Identification of the typical frequency range associated with subsurface gas zones: a study using Hilbert-Huang transform and wavelet analysis

Jayaswal, Vaibhav; Gairola, Gaurav Siddharth; Chandrasekhar, E.

doi:10.1007/s12517-021-06606-5

Cited by 5 publications

(1 citation statement)

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“…e WA has developed a powerful tool for converting a signal into a stationary signal with specific effectiveness. ere are many articles in the literature in which different hybrid models with wavelet decomposition are used to predict different kinds of nonlinear and nonstationary time-series data [18,19]. Singh et al [19] proposed hybrid models based on the discrete wavelet decomposition which can partition the data into series.…”

Section: Introductionmentioning

confidence: 99%

Improvement towards Prediction Accuracy of Principle Mineral Resources Using Threshold

Qurban

Almazah

Nazir³

et al. 2022

Mathematical Problems in Engineering

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The production data of mineral resources are noisy, nonstationary, and nonlinear. Therefore, some techniques are required to address the problem of nonstationarity and complexity of noises in it. In this paper, two hybrid models (EMD-CEEMDAN-EBT-MM and WA-CEEMDAN-EBT-MM) flourish to improve mineral production prediction. First, we use empirical mode decomposition (EMD) and wavelet analysis (WA) to denoise the data. Second, ensemble empirical mode decomposition (EEMD) and complete ensemble empirical mode decomposition (CEEMDAN) are used for the decomposition of nonstationary data into intrinsic mode function (IMF). Then, empirical Bayesian threshold (EBT) is applied on noise dominant IMFs to consolidate noises, which are further used as input in the data-driven model. Next, other noise-free IMFs are used in the stochastic model as input for the prediction of minerals. At last, the predicted IMFs are ensemble for final prediction. The proposed strategy is exemplified using Pakistan's four major mineral resources. To measure the prediction performance of all the models, three methods, that is, mean relative error, mean square error, and mean absolute percentage error, are used. Our proposed framework WA-CEEMDAN-EBT-MM has shown improvement with minimum mean absolute percentage error value compared to other existing models in prediction accuracy for all four minerals. Therefore, our proposed strategy can predict the noisy and nonstationary time-series data with an efficient mechanism. Hence, it will be helpful to the policymakers for making policies and planning in mineral resource management.

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Section: Introductionmentioning

confidence: 99%