Volcanic Tremor Extraction and Earthquake Detection Using Music Information Retrieval Algorithms

2021 ◽  
Author(s):  
Zahra Zali ◽  
Matthias Ohrnberger ◽  
Frank Scherbaum ◽  
Fabrice Cotton ◽  
Eva P. S. Eibl
Keyword(s):  
Amplitude Spectrum ◽  
Volcanic Tremor ◽  
Volcanic Eruptions ◽  
Detection Algorithm ◽  
Transient Signal ◽  
Volcanic Origin ◽  
Time Frequency ◽  
Reconstructed Signal ◽  
Single Station ◽  
Transient Events

Abstract Volcanic tremor signals are usually observed before or during volcanic eruptions and must be monitored to evaluate the volcanic activity. A challenge in studying seismic signals of volcanic origin is the coexistence of transient signal swarms and long-lasting volcanic tremor signals. Separating transient events from volcanic tremors can, therefore, contribute to improving upon our understanding of the underlying physical processes. Exploiting the idea of harmonic–percussive separation in musical signal processing, we develop a method to extract the harmonic volcanic tremor signals and to detect transient events from seismic recordings. Based on the similarity properties of spectrogram frames in the time–frequency domain, we decompose the signal into two separate spectrograms representing repeating (harmonic) and nonrepeating (transient) patterns, which correspond to volcanic tremor signals and earthquake signals, respectively. We reconstruct the harmonic tremor signal in the time domain from the complex spectrogram of the repeating pattern by only considering the phase components for the frequency range in which the tremor amplitude spectrum is significantly contributing to the energy of the signal. The reconstructed signal is, therefore, clean tremor signal without transient events. Furthermore, we derive a characteristic function suitable for the detection of transient events (e.g., earthquakes) by integrating amplitudes of the nonrepeating spectrogram over frequency at each time frame. Considering transient events like earthquakes, 78% of the events are detected for signal-to-noise ratio = 0.1 in our semisynthetic tests. In addition, we compared the number of detected earthquakes using our method for one month of continuous data recorded during the Holuhraun 2014–2015 eruption in Iceland with the bulletin presented in Ágústsdóttir et al. (2019). Our single station event detection algorithm identified 84% of the bulletin events. Moreover, we detected a total of 12,619 events, which is more than twice the number of the bulletin events.

Automatic transient signal detection and volcanic tremor extraction using music information retrieval strategies

2020 ◽  
Author(s):  
Zahra Zali ◽  
Frank Scherbaum ◽  
Matthias Ohrnberger ◽  
Fabrice Cotton
Keyword(s):  
Signal Processing ◽  
Volcanic Tremor ◽  
Transient Signal ◽  
Volcano Seismology ◽  
Generation Process ◽  
Transient Signals ◽  
Transient Event ◽  
Time Frequency ◽  
Seismic Waveform ◽  
Transient Events

<p>Volcanic tremor is one of the most important signal in volcano seismology because of its potential to be a tool for forecasting eruptions and better understanding of underlying volcanic process. Despite different suggested mechanisms for volcanic tremor generation, the exact process of that is not well understood yet. This signal usually comes along with large number of earthquakes happening during unrest period that affect the shape and amplitude of tremor. A delicate signal processing is required to separate earthquakes and other transient signals from seismic waveform to derive a time series of volcanic tremor which can provide a new insight into tremor source investigations. Exploiting the idea of harmonic and percussive separation in musical signal processing we have developed a method to extract volcanic tremor and transient events from the seismic signal. By using the concept of periodicity as underlying generation process of tremor, we are able to extract the volcanic tremor signal based on the self similarity properties of spectra in time-frequency domain. The separation process results in two spectrograms representing repeating (long-lasting) and non-repeating (short-lived) patterns.</p><p>From the spectrogram of the repeating pattern we reconstruct the signal in time domain by adding the original spectrogram’s phase information, thus creating an modified version of the long-lasting tremor signal.</p><p>Further, we can derive a characteristic function for transient events by integrating the amplitude of the non-repeating spectrogram in each time frame. This function has non zero value in transient event instances and zero value in time periods devoid of such events. Considering transient events as earthquakes we apply an onset detector to time first arrivals of the transient signal by using the slope of the function. First we determine local maxima of the function showing good correspondence to even the tiniest transient signals. From the peak locations we calculate the slope of each point within a period of 6 seconds preceding each peak. The uncertainty of positive P peaks is up to 0.32 seconds which is equal to the hope size of the calculated spectrogram. The advantage of timing earthquakes through this method is the ability of detecting very low seismic events, although due to the small window size of short time Fourier transform the process is time consuming. The result of this study is promising, while further testing is on-going to validate the method as well as determine applications and limitations.</p>

Volcanic Tremor and its Relation to Volcano- and Glacier-related Processes

2021 ◽  
Author(s):  
Eva P. S. Eibl
Keyword(s):  
Early Warning ◽  
Volcanic Tremor ◽  
Volcanic Eruptions ◽  
Seismic Signal ◽  
Magma Flow ◽  
Rotational Seismology ◽  
Multidisciplinary Data ◽  
Single Station ◽  
Glacial Environments ◽  
Remote Sites

<p>Volcanic eruptions can affect the climate system, the environment and society. On ice covered volcanoes this threat intensifies due to the increasing explosivity in contact with water. Monitoring and early-warning of such eruptions is closely linked to real-time, multidisciplinary data analysis. This builds on a good understanding and location of the recorded signals.</p><p>I will summarize my work on understanding and modelling volcanic tremor, a long-lasting seismic signal with emergent onset. This tremor accompanies various volcano- and glacier-related processes and has to be reliably detected and distinguished from other sources. My examples range from modelling pre-eruptive subglacial tremor and silent magma flow, to monitoring eruptive tremor, to early warning of subglacial flooding, to hydrothermal explosions and boiling and other sources such as helicopters. These results are based on array analysis, amplitude location techniques and single-station arrays but I will also risk a look into the future embracing the emerging field of rotational seismology which might solve some challenges we face in volcanic and glacial environments and advance our understanding and modelling of volcanic signals at remote sites.</p>

Transient ULF pulsations: time dependence of magnetic fields observed at the ground

1995 ◽  
Vol 13 (9) ◽  
pp. 938-945 ◽  
Author(s):  
W. Allan
Keyword(s):  
Time Dependence ◽  
Least Squares ◽  
Analytical Signal ◽  
Basic Feature ◽  
Ionospheric Current ◽  
Time Frequency ◽  
Least Squares Fitting ◽  
Single Station ◽  
Damping Rate ◽  
Transient Events

Abstract. The continuum oscillation of a latitudinal range of closed geomagnetic field lines or shells appears to be a basic feature of the magnetosphere. Such oscillations are observed at the ground, and have been termed transient ULF pulsations. Earlier modelling showed that the apparent mean damping rate at the ground should be much greater than that in the magnetosphere. This modelling is extended to examine the time dependence of the magnetic field of transient pulsations as seen by a latitudinal chain of magnetometers. It is found that there should be significant temporal variation of both period and damping decrement observed at a given latitude, which could help to identify transient events even when the period variation with latitude is not obvious. Time-frequency analysis and analytical signal analysis do not seem to be effective in determining temporal parameter variation for the short, highly damped data segments typical of transient events. Least squares fitting of two decaying sinusoids gives surprisingly good results, but seems to have no physical basis, is difficult to interpret, and may be misleading. Least squares fitting of a single sinusoid with time-varying period and damping rate gives reasonably good fits. The resulting parameter variations with latitude may help to determine the structures of ionospheric current systems associated with transient ULF events. In particular, the time change of the period at a single station can determine where that station is relative to the ionospheric current maximum.

scholarly journals Detection of Ventricular Fibrillation Based on Ballistocardiography by Constructing an Effective Feature Set

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3524
Author(s):  
Rongru Wan ◽  
Yanqi Huang ◽  
Xiaomei Wu
Keyword(s):  
Ventricular Fibrillation ◽  
Cross Validation ◽  
Rhythmic Activity ◽  
Detection Algorithm ◽  
Motion Artifacts ◽  
Cardiac Monitoring ◽  
Mechanical Activity ◽  
Time Frequency ◽  
S Transform ◽  
Fatal Arrhythmia

Ventricular fibrillation (VF) is a type of fatal arrhythmia that can cause sudden death within minutes. The study of a VF detection algorithm has important clinical significance. This study aimed to develop an algorithm for the automatic detection of VF based on the acquisition of cardiac mechanical activity-related signals, namely ballistocardiography (BCG), by non-contact sensors. BCG signals, including VF, sinus rhythm, and motion artifacts, were collected through electric defibrillation experiments in pigs. Through autocorrelation and S transform, the time-frequency graph with obvious information of cardiac rhythmic activity was obtained, and a feature set of 13 elements was constructed for each 7 s segment after statistical analysis and hierarchical clustering. Then, the random forest classifier was used to classify VF and non-VF, and two paradigms of intra-patient and inter-patient were used to evaluate the performance. The results showed that the sensitivity and specificity were 0.965 and 0.958 under 10-fold cross-validation, and they were 0.947 and 0.946 under leave-one-subject-out cross-validation. In conclusion, the proposed algorithm combining feature extraction and machine learning can effectively detect VF in BCG, laying a foundation for the development of long-term self-cardiac monitoring at home and a VF real-time detection and alarm system.

scholarly journals Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1248
Author(s):  
Rafia Nishat Toma ◽  
Cheol-Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Fault Classification ◽  
Original Signal ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal

Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of vibration signals, it is quite difficult to achieve high classification accuracy when directly using the original signal as the input of a convolution neural network. To evaluate the fault characteristics, ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration signal is converted into a 2-D image using a continuous wavelet transform with information from the damage frequency band. This also transfers the signal into a time-frequency domain and reduces the nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions, which possess a discriminative pattern relative to the types of faults, are used to train an appropriate CNN model. Additionally, with the reconstructed signal, two different methods are used to create an image to compare with our proposed image creation approach. The vibration signal is collected from a self-designed testbed containing multiple bearings of different fault conditions. Two other conventional CNN architectures are compared with our proposed model. Based on the results obtained, it can be concluded that the image generated with fault signatures not only accurately classifies multiple faults with CNN but can also be considered as a reliable and stable method for the diagnosis of fault bearings.

scholarly journals New Time-Frequency Transient Features for Nonintrusive Load Monitoring

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1437
Author(s):  
Mahfoud Drouaz ◽  
Bruno Colicchio ◽  
Ali Moukadem ◽  
Alain Dieterlen ◽  
Djafar Ould-Abdeslam
Keyword(s):  
Transient Signal ◽  
Learning Tools ◽  
Time Frequency ◽  
Energy Measure ◽  
Load Monitoring ◽  
Feature Based ◽  
Stockwell Transform ◽  
Processing Techniques ◽  
Equipment Laboratory ◽  
New Time

A crucial step in nonintrusive load monitoring (NILM) is feature extraction, which consists of signal processing techniques to extract features from voltage and current signals. This paper presents a new time-frequency feature based on Stockwell transform. The extracted features aim to describe the shape of the current transient signal by applying an energy measure on the fundamental and the harmonic frequency voices. In order to validate the proposed methodology, classical machine learning tools are applied (k-NN and decision tree classifiers) on two existing datasets (Controlled On/Off Loads Library (COOLL) and Home Equipment Laboratory Dataset (HELD1)). The classification rates achieved are clearly higher than that for other related studies in the literature, with 99.52% and 96.92% classification rates for the COOLL and HELD1 datasets, respectively.

An automatic phase picker for local and teleseismic events

1987 ◽  
Vol 77 (4) ◽  
pp. 1437-1445
Author(s):  
M. Baer ◽  
U. Kradolfer
Keyword(s):  
Arrival Time ◽  
Detection Algorithm ◽  
Dynamic Threshold ◽  
Data Set ◽  
Automatic Phase ◽  
Nonlinear Amplifier ◽  
First Motion ◽  
Transient Events ◽  
Teleseismic Events ◽  
Automatic Detection Algorithm

Abstract An automatic detection algorithm has been developed which is capable of time P-phase arrivals of both local and teleseismic earthquakes, but rejects noise bursts and transient events. For each signal trace, the envelope function is calculated and passed through a nonlinear amplifier. The resulting signal is then subjected to a statistical analysis to yield arrival time, first motion, and a measure of reliability to be placed on the P-arrival pick. An incorporated dynamic threshold lets the algorithm become very sensitive; thus, even weak signals are timed precisely. During an extended performance evaluation on a data set comprising 789 P phases of local events and 1857 P phases of teleseismic events picked by an analyst, the automatic picker selected 66 per cent of the local phases and 90 per cent of the teleseismic phases. The accuracy of the automatic picks was “ideal” (i.e., could not be improved by the analyst) for 60 per cent of the local events and 63 per cent of the teleseismic events.

Detection of EEG-Based Eye-Blinks Using A Thresholding Algorithm

2021 ◽  
Vol 6 (4) ◽  
pp. 6-12
Author(s):  
Dang-Khoa Tran ◽  
Thanh-Hai Nguyen ◽  
Thanh-Nghia Nguyen
Keyword(s):  
Detection Algorithm ◽  
Time Frequency ◽  
Eye Blink ◽  
Eye Blinks ◽  
User Training ◽  
Electrical Potentials ◽  
Ocular Artifact ◽  
Blink Detection ◽  
Thresholding Algorithm ◽  
Frequency Properties

In the electroencephalography (EEG) study, eye blinks are a commonly known type of ocular artifact that appears most frequently in any EEG measurement. The artifact can be seen as spiking electrical potentials in which their time-frequency properties are varied across individuals. Their presence can negatively impact various medical or scientific research or be helpful when applying to brain-computer interface applications. Hence, detecting eye-blink signals is beneficial for determining the correlation between the human brain and eye movement in this paper. The paper presents a simple, fast, and automated eye-blink detection algorithm that did not require user training before algorithm execution. EEG signals were smoothed and filtered before eye-blink detection. We conducted experiments with ten volunteers and collected three different eye-blink datasets over three trials using Emotiv EPOC+ headset. The proposed method performed consistently and successfully detected spiking activities of eye blinks with a mean accuracy of over 96%.

scholarly journals Satellite-based detection of volcanic sulphur dioxide from recent eruptions in Central and South America

2008 ◽  
Vol 14 ◽  
pp. 35-40 ◽  
Author(s):  
D. Loyola ◽  
J. van Geffen ◽  
P. Valks ◽  
T. Erbertseder ◽  
M. Van Roozendael ◽  
...  
Keyword(s):  
Sulphur Dioxide ◽  
Fine Particles ◽  
Local Population ◽  
Volcanic Eruptions ◽  
Quantitative Information ◽  
Visible Spectral Range ◽  
Trace Gas ◽  
Detection Scheme ◽  
Volcanic Origin ◽  
Rock Fragments

Abstract. Volcanic eruptions can emit large amounts of rock fragments and fine particles (ash) into the atmosphere, as well as several gases, including sulphur dioxide (SO2). These ejecta and emissions are a major natural hazard, not only to the local population, but also to the infrastructure in the vicinity of volcanoes and to aviation. Here, we describe a methodology to retrieve quantitative information about volcanic SO2 plumes from satellite-borne measurements in the UV/Visible spectral range. The combination of a satellite-based SO2 detection scheme and a state-of-the-art 3D trajectory model enables us to confirm the volcanic origin of trace gas signals and to estimate the plume height and the effective emission height. This is demonstrated by case-studies for four selected volcanic eruptions in South and Central America, using the GOME, SCIAMACHY and GOME-2 instruments.

Abstract 309: A Novel Method to Predict Survival From Ventricular Fibrillation Arrest During Chest Compressions

Circulation ◽  
2018 ◽  
Vol 138 (Suppl_2) ◽  
Author(s):  
Jason Coult ◽  
Lawrence D Sherman ◽  
Jennifer Blackwood ◽  
Heemun Kwok ◽  
Peter J Kudenchuk ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Ventricular Fibrillation ◽  
Characteristic Curve ◽  
Amplitude Spectrum ◽  
Support Vector ◽  
Chest Compressions ◽  
Time Frequency ◽  
Model Combining ◽  
Spectrum Area ◽  
Care Guidelines

Background: Quantitative measures of the ventricular fibrillation (VF) electrocardiogram (ECG) such as Amplitude Spectrum Area (AMSA) assess myocardial physiology and predict cardiac arrest outcomes, offering the potential to guide resuscitation care. Guidelines recommend minimally-interrupted chest compressions (CCs) during resuscitation, but CCs corrupt the ECG and must be paused for analysis. We therefore sought to develop a novel measure to predict survival without requiring CC pause. Methods and Results: Five-second VF ECG segments were collected with CCs and without CCs prior to 2755 defibrillation shocks in 1151 patients with out-of-hospital cardiac arrest. The cohort was divided into a training set to develop the measure and a test set to evaluate performance. Using segments from 460 training patients, we designed an adaptive filter to remove CC artifacts based on chest impedance and ECG characteristics, derived novel time-frequency and amplitude features of the filtered VF ECG, and trained a Support Vector Machine (SVM) model combining these novel features to predict survival with favorable neurologic status. In 691 test cases, area under the receiver operating characteristic curve (AUC) for predicting survival using the SVM was 0.74 (95% CI: 0.71-0.77) with CCs and 0.74 (95% CI: 0.71-0.76) without CCs (Figure 1). By comparison, AUC for predicting survival using AMSA was 0.70 (95% CI: 0.67-0.73) with CCs (p=0.001 for difference versus SVM) and 0.73 (95% CI: 0.71-0.76) without CCs (p=0.68 for difference versus SVM). Conclusions: VF waveform measures such as AMSA predict functional survival when obtained during ongoing CCs, but prognostic performance is reduced compared to CC-free analysis. However, an SVM-based measure combining novel VF waveform features enabled similar prediction with and without CCs. Machine learning combinations of features optimized for use during CCs may thus afford a means for VF prognosis during uninterrupted CCs.

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