The authors have no conflicts of interest to declare.
Geoelectrical resistivity data is used for estimating the subsurface features of earth. It is very difficult to estimate the depth and true resistivity analytically, therefore many mathematical models approximates the result. The approximation relies on many parameters as the heterogenous model of earth is difficult to map. Conventional interpretation algorithm mostly uses the forward modelling technique which is limited for different lithologies. Here we presented ResinvANFIS v1.0 software platform to invert any type (A, Q, K, H or any mixed data types) of resistivity data having AB/2 and apparent resistivity data as input. This kind of generalised platform has not been done elsewhere to invert data directly using soft computing approach.
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
Few researchers applied neuro fuzzy algorithm to interpret geoelectrical resistivity data
The electrical resistivity survey involved vertical electrical sounding (VES) is based on measuring the potentials between one pair of electrodes while transmitting a direct current between another pair of electrodes. Depth of current penetration is proportional to the spacing between the electrodes in homogenous ground, and varying the electrode separation provides information about the stratification of the ground. Vertical electrical soundings are applied to a horizontally or approximately horizontally layered earth. Geological targets may be, e.g., sedimentary rocks of different lithologies, layered aquifers of different properties, sedimentary rocks overlaid igneous rocks, or the weathering zones of igneous rock in the most favourable case, the number of layers, their thickness and resistivity are outcome of a VES survey.
The Vertical Electrical Sounding (VES) is a very appropriate method to explore layered underground. By performing a VES, a linear electrode array is laid out in the field injecting a direct or a slow alternating current into the underground. In the center, the voltage response is measured simultaneously between two electrodes. Increasing depths are realized by enlargement of the current electrode from small distances in the beginning to larger at end.
It is most commonly used electrode system. In the Wenner spread the electrodes are uniformly spaced in a line. It is the simplest and most asymmetrical arrangement. It, or modification of it, has been used widely in UK, USA, Canada and few other countries. For depth exploration using the Wenner spread, the exploration are mapping, the spacing remains constant and all four electrodes are moved electrodes are expanded about a fixed centre, increasing the spacing in steps. In mapping the apparent resistivity for each array position is plotted against the centre of the spread. The Wenner array on particular demands less instrument sensitivity, and reduction of data is marginally easier. The equation for calculating geometric factor Kequals to
This differs from the Wenner configuration in placing the two current electrodes with a much larger interval than that between the two inner potential electrodes. In Schlumberger configuration the current electrodes are denoted by A and B while the potential electrodes are denoted by M and N. The interval between M and N may be denoted by b while interval AB/2 is denoted by s. The current electrodes AB may be placed as inner electrodes, and the two potential electrodes M and N as outer electrodes. This arrangement has the advantage that in depth sounding, long current carrying cables may be avoided.
For this array, the geometric factor (K) is:
In a survey with varying electrode spacing, field operations with the Schlumberger array are faster, because all four electrodes of the Wenner array are moved between successive observations, but with the Schlumberger array, only the outer ones need to be moved. The Schlumberger array is also said to be superior in distinguishing lateral from vertical variations in resistivity. So the survey was carried out in our study area with Schlumberger Configuration.
Due to different geological settings the software uses multilayer model to evaluate the thickness and true resistivity. As the variations in resistivity with respect to the geology is much larger to estimate it. Thus the soft computing algorithm is designed to estimate the thickness and true resistivity with maximum number of iterations to optimize the result.
Electrical resistivity methods are successful in delineating lateral and vertical variation of subsurface geology. Detailed survey has been carried out with DC Resistivity meter IGIS DDR3. This is very compact and highly reliable equipment, which is used for resistivity measurement. This DDR3 resistivity meter poses two main units such as Current unit and potential unit. This unit measures the potential difference across the potential electrodes as also the resistance values provide direct display over digital panel metre. The purpose of current unit is to send the current in a constant manner to the subsurface and the potential unit provides the accurate measurement of potential variations and displays it.
The current and potential electrodes which are made up of conducting metals such as iron, copper, steel etc., are connected to the DDR3 resistivity meter through well insulated cables wounded on the winch.
RESinvANFIS was created under the Graphical User Interface (GUI) of MATLAB environment. The package has been developed using the deploy tool of MATLAB and work as a stand alone application in windows. It is written primarily in MATLAB platform and the entire program is executed through series of automated instructions that will proceeds the inversion algorithm without any interruption. The MATLAB platform was chosen for applying the soft computing inversion algorithm efficiently.




1. 


Granite  5×10^{3}10^{6}  
Basalt  10^{3}10^{6}  
Marble  10^{2}2.5×10^{8}  
Quartzite  10^{2}2.5×10^{8}  
Slate  6×10^{2}4×10^{7}  
2. 


Sandstone  84×10^{3}  
Shale  202×10^{3}  
Limestone  504×10^{2}  
3. 


Clay  1100  
Alluvium  10800  
Groundwater (Fresh)  10100  
Sea water  0.2 
RESinvANFIS is a standalone application that provides the output subsurface layer model (e.g. True resistivity and Thickness).
The software module runs in MATLAB 2012 or newer version installation. If MATLAB software is not available then install MCR (Matlab run time environment) installer to run the software for the system without MATLAB.
The basic structure of the fuzzy inference system maps input characteristics to input membership functions, input membership functions to rules, rules to a set of output characteristics, output characteristics to output membership functions, and the output membership functions to a singlevalued output or a decision associated with the output. An example structure of the ANFIS with rule evaluation is shown in
The selection of the FIS is the major concern when designing an ANFIS to model a specific target system. In the current study, the Sugeno type FIS model is chosen, since the consequent part of this FIS is a linear equation, and the parameters can be estimated by a hybrid error evaluation method. A typical fuzzy logic system with neural network structure consists of four major components: fuzzification interface, fuzzy rule base, fuzzy inference engine and defuzzification interface. The fuzzification interface (fuzzifier) converts numerical input data into suitable linguistic terms, which may be viewed as labels of the fuzzy sets. A fuzzy rule represents a fuzzy relation between two fuzzy sets. It takes the form such as “If X is A then Y is B”. Each fuzzy set is characterized by appropriate membership functions that map each element to a membership value between 0 and 1. A fuzzy rule base contains a set of fuzzy rules, where each rule may have multiple inputs and multiple outputs. Fuzzy inference can be realized by using a series of fuzzy operations. The defuzzification interface (defuzzifier) combines and converts linguistic conclusions (fuzzy membership functions) into crisp numerical outputs. The crisp output is generally obtained using different defuzzification strategies. It amalgamates two procedures, the logic decision and defuzzification procedures into one composite procedure
Initially, the data have been subjected to clustering analysis where subtractive clustering technique has been used before applying ANFIS algorithm. The resultant membership functions have been mapped to raise certain degree of membership grade between the input and output, and serves to raise the firing strengths for each membership function. Each function has a significant feature on rule framing and consequent parameters.
After importing AB/2 and apparent resistivity data, it is subjected to subtractive clustering algorithm (“
ANFIS system consists of 5 layers; layer symbolized by the box is a layer that is adaptive and symbolized by the circle is fixed (
Serves to raise the degree of membership and the membership used here is Gaussian membership function.
and
_{ }with
By { σand c} are the parameters of membership function or called as a parameter premise. σ signifies the cluster bandwidth, and c represents the cluster center.
Serves to evoke
Normalizes the
Calculates the output based on the parameters of the rule
Counts the ANFIS output signal by summing all incoming signals will produce
An adaptive network is a multilayer feedforward network in which each node performs a particular function (
The parameter set of an adaptive network is the union of the parameter sets of each adaptive node. In order to achieve a desired inputoutput mapping, these parameters are updated according to the given training data. A hybrid method consists of two phases
1. Gradient descent which computes error signals recursively from the output layer backward to the input nodes, and
2. Least squares method, which finds a feasible set of consequent parameters
Data 1 collected from published work of Banerjee et al. in 2011
RESinvANFIS provides excellent platform for geophysical researchers who got involved in interpreting data with soft computing approach. With this new algorithm, anyone can invert the geoelectrical sounding data. The modelling methodology will improve the pursuit of existing conventional interpretations algorithm that will rapidly allow the researchers to rely on soft computing methods in order to promote more number of research works which are helpful to the scientific societies. Below mentioned are some of the avenues of research that will benefit from RESinvANFIS. The software can be downloaded from the github repository (
In recent years, ANFIS modelling technique has been successfully employed to model complex systems, where classical methods e.g. mathematical and modelfree methods are inapplicable due to lack of sufficient information
ANFISis much more complex than the fuzzy inference systems discussed so far, and is not available for all of the fuzzy inference system options. Specifically, ANFIS only supports Sugenotype systems, and these must have the following properties (MATLAB, 2008):
Have a single output, obtained using defuzzification methods (weighted average method used here). All output membership functions must be of the same type and either be linear or constant.
Have no rule sharing. Different rules cannot share the same output membership function, namely the number of output membership functions must be equal to the number of rules.
Have unity weight for each rule.
An error occurs if FIS structure does not comply with these constraints.
Moreover, ANFIS cannot accept all the customization options that basic fuzzy inference allows. That is, one cannot create their own membership or defuzzification functions in the ANFIS architecture, unless one can use the available membership and defuzzification functions.
The main areas of ANFIS technique have been covered in the previous sections. The major impact of applying these logics in geophysical, geological and reservoir engineering applications will produce a promising and more reliable output. Finally, quantification and reduction of uncertainty and the confidence interval are possible by more comprehensive use of fuzzy logic and neural networks. The true benefit of soft computing algorithm is to use the intelligent techniques in combination (hybrid) rather than isolation, has not been demonstrated to a full extent. This research addresses the particular areas for future research: hybrid systems and their application.
Many hybrid systems can be built on the combining platform of neural networks, fuzzy logic and neuro fuzzy networks. For example, fuzzy logic can be used to combine results from several neural networks; Although some hybrid systems have been built, this present work has attained promising results when combining the fuzzy logic and neural networks. The field validation proves that this algorithm can have the bright future for estimating many nonlinear problems.
We thank Manonmaniam Sundaranar University for carrying out the research by providing enough software package and environment necessary for developing the software. Also, the author is willing to thank the management of Loyola College to publish this article. Moreover the improvement comes from the criticism of notable professors Dr. KK Sharma, Dr. N. Sundararajan and friends Dr. Joe Vivek and Dr. Saravanan. Thanks for them too.