Subsurface Fiber Optic Event Detection via Signal Refinement
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Solution Overview
Problem
Current subsurface event detection methods using fiber optic apparatuses face challenges such as losing relevant information due to amplitude variation, phase rotation, and polarity flip, and require extensive preprocessing to improve signal-to-noise ratios.
Innovation Solution
The system processes subsurface fiber optic data by constraining and refining it using multiple signal classification and semblance-based models, generating a moment tensor, and creating a digital seismic image to accurately depict event locations and origins within the subsurface volume of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If fiber optic apparatus is used for subsurface event detection, then measurement coverage is improved, but information loss occurs due to amplitude variation, phase rotation, and polarity flip
Solution Approach 1:
The patent transforms the fiber optic data from its original parameter space (amplitude, phase, polarity) to a new parameter space using multiple signal classification and semblance-based models. This parameter transformation resolves the information loss by mapping distorted signals to a representation where subsurface event characteristics can be accurately recovered despite the original distortions.
Solution Approach 2:
The patent introduces intermediate processing models (multiple signal classification model and semblance-based model) that act as mediators between the raw fiber optic data and the final subsurface event detection. These intermediary models process the distorted signals through multiple transformation stages, progressively recovering the lost information while maintaining the broad measurement coverage advantage.
2Reliability
If extensive preprocessing is applied to improve signal-to-noise ratio, then detection reliability is improved, but processing complexity increases
Solution Approach 1:
The patent segments the preprocessing task into distinct modular stages: initial signal acquisition, multiple signal classification processing, semblance-based model processing, and final event detection. Each segment handles a specific aspect of signal enhancement, allowing the system to achieve high detection reliability through systematic multi-stage processing while managing complexity through modular organization.
Solution Approach 2:
The patent employs dynamic processing where the complexity of preprocessing operations is adaptively adjusted based on the characteristics of the incoming fiber optic data. The system dynamically selects and applies appropriate processing intensity and model complexity levels, ensuring high detection reliability while avoiding unnecessary processing complexity for already high-quality signals.
3Measurement precision
If multiple signal classification and semblance-based models are used to refine data, then location accuracy is improved, but computational requirements increase
Solution Approach 1:
The patent applies preliminary filtering and constraint operations before the computationally intensive multiple signal classification and semblance-based modeling. By pre-constraining the search space and eliminating obviously incorrect solutions early in the processing chain, the system reduces the computational energy required for the subsequent high-precision location accuracy calculations while maintaining measurement precision.
Data Source
Figure 1A
Figure 1B~2A-2
Figure 2B-1~2B-2
AI summary
Systems, methods, and storage media for detecting a given subsurface event in a subsurface volume of interest are disclosed. Exemplary implementations may: receive a subsurface fiber optic data set; receive a sensor data set using one or more sensors; constrain the subsurface fiber optic data set based on a given parameter value of a given parameter within a certain range to generate a constrained subsurface fiber optic data set; use sets of models to refine the constrained subsurface fiber optic data set to generate a refined subsurface fiber optic data set; estimate an event location of the given subsurface event based on the refined subsurface fiber optic data set; and estimate an origin time based on the event location.