Heterogeneity analysis of geophysical well-log data using Hilbert–Huang transform

Gairola, Gaurav Siddharth; Chandrasekhar, E.

doi:10.1016/j.physa.2017.02.029

Cited by 22 publications

(6 citation statements)

References 20 publications

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“…The whole process is an adaptive process, fully data-driven, without the need to define functions in advance. The main steps of EMD are as follows [159][160][161][162][163][164][165]:…”

Section: Theorymentioning

confidence: 99%

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A Review of Global Navigation Satellite System (GNSS)-Based Dynamic Monitoring Technologies for Structural Health Monitoring

et al. 2019

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In the past few decades, global navigation satellite system (GNSS) technology has been widely used in structural health monitoring (SHM), and the monitoring mode has evolved from long-term deformation monitoring to dynamic monitoring. This paper gives an overview of GNSS-based dynamic monitoring technologies for SHM. The review is classified into three parts, which include GNSS-based dynamic monitoring technologies for SHM, the improvement of GNSS-based dynamic monitoring technologies for SHM, as well as denoising and detrending algorithms. The significance and progress of Real-Time Kinematic (RTK), Precise Point Position (PPP), and direct displacement measurement techniques, as well as single-frequency technology for dynamic monitoring, are summarized, and the comparison of these technologies is given. The improvement of GNSS-based dynamic monitoring technologies for SHM is given from the perspective of multi-GNSS, a high-rate GNSS receiver, and the integration between the GNSS and accelerometer. In addition, the denoising and detrending algorithms for GNSS-based observations for SHM and corresponding applications are summarized. Challenges of low-cost and widely covered GNSS-based technologies for SHM are discussed, and problems are posed for future research.

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“…The whole process is an adaptive process, fully data-driven, without the need to define functions in advance. The main steps of EMD are as follows [159][160][161][162][163][164][165]:…”

Section: Theorymentioning

confidence: 99%

“…The EMD decomposition process is shown in Figure 14: [159][160][161] An example of EMD decomposition is shown in Figure 15: The existing body of research on EMD suggests that it is a powerful decomposition tool in time series analysis. Therefore, EMD is usually combined with other methods for data processing.…”

Section: Theorymentioning

confidence: 99%

A Review of Global Navigation Satellite System (GNSS)-Based Dynamic Monitoring Technologies for Structural Health Monitoring

et al. 2019

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“…By integrating the Hilbert spectrum, the energy spectrum could be well obtained, which can be used to analyze the energy distribution of vibration signals varied with time and frequency. It breaks the limitation of the uncertainty principle and can accurately express all kinds of information on the time-frequency plane [22,23].…”

Section: Introductionmentioning

confidence: 99%

Blasting Vibration Signal Transform in Geotechnical Engineering Applied HHT and S Transform for Time-Frequency Analysis

Man

Song

Liu

et al. 2022

Shock and Vibration

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Based on the same blasting vibration signal, the S transform and HHT method have been applied for the time and frequency analysis. Both the S transform and HHT method are analyzed and compared from the point of decomposition path and analysis function. It expressed that these two methods are complete and effective and have the characteristics of adaptive multiresolution. Especially, the time resolution of the HHT method is invariable and its accuracy is high. Moreover, its frequency resolution can be adaptively adjusted with the inherent characteristics of the signal. However, the time and frequency resolution of the S transform are limited by the uncertainty principle, and the resolution improvement is at the cost of reducing the resolution of another. Furthermore, the HHT method has much more adaptability and is superior to S transform in the analysis of nonstationary signals. In advance, the waveform curve and power spectral density of each detonator are extracted, which indicates that it is not scientific to use different segment detonators for the same circle of the auxiliary hole. Under the premise of the same charge quantity, the vibration energy peak value produced by the same segment detonator is less than that of several segments of detonators. Therefore, it is believed that the HHT method has broad application prospects in the study of wave propagation, attenuation law, structural dynamic response characteristics, and blasting vibration damage in rocks.

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“…While wavelet analysis still requires one to choose basis functions, the Hilbert–Huang Transform (HHT) introduced by [ 14 ] is truly data adaptive and therefore, well suited for the analysis of general time series of unknown origin that include non-stationarity. HHT, and its fundamental engine, the Empirical Mode Decomposition (EMD), have become a widely used tool to analyze time series measurements that perform equally well for stationary as for non-stationary signals, e.g., [ 15 , 16 , 17 , 18 ]. Whether or not non-stationary signals should be removed from time series for HVSR processing is still debated between authors who consider it necessary to exclude spikes and transients in microtremors, e.g., [ 19 , 20 , 21 ] who suggest that non-stationary large amplitude noise windows should not be removed, because these can carry subsoil information, potentially improving the correlation between noise and earthquakes in HVSR curves.…”

Section: Introductionmentioning

confidence: 99%

Horizontal-to-Vertical Spectral Ratio of Ambient Vibration Obtained with Hilbert–Huang Transform

Neukirch

García‐Jerez

Villaseñor

et al. 2021

Sensors

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The Horizontal-to-Vertical Spectral Ratio (HVSR) of ambient vibration measurements is a common tool to explore near surface shear wave velocity (Vs) structure. HVSR is often applied for earthquake risk assessments and civil engineering projects. Ambient vibration signal originates from the combination of a multitude of natural and man-made sources. Ambient vibration sources can be any ground motion inducing phenomena, e.g., ocean waves, wind, industrial activity or road traffic, where each source does not need to be strictly stationary even during short times. Typically, the Fast Fourier Transform (FFT) is applied to obtain spectral information from the measured time series in order to estimate the HVSR, even though possible non-stationarity may bias the spectra and HVSR estimates. This problem can be alleviated by employing the Hilbert–Huang Transform (HHT) instead of FFT. Comparing 1D inversion results for FFT and HHT-based HVSR estimates from data measured at a well studied, urban, permanent station, we find that HHT-based inversion models may yield a lower data misfit χ2 by up to a factor of 25, a more appropriate Vs model according to available well-log lithology, and higher confidence in the achieved model.

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Heterogeneity analysis of geophysical well-log data using Hilbert–Huang transform

Cited by 22 publications

References 20 publications

A Review of Global Navigation Satellite System (GNSS)-Based Dynamic Monitoring Technologies for Structural Health Monitoring

A Review of Global Navigation Satellite System (GNSS)-Based Dynamic Monitoring Technologies for Structural Health Monitoring

Blasting Vibration Signal Transform in Geotechnical Engineering Applied HHT and S Transform for Time-Frequency Analysis

Horizontal-to-Vertical Spectral Ratio of Ambient Vibration Obtained with Hilbert–Huang Transform

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