Using gravity-data uncertainties in forward modeling to estimate uncertainties in model parameters: a case history in estimating the dip and the dip uncertainty of the Porcupine Destor Fault

Abstract

When using forward modeling to estimate model parameters, such as the dip, it is also important to estimate the corresponding uncertainty in the model parameters. For gravity data, these uncertainties are dependent on the uncertainty in the Bouguer corrected data. The uncertainty in the gravity meter reading and the height used in the free-air and Bouguer corrections are amongst the most important factors influencing the uncertainty in the Bouguer-corrected data. We used two methods for estimating the uncertainty in the Bouguer corrected data, which give similar answers (0.121 and 0.109 mGal). The uncertainty in the model parameters can be estimated by perturbing the corrected data multiple times by amounts consistent with the estimated uncertainty in the corrected gravity. The standard deviation of the model parameters derived from each perturbed dataset gives an estimate of their uncertainty. Using this procedure for Bouguer gravity profiles that cross the Porcupine Destor fault (a fault that is prospective for gold in the Timmins camp of Ontario, Canada), we found the uncertainty in the dip was one or two degrees, assuming a planar or linear fault. If the uncertainty in the corrected data had been 1 mGal (a value typical of regional surveys, instead of 0.1 mGal for a local survey), then the uncertainty in the dip is 41 degrees for the same model. Knowing the uncertainties in the corrected data is thus very important for estimating the uncertainty in model parameters. Conversely, if a model parameter is known to be required to a specific precision, the survey can be planned so that the corrected gravity has an uncertainty appropriate to achieve that precision.