Seismic Data Interpolation via Gradient Sensors
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Solution Overview
Problem
Deploying a dense array of seismic sensors for subterranean surveying is costly and time-consuming, and existing methods fail to efficiently interpolate translational data at points between sensors, limiting data density and accuracy.
Innovation Solution
The use of gradient sensors to measure gradient data, which allows for interpolation and extrapolation of translational data at geometric points where seismic sensors are not deployed, enabling denser sampling with a sparser arrangement of seismic sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a dense array of seismic sensors is deployed, then data density and accuracy are improved, but deployment cost and time increase
Solution Approach 1:
The patent introduces gradient sensors as intermediary devices that measure spatial gradients of seismic wavefields. These gradient measurements serve as mediators that enable mathematical reconstruction of translational data at locations where physical seismic sensors are not deployed, thus achieving dense sampling coverage without deploying a dense array of expensive seismic sensors.
Solution Approach 2:
The patent replaces the mechanical approach of deploying numerous physical seismic sensors with a hybrid system that combines sparse seismic sensors with gradient sensors. The gradient sensor data is then processed through mathematical operations (spatial differentiation, interpolation, extrapolation) to substitute for the missing seismic sensor measurements, reducing the need for extensive mechanical deployment.
2Measurement precision
If a dense array of seismic sensors is deployed, then data density and accuracy are improved, but deployment cost increases
Solution Approach 1:
The patent introduces gradient sensors as intermediary devices that measure spatial gradients of seismic wavefields. These gradient measurements serve as mediators that enable mathematical reconstruction of translational data at locations where physical seismic sensors are not deployed, thus achieving dense sampling coverage without deploying a dense array of expensive seismic sensors.
Solution Approach 2:
The patent creates virtual copies of seismic sensor measurements through mathematical reconstruction techniques. By using gradient sensor data to interpolate and extrapolate translational data, the system generates synthetic seismic sensor readings at locations where no physical sensors exist, effectively copying the information that would have been obtained from additional expensive hardware.
3Loss of information
If existing interpolation methods are used, then data at points between sensors is obtained, but accuracy is limited
Solution Approach 1:
The patent fundamentally changes the parameter being measured from translational displacement/velocity (traditional seismic sensors) to spatial gradients of the wavefield (gradient sensors). This parameter change enables more accurate reconstruction because gradient information contains higher-order spatial derivatives that, when integrated through mathematical operations, yield more precise translational data at interpolated locations.
Solution Approach 2:
The patent replaces the mechanical approach of placing sensors at every desired measurement point with a mathematical system that uses gradient sensor data to compute translational data through spatial differentiation and interpolation algorithms, achieving higher accuracy without additional physical sensors.
Data Source
AI summary
Translational data acquired by at least one seismic sensor is received. Gradient sensor data acquired by at least one gradient sensor is received. Estimated translational data at a position away from at least one position of the at least one seismic sensor is computed, where the computing is based on the gradient sensor data and the translational data.