Seismic random noise attenuation using artificial neural network and wavelet packet analysis

Kimiaefar, Roohollah; Siahkoohi, Hamid Reza; Hajian, Alireza; Kalhor, Ahmad

doi:10.1007/s12517-015-2067-1

Cited by 18 publications

(5 citation statements)

References 11 publications

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“…In recent years, the soft computing method, especially the artificial neural network method [20][21][22][23][24], has been widely used in geotechnical engineering to determine the bearing capacity of shallow pile foundations, the settlement and stability of soil slopes and the behavior, such as compressibility parameters, in actual solutions. In addition, in the past 20 years, researchers have tried to use different artificial intelligence methods to predict the liquefaction potential of soil.…”

Section: Introductionmentioning

confidence: 99%

A Threshold Model of Tailings Sand Liquefaction Based on PSO-SVM

et al. 2022

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The liquefaction of tailings sand caused by seismic loads is a major problem in ensuring the safety of tailings ponds. Liquefaction may cause uncontrolled fluidized failure of the dam body, causing considerable damage to the lives, property and environment of people downstream. In this paper, a prototype tailings sand is used as the material to consider the main factors affecting liquefaction (i.e., dynamic load, soil quality, burial and static conditions). By embedding acceleration, pore pressure and earth pressure sensors in the rigid design of the self-designed rigid model box, different types of seismic waves of different ground motion amplitudes (PGA) were induced in a shaking table test of tailings sand liquefaction. The seismic intensity, waveform (class II, III and IV seismic waves) and active earth pressure of the PGA characterizing dynamic factors were obtained, and the static factors were characterized. The dynamic shear stress ratio, the peak acceleration of the earthquake, the pore pressure of the drainage factor and the buried depth (overlying effective pressure) characterize the soil conditions. SPSS software was used to analyze the factor dimension reduction, and the most suitable factors for factor analysis were obtained. Particle Swarm Optimization (PSO) was used to optimize the parameters, and the improved PSO-SVM algorithm was compared with the existing genetic algorithm (GA) and grid node search (GS). The algorithm used in this paper is fast and has a relatively high accuracy rate of 92.7%. The established threshold model method is of great significance to predict the liquefaction of tailings sand soil under the action of ground motions and to carry out safety managemenin advance, which can provide a certain reference for the project.

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Section: Introductionmentioning

confidence: 99%

A Threshold Model of Tailings Sand Liquefaction Based on PSO-SVM

et al. 2022

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“…Learning-based denoising approaches are emerging in the form of deep neural networks to remove background random noise from earthquake signals [17], [9]. However, these approaches require a large amount of labeled training data in the form of recordings or seismograms.…”

Section: Introductionmentioning

confidence: 99%

Automated Platform for Microseismic Signal Analysis: Denoising, Detection, and Classification in Slope Stability Studies

Stanković

Pytharouli

2021

IEEE Trans. Geosci. Remote Sensing

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Microseismic monitoring has been increasingly used in the past two decades to illuminate (sub)surface processes such as landslides, due to its ability to record small seismic waves generated by soil movement and/or brittle behaviour of rock. Understanding the evolution of landslide processes is of paramount importance in predicting or even avoiding an imminent failure. Microseismic monitoring recordings are often continuous, noisy and consist of signals emitted by various sources. Manually detecting and distinguishing the signals emitted by an unstable slope is challenging. Research on automated end-to-end denoising, detection, and classification of microseismic events, as an early warning system, is still in its infancy. To this effect, our work is focused on jointly evaluating and developing suitable approaches for signal denoising, accurate event detection, non site-specific feature construction, feature selection and event classification. We propose an automated end-to-end system that can process big data sets of continuous seismic recordings fast and demonstrate applicability and robustness to a wide range of events (distant and local earthquakes, slidequakes, anthropogenic noise etc.). Algorithmic contributions lie in novel signal processing and analysis methods with fewer tunable parameters than the state of the art, evaluated on two field datasets and benchmarked against the state of the art.

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“…Zhu et al [18] investigated the energy distribution characteristics for two common types of microseismic signals (i.e., blasting-induced and roof-breakinduced signals) in mines using WPT and proposed a quantitative method for distinguishing the two signals. e authors in [19] employed artificial neural network and WPT to suppress random noise in seismic signals. e authors in [20] applied HHT to seismic reflection data to identify the instantaneous attributes in order to develop a superior filter for enhancing the signal-to-noise ratio.…”

Section: Introductionmentioning

confidence: 99%

A Combined Denoising Method for Microseismic Signals from Coal Seam Hydraulic Fracturing: Multithreshold Wavelet Packet Transform and Improved Hilbert‐Huang Transform

Wen

Zhu

et al. 2021

Shock and Vibration

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Coal seam hydraulic fracturing (CSHF) has recently been applied to mitigate frequent regional rockburst risk in deep mines before mining practice, as an effective substitute for conventional labor-intensive and time-consuming rockburst prevention measures. Due to the complex nature of CSHF microseismic signals—e.g., nonstationary, transient, and low signal-to-noise ratio—conventional denoising methods tend to yield undesirable results that may preclude reliable evaluation of hydraulic fracturing performance using microseismic data. We propose an advanced denoising method MWPT-IHHT to achieve twice denoising in a fine and adaptive manner. This method combines a multithreshold wavelet packet transform (MWPT) and an improved Hilbert-Huang transform (IHHT), with each being improved compared to their conventional counterparts. A quantitative comparison using synthetic signals suggests the outperformance of the proposed method over the commonly used denoising methods in suppressing noises in terms of signal-to-noise ratio, signal similarity, and energy percentage. The desirable denoising results of two typical real CSHF signals in a CSHF test at Huafeng Coal Mine further demonstrate the applicability and effectiveness of the proposed MWPT-IHHT method.

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Seismic random noise attenuation using artificial neural network and wavelet packet analysis

Cited by 18 publications

References 11 publications

A Threshold Model of Tailings Sand Liquefaction Based on PSO-SVM

A Threshold Model of Tailings Sand Liquefaction Based on PSO-SVM

Automated Platform for Microseismic Signal Analysis: Denoising, Detection, and Classification in Slope Stability Studies

A Combined Denoising Method for Microseismic Signals from Coal Seam Hydraulic Fracturing: Multithreshold Wavelet Packet Transform and Improved Hilbert‐Huang Transform

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