stanley raj - Academia.edu (original) (raw)

Papers by stanley raj

This paper presents a denoising technique based on wavelet algorithm for inverting geoelectrical ... more This paper presents a denoising technique based on wavelet algorithm for inverting geoelectrical resistivity data. The presented work compares different denoising process by thresholding wavelet algorithm. Discrete wavelet transform is used to denoise the geoelectrical resistivity data. It is suitable for applying vertical electrical sounding data. The optimum performance is obtained and the result is investigated under several constraints. This method can be adopted to any geophysical data for pre-processing. Dau-bechies wavelet functions ('db') of different decomposition levels with four (''rigsure'',''universal thresholding'',''mini-max'',''heursure'') thresholds were attempted and the significant reduction of noise is effectively done. The data is initially subjected to synthetic noisy data with various levels of signal to noise ratio (SNR) and tested results with optimum condition is implemented to the noisy field data which is verified with the nearby ground truth information. Error measures reveal that is algorithm is best suited for denoising the geoelectrical resistivity data.

Estimation of subsurface parameters of earth need an efficient and knowledge based algorithm to e... more 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.

The non-linear apparent resistivity problem in the subsurface study of the earth takes into accou... more 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.

Arabian Journal of Geosciences, 2015

International Journal of Geophysics, 2015

Applied Water Science, 2013

This study was made to find the ground water quality for samples of the town located in the south... more 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 Cland HCO 3 as the dominant anion. The abundant of the major is as follows: SO 4 . 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.

Environmental Earth Sciences, 2013

ABSTRACT Coastal aquifers can become polluted due to natural and human activities, such as intrus... more ABSTRACT 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.

Chinese Journal of Geochemistry, 2014

ABSTRACT The present study investigates the hydrogeochemical characteristics of groundwater quali... more ABSTRACT The present study investigates the hydrogeochemical characteristics of groundwater quality in Agastheeswaram taluk of Kanyakumari district, Tamil Nadu, India. A total of 69 groundwater samples were collected during pre- and post-monsoon periods of 2011–2012. The groundwater quality assessment has been carried out by evaluating the physicochemical parameters such as pH, EC, TDS, HCO3−, Cl−, SO42−, Ca2+, Mg2+, Na+ and K+ for both the seasons. Based on these parameters, groundwater has been assessed in favor of its suitability for drinking and irrigation purpose. Dominant cations for both the seasons are in the order of Na+ > Ca2+ > Mg2+ > K+ while the dominant anions for post monsoon and pre monsoon have the trends of Cl− > HCO3− > SO42− and HCO3− > Cl− > SO42−, respectively. Analytical results observed from various indices reveal that the groundwater quality is fairly good in some places. Analytical results of few samples show that they are severely polluted and incidentally found to be near the coasts, estuaries and salt pans in the study area. The Gibbs plot indicates that the majority of groundwater samples fall in rock dominant region, which indicates rock water interaction in the study area. The United States salinity (USSL) diagram shows that the groundwater is free from sodium hazards but the salinity hazard varies from low to very high throughout the study area. This reveals that the groundwater is moderately suitable for agricultural activities. The observed chemical variations in pre-monsoon and post-monsoon seasons may be the effect to rock-water interactions, ion-exchange reactions, and runoff of fertilizers from the surrounding agricultural lands.

This paper presents a denoising technique based on wavelet algorithm for inverting geoelectrical ... more This paper presents a denoising technique based on wavelet algorithm for inverting geoelectrical resistivity data. The presented work compares different denoising process by thresholding wavelet algorithm. Discrete wavelet transform is used to denoise the geoelectrical resistivity data. It is suitable for applying vertical electrical sounding data. The optimum performance is obtained and the result is investigated under several constraints. This method can be adopted to any geophysical data for pre-processing. Dau-bechies wavelet functions ('db') of different decomposition levels with four (''rigsure'',''universal thresholding'',''mini-max'',''heursure'') thresholds were attempted and the significant reduction of noise is effectively done. The data is initially subjected to synthetic noisy data with various levels of signal to noise ratio (SNR) and tested results with optimum condition is implemented to the noisy field data which is verified with the nearby ground truth information. Error measures reveal that is algorithm is best suited for denoising the geoelectrical resistivity data.

Estimation of subsurface parameters of earth need an efficient and knowledge based algorithm to e... more 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.

The non-linear apparent resistivity problem in the subsurface study of the earth takes into accou... more 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 paper presents applied research on fuzzy logic modeling to forecast the distribution of sali... more 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.

Electrical Resistivity Method is one of the Geophysical techniques used to investigate the nature... more Electrical Resistivity Method is one of the Geophysical techniques used to investigate the nature of the subsurface formations of earth. Generally, this kind of non-linear parameter estimation problem needs high computation to obtain the probable model. Moreover the error performance and computational time are more important while interpreting the model parameters of the subsurface viz., true resistivity and depth. But recent development in computational techniques paves way for producing approximate solutions to the non linear problem that are very much resembling the true nature of the earth. One of the most emerging soft computing techniques is Adaptive Neuro Fuzzy Inference System (ANFIS) in which the concepts of Artificial Neural Networks and Fuzzy logic have been integrated. This integrated concept helps the algorithm to generate more synthetic data to obtain best fit model on the basis of minimizing the root mean square error percent. In this paper, Vertical Electrical Sounding (VES) data has been interpreted by newly proposed efficient algorithm supported by ANFIS to identify the subsurface strata of the earth. The inverted results have been correlated with available lithologs and found to be correlating very well. Thus this paper projects a different approach in interpreting the geoelectrical resistivity data using ANFIS. In this novel and generalized algorithm, the interpretation of the vertical electrical sounding has done successfully with more accurate layer model and is represented as Graphical User Interface (GUI).

Soft computing techniques are widely used for the applications on most of the nonlinear problems ... more Soft computing techniques are widely used for the applications on most of the nonlinear problems related to the real world. Earth's most of the nonlinear characteristics exhibit the uncertainty problem that has to be interpreted with most of the advanced soft computing tools. Here the three layer electrical resistivity data has taken for interpreting the subsurface parameters of the earth using Adaptive Neuro-Fuzzy inference (ANFIS) technique. ANFIS can be predictably used for most of the nonlinear problems. Its membership functions and rules with adjustable parameters will help the interpretation technique with less error percentage results. In the present study, the program is specially designed for the interpretation of three layer electrical resistivity data. The network model is successful in training with large number of data sets available. Interpretation using ANFIS technique will give the promising results with good accuracy. With much less error percentage, the program supports all types of three layer electrical resistivity data more than a conventional method can do. Typical problems with parameter estimation can be done more efficiently with this ANFIS program.

The applications of intelligent techniques have increased exponentially in recent days to study m... more 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 nonlinearity 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 singlelayer feed-forward neural network with the back propagation algorithm is chosen as one of the wellsuited 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 0 30"E and 8 48 0 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

irphouse.com

The Non-linear apparent resistivity problem of three layer case in the sub surface study of the e... more The Non-linear apparent resistivity problem of three layer case in the sub surface study of the earth is taken into account to get the model parameters in terms of resistivity and thickness of individual subsurface layers using the trained synthetic data by means of Artificial Neural Network toolbox in MATLAB software. 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 earlier 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 will be minimized and get the better result using the artificial neural network .The network is trained with more number of VES data and this trained network is demonstrated with the field data. The accuracy of inversion depends upon the number of data trained. In the present study the interpretation of the vertical electrical sounding has been done successfully with more accurate layer model and presented in the text.

This paper presents a denoising technique based on wavelet algorithm for inverting geoelectrical ... more This paper presents a denoising technique based on wavelet algorithm for inverting geoelectrical resistivity data. The presented work compares different denoising process by thresholding wavelet algorithm. Discrete wavelet transform is used to denoise the geoelectrical resistivity data. It is suitable for applying vertical electrical sounding data. The optimum performance is obtained and the result is investigated under several constraints. This method can be adopted to any geophysical data for pre-processing. Dau-bechies wavelet functions ('db') of different decomposition levels with four (''rigsure'',''universal thresholding'',''mini-max'',''heursure'') thresholds were attempted and the significant reduction of noise is effectively done. The data is initially subjected to synthetic noisy data with various levels of signal to noise ratio (SNR) and tested results with optimum condition is implemented to the noisy field data which is verified with the nearby ground truth information. Error measures reveal that is algorithm is best suited for denoising the geoelectrical resistivity data.

Estimation of subsurface parameters of earth need an efficient and knowledge based algorithm to e... more 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.

The non-linear apparent resistivity problem in the subsurface study of the earth takes into accou... more 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.

Arabian Journal of Geosciences, 2015

International Journal of Geophysics, 2015

Applied Water Science, 2013

This study was made to find the ground water quality for samples of the town located in the south... more 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 Cland HCO 3 as the dominant anion. The abundant of the major is as follows: SO 4 . 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.

Environmental Earth Sciences, 2013

ABSTRACT Coastal aquifers can become polluted due to natural and human activities, such as intrus... more ABSTRACT 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.

Chinese Journal of Geochemistry, 2014

ABSTRACT The present study investigates the hydrogeochemical characteristics of groundwater quali... more ABSTRACT The present study investigates the hydrogeochemical characteristics of groundwater quality in Agastheeswaram taluk of Kanyakumari district, Tamil Nadu, India. A total of 69 groundwater samples were collected during pre- and post-monsoon periods of 2011–2012. The groundwater quality assessment has been carried out by evaluating the physicochemical parameters such as pH, EC, TDS, HCO3−, Cl−, SO42−, Ca2+, Mg2+, Na+ and K+ for both the seasons. Based on these parameters, groundwater has been assessed in favor of its suitability for drinking and irrigation purpose. Dominant cations for both the seasons are in the order of Na+ > Ca2+ > Mg2+ > K+ while the dominant anions for post monsoon and pre monsoon have the trends of Cl− > HCO3− > SO42− and HCO3− > Cl− > SO42−, respectively. Analytical results observed from various indices reveal that the groundwater quality is fairly good in some places. Analytical results of few samples show that they are severely polluted and incidentally found to be near the coasts, estuaries and salt pans in the study area. The Gibbs plot indicates that the majority of groundwater samples fall in rock dominant region, which indicates rock water interaction in the study area. The United States salinity (USSL) diagram shows that the groundwater is free from sodium hazards but the salinity hazard varies from low to very high throughout the study area. This reveals that the groundwater is moderately suitable for agricultural activities. The observed chemical variations in pre-monsoon and post-monsoon seasons may be the effect to rock-water interactions, ion-exchange reactions, and runoff of fertilizers from the surrounding agricultural lands.

This paper presents a denoising technique based on wavelet algorithm for inverting geoelectrical ... more This paper presents a denoising technique based on wavelet algorithm for inverting geoelectrical resistivity data. The presented work compares different denoising process by thresholding wavelet algorithm. Discrete wavelet transform is used to denoise the geoelectrical resistivity data. It is suitable for applying vertical electrical sounding data. The optimum performance is obtained and the result is investigated under several constraints. This method can be adopted to any geophysical data for pre-processing. Dau-bechies wavelet functions ('db') of different decomposition levels with four (''rigsure'',''universal thresholding'',''mini-max'',''heursure'') thresholds were attempted and the significant reduction of noise is effectively done. The data is initially subjected to synthetic noisy data with various levels of signal to noise ratio (SNR) and tested results with optimum condition is implemented to the noisy field data which is verified with the nearby ground truth information. Error measures reveal that is algorithm is best suited for denoising the geoelectrical resistivity data.

Estimation of subsurface parameters of earth need an efficient and knowledge based algorithm to e... more 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.

The non-linear apparent resistivity problem in the subsurface study of the earth takes into accou... more 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 paper presents applied research on fuzzy logic modeling to forecast the distribution of sali... more 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.

Electrical Resistivity Method is one of the Geophysical techniques used to investigate the nature... more Electrical Resistivity Method is one of the Geophysical techniques used to investigate the nature of the subsurface formations of earth. Generally, this kind of non-linear parameter estimation problem needs high computation to obtain the probable model. Moreover the error performance and computational time are more important while interpreting the model parameters of the subsurface viz., true resistivity and depth. But recent development in computational techniques paves way for producing approximate solutions to the non linear problem that are very much resembling the true nature of the earth. One of the most emerging soft computing techniques is Adaptive Neuro Fuzzy Inference System (ANFIS) in which the concepts of Artificial Neural Networks and Fuzzy logic have been integrated. This integrated concept helps the algorithm to generate more synthetic data to obtain best fit model on the basis of minimizing the root mean square error percent. In this paper, Vertical Electrical Sounding (VES) data has been interpreted by newly proposed efficient algorithm supported by ANFIS to identify the subsurface strata of the earth. The inverted results have been correlated with available lithologs and found to be correlating very well. Thus this paper projects a different approach in interpreting the geoelectrical resistivity data using ANFIS. In this novel and generalized algorithm, the interpretation of the vertical electrical sounding has done successfully with more accurate layer model and is represented as Graphical User Interface (GUI).

Soft computing techniques are widely used for the applications on most of the nonlinear problems ... more Soft computing techniques are widely used for the applications on most of the nonlinear problems related to the real world. Earth's most of the nonlinear characteristics exhibit the uncertainty problem that has to be interpreted with most of the advanced soft computing tools. Here the three layer electrical resistivity data has taken for interpreting the subsurface parameters of the earth using Adaptive Neuro-Fuzzy inference (ANFIS) technique. ANFIS can be predictably used for most of the nonlinear problems. Its membership functions and rules with adjustable parameters will help the interpretation technique with less error percentage results. In the present study, the program is specially designed for the interpretation of three layer electrical resistivity data. The network model is successful in training with large number of data sets available. Interpretation using ANFIS technique will give the promising results with good accuracy. With much less error percentage, the program supports all types of three layer electrical resistivity data more than a conventional method can do. Typical problems with parameter estimation can be done more efficiently with this ANFIS program.

The applications of intelligent techniques have increased exponentially in recent days to study m... more 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 nonlinearity 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 singlelayer feed-forward neural network with the back propagation algorithm is chosen as one of the wellsuited 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 0 30"E and 8 48 0 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

irphouse.com

The Non-linear apparent resistivity problem of three layer case in the sub surface study of the e... more The Non-linear apparent resistivity problem of three layer case in the sub surface study of the earth is taken into account to get the model parameters in terms of resistivity and thickness of individual subsurface layers using the trained synthetic data by means of Artificial Neural Network toolbox in MATLAB software. 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 earlier 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 will be minimized and get the better result using the artificial neural network .The network is trained with more number of VES data and this trained network is demonstrated with the field data. The accuracy of inversion depends upon the number of data trained. In the present study the interpretation of the vertical electrical sounding has been done successfully with more accurate layer model and presented in the text.