Many conventional methods were adopted to invert geoelectrical resistivity data. A tool with soft computing approach is new in the field of inverting resistivity data. Previous researchers’ works on soft computing research acclaims the ‘conventional/traditional’ approach on inversion. For example, the system and architecture of soft computing were designed on the basis of previously learned examples. Training is a major part that is the primary requirement for any artificial intelligent technique. Researchers studied the artificial intelligent techniques to predict lost circulation 111. Additionally, with lack of training datasets will result in inaccuracy of bringing out good optimisation. Training datasets will precise the result invariably. This needs extra time to spend for training. Field datasets with much complex geological settings will definitely make the problem more ill-posed. Ten datasets of two square kilometre radius would not be a good optional for training and testing for eleventh dataset. The reliability and performance lies in the training datasets and the way of training. This research work prevails in modifying the training database in such a way that generating the synthetic datasets of its own.

Few researchers applied neuro fuzzy algorithm to interpret geoelectrical resistivity data 121314. This software provides the platform of neurofuzzy inversion technique for inverting geoelectrical data. It is a novel method in the sense that applying the generalised approach for any kind of field datasets. This software will work for any field data collected around the world with any kind of geological settings. It is not so in the conventional artificial intelligent techniques where it needs more training datasets to enhance the performance. This proved to be the versatile algorithm. Over fitting problem has been avoided by automatic adjustment of training parameters with respect to the output error percent. To restrict the output to minimum error percent the system will adjust the parameters in the mean time while training. This proposed technique proved to be helpful for researchers relying on 1D vertical electrical sounding (Wenner/ Schlumberger methods) data. The intention of this development of software is to promote the soft computing inversion techniques which are more applicable and reliable in the field of earth sciences. The non-availability of any soft computing based software for inverting geoelectrical data in the market as ‘generalisation’ is difficult to build upon. This has been overcome in this algorithm. The overall impact of this software is it will generate a new platform in soft computing experts to move on generalisation to unveil the heterogeneity of earth’s subsurface. Disparities between conventional and soft computing inversion, is now descending to the level of competing, each other in terms of attaining uniqueness. The experimental evidences have been presented for validating the algorithm.