Real-Time Seismic Noise Detection via Hough Tensor Analysis
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Solution Overview
Problem
Seismic surveys face challenges in real-time detection and mitigation of extraneous noise, which can degrade the quality of seismic data and require resource-intensive offline processing, leading to delays and potential re-performance of surveys.
Innovation Solution
The system employs Hough transforms to generate tensors, using machine learning to detect extraneous noise by comparing eigenvalues and eigenvectors with historical data, allowing for real-time notification and adjustment of seismic survey characteristics to reduce noise levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional offline processing methods are used to detect and mitigate extraneous noise in seismic surveys, then noise removal can be performed with comprehensive data analysis, but the process causes delays and may require re-performance of surveys
Solution Approach 1:
The system performs preliminary noise detection and characterization during the seismic survey operation itself, rather than waiting for offline processing. By continuously monitoring seismic data and detecting extraneous noise patterns in real-time, the system enables immediate identification of noise sources and conditions, allowing for timely adjustments before the survey is completed.
Solution Approach 2:
The system implements a feedback mechanism where detected extraneous noise characteristics are used to generate notifications that can trigger adjustments to survey parameters or operations. This closed-loop feedback allows the survey process to adapt dynamically to noise conditions, improving data quality without requiring complete re-surveying.
2Productivity
If real-time noise detection is implemented during seismic surveys, then noise can be identified and mitigated immediately, but the system complexity increases
Solution Approach 1:
The system introduces a specialized data processing intermediary that sits between the seismic data acquisition and the final processing stages. This intermediary component continuously analyzes seismic data for extraneous noise patterns, transforms the data through Fourier transforms, and generates notifications when noise is detected. By placing this intelligence in the data flow, the system achieves real-time detection without fundamentally redesigning the entire survey infrastructure.
Solution Approach 2:
The system replaces manual or post-survey noise analysis with automated computational methods. By using Fourier transforms and algorithmic noise pattern recognition, the system substitutes human analysis and offline processing with automated real-time computational detection, reducing the need for complex manual intervention while maintaining high detection accuracy.
3Reliability
If comprehensive offline processing is performed to ensure high data quality, then noise can be thoroughly removed, but resource utilization increases
Solution Approach 1:
By detecting and flagging extraneous noise during the survey operation itself, the system performs preliminary data quality assurance before the full offline processing pipeline is engaged. This preliminary action allows for early identification of problematic data segments, enabling targeted processing resources to be applied only where needed rather than uniformly across all data.
Solution Approach 2:
The system monitors changes in seismic data parameters (such as frequency content, amplitude patterns, and temporal characteristics) to detect extraneous noise. By tracking parameter variations in real-time and comparing them against expected patterns, the system can identify noise conditions and trigger appropriate responses, maintaining data quality through parameter-based detection rather than resource-intensive comprehensive processing.
Data Source
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AI summary
A system to detect and control noise in seismic surveys is provided. The system receives, responsive to a seismic wave generated by a source, seismic data detected by a sensor component of a seismic data acquisition unit. The system generates, for windows of the seismic data, Hough tensors for seismic data transforms in multiple dimensions. The system detects, based on a comparison of an eigenvector and eigenvalue of a canonical matrix of the Hough tensors with a historical eigenvector and eigenvalue of a historical canonical matrix of historical Hough tensors of historical seismic data, a first presence of noise in the seismic data. The first presence of noise can correspond to a noisy spectra pattern in a seismic data transform of the seismic data. The system provides, responsive to detection of the first presence of noise in the seismic data, a notification to adjust a characteristic of the seismic survey.