Extrapolating Specular Energy in RTM 3D Angle Gathers
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Solution Overview
Problem
Reverse time migration 3D angle domain common image gathers (ADCIGs) face significant under-sampling issues, particularly at shallow angles, leading to poor signal-to-noise ratios and challenges in automatic event picking, due to coarse sampling and increased numerical costs associated with five-dimensional mapping processes.
Innovation Solution
A method that involves receiving seismic data, calculating shot and receiver wave-fields, applying a wave-fields decomposition algorithm, determining under-sampled specular energies, and extrapolating these energies to improve sampling rates using a kernel match method, thereby enhancing the quality of ADCIGs without increasing computational costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional RTM 3D ADCIG processing is used, then computational cost is reduced, but sampling rate in angle domain is insufficient leading to poor signal-to-noise ratio
Solution Approach 1:
The patent applies preliminary action by performing wavefield decomposition and specular energy identification before the main imaging process. By pre-identifying specular energy components and their corresponding angles, the method prepares the data in advance to enable accurate interpolation without requiring excessive computational resources during the main processing stage.
Solution Approach 2:
The patent introduces an intermediary approach by using interpolation as a mediator between the sparsely sampled specular energy data and the densely sampled angle domain common image gathers. This intermediary step fills in the sampling gaps by estimating missing values based on neighboring data points, thereby improving the signal-to-noise ratio without requiring additional computational expensive measurements.
2Productivity
If coarse sampling is used in five-dimensional mapping, then computational cost is reduced, but under-sampling noise increases
Solution Approach 1:
The patent applies the extraction principle by separating and identifying only the specular energy components from the total wavefield. By extracting these specific components and their corresponding angles, the method focuses computational resources on the most important signal portions, allowing coarse sampling to be used without losing critical information while reducing overall computational cost.
Solution Approach 2:
The patent changes parameters by transforming the data from the conventional offset-domain representation to an angle-domain representation through wavefield decomposition. This parameter transformation allows the use of interpolation techniques that work effectively in the angle domain, reducing under-sampling noise while maintaining computational efficiency through the changed parameter space.
3Measurement precision
If specular energy interpolation is applied, then sampling rate is improved, but processing complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex processing task into distinct segments: wavefield decomposition, specular energy identification, angle determination, and interpolation. By segmenting the process, each step can be optimized independently and implemented using straightforward algorithms, reducing overall processing complexity while achieving improved sampling rates.
Data Source
AI summary
Computer instructions, computing device and method for processing seismic data under-sampled in an angle domain, the seismic data corresponding to a reverse time migration, three-dimensional, angle domain common image gather (ADCIG). The method includes receiving the seismic data; calculating, based on the seismic data, shot and receiver wave-fields with an RTM wave propagation engine; applying a wave-fields decomposition algorithm to obtain a propagation direction for the shot and receiver wave-fields; forming the ADCIG by applying an image condition to the shot and receiver wave-fields; determining that specular energies of the ADCIG are under-sampled around a reflection angle; during the step of forming the ADCIG, extrapolating the specular energies to a neighborhood of the reflection angle; and generating an image of a subsurface that is being surveyed based on the extrapolated specular energies.