New fast least-squares algorithm for estimating the best-fitting parameters due to simple geometric-structures from...

Area of Science:

• Geophysics
• Geophysical exploration
• Inversion methods

Background:

• Gravity data analysis is crucial for subsurface structure interpretation.
• Estimating parameters of buried structures from gravity anomalies presents challenges.
• Existing methods can be computationally intensive or less accurate for certain parameters.

Purpose of the Study:

• To develop a novel, fast least-squares method for estimating the shape factor (q-parameter) of buried structures.
• To formulate procedures for simultaneously estimating depth (z-parameter) and amplitude coefficient (A-parameter).
• To validate the method's efficacy on theoretical models and real-world gravity data.

Main Methods:

• Transforming shape factor estimation into solving a non-linear equation f(q)=0.
• Utilizing normalized residual anomalies and N-values derived from gravity profiles.
• Applying the method to geometrically simple bodies: vertical cylinders, horizontal cylinders, and spheres.

Main Results:

• The developed method provides a simple and rapid estimation of gravity anomaly parameters.
• Successful verification on theoretical models, including those with random errors.
• Accurate parameter estimation when applied to real gravity data from Senegal and India.

Conclusions:

• The fast least-squares method is effective for estimating shape, depth, and amplitude of buried structures from gravity data.
• The technique offers a computationally efficient alternative for geophysical exploration.
• The method's successful application to real data demonstrates its practical utility in geological surveys.