Global nonlinear optimization for the interpretation of source parameters from total gradient of gravity and magnetic anomalies caused by thin dyke

Arkoprovo Biswas, Mahesh Prasad Parija, Sushil Kumar

Abstract


An efficient approach to estimate model parameters from total gradient of gravity and magnetic data based on Very Fast simulated Annealing (VFSA) has been presented. This is the first time of applying VFSA in interpreting total gradient of potential field data with a new formulation estimation caused due to isolated causative sources embedded in the subsurface. The model parameters interpreted here are the amplitude coefficient (k), exact origin of causative source (x0) depth (z0) and the shape factors (q). The results of VFSA optimization show that it can uniquely determine all the model parameters when shape factor is constrained. The model parameters estimated by the present method, mostly the shape and depth of the buried structures were found to be in excellent agreement with the actual parameters. The method has also the proficiency of evading highly noisy data points and improves the interpretation results. Study of histogram and cross-plot analysis also suggests the interpretation within the estimated uncertainty.  Inversion of noise-free and noisy synthetic data for single structures as well as field data demonstrates the efficacy of the approach. The technique has been warily and effectively applied to real data examples (Leona Anomaly, Senegal for gravity and Pima copper deposit, USA for magnetic) with the presence of ore bodies. The present method can be extremely applicable for mineral exploration or ore bodies of dyke-like structure embedded in the shallow and deeper subsurface. The computation time for the whole process is very short.


Keywords


Gravity and Magnetic anomaly, dyke-type structure, VFSA, Uncertainty estimation, ore exploration

Full Text:

PDF

References


DOI: http://dx.doi.org/10.4401/ag-7129


 

Published by INGV, Istituto Nazionale di Geofisica e Vulcanologia - ISSN:  2037-416X