Modelling The Progression Of AMD Plume In Karst Aquifer Using Complex Resistivity Tomography Model

The costs of acid mine drainage (AMD) monitoring result in the quest for alternative non-invasive method that can provide qualitative data on the progression of the pollution plume and ground geophysics was the ideal solution. However, the monitoring of AMD plume progression by ground geophysics (time-lapse electrical resistance) proves to be non-invasive but also time consuming. This gave way to a study that focuses on the modeling of different scenarios of the karstic aquifer. The models use the field parameters such as the electrical resistivity of the host rock and the target rock, depth to the target, noise level and electrode configuration in order to ensure that the model outcomes represent the field data as much as possible. This geoelectric modeling process uses Complex Resistivity Model (CRMod) and Complex Resistivity Tomography (CRTomo) to generate geoelectric subsurface images. Different resistivity values are applied to targets in order to assess the difference against the baseline model for each target scenario. The model resistivity difference is reduced to the smallest difference possible between the reference and new models in order to gauge the lowest percentage change in the model at which the background noises start to have impact on the results. The study shows that the behavior of targets (aquifer) could be clearly detected through resistivity difference tomography rather than inversion tomography. The electrode array plays a significant part in the detection of target areas and their differences in resistance because of its sensitivity. This therefore indicates that the electrode array should be chosen according to study requirements. Furthermore, the model geometry also plays a role and this can be seen with the modelling of different target sizes, alignments and shapes. Future studies that can provide a correlation between the field quantitative data from sampling and the model outcomes have the ability to add to the knowledge field of geophysical modelling therefore reducing costs associated with field based plume AMD monitoring300-500 words without references; reach your conclusions rather than only delivering promises.