Gas chimney and hydrocarbon detection using combined BBO and artificial neural network with hybrid seismic attributes

Ramya, J.; Somasundareswari, D.; Vijayalakshmi, P.

doi:10.1007/s00500-019-04064-6

Cited by 13 publications

(4 citation statements)

References 28 publications

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“…o BBO is an evolutionary-based genetic algorithm developed by Dan Simon [17], which is referred to as the BBO. Motivated by the natural dynamic of biogeography, which involves species being bound to their natural habitat, the method models candidate solutions as habitats called species.…”

Section: Population-based Algorithm (Bbo)mentioning

confidence: 99%

Optimizing Marketing Strategies: Integration of Al-Biruni Earth Radius Algorithm for Feature Selection and Pipeline Regression Model

Gaber,

Zaki,

Eid

et al. 2024

Journal of Artificial Intelligence in Engineering Practice

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With the current business environment becoming increasingly ferocious, the effectiveness of digital marketing strategies is no longer a matter of debate as many organizations have realized the need to gain an edge over competition and improve the ROI with their marketing efforts. This study looks into the specifics of digital marketing effectiveness by, in the process, analyzing true indicators and key metrics. Demonstrating an understanding of the complexity of online marketing operations and the diversity of the variables involved, econometric techniques provide feature choice that affects campaign outcomes the most. At first, the variety of performance between different algorithms from feature selection gave the average error ranging from 0.38264 to 0.44194. However, following the optimization provides the tendency to see a decrease in mean errors and an improving performance. Afterward, the step of predictive modeling is implemented, employing diverse machine learning algorithms including ExtraTreesRegressor, GradientBoostingRegressor, SVR, and CatBoost to assess the effectiveness of foreshowing marketing outcomes. Before the optimization, the recommendations made by the predictive modeling are not too accurate and uniform for each algorithm. That being said, however, once the optimization is done, enhancement in prediction accuracy to the tune of substantial improvement is observed with metrics indicating the same as less MSE, RMSE, and R2. Contributing to a more thorough comprehension of the issue of selecting features and models for predicting as well as efficiency of digital marketing, the study also offers an understanding of the opportunities and obstacles that are present in the process of building digital marketing strategies. A thorough evaluation of top metrics and KPIs gives decision-makers data-driven tools to define their marketing activities, deliver tangible results, and stay relevant in the fast-paced digital environment of today.

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Section: Population-based Algorithm (Bbo)mentioning

confidence: 99%

Optimizing Marketing Strategies: Integration of Al-Biruni Earth Radius Algorithm for Feature Selection and Pipeline Regression Model

Gaber,

Zaki,

Eid

et al. 2024

Journal of Artificial Intelligence in Engineering Practice

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show abstract

“…It is inspired by the structure of biological neurons that receive signals from other neurons via interconnections [60,61]. It has been frequently applied in the geosciences [17][18][19][20][21][22][23]25,27,29,31].…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

“…Supervised machine learning models have been applied to marine geology [17][18][19][20][21][22] and geophysics [23,24], geochemistry [25,26], and gas hydrate [26][27][28][29] datasets to predict different physical properties. For example, Graw et al [30] used the random forest algorithm to predict global seafloor sediment bulk density using core measurements acquired with scientific ocean drilling programs.…”

Section: Introductionmentioning

confidence: 99%

“…Sain and Kumar [31] used artificial neural networks to interpret subsurface geological features with a combination of seismic attributes. Similarly, Farfour, and Mesbah; Ismail et al; and Ramya et al [17][18][19] used artificial neural networks to interpret subsurface features such as gas chimneys, channels, and hydrocarbon-saturated rocks using marine seismic data. Dumke and Berndt [32] used V p logs, local geological information (such as water depth and distance to the basement), and the random forest algorithm to predict subseafloor V p trends worldwide.…”

Section: Introductionmentioning

confidence: 99%

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Estimating Compressional Velocity and Bulk Density Logs in Marine Gas Hydrates Using Machine Learning

Naim,

Cook,

Moortgat

2023

Energies

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Compressional velocity (Vp) and bulk density (ρb) logs are essential for characterizing gas hydrates and near-seafloor sediments; however, it is sometimes difficult to acquire these logs due to poor borehole conditions, safety concerns, or cost-related issues. We present a machine learning approach to predict either compressional Vp or ρb logs with high accuracy and low error in near-seafloor sediments within water-saturated intervals, in intervals where hydrate fills fractures, and intervals where hydrate occupies the primary pore space. We use scientific-quality logging-while-drilling well logs, gamma ray, ρb, Vp, and resistivity to train the machine learning model to predict Vp or ρb logs. Of the six machine learning algorithms tested (multilinear regression, polynomial regression, polynomial regression with ridge regularization, K nearest neighbors, random forest, and multilayer perceptron), we find that the random forest and K nearest neighbors algorithms are best suited to predicting Vp and ρb logs based on coefficients of determination (R2) greater than 70% and mean absolute percentage errors less than 4%. Given the high accuracy and low error results for Vp and ρb prediction in both hydrate and water-saturated sediments, we argue that our model can be applied in most LWD wells to predict Vp or ρb logs in near-seafloor siliciclastic sediments on continental slopes irrespective of the presence or absence of gas hydrate.

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Distribution and characteristics of gas chimneys in the passive margin offshore SW Taiwan

Han¹,

Chen²,

Liu

2021

Mar Geophys Res

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Gas chimney and hydrocarbon detection using combined BBO and artificial neural network with hybrid seismic attributes

Cited by 13 publications

References 28 publications

Optimizing Marketing Strategies: Integration of Al-Biruni Earth Radius Algorithm for Feature Selection and Pipeline Regression Model

Optimizing Marketing Strategies: Integration of Al-Biruni Earth Radius Algorithm for Feature Selection and Pipeline Regression Model

Estimating Compressional Velocity and Bulk Density Logs in Marine Gas Hydrates Using Machine Learning

Distribution and characteristics of gas chimneys in the passive margin offshore SW Taiwan

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