Feedforward Neural Network for joint inversion of geophysical data to identify geothermal sweet spots in Gandhar, Gujarat, India

Yadav, Apurwa; Yadav, Kriti; Sircar, Anirbid

doi:10.1016/j.engeos.2021.01.001

Cited by 16 publications

(6 citation statements)

References 24 publications

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“…ANNs have been especially useful in mineral resource estimation (e.g., [85][86][87]), comprising ~46% of the ML techniques used in this area [68]. Other prominent applications include mineral prospecting and mapping [88,89], geophysics and remote sensing [90,91], ore classification [92,93], drilling and blasting operations [94,95], mining method selection, equipment utilization and production planning [96,97], ore beneficiation and mineral recovery [98,99] and mine site reclamation [100,101], among others.…”

Section: Appendix B3 Mlp Design Training and Mining Applicationsmentioning

confidence: 99%

Integrated Artificial Neural Network and Discrete Event Simulation Framework for Regional Development of Refractory Gold Systems

Wilson

Mercier²,

Navarra

2022

Mining

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Mining trends in the gold sector indicate a growing imbalance in global supply and demand chains, especially in light of accelerated efforts towards industrial electrification and automation. As such, it is important that research and development continue to focus on processing options for more complex and refractory ores. Unlike conventional (i.e., free-milling) ore feeds, refractory gold is not amenable to standard cyanidation, and requires additional pretreatment prior to leaching and recovery. With recent technological advancements, such as sensor-based ore sorting, there is opportunity to advance the development of smaller untapped refractory resources with marginal economics, particularly those in proximity to processing infrastructure within major gold districts. However, it will be critical that the necessary tools are developed to capture the potential system-wide effects caused by varied ore feeds and improve related decision-making processes earlier in the value chain. Discrete event simulation (DES) is a powerful computational technique that can be used to monitor the interactions between important processes and parameters in response to random natural variations; the approach is thus suitable for the modelling of complex mining systems that deal with significant geological uncertainty. This work implements an integrated artificial neural network (ANN) and DES framework for the regional coordination of conventional and preconcentrated refractory gold ores to be processed at a centralized plant. Sample calculations are presented that are based on a generated dataset reflective of sediment-hosted refractory gold systems.

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Section: Appendix B3 Mlp Design Training and Mining Applicationsmentioning

confidence: 99%

Integrated Artificial Neural Network and Discrete Event Simulation Framework for Regional Development of Refractory Gold Systems

Wilson

Mercier²,

Navarra

2022

Mining

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“…An ANN is a set of artificially structured neurons that are trained from a specific sequence of steps (algorithm) to obtain the desired output. The main design of ANNs is adapted from the fundamental structure of a biological neuron that resides in our brains [32].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Pitts in 1943 to replicate the functioning of a biological neuron [32]. Even though research in neural modeling started circa 1940 (i.e., McCulloch & Pitts in 1943), there was little active development of the field prior to the late fifties and early sixties when Rosenblatt introduced the perceptron in 1958, in figure 7 the basic structure of a perceptron [33].…”

Section: Perceptron a Mathematical Perceptron Of A Biological Neuron Was Proposed Almost 65 Years Ago By Mcculloch Andmentioning

confidence: 99%

Artificial Neural Networks for Predicting the Generation of Acetaldehyde in Pet Resin in the Process of Injection of Plastic Packages

Nascimento

Alencar

Nascimento

et al. 2021

Int J Innov Educ Res

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The industrial production of preforms for the manufacture of PET bottles, during the plastic injection process, is essential to regulate the drying temperature of the PET resin, to control the generation of Acetaldehyde (ACH), which alters the flavor of carbonated or non-carbonated drinks, giving the drink a citrus flavor and putting in doubt the quality of packaged products. In this work, an Artificial Neural Network (ANN) of the Backpropagation type (Cascadeforwardnet) is specified to support the decision-making process in controlling the ideal drying temperature of the PET resin, allowing specialists to make the necessary temperature regulation decisions for the best performance by decreasing ACH levels. The materials and methods were applied according to the manufacturer's characteristics on the moisture in the PET resin grain, which may contain between 50 ppm and 100 ppm of ACH. Data were collected for the method analysis, according to temperatures and residence times used in the blow injection process in the manufacture of the bottle preform, the generation of ACH from the PET bottle after solid post-condensation stage reached residual ACH levels below (3-4) ppm, according to the desired specification, reaching levels below 1 ppm. The results found through the Computational Intelligence (IC) techniques applied by the ANNs, where they allowed the prediction of the ACH levels generated in the plastic injection process of the bottle packaging preform, allowing an effective management of the parameters of production, assisting in strategic decision making regarding the use of temperature control during the drying process of PET resin.

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“…Traditional linear iterative regularization inversion is prone to getting stuck in local minima, and global optimization inversion methods have received widespread attention. Nonlinear methods such as Monte Carlo (MC) [3], simulated annealing (SA) [4], genetic algorithm (GA) [5], artificial neural network (ANN) [6], particle swarm optimization (PSO) [7], differential evolution [8], ant colony optimization (ACO) [9] have been widely used in geophysical inversion.…”

Section: Introductionmentioning

confidence: 99%

2D Inversion of Magnetic Anomaly data based on Deep Learning

2024

International Journal of Science and Engineering Applications

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This Magnetic exploration is a geophysical exploration method to study geological structure and mineral resources. Inversion is an effective method to estimate the horizontal location,depth,and geometry of subsurface geological bodies. To resolve the problems of traditional inversion methods, such as dependence on initial model and long calculation time, we proposed a 2D inversion method of magnetic anomaly data based on Deep Learning.With this method, a number of magnetic anomalous body models were designed to perform forward simulation, which generated sample dateset, firstly; a new convolution neural network(CNN) magnetic inversion network was designed, secondly; the sample dataset was used to train the network thirdly; and inversion experimental was performed to evaluate the proposed method lastly. The experimental results show that the proposed method can invert position and magnetization of magnetic anomaly, with strong learning ability and certain generalization ability, and can solve the magnetic inversion problem effectively.

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Feedforward Neural Network for joint inversion of geophysical data to identify geothermal sweet spots in Gandhar, Gujarat, India

Cited by 16 publications

References 24 publications

Integrated Artificial Neural Network and Discrete Event Simulation Framework for Regional Development of Refractory Gold Systems

Integrated Artificial Neural Network and Discrete Event Simulation Framework for Regional Development of Refractory Gold Systems

Artificial Neural Networks for Predicting the Generation of Acetaldehyde in Pet Resin in the Process of Injection of Plastic Packages

2D Inversion of Magnetic Anomaly data based on Deep Learning

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