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Geoelectrical inversion has some problems in inverting data due to the heterogeneous behaviour of Earth. One of the major concerns in inverting the data is due to the influence of noises, which com...

Intelligent algorithms are used to optimize the process in geophysics and applied in evaluating ground or subsurface parameters. This neurofuzzy sampling model is a great replacement for conventional data interpretation approach using mathematical models. In this algorithm, a novel sampling method is involved to filter noises and errors in each data. Neurofuzzy can train the data, and after training, the fuzzy structure predicts the exact parameter necessary for inversion. During sampling process, the algorithm samples each datum and produces synthetic data in correlation with the neighbouring data. After the sampling process, ANFIS intelligent algorithm learns the data, and based on the inputs and the number of samples, it interprets the appropriate functional output with high-level accuracy. This novel method of sampling algorithm provides a framework for efficient parameter optimization. These implications made to create the specific Graphical User Interface (GUI) for the algorithm and it works well for all types of Vertical Electrical Sounding (VES) data with good performance results.

Rainfall is one of the most complex phenomena occurring on earth to study with extreme and advanced soft computing engine that can perform well with adaptive perception. Here, in this study, an attempt has been made to study the behaviour of rainfall in Chennai district, Tamil Nadu, India, using wavelet tool. Wavelet is one of the prominent tools for analysing the data in various ways. One such technique called multiresolution wavelet analysis is implemented here to study the pattern of rainfall in Chennai. This approach applied to the data works well for and can be implemented for complex data. The above approach fits not only to simple data but also for complex data. Rainfall data from 2001 to 2017 have been taken into account for studying the pattern. The influence of change due to groundwater is investigated in different criteria, and future threats posed by climate change are also analysed. The key factors involved in understanding climate change and hydrological regime of groundwater were identified, and the residents of the Chennai locality could plan for better management methodologies, especially for those who are residing in coastal regions of the district. Better management methodologies with recommendations are suggested to attain sustainability in groundwater resources. The research was carried out based on sustainable groundwater managements for 12th plan, submitted to planning commission of India. The reports state that in 2004, 28% of India’s blocks were showing alarmingly high levels of groundwater use.

A combined study of both geoelectrical and geochemical methods was used to find the quality of groundwater in the Agastheeswaram Taluk of Kanyakumari District, Tamil Nadu, India. The vertical electrical sounding (VES) method was applied to identify the variations in electrical resistivity of the subsurface. Three to five geoelectric layers are interpreted in the study area from the observed VES data. The aquifer was mostly found in the second or third layer. Analytical results from the 14 groundwater samples collected near the VES stations are in good correlation with the VES outcomes. Also, the interpreted results of VES data collected from the field were compared with the existing subsurface data and correlation was made, which proved the consistency of the VES method. Iso-resistivity contour maps for different depths and pseudo cross sections showing the resistivity distribution with depth are prepared for detailed analysis. More than 50 % of the area is having aquifer resistivity value less than 10 Ωm. This indicates that the aquifer is found to get polluted by saline water intrusion or some other anthropogenic activities, like intense agricultural practices and poor sewage management. From the geochemical analysis of the groundwater samples, it is found that the Na+ and Cl− are the dominant cation and anion in the study area. Evaporation and rock-water interaction are found to be the major processes controlling the ion concentration in the groundwater. The results obtained from geochemical analysis are in good correlation with the VES results.

The non-linear apparent resistivity problem in the subsurface study of the earth takes into account the model parameters in terms of resistivity and thickness of individual subsurface layers using the trained synthetic data by means of Artificial Neural Networks (ANN). Here we used a single layer feed-forward neural network with fast back propagation learning algorithm. So on proper training of back propagation networks it tends to give the resistivity and thickness of the subsurface layer model of the field resistivity data with reference to the synthetic data trained in the appropriate network. During training, the weights and biases of the network are iteratively adjusted to make network performance function level more efficient. On adequate training, errors are minimized and the best result is obtained using the artificial neural networks. The network is trained with more number of VES data and this trained network is demonstrated by the field data. The accuracy of inversion depends upon the number of data trained. In this novel and specially designed algorithm, the interpretation of the vertical electrical sounding has been done successfully with the more accurate layer model.

This research work investigates the possibility of shielding gravity. The ultimate purpose of this work is to understand the reality behind the concept of Gravitational Shielding (GS) and time dilation. Since the 19th century, scientists have tried to arrive at an understanding of GS via the use of various experiments. Unfortunately, some experiments failed to prove the existence of gravitational shielding, whereas some results proclaimed the possibility of attaining GS. The original phenomenon exhibited by nature cannot easily be understood, but some experiments have demonstrated that the answer may lie behind the mysterious GS. If GS is proved, then in the future, it would be possible to travel across black holes by defying gravity or through any bigger mass having high gravitational field. To unravel the mystery of GS, this work investigates the history of GS and considers the future vision of technologically advanced spacecraft or other warp drive mechanisms with appropriate gravitational shielding. Though the problem is very complex, this research work tries to come to a deeper understanding and explanation of the complexity involved in achieving gravitational shielding.

This study was made to find the ground water quality for samples of the town located in the southern most end of India. The study was carried out to evaluate the major ion chemistry, the factors controlling water composition, and suitability of water for both drinking and irrigation purposes. Totally, 21 ground water samples were collected randomly from bore wells and hand pumps throughout the Nagercoil town and its surroundings. The collected samples were analyzed for major ions and the analytical data were interpreted according to published guide lines. The spatial maps show that the concentration of the chemical constituent in ground water varies spatially and temporarily. Sodium is the most dominant cation with Cl− and HCO3− as the dominant anion. The abundant of the major is as follows: Na+ > Cl− > Mg2+ > K+ which is equal to HCO3− > Cl− > SO4. Only one-third of the samples best fit for both consumption and agricultural purposes. The spatial maps show high contamination along the southern region of the study area. Total hardness of the collected samples lies between 60 and 490 mg/l reveals that the 33 % groundwater samples exceeds the safe limit of 300 mg/l. Total dissolved solids (TDS) in the study area ranges between 67 and 2,086 mg/l with a mean value of 523 mg/l. High total hardness and TDS in few places identified that the ground water is unsuitable for drinking and irrigation. In these places, the aquifers are subject to contamination from sewage effluents and excess use of fertilizer and pesticides in agriculture. Such areas require adequate drainage and introduction of alternative salt tolerance cropping.

Coastal aquifers can become polluted due to natural and human activities, such as intrusion of saline water, discharge of effluents in industrial areas and chemical weathering of natural geological deposits. The present study is aimed mainly at understanding the geophysical and chemical characteristics of groundwater near Tuticorin, Tamilnadu, India by studying the electrical resistivity distribution of the subsurface groundwater by applying the Schlumberger vertical electrical sounding (VES) technique followed by chemical analysis of water samples. A total of 20 VES soundings were carried out to understand the resistivity distribution of the area and 21 water samples were collected to analyze the chemical quality. The interpretation and analysis of the results have identified different hydrogeologic behaviors, a highly saline coastal aquifer and freshwater locations. The results obtained from geophysical and geochemical sampling are in good agreement with each other. The approach shows the efficacy of the combination of geophysical and geochemical methods to map groundwater contamination zones in the study area.

This paper presents applied research on fuzzy logic modeling to forecast the distribution of salinity in the coastal region of southern Tamil Nadu, India. Geoelectrical resistivity data has been used in this research, apart from nominal approach of salinity forecasting using geochemical data analysis. The data collected using vertical electrical sounding (VES) method was fed as an input for fuzzy inverting algorithm to evaluate true resistivity and thickness of subsurface layers. Inverted resistivity values have been subjected to fuzzy rule-based approach for salinity forecasting. Classifications have been made on the basis of linguistic variables with five linguistic terms of resistivity range using a triangular membership function of fuzzy logic. The purpose is to find the saltwater intrusion in the coastal region of Tuticorin district, Tamil Nadu, India. This research work reveals that fuzzy logic would be the effective tool for solving complex problems as well as enhancement in integrating multiple features necessary for the study. The results overlain in the surrounding regions which were mapped the threatening zones; hence, to mark pre-awareness in the regions, more rainwater harvesting system and avoidance of human anthropogenic activities need to be implemented.

Estimation of subsurface parameters of earth need an efficient and knowledge based algorithm to enthrall the real world truth clearly. Implementing the adaptive neuro fuzzy inference system (ANFIS) is worthwhile in this case of non-linear parametric approach. The ambiguous property of the conventional inversion technique results can be prevailing over by implementing the soft computing tool. The coalesce behavior of neural networks logics and fuzzy sets with certain rule based logics will concise the inversion technique to obtain the preferred result. In the present study, ANFIS algorithm was applied in direct inversion approach and the most prominent of this approach is supervised learning techniques adapted in the algorithm specially to enroll the concepts of inverting the geoelectrical data in a systematic way. The subsurface parameters of earth are mysteriously identified by sounding or direct bore techniques. Sounding method in geophysics plays the prominent role in understanding the subsurface features of earth. But major part of the sounding method relies on inversion techniques. Since the data obtained from the earth is non-linear and heterogeneous it is difficult to estimate the parameters more clearly. Thus apart from using any conventional inversion techniques which are mainly focusing on initial model layer parameters. If the initial layer parameters are not given in the particular range, then the forward modeling solution tends dissimilarity of observed bore hole/litholog data. Thus direct inversion dominates in estimating the parameters with the help of soft computing inversion techniques. The proposed technique solves most of the subsurface problems since it depends on the trained knowledge. The supervised learning technique has been validated with Tuticorin and Kanyakumari coastal region data and found to be successful.

Soft computing based geoelectrical data inversion differs from conventional computing in fixing the uncertainty problems. It is tractable, robust, efficient, and inexpensive. In this paper, fuzzy logic clustering methods are used in the inversion of geoelectrical resistivity data. In order to characterize the subsurface features of the earth one should rely on the true field oriented data validation. This paper supports the field data obtained from the published results and also plays a crucial role in making an interdisciplinary approach to solve complex problems. Three clustering algorithms of fuzzy logic, namely, fuzzyC-means clustering, fuzzyK-means clustering, and fuzzy subtractive clustering, were analyzed with the help of fuzzy inference system (FIS) training on synthetic data. Here in this approach, graphical user interface (GUI) was developed with the integration of three algorithms and the input data (AB/2 and apparent resistivity), while importing will process each algorithm and interpret the layer model parameters (true resistivity and depth). A complete overview on the three above said algorithms is presented in the text. It is understood from the results that fuzzy logic subtractive clustering algorithm gives more reliable results and shows efficacy of soft computing tools in the inversion of geoelectrical resistivity data.

Geoinversion takes off different forms to assess the subsurface formations more noticeably. The evolution of soft computing inversion technique makes the geoinversion to the next level of modelling parameters. In this research work, the novel neuro fuzzy pattern recognition approach was introduced to solve the non-linearity involved in geoelectrical resistivity data for appraising the subsurface parameters. The novelty encompasses in generating the patterns using Artificial Neural Networks (ANN) for the geoelectrical resistivity data obtained from the Vertical Electrical Sounding (VES) data as well as the pattern recognition done by the Adaptive Neuro Fuzzy Inference System (ANFIS) algorithm is predominant in mitigating the near world truth information that is available. Moreover, the ambiguities of the principle of equivalence have been reduced further by incorporating the Dar-Zarrouk parameter evaluation of longitudinal conductance and transverse resistivity. Thus, this tool could be a good alternate for any conventional algorithm for unravelling such complex problems.

This paper aimed mainly to understand the geophysical characteristics of groundwater in tsunami affected areas, Kanyakumari district, Tamilnadu, India by applying the Schlumberger vertical electrical sounding techniques. In the study area seven vertical electrical soundings were conducted between April 2010 to October 2010. Based on study, Uthayamarthandam (VES 6) top layer consists of high resistive surface layer with 2000 ohm-m and second layer is 83 ohm-m which indicates its suitability of drinking water and followed by high resistive basement. Mela Kurumpanai (VES 1) is located very nearer to the coast; at this point the aquifer resisitivity is low, i.e., very below the 10 ohm-m which indicates highly saline in nature. Alanchi School (VES 2), Paravilai (VES 4) and Mel Midalam (VES 5) also have aquifer resistivity very low, this indicates highly saline water due to inundated sea water pass through at the time of 26th December 2004 Tsunami waves.

The applications of intelligent techniques have increased exponentially in recent days to study most of the non-linear parameters. In particular, the behavior of earth resembles the non-linearity applications. An efficient tool is needed for the interpretation of geophysical parameters to study the subsurface of the earth. Artificial Neural Networks (ANN) perform certain tasks if the structure of the network is modified accordingly for the purpose it has been used. The three most robust networks were taken and comparatively analyzed for their performance to choose the appropriate network. The single-layer feed-forward neural network with the back propagation algorithm is chosen as one of the well-suited networks after comparing the results. Initially, certain synthetic data sets of all three-layer curves have been taken for training the network, and the network is validated by the field datasets collected from Tuticorin Coastal Region (78°7′30"E and 8°48′45"N), Tamil Nadu, India. The interpretation has been done successfully using the corresponding learning algorithm in the present study. With proper training of back propagation networks, it tends to give the resistivity and thickness of the subsurface layer model of the field resistivity data concerning the synthetic data trained earlier in the appropriate network. The network is trained with more Vertical Electrical Sounding (VES) data, and this trained network is demonstrated by the field data. Groundwater table depth also has been modeled.