Distributed Acoustic Sensing Phase Pick Correction
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Solution Overview
Problem
Current seismic phase picking methods in distributed acoustic sensing (DAS) data are inefficient, requiring significant manual correction and time due to the large volume of data, and often fail to accurately select all necessary picks, leading to delayed analysis and potential inaccuracies in identifying subsurface features and microseismic events.
Innovation Solution
A computer-implemented method that uses cross-correlation with all available traces to generate corrected seismic phase picks, employing probability density functions to automate the correction process, eliminating the need for manual selection of reference traces and improving the accuracy of picks by interpolating gaps and applying amplitude gain control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual phase picking correction is used on DAS data, then accuracy of seismic picks can be improved, but time consumption and labor effort increase significantly
Solution Approach 1:
The system performs automatic self-correction of phase picks using cross-correlation algorithms and probability density functions, eliminating the need for manual intervention. The computer automatically identifies and corrects inaccurate picks by comparing seismic traces against a library of reference traces and selecting the best matches based on correlation coefficients.
Solution Approach 2:
The manual mechanical process of phase pick correction is replaced with an automated computational system using cross-correlation algorithms and probability density function calculations. The system substitutes human operators with computer-based signal processing methods that automatically analyze DAS data and generate corrected phase picks.
2Productivity
If automated phase picking algorithms are used, then processing speed is improved, but accuracy of selected picks deteriorates due to missed picks and false positives
Solution Approach 1:
The system incorporates feedback mechanisms where cross-correlation results are used to validate and refine phase pick selections. The probability density function provides feedback on the quality of matches, allowing the system to iteratively improve pick accuracy by comparing initial automated picks against correlation-based corrections and adjusting selections based on correlation coefficients.
Solution Approach 2:
The system combines multiple approaches into a composite solution: automated phase picking algorithms provide initial picks, cross-correlation with reference traces provides validation, and probability density functions provide quality assessment. This composite methodology integrates the speed of automated algorithms with the accuracy of correlation-based verification.
3Measurement precision
If all available traces are used for cross-correlation, then accuracy of corrected picks is improved, but computational complexity increases
Solution Approach 1:
The computational process is segmented into distinct stages: initial automated phase picking, cross-correlation with reference traces, probability density function calculation, and final correction selection. This segmentation allows the system to manage computational complexity by breaking down the complex task into manageable steps, processing data in organized phases rather than attempting all calculations simultaneously.
4Measurement precision
If manual correction of phase picks is performed, then accuracy can be improved, but the process becomes bottlenecked by operator availability and consistency
Solution Approach 1:
The system performs automatic self-correction of phase picks using cross-correlation algorithms and probability density functions, eliminating the need for manual intervention. The computer automatically identifies and corrects inaccurate picks by comparing seismic traces against a library of reference traces and selecting the best matches based on correlation coefficients.
Solution Approach 2:
The manual mechanical process of phase pick correction is replaced with an automated computational system using cross-correlation algorithms and probability density function calculations. The system substitutes human operators with computer-based signal processing methods that automatically analyze DAS data and generate corrected phase picks.
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
This method significantly reduces the time and effort required for seismic phase pick correction, enhances the accuracy of seismic data interpretation, and automates the process, allowing for more efficient identification of subsurface features and microseismic events, thereby improving project planning and decision-making in seismic exploration.
Implementation Method 1
In DAS data, seismic sensors comprise fiber optic cables configured to sense changes in strain along the cable
Implementation Method 2
cross-correlate each of the plurality of initial seismic phase picks using the plurality of traces as reference traces
Data Source
AI summary
Systems and methods are provided for correcting distributed acoustic sensing (DAS) data. The system can receive a seismic dataset with a plurality of initial seismic phase picks and a plurality of traces, and cross-correlate each of the plurality of initial seismic phase picks using the plurality of traces as reference traces. Each initial seismic phase pick can receive a set of corrected phase picks. The system can calculate a probability density function for each set of corrected phase picks. The system can select a peak of each probability density functions as accurate seismic phase picks. These accurate seismic phase picks can be used for event location in the DAS data.