Estimation of subsurface strata of earth using Adaptive Neuro-Fuzzy inference system (ANFIS)

Srinivas, Y.; Raj, Abhishek; Oliver, D. Hudson; Muthuraj, D.; Chandrasekar, N.

doi:10.1556/ageod.47.2012.1.7

Cited by 12 publications

(9 citation statements)

References 14 publications

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“…Masoud Nikravesh (2004) applied intelligent techniques in the oil and gas industry for multipurposes such as risk management, uncertainty analysis and interpretation of geological data. Yasala Srinivas (2013) applied the ANFIS model to find the lithology of the study area. The last decade has witnessed significant advances in inverting geosciences data with associated characteristics.…”

Section: Fundamentals Of Anfis -Theory and Applicationsmentioning

confidence: 99%

Optimising noise intervened data processes for inverse geoelectrical problem using adaptive neuro fuzzy inference system (ANFIS)

Raj¹,

Oliver²,

Srinivas

et al. 2021

NRIAG Journal of Astronomy and Geophysics

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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 comes from the disturbance due to human interventions, atmospheric variations, and electromagnetic disturbance, etc. . In this paper, we have presented a concept of Neuro Fuzzy algorithm which can interpret the noisy data successfully. Moreover, the data were tested with artificially generated random noise, gaussian noise and missing data. Kanyakumari field region having complex geological structures and its performance is validated with a maximum threshold. Kanyakumari field region having complex geological structures is used and the performance is validated with a maximum threshold. Neuro fuzzy technique has the dominant feature of training and testing the data with utmost accuracy. These implications are 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.

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Section: Fundamentals Of Anfis -Theory and Applicationsmentioning

confidence: 99%

Optimising noise intervened data processes for inverse geoelectrical problem using adaptive neuro fuzzy inference system (ANFIS)

Raj¹,

Oliver²,

Srinivas

et al. 2021

NRIAG Journal of Astronomy and Geophysics

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“…For simplicity, the Euclidean distance is used as the dissimilarity measure and the overall cost function is expressed as in (7). The partitioned groups are typically defined by a × binary membership matrix , where the element is 1 if the th data point belongs to group and 0 otherwise.…”

Section: Fuzzy -Means Clusteringmentioning

confidence: 99%

“…Step 3. Compute the cost function according to (7). Stop if either it is below a certain tolerance value or its improvement over previous iteration is below a certain threshold.…”

Section: Fuzzy -Means Clusteringmentioning

confidence: 99%

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Geoelectrical Data Inversion by Clustering Techniques of Fuzzy Logic to Estimate the Subsurface Layer Model

Raj

Oliver²,

Srinivas

2015

International Journal of Geophysics

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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.

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“…Among them is estimation of evaporation using Artificial Neural Networks (AAN) and ANFIS techniques [4], which found that the ANFIS models are better than ANN although the difference is small. Estimation of time series on earthquake events with mapping function [5], and estimation of subsurface strata of Earth [6] reported that the interpretation using ANFIS technique will give the promising results with much fewer percentage error.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Precipitable Water Vapor Using an Adaptive Neuro-fuzzy Inference System Technique

Suparta

Alhasa

2013

Lecture Notes in Computer Science

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Part 1: Information and Communication Technology- Eurasia Conference (ICT-EurAsia)International audienceWater vapor has an important role in the global climate change development. Because it is essential to human life, many researchers proposed the estimation of atmospheric water vapor values such as for meteorological applications. Lacking of water vapor data in a certain area will a problem in the prediction of current climate change. Here, we reported a novel precipitable water vapor (PWV) estimation using an adaptive neuro-fuzzy inference system (ANFIS) model that has powerful accuracy and higher level. Observation of the surface temperature, barometric pressure and relative humidity from 4 to 10 April 2011 has been used as training and the PWV derived from GPS as a testing of these models. The results showed that the model has demonstrated its ability to learn well in events that are trained to recognize. It has been found a good skill in estimating the PWV value, where strongest correlation was observed for UMSK station (r = 0.95) and the modest correlation was for NTUS station (r = 0.73). In general, the resulting error is very small (less than 5%). Thus, this model approach can be proposed as an alternative method in estimating the value of PWV for the location where the GPS data is inaccessible

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Estimation of subsurface strata of earth using Adaptive Neuro-Fuzzy inference system (ANFIS)

Cited by 12 publications

References 14 publications

Optimising noise intervened data processes for inverse geoelectrical problem using adaptive neuro fuzzy inference system (ANFIS)

Optimising noise intervened data processes for inverse geoelectrical problem using adaptive neuro fuzzy inference system (ANFIS)

Geoelectrical Data Inversion by Clustering Techniques of Fuzzy Logic to Estimate the Subsurface Layer Model

Estimation of Precipitable Water Vapor Using an Adaptive Neuro-fuzzy Inference System Technique

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