Utilization of multimode Love wave dispersion curve inversion for geotechnical site investigation

Hamimu, La; Nawawi, Mohd; Safani, Jamhir

doi:10.1088/1742-2132/8/2/019

Cited by 7 publications

(9 citation statements)

References 21 publications

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“…In general, the numerical approach can be certainly carried out if the algorithm of the actual calculation from an empirical system characteristic is obviously established. Based on the lists of equation presented in the literature review section, the actual calculation of the multimode Love wave dispersion curves using Haskell layer matrix method can be succinctly outlined as follow [21]:…”

Section: Methodology/approachmentioning

confidence: 99%

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Numerical analysis of the geophysical parameter sensitivity on the multimode Love wave dispersion curves

Hamimu,

Cahyono,

Budiman

et al. 2024

J. Phys. Commun.

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The characteristics of multimode Love wave dispersion curves are dependent on various geophysical parameters, namely shear wave velocity (SWV), layer thickness and density.This paper presents a novel numerical analysis of these parameters to determine which ones have a significant effect on the dispersion curves of multimode Love waves. Identifying parameter sensitivity is a crucial step in the inversion procedure of geophysical modeling.Our numerical analysis focuses on perturbing three geophysical parameters that influence the dispersion curves. The results confirm that SWV parameter is the most sensitive. By employing the same perturbation procedure, our numerical analysis reveals that the SWV parameter has a highly significant impact on the multimode Love wave dispersion curves.The average relative error (RE) values are found to be 27.33% for alpha=-0.2 and 25.51% for alpha=+0.2. Conversely, perturbing the layer thickness parameter demonstrates no significant influence on the dispersion curves, resulting in average RE values of 5.69% for alpha=-0.2 and 7.96% for alpha=+0.2. Furthermore, the perturbation of the density parameter exhibits an extremely negligible impact on the multimode Love wave dispersion curves, with average RE values on the order of 10^(−14)%.

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Section: Methodology/approachmentioning

confidence: 99%

“…According to main equation listed in this algorithm, there are three geophysical parameters should be sensitively affected the characteristics of the multimode Love wave dispersion curve, that is [22]:…”

Section: Methodology/approachmentioning

confidence: 99%

Numerical analysis of the geophysical parameter sensitivity on the multimode Love wave dispersion curves

Hamimu,

Cahyono,

Budiman

et al. 2024

J. Phys. Commun.

Self Cite

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“…In this study, inversion of apparent resistivity is developed by adopting the FORTRAN GA inversion codes of Hamimu et al, [8,9] which were used for inverting surface wave phase velocity curves. For 1D resistivity inversion, GA inversion seeks to find the best combination of resistivity and thickness values for each layer to minimize the difference between the experimental and the theoretical apparent resistivity curve.…”

Section: Inversion Using the Ga Optimization Techniquementioning

confidence: 99%

“…Nagai et al, [6], Song and Gu, [7], Hamimu et al, [8,9]). These authors have used GA optimization techniques for inversion of surface wave dispersion curves.…”

Section: Introductionmentioning

confidence: 99%

“…Davis [2], Nagai et al, [6]; Jha et al, [10]; Kohlbeck and Mawlood [11], Hamimu et al, [8,9]. However, FORTRAN and C programming languages impose a rather expensive burden on users in integrating complicated numerical algorithms with visual data analysis compared with MATLAB, which provides integrated functionality of computation and visualization.…”

Section: Introductionmentioning

confidence: 99%

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Developed Forward and Inverse Modelling of Vertical Electrical Sounding (VES) Using MATLAB Implementation

2015

IJSR

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Abstract:We have implemented a MATLAB source code for VES forward modeling and its inversion using a genetic algorithm (GA) optimization technique. The codes presented here are applied to the Schlumberger electrode arrangement. In the forward modeling computation, we have developed code to generate theoretical apparent resistivity curves from a specified layered earth model. The input to this program consists of the number of layers, the layer resistivity and thickness. The output of this program is apparent resistivity versus electrode spacing incorporated in the inversion process as apparent resistivity data. For the inversion, we have developed a MATLAB code to invert (for layer resistivity and thickness) the apparent resistivity data by the genetic algorithm optimization technique. The code also has some function files involving the basic stages in the GA inversion. Our inversion procedure addressed calculates forward solutions from sets of random input, to find the apparent resistivity. Then, it evolves the models by better sets of inputs through processes that imitate natural mating, selection, crossover, and mutation in each generation. The aim of GA inversion is to find the best correlation between model and theoretical apparent resistivity curves. In this study, we present three synthetic examples that demonstrate the effectiveness and usefulness of this program. Our numerical modeling shows that the GA optimization technique can be applied for resolving layer parameters with reasonably low error values.

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Evaluating the overconsolidation ratios and peak friction angles of granular soil deposits using noninvasive seismic surveying

Pegah

Liu

2020

Acta Geotech.

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Utilization of multimode Love wave dispersion curve inversion for geotechnical site investigation

Cited by 7 publications

References 21 publications

Numerical analysis of the geophysical parameter sensitivity on the multimode Love wave dispersion curves

Numerical analysis of the geophysical parameter sensitivity on the multimode Love wave dispersion curves

Developed Forward and Inverse Modelling of Vertical Electrical Sounding (VES) Using MATLAB Implementation

Evaluating the overconsolidation ratios and peak friction angles of granular soil deposits using noninvasive seismic surveying

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