Seismic Wavefront Attribute Estimation for Low-SNR 3D Land Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Processing of three-dimensional land seismic data acquired with small field arrays or single-sensor systems is challenging due to low signal-to-noise ratios and complex wavefields, leading to inefficient data enhancement and loss of high-frequency content.
Innovation Solution
An automated process to constrain prestack seismic wavefront attributes by estimating initial search intervals on a representative subset of the dataset, applying semblance thresholds, and transforming attributes to reduce ranges, using statistical and physical properties to enhance seismic data quality while preserving high-frequency content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to process three-dimensional land seismic data, then data enhancement can be achieved, but computational time is excessive and high-frequency content is lost
Solution Approach 1:
The patent segments the seismic data processing into distinct stages: estimating wavefront attributes on a representative subset, applying semblance thresholds, transforming attributes to reduce ranges, and then estimating attributes for the full dataset. This segmentation allows efficient processing while maintaining data quality.
Solution Approach 2:
The patent performs preliminary actions by first estimating wavefront attributes and applying semblance thresholds to a representative subset of the dataset before processing the entire dataset. This preliminary processing establishes constraints that guide the subsequent full dataset processing, reducing overall computational time.
2Measurement precision
If traditional methods are used to process three-dimensional land seismic data, then data enhancement can be achieved, but high-frequency content is lost
Solution Approach 1:
The patent changes parameters by transforming wavefront attributes to reduce their ranges and applying optimized search intervals. This parameter optimization allows accurate data enhancement while preserving high-frequency content that would otherwise be lost in traditional processing methods.
3Measurement precision
If comprehensive wavefront attribute estimation is performed on the entire dataset, then processing accuracy is improved, but computational cost increases significantly
Solution Approach 1:
The patent divides the dataset into a representative subset for initial attribute estimation and the full dataset for final processing. This segmentation enables accurate attribute estimation without the prohibitive computational cost of processing the entire dataset with the same level of detail.
Solution Approach 2:
The patent implements an automated process that uses statistical and physical properties of the seismic data to self-determine optimal search intervals and processing parameters. This automation eliminates the need for manual parameter tuning while maintaining processing accuracy.
Data Source
AI summary
A computer implemented method that constrains prestack seismic wavefront attributes is described. The method includes estimating wavefront attributes in intervals sized to include a representative subset of an original seismic dataset and applying a semblance threshold to the wavefront attributes based on an estimated semblance value, wherein wavefront attributes that satisfy the semblance threshold are retained. The method also includes transforming the retained wavefront attributes to reduce a range of possible values and selecting minimum and maximum values of the wavefront attributes based on statistical criteria. The method includes estimating wavefront attributes for the original seismic dataset using the selected minimum and maximum values.