Automatic segmentation of seismic signal with support of innovative filtering

Hossa, R.; Makowski, Ryszard; Zimroz, Radosław

doi:10.1016/j.ijrmms.2016.11.003

Cited by 9 publications

(7 citation statements)

References 33 publications

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“…Secondly, data distributions within segments are modeled with smoothed histograms to visually observe the differences between the classes. In this work segmentation has been done manually, however in future work we plan to do segmentation automatically [1,2,[12][13][14].…”

Section: Methodsmentioning

confidence: 99%

Mobile based vibration monitoring and its application to road quality monitoring in deep underground mine

Zimoz¹,

Wodecki²,

Hebda-Sobkowicz³

et al. 2018

Vib. proced.

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Road quality is an important issue in everyday life for all car owners. This issue seems to be critically important in underground mines, where LHD machines are used for material transport. One of the biggest problems for LHD operation is relatively quick tires degradation. One of possible reasons might be road surface quality, indeed. However, driver's skills as well as ways of machine operation (loading, acceleration, breaking...) might also play a crucial role. Nowadays, many of machines are equipped with onboard monitoring system that allows to monitor basic parameters (speed, torque, temperatures, pressures etc.) at some predefined components. To complete the picture, we propose to use proposed already (but not for mining applications) vibration measurement for road roughness evaluation. To measure vibration acceleration is relatively easy task (we used simple smartphone here), unfortunately method of parametrization and concluding about road quality is still a challenge in mining case. In this paper we have presented a short communication related to first experimental work and some ideas how to deal with this problem using statistical tools for signal modeling.

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Section: Methodsmentioning

confidence: 99%

Mobile based vibration monitoring and its application to road quality monitoring in deep underground mine

Zimoz¹,

Wodecki²,

Hebda-Sobkowicz³

et al. 2018

Vib. proced.

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show abstract

“…us, in order to acquire onset time, it seems to be reasonable to utilize tools used to solve the isometric problems. e exemplary algorithms solving these parallel issues have been included in the following articles [30][31][32][33][34][35][36][37][38][39][40]. As one might see, the segmentation problem is a fundamental task in the signal processing, and it might be applied to various types of signals.…”

Section: Introductionmentioning

confidence: 99%

“…e STFT time-frequency representation could be considered as a set of signals (we call them subsignals) for each frequency bin Δf which is easier to process due to better signal-to-noise ratio. In the case of very noisy signals, there is a possibility to use preprocessing techniques such as denoising [11] or mentioned segmentation, especially if the signal covers multiple events [31,41].…”

Section: Introductionmentioning

confidence: 99%

Combination of Principal Component Analysis and Time‐Frequency Representation for P‐Wave Arrival Detection

Wodecki

Hebda-Sobkowicz

Mirek

et al. 2019

Shock and Vibration

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Seismic events are phenomena which commonly occur in the mining industry. Due to their dangerous character, such information as the energy of the potential event, the location of hazardous regions with higher seismic activity is considered valuable. However, the acquisition of this information is almost impossible without the ability to detect the onset time of the seismic event. The main objectives of algorithms in finding P-wave are high accuracy, reasonable time of operation, and automatic detection of wave arrival. In this paper, an innovative method which incorporates principal component analysis (PCA) with time-frequency representation of the signal is proposed. Due to the significant difference between the spectra of recorded seismic wave and pure noise which precedes the event, time-frequency representation allows for better accuracy of signal change detection. However, with an additional domain, the complexity rises. Thus, the incorporation of PCA (which is known for high efficiency in lowering data dimensions while maintaining original information) seems to be recommended. In order to show the feasibility of the method, it will be tested on real data originating from monitoring system used in underground mine.

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“…When the sensors are relatively close to the event epicentre the delay between event's onset moments are small thus the indication of arrivals other than P-wave is difficult. The indication of waves arrival may be considered as a case of finding a structural break detection [7][8][9][10]. Recently couple new methods to indicate the S-wave arrival were created.…”

Section: Introductionmentioning

confidence: 99%

Comparison of recent S-wave indicating methods

Hubicka

Sokołowski

2018

E3S Web Conf.

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Abstract. Seismic event consists of surface waves and body waves. Due to the fact that the body waves are faster (P-waves) and more energetic (S-waves) in literature the problem of their analysis is taken more often. The most universal information that is received from the recorded wave is its moment of arrival. When this information is obtained from at least four seismometers in different locations, the epicentre of the particular event can be estimated [1]. Since the recorded body waves may overlap in signal, the problem of wave onset moment is considered more often for faster P-wave than S-wave. This however does not mean that the issue of S-wave arrival time is not taken at all. As the process of manual picking is time-consuming, methods of automatic detection are recommended (these however may be less accurate). In this paper four recently developed methods estimating S-wave arrival are compared: the method operating on empirical mode decomposition and Teager-Kaiser operator [2], the modification of STA/LTA algorithm [3], the method using a nearest neighbour-based approach [4] and the algorithm operating on characteristic of signals' second moments. The methods will be also compared to wellknown algorithm based on the autoregressive model [5]. The algorithms will be tested in terms of their S-wave arrival identification accuracy on real data originating from International Research Institutions for Seismology (IRIS) database.

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Automatic segmentation of seismic signal with support of innovative filtering

Cited by 9 publications

References 33 publications

Mobile based vibration monitoring and its application to road quality monitoring in deep underground mine

Mobile based vibration monitoring and its application to road quality monitoring in deep underground mine

Combination of Principal Component Analysis and Time‐Frequency Representation for P‐Wave Arrival Detection

Comparison of recent S-wave indicating methods

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