Seismic Data Preprocessing for Full Waveform Inversion
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Solution Overview
Problem
Conventional seismic data processing methods are inadequate for handling the increased data volumes from modern seismic acquisition systems, particularly due to low signal-to-noise ratio (S/N) quality in land seismic data, which hinders accurate subsurface analysis and imaging.
Innovation Solution
The described processes involve preprocessing seismic data to enhance S/N quality through iterative rejection of anomalous traces, surface-consistent amplitude corrections, and generation of virtual super shot gathers, enabling 1.5 dimensional full waveform inversion (FWI) for improved seismic imaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional seismic data processing methods are used, then the workflow is simple and requires minimal human intervention, but the S/N quality of land seismic data is insufficient for accurate subsurface analysis
Solution Approach 1:
The patent applies preliminary action by performing iterative preprocessing steps before the main FWI analysis. Specifically, it iteratively rejects anomalous traces, corrects surface-consistent amplitude anomalies, and generates virtual super shot gathers in advance to enhance S/N quality, ensuring the data is optimized for subsequent inversion processing
Solution Approach 2:
The patent implements feedback mechanisms through iterative processing loops. The method repeatedly evaluates S/N quality metrics, rejects traces based on amplitude deviations from median values, and corrects anomalies using feedback from previous iterations until convergence criteria are met, thereby progressively improving data quality
2Measurement precision
If manual interactive procedures are used for data quality control, then the S/N quality can be improved, but the processing time increases significantly
Solution Approach 1:
The patent applies self-service by implementing automated algorithms that independently perform quality control without requiring manual analyst intervention. The system automatically identifies and rejects anomalous traces based on amplitude deviations, corrects surface-consistent anomalies through iterative algorithms, and generates virtual super shot gathers, thereby improving S/N quality while eliminating time-consuming manual operations
Solution Approach 2:
The patent changes processing parameters by transitioning from manual interactive procedures to automated iterative algorithms. It uses objective criteria such as amplitude deviation thresholds, median-based normalization, and convergence thresholds to control the processing, thereby maintaining high S/N quality while significantly reducing processing time through computational automation
3Quantity of substance
If the number of seismic acquisition channels is increased, then the data availability increases, but the difficulty of handling and analyzing the data increases
Solution Approach 1:
The patent applies segmentation by organizing the large volume of seismic data into manageable common midpoint-offset bins. It processes data in discrete spatial-frequency domains, separating and analyzing specific trace groups based on their geometric relationships, thereby reducing the complexity of handling large datasets while maintaining comprehensive data coverage
Solution Approach 2:
The patent implements universality by developing a multi-functional processing framework that simultaneously performs trace rejection, amplitude correction, and virtual super shot gather generation within a single integrated workflow. This universal approach handles diverse data characteristics and processing requirements through unified algorithms, reducing the overall difficulty of data analysis
Data Source
AI summary
This specification describes workflows for, but is not limited to, performing full waveform inversion (FWI) to build high resolution velocity models to improve the accuracy of seismic imaging of a subterranean formation. This specification describes processes to automatically edit and enhance S/N quality of seismic data (such as land seismic data) to prepare the datasets for FWI. The methods for automatic corrections and pre-processing include: automatic iterative surface-consistent residual statics calculation, automatic rejection of anomalous traces (such as dead traces), and the automatic correction of surface-consistent amplitude anomalies (such as by scalar or deconvolution approaches). The operations include automatic “muting” of noise before first arrivals.