Wavelet analysis in determination of reservoir fluid contacts

Heidary, Mohammad; Javaherian, Abdolrahim

doi:10.1016/j.cageo.2012.10.002

Cited by 10 publications

(2 citation statements)

References 7 publications

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“…Wavelet method is a powerful tool to detect multiscale and localized features of time-series data for transient processes. Wavelet coherence has been used to distinguish the localized and scale-specific relationship embedded in nonstationary time-series data sets observed in various scientific disciplines such as geochemistry (Chen and Cheng 2016), geophysics (Heidary and Javaherian 2013), and hydrology (Pellegrini et al 2012;Carey et al 2013;Graf et al 2014;Song et al 2018). These studies usually considered only one influencing factor on the dynamic process, although the combination of influencing factors may exist.…”

Section: Introductionmentioning

confidence: 99%

Multiple Wavelet Coherence to Evaluate Local Multivariate Relationships in a Groundwater System

Sun

Zhang

et al. 2021

Groundwater

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Groundwater level fluctuations are affected by surface properties due to complex correlations of groundwatersurface water interaction and/or other surface processes, which are usually hard to be accurately quantified. Previous studies have assessed the relationship between groundwater level fluctuations and specific controlling factors. However, few studies have been conducted to explore the impact of the combination of multiple factors on the groundwater system. Hence, this paper tries to explore the localized and scale-specific multivariate relationships between the groundwater level and controlling factors (such as hydrologic and meteorological factors) using bivariate wavelet coherence and multiple wavelet coherence. The groundwater level fluctuations of two wells in areas covered by different plant densities (i.e., the riparian zone of the Colorado River, USA) are analyzed. Main findings include three parts. First, barometric pressure and river stage are the best factors to interpret the groundwater level fluctuations at small scales (<1 day) and large scales (>1 day) at the well of low-density plants stand, respectively. Second, at the well of high-density plants stand, the best predictors to control the groundwater level fluctuations include barometric pressure (<1 day), the combination of barometric pressure and temperature (1-7 days), temperature (7-30 days), and the combination of barometric pressure, temperature, and river stage (>30 days). The best predictor of groundwater head fluctuations depends on the variance of the vegetation coverage and hydrological processes. Third, these results provide a suite of factors to explain the groundwater level variations, which is an important topic in water-resource prediction and management.

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Section: Introductionmentioning

confidence: 99%

Multiple Wavelet Coherence to Evaluate Local Multivariate Relationships in a Groundwater System

Sun

Zhang

et al. 2021

Groundwater

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“…Hence, WA based AI models firstly decompose a time series into several multi-resolution frequency components and then these components are respectively predicted in the AI models with higher prediction performance 13,14 . Recently, WA-AI models have been effectively introduced to the areas of reservoir fluid contacts prediction 26 , stream flow data series prediction 14 and seasonal variation of landslide displacement 27 . This study also introduces WA into AI methods for non-linear and non-stationary cumulative landslide displacements prediction.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of landslide displacements using a chaos theory based wavelet analysis-Volterra filter model

Sun

Yin

et al. 2019

Sci Rep

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Landslide displacement time series can directly reflects landslide deformation and stability characteristics. Hence, forecasting of the non-linear and non-stationary displacement time series is necessary and significant for early warning of landslide failure. Traditionally, conventional machine learning methods are adopted as forecasting models, these forecasting models mainly determine the input and output variables experientially and does not address the non-stationary characteristics of displacement time series. However, it is difficult for these conventional machine learning methods to obtain appropriate input-output variables, to determine appropriate model parameters and to acquire satisfied prediction performance. To deal with these drawbacks, this study proposes the wavelet analysis (WA) to decompose the displacement time series into low- and high-frequency components to address the non-stationary characteristics; then proposes thee chaos theory to obtain appropriate input-output variables of forecasting models, and finally proposes Volterra filter model to construct the forecasting model. The GPS monitoring cumulative displacement time series, recorded on the Shuping and Baijiabao landslides, distance measuring equipment monitoring displacements on the Xintan landslide in Three Gorges Reservoir area of China, are used as test data of the proposed chaotic WA-Volterra model. The chaotic WA-support vector machine (SVM) model and single chaotic Volterra model without WA method, are used as comparisons. The results show that there are chaos characteristics in the GPS monitoring displacement time series, the non-stationary characteristics of landslide displacements are captured well by the WA method, and the model input-output variables are selected suitably using chaos theory. Furthermore, the chaotic WA-Volterra model has higher prediction accuracy than the chaotic WA-SVM and single chaotic Volterra models.

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Joint principal component: wavelet analysis of atmospheric teleconnection: the North Atlantic Oscillation case

Khokhlov

Romanova

2013

Stoch Environ Res Risk Assess

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Wavelet analysis in determination of reservoir fluid contacts

Cited by 10 publications

References 7 publications

Multiple Wavelet Coherence to Evaluate Local Multivariate Relationships in a Groundwater System

Multiple Wavelet Coherence to Evaluate Local Multivariate Relationships in a Groundwater System

Forecasting of landslide displacements using a chaos theory based wavelet analysis-Volterra filter model

Joint principal component: wavelet analysis of atmospheric teleconnection: the North Atlantic Oscillation case

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