Subsurface geology detection from application of the gravity-related dimensionality constraint

Area of Science:

• Geophysics
• Potential Field Geophysics
• Geophysical Data Interpretation

Background:

• Accurate subsurface body depth estimation is crucial in geophysics.
• Existing methods like Euler Deconvolution have limitations in certain scenarios.
• Understanding causative body dimensionality is key for improved interpretation.

Purpose of the Study:

• To develop an innovative geophysics method for estimating the depth of causative bodies.
• To utilize a dimensionality indicator (I) based on relative horizontal dimensions.
• To categorize bodies into Line of Poles and Point Pole (LOP-PP) and Line of Poles and Plane of Poles (LOP-POP) for distinct solutions.

Main Methods:

• Introduced a dimensionality indicator (I) to classify causative bodies.
• Developed two distinct solutions based on LOP-PP and LOP-POP categories.
• Analyzed the influence of depth extent and dimensionality on depth estimates (z^0).
• Validated the method with synthetic models and real lunar data, comparing with Euler Deconvolution.

Main Results:

• The method accurately estimates depth (z^0) as either center of mass or top surface, depending on depth extent.
• Depth estimates show lower errors for larger dimensionality indicator (I) values in LOP-POP solutions.
• Successful application to lunar data, aligning with geological features.
• Demonstrated effectiveness for bodies ranging from 2D to 3D.

Conclusions:

• The novel geophysics approach provides a straightforward, reliable, and efficient means for depth estimation.
• It offers valuable insights into subsurface structures for planetary and engineering applications.
• The method requires only one data point and is adaptable to various body dimensionalities.