Development of novel velocity–resistivity relationships for granitic terrains based on complex collocated geotomographic modeling and supervised statistical analysis

Akingboye, Adedibu Sunny; Bery, Andy Anderson

doi:10.1007/s11600-023-01049-w

Cited by 12 publications

(14 citation statements)

References 59 publications

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“…The procedure for data collection as shown in Fig. 3 included the use of both forward and reverse ring, as well as the application of SeisOpt@2D software, as explained by (Akingboye and Bery, 2023), was employed for the data inversion. The optimal range for shots per point was within the interval of 7-10.…”

Section: Srt Eld Data Acquisitionmentioning

confidence: 99%

“…The inversion of refraction-type rst-arrival time was carried out by combining the nite-difference solution of the Eikonal problem with the multi-stencils fast marching method and simulated annealing global optimization, as explained in (Hiltunen et al, 2014;Akingboye and Bery, 2023). The inversion models were produced by the application of the refraction inversion and optimization method (RIOT), which is an interface of the SeisOpt@2D, in high-resolution mode.…”

Section: Modeling and Processing Of Srt Field Datamentioning

confidence: 99%

“…This approach outperforms the use of these methods independently or its sole reliance on conventional techniques. By integrating these two inversion outcomes to describe environmental characteristics, their collective analysis may effectively address the problem of the lack of uniqueness in the inverse problem as well as the inadequate comprehension of the subsurface (Akingboye and Bery, 2023).…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, cluster analysis was used to integrate the images and construct a conceptual subsurface model. The characteristics features of soil-rock and the geology of the subsurface have been examined in different environments through the utilization of Vp and resistivity, with ML, as demonstrated in previous studies (Akingboye and Bery, 2023;Hellman et al, 2017;Ronczka et al, 2017). The work by Akingboye and Bery (2023) demonstrated clear empirical connections between Vp and resistivity in tropical granitic terrains using geotomographic data and supervised statistical modeling.…”

Section: Introductionmentioning

confidence: 99%

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Joint K-means clustering and statistical analytical modeling of P- wave velocity and resistivity datasets for subsurface lithologic differentiation

Dick,

Bery,

Bala

et al. 2024

Preprint

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Given the hazards linked to unstable ground conditions, it is vital to grasp the soil-rock characteristics essential for foundation construction and groundwater development. However, the inherent challenges in geophysics, such as the non-uniqueness of the inverse problem and incomplete subsurface knowledge, hinder the direct interpretation of geophysical data in terms of geological units. Traditional soil exploration methods or relying solely on one geophysical survey method often yield inaccurate results due to limitations in mapping subsurface complexities and heterogeneities. This study addresses these challenges by applying K-means cluster analysis to a univariate geophysical parameter set spanning an 800 m section in the geothermally active Kabota-Tawau area of Sabah, Malaysia. Leveraging unsupervised machine learning techniques like principal component analysis, involving Silhouette and elbow methods, the research determines the optimal number of clusters (k) and validates their accuracy. The analysis identifies four distinct lithologic units, serving as proxies for soil/rock properties in the study area. With an R-squared value nearing 1 and an average Silhouette score of 0.67 for \(k=4\), the results indicate a high level of satisfaction in cluster separation, supported by a percentage sum of square error exceeding 88%. This approach enhances our ability to accurately identify lithologic units critical for improving the reliability of foundation construction and groundwater development efforts.

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Section: Srt Eld Data Acquisitionmentioning

confidence: 99%

Section: Modeling and Processing Of Srt Field Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Joint K-means clustering and statistical analytical modeling of P- wave velocity and resistivity datasets for subsurface lithologic differentiation

Dick,

Bery,

Bala

et al. 2024

Preprint

Self Cite

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“…The four models were significantly predictive (PC > CPC). All MLRs met the assumptions of no autocorrelation [30], linearity and severe non-multicollinearity [31][32][33], homogeneity, and normality.…”

Section: Introductionmentioning

confidence: 99%

Modeling Yield of Irrigated and Rainfed Bean in Central and Southern Sinaloa State, Mexico, Based on Essential Climate Variables

Llanes Cárdenas,

Estrella Gastélum,

Parra Galaviz

et al. 2024

Atmosphere

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The goal was to model irrigated (IBY) and rainfed (RBY) bean yields in central (Culiacán) and southern (Rosario) Sinaloa state as a function of the essential climate variables soil moisture, temperature, reference evapotranspiration, and precipitation. For Sinaloa, for the period 1982–2013 (October–March), the following were calculated: (a) temperatures, (b) average degree days for the bean, (c) cumulative reference evapotranspiration, and (d) cumulative effective precipitation. For essential climate variables, (e) daily soil moisture obtained from the European Space Agency and (f) IBY and RBY from the Agrifood and Fisheries Information Service were used. Multiple linear regressions were significant for predicting IBY–RBY (dependent variables) as a function of essential climate variables (independent variables). The four models obtained were significantly predictive: IBY–Culiacán (Pearson correlation (PC) = 0.590 > Pearson critical correlation (CPC) = |0.349|), RBY–Culiacán (PC = 0.734 > CPC = |0.349|), IBY–Rosario (PC = 0.621 > CPC = |0.355|), and RBY–Rosario (PC = 0.532 > CPC = |0.349|). Due to the lack of irrigation depth data, many studies only focus on modeling RBY; this study is the first in Sinaloa to predict IBY and RBY based on essential climate variables, contributing to the production of sustainable food.

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Rock mass quality evaluation via statistically optimized geophysical datasets

Akingboye,

Bery

2023

Bull Eng Geol Environ

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Development of novel velocity–resistivity relationships for granitic terrains based on complex collocated geotomographic modeling and supervised statistical analysis

Cited by 12 publications

References 59 publications

Joint K-means clustering and statistical analytical modeling of P- wave velocity and resistivity datasets for subsurface lithologic differentiation

Joint K-means clustering and statistical analytical modeling of P- wave velocity and resistivity datasets for subsurface lithologic differentiation

Modeling Yield of Irrigated and Rainfed Bean in Central and Southern Sinaloa State, Mexico, Based on Essential Climate Variables

Rock mass quality evaluation via statistically optimized geophysical datasets

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