems and method according to aspects of the present disclosure perform favorably as compared with alternative, state-of-the-art approaches.