An ML scale for Eastern Dharwar Craton and adjoining regions

Sharma, Aman; Vashisth, Divakar; Naresh, B.; Suresh, G.; Raju, P. V. Sunder; Raghavan, Ramesh; Srinagesh, D.

doi:10.1007/s10950-021-10028-x

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…Global optimization techniques inspired by swarm intelligence have found increasing applications in geophysical inverse problems due to their ability to find global solutions by using simple tuning parameters, flexible applicability and easy implementation. Some of the popular swarm intelligencebased global optimization techniques include, but are not limited to, particle swarm Optimization (PSO) (Kennedy and Eberhart, 1995;Shaw and Srivastava, 2007;Naudet et al, 2008;Song et al, 2012;Pace et al, 2021), ant colony optimization (Yuan et al, 2009;Srivastava et al, 2014), grey wolf algorithm (GWO) (Mirjalili et al, 2014;Song et al, 2015;Agarwal et al, 2018;Vashisth and Shekar, 2019;Sharma et al, 2021) and whale optimization algorithm (Mirjalili and Lewis, 2016;Vashisth et al, 2018;. A couple of recently proposed swarm intelligence-based global optimization techniques include African vultures optimization (Abdollahzadeh et al, 2021a) and artificial gorilla troops optimization (Abdollahzadeh et al, 2021b) that mimic foraging and navigation behaviour of African vultures and social intelligence of gorilla troops, respectively.…”

Section: Introductionmentioning

confidence: 99%

Joint inversion of Rayleigh wave fundamental and higher order mode phase velocity dispersion curves using multi‐objective grey wolf optimization

Vashisth

Shekar

Srivastava

2022

Geophysical Prospecting

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Rayleigh wave dispersion curves can be inverted to retrieve subsurface seismic velocity profiles. The inverse problem is ill-posed, nonlinear and poorly conditioned, necessitating the application of global optimization methods. We present the application of the multi-objective grey wolf optimization algorithm to perform joint inversion of the phase velocity dispersion curves corresponding to the fundamental and higher order modes of Rayleigh waves to obtain shear (S-) and primary (P-) wave velocity profiles. Multi-objective grey wolf optimization is an extension of the grey wolf optimization algorithm for application to multi-objective optimization and can be adapted to solve joint inversion problems. We compare the joint inversion results obtained from the multi-objective grey wolf optimizer with those obtained from Markov chain Monte Carlo and fundamental mode inversion using the grey wolf optimizer on synthetic examples. The errors associated with phase velocity measurements are simulated by adding frequency-dependent noise, with a higher level of noise added to the phase velocities corresponding to lower frequencies as compared to the higher frequencies. In the multimode joint inversion problem, the multi-objective grey wolf optimizer gives a suite of solutions corresponding to each model parameter. The suite of S-wave and P-wave velocity profiles estimated from the multi-objective grey wolf optimizer matches closely with the true model for the synthetic case studies even in the presence of noise. However, the suite of solutions has a greater spread for the last few layers, qualitatively indicating a higher degree of uncertainty in the predicted model parameter. The uncertainty in the solution for the deeper layers is a consequence of the uncertainty in the phase velocity at lower frequencies. We demonstrate the efficacy of the algorithm on recorded data from a shallow seismic survey conducted at the Indian Institute of Technology Bombay. The results from the multi-objective grey wolf optimizer are in close agreement with those from Markov chain Monte Carlo, and the depth of investigation is found to be greater in comparison to results from refraction traveltime inversion.

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Section: Introductionmentioning

confidence: 99%

Joint inversion of Rayleigh wave fundamental and higher order mode phase velocity dispersion curves using multi‐objective grey wolf optimization

Vashisth

Shekar

Srivastava

2022

Geophysical Prospecting

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A Comparative Analysis of Three Computational-Intelligence Metaheuristic Methods for the Optimization of TDEM Data

Pace

Raftogianni

Godio

2022

Pure Appl. Geophys.

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We focus on the performances of three nature-inspired metaheuristic methods for the optimization of time-domain electromagnetic (TDEM) data: the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO) and the Grey Wolf Optimizer (GWO) algorithms. While GA and PSO have been used in a plethora of geophysical applications, GWO has received little attention in the literature so far, despite promising outcomes. This study directly and quantitatively compares GA, PSO and GWO applied to TDEM data. To date, these three algorithms have only been compared in pairs. The methods were first applied to a synthetic example of noise-corrupted data and then to two field surveys carried out in Italy. Real data from the first survey refer to a TDEM sounding acquired for groundwater prospection over a known stratigraphy. The data set from the second survey deals with the characterization of a geothermal reservoir. The resulting resistivity models are quantitatively compared to provide a thorough overview of the performances of the algorithms. The comparative analysis reveals that PSO and GWO perform better than GA. GA yields the highest data misfit and an ineffective minimization of the objective function. PSO and GWO provide similar outcomes in terms of both resistivity distribution and data misfits, thus providing compelling evidence that both the emerging GWO and the established PSO are highly valid tools for stochastic inverse modeling in geophysics.

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An ML scale for Eastern Dharwar Craton and adjoining regions

Cited by 2 publications

References 16 publications

Joint inversion of Rayleigh wave fundamental and higher order mode phase velocity dispersion curves using multi‐objective grey wolf optimization

Joint inversion of Rayleigh wave fundamental and higher order mode phase velocity dispersion curves using multi‐objective grey wolf optimization

A Comparative Analysis of Three Computational-Intelligence Metaheuristic Methods for the Optimization of TDEM Data

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