Sharpening Function for Anomalous Density Zone Boundary Modeling
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Solution Overview
Problem
Current seismic imaging techniques fail to accurately model subsurface regions with anomalous densities, such as the base of salt, leading to poor interpretations of mineral deposits or petroleum reservoirs, and existing inversion methods are complex, resource-intensive, and produce non-unique density models.
Innovation Solution
A method involving forming a density model, computing a response, inverting the measured gravity response to obtain a geometric model, and applying a sharpening function with iterative weighting to emphasize anomalous and surrounding densities, while suppressing transition densities, using formulae like S˜((m−m0)γ+ε)α, and incorporating constraints from seismic, magnetic, and bathymetry data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional seismic imaging techniques are used to model subsurface regions with anomalous densities, then the imaging process is simple and fast, but the accuracy and precision of boundary modeling deteriorates significantly
Solution Approach 1:
The patent combines multiple data sources (seismic data, gravity data, magnetic data, bathymetry data) into an integrated modeling approach. By merging these complementary datasets, the method achieves accurate boundary modeling of anomalous density zones while compensating for the limitations of individual techniques, particularly seismic imaging alone.
Solution Approach 2:
The patent creates a composite modeling approach that integrates multiple geophysical disciplines (seismics, gravity, magnetism) and data types. This composite methodology produces a more robust and accurate subsurface model than any single technique could achieve alone, particularly for defining boundaries of salt bodies and other anomalous density zones.
2Manufacturing precision
If inversion techniques are used to model gravity data for accurate boundary definition, then the boundary modeling precision improves, but the computational time and hardware resources required increase significantly
Solution Approach 1:
The patent performs preliminary processing and integration of multiple datasets (seismic, gravity, magnetic, bathymetry) before conducting the inversion process. By preparing a well-constrained initial model and integrating relevant information in advance, the actual inversion requires fewer iterations and less computational time while still achieving high precision boundary definition.
Solution Approach 2:
The patent applies inversion techniques selectively to specific regions of interest (anomalous density zones) rather than processing the entire subsurface volume uniformly. By focusing computational resources on localized areas where boundaries need precise definition, the method achieves high precision without proportionally increasing overall processing time and resource requirements.
3Measurement precision
If inversion techniques are used to interpret gravity data, then the boundary model accuracy improves, but the reliability of the density model deteriorates due to non-uniqueness of the solution
Solution Approach 1:
The patent combines multiple independent geophysical datasets (seismic, gravity, magnetic, bathymetry) that each provide different constraints on the subsurface model. By merging these datasets, the method resolves the non-uniqueness problem of individual inversion techniques, as each dataset independently constrains the solution space, leading to a more reliable and unique density model that accurately defines boundaries.
Solution Approach 2:
The patent implements an iterative feedback process where the inversion model is continuously refined by comparing predicted responses with actual measurements from multiple geophysical datasets. This feedback loop allows the model to converge toward a unique solution that satisfies all observational constraints simultaneously, improving both boundary accuracy and model reliability.
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 approach provides more accurate and precise boundary modeling of subsurface anomalous density zones with reduced computational demands, improving the definition of boundaries and enhancing the interpretation of mineral deposits or petroleum reservoirs.
Implementation Method 1
the geologic component of the gravity field produced by such zones of anomalous densities
Data Source
AI summary
A method for modeling a subsurface anomalous density zone including the steps of forming a density model, computing a response to the density model, inverting the response to arrive at a geometric model of the anomalous density zone, and applying a sharpening function to boundary regions of the geometric model to distinguish between the anomalous density zone and a surrounding region.