scholarly journals Reply to comment by Vallée et al. on “Earthquake-induced prompt gravity signals identified in dense array data in Japan”

2019 ◽  
Vol 71 (1) ◽  
Author(s):  
Masaya Kimura ◽  
Nobuki Kame ◽  
Shingo Watada ◽  
Makiko Ohtani ◽  
Akito Araya ◽  
...  
Keyword(s):  
Signal Processing ◽  
Time Window ◽  
Statistical Significance ◽  
Low Frequency ◽  
Maximum Amplitude ◽  
P Wave ◽  
Full Account ◽  
Frequency Components ◽  
Input Acceleration ◽  
Broadband Seismograms

AbstractDensity perturbations accompanying seismic waves are expected to generate prompt gravity perturbations preceding the arrival of P-waves. Vallée et al. (Science 358:1164–1168, 2017, https://doi.org/10.1126/science.aao0746) reported the detection of such pre-P-wave signals in broadband seismograms during the 2011 Tohoku-oki earthquake. Kimura et al. (Earth Planets Space 71:27, 2019, https://doi.org/10.1186/s40623-019-1006-x) considered that their detection involved some uncertain points, including a concern regarding their signal processing procedure. Specifically, to remove the instrumental response, Vallée et al. (2017) applied acausal deconvolution to the seismograms truncated at the P-wave arrivals. Generally, acausal deconvolution produces artifacts at the edge of the time window. However, they did not present quantitative assessment whether the detected signals were artifacts due to the signal processing. To avoid this concern, Kimura et al. (2019) employed another procedure that eliminated acausal processes, resulting in the detection of a pre-P-wave signal with a statistical significance of 7σ in stacked broadband seismograms. Subsequently, Vallée et al. (Earth Planets Space 71:51, 2019, https://doi.org/10.1186/s40623-019-1030-x) commented that the procedure employed by Kimura et al. (2019) for the signal detection was inappropriate because it dismissed the low-frequency components of data. Although we admit the loss of low-frequency components in the data in Kimura et al. (2019), Vallée et al. (2019) have not yet provided a full account of the validity of their own procedure. Here, we assessed the validity of the procedure employed by Vallée et al. (2017) by quantitatively evaluating the magnitude of the acausal artifacts. First, we investigated how the input acceleration waveform, having an ideal signal-like shape, was distorted by their procedure. Their acausal deconvolution indeed generated a large-amplitude terminal artifact; however, it was removed by the causal band-pass filtering performed after the deconvolution and consequently became negligible. Next, we constrained the maximum amplitude of the artifact due to the noise in a seismogram and showed that it was sufficiently small compared to the reported signal amplitudes. These results suggest that the signal waveforms seen after their procedure were not artifacts but were representing the input acceleration with sufficient accuracy. Namely, their procedure well functions as a detection method for pre-P-wave signals. In the context of this validation, we replied to the comments of Vallée et al. (2019).

scholarly journals Analytical Derivation of Nonlinear Spectral Effects and 1/f Scaling Artifact in Signal Processing of Real-World Data

2017 ◽  
Vol 29 (7) ◽  
pp. 2004-2020 ◽  
Author(s):  
Claudia Lainscsek ◽  
Lyle E. Muller ◽  
Aaron L. Sampson ◽  
Terrence J. Sejnowski
Keyword(s):  
Signal Processing ◽  
Real World ◽  
Time Series Data ◽  
Time Window ◽  
Low Frequency ◽  
Series Data ◽  
Infinite Length ◽  
Scaling Exponent ◽  
Sampled Data ◽  
Frequency Components

In estimating the frequency spectrum of real-world time series data, we must violate the assumption of infinite-length, orthogonal components in the Fourier basis. While it is widely known that care must be taken with discretely sampled data to avoid aliasing of high frequencies, less attention is given to the influence of low frequencies with period below the sampling time window. Here, we derive an analytic expression for the side-lobe attenuation of signal components in the frequency domain representation. This expression allows us to detail the influence of individual frequency components throughout the spectrum. The first consequence is that the presence of low-frequency components introduces a 1/f[Formula: see text] component across the power spectrum, with a scaling exponent of [Formula: see text]. This scaling artifact could be composed of diffuse low-frequency components, which can render it difficult to detect a priori. Further, treatment of the signal with standard digital signal processing techniques cannot easily remove this scaling component. While several theoretical models have been introduced to explain the ubiquitous 1/f[Formula: see text] scaling component in neuroscientific data, we conjecture here that some experimental observations could be the result of such data analysis procedures.

Iterative P-wave velocity inversion using Markov chain Monte Carlo method

2020 ◽  
Author(s):  
Hyunggu Jun ◽  
Hyeong-Tae Jou ◽  
Han-Joon Kim ◽  
Sang Hoon Lee
Keyword(s):  
Wave Velocity ◽  
Seismic Data ◽  
Velocity Structure ◽  
Low Frequency ◽  
P Wave ◽  
Velocity Analysis ◽  
Seismic Section ◽  
Traveltime Tomography ◽  
P Wave Velocity ◽  
Frequency Components

<p>Imaging the subsurface structure through seismic data needs various information and one of the most important information is the subsurface P-wave velocity. The P-wave velocity structure mainly influences on the location of the reflectors during the subsurface imaging, thus many algorithms has been developed to invert the accurate P-wave velocity such as conventional velocity analysis, traveltime tomography, migration velocity analysis (MVA) and full waveform inversion (FWI). Among those methods, conventional velocity analysis and MVA can be widely applied to the seismic data but generate the velocity with low resolution. On the other hands, the traveltime tomography and FWI can invert relatively accurate velocity structure, but they essentially need long offset seismic data containing sufficiently low frequency components. Recently, the stochastic method such as Markov chain Monte Carlo (McMC) inversion was applied to invert the accurate P-wave velocity with the seismic data without long offset or low frequency components. This method uses global optimization instead of local optimization and poststack seismic data instead of prestack seismic data. Therefore, it can avoid the problem of the local minima and limitation of the offset. However, the accuracy of the poststack seismic section directly affects the McMC inversion result. In this study, we tried to overcome the dependency of the McMC inversion on the poststack seismic section and iterative workflow was applied to the McMC inversion to invert the accurate P-wave velocity from the simple background velocity and inaccurate poststack seismic section. The numerical test showed that the suggested method could successfully invert the subsurface P-wave velocity.</p>

scholarly journals A LSTM-STW and GS-LM Fusion Method for Lithium-Ion Battery RUL Prediction Based on EEMD

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2380 ◽  
Author(s):  
Ling Mao ◽  
Jie Xu ◽  
Jiajun Chen ◽  
Jinbin Zhao ◽  
Yuebao Wu ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
High Frequency ◽  
Short Term Memory ◽  
Time Window ◽  
Low Frequency ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Marquardt Algorithm ◽  
Starting Point ◽  
Frequency Components

To address inaccurate prediction in remaining useful life (RUL) in current Lithium-ion batteries, this paper develops a Long Short-Term Memory Network, Sliding Time Window (LSTM-STW) and Gaussian or Sine function, Levenberg-Marquardt algorithm (GS-LM) fusion batteries RUL prediction method based on ensemble empirical mode decomposition (EEMD). Firstly, EEMD is used to decompose the original data into high-frequency and low-frequency components. Secondly, LSTM-STW and GS-LM are used to predict the high-frequency and low-frequency components, respectively. Finally, the LSTM-STW and GS-LM prediction results are effectively integrated in order to obtain the final prediction of the lithium-ion battery RUL results. This article takes the lithium-ion battery data published by NASA as input. The experimental results show that the method has higher accuracy, including the phenomenon of sudden capacity increase, and is less affected by the prediction starting point. The performance of the proposed method is better than other typical battery RUL prediction methods.

scholarly journals Comparison and Noise Suppression of the Transmitted and Reflected Photoplethysmography Signals

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Suyi Li ◽  
Lijia Liu ◽  
Jiang Wu ◽  
Bingyi Tang ◽  
Dongsheng Li
Keyword(s):  
Real Time ◽  
Time Window ◽  
Median Filter ◽  
Low Frequency ◽  
Measuring Instruments ◽  
Frequency Spectra ◽  
Median Filters ◽  
Compact Size ◽  
Frequency Components

The photoplethysmography (PPG) is inevitably corrupted by many kinds of noise no matter whether its acquisition mode is transmittance or reflectance. To enhance the quality of PPG signals, many studies have made great progress in PPG denoising by adding extra sensors and developing complex algorithms. Considering the reasonable cost, compact size, and real-time and easy implementation, this study proposed a simple real-time denoising method based on double median filters which can be integrated in microcontroller of commercial or portable pulse oximeters without adding extra hardware. First, we used the boundary extension to preserve the signal boundary distortion and designed a first median filter with the time window at approximately 78 ms to eliminate the high-frequency components of the signal. Then, through the second median filter with a time window which was about 780 ms, we estimated the low-frequency components. Finally, we removed the estimated low-frequency components from the signal to obtain the denoised signal. Through comparing the multiple sets of signals under calmly sitting and slightly moving postures, the PPG signals contained noises no matter whether collected by the transmittance-mode or the reflectance-mode. To evaluate the proposed method, we conducted measured, simulated experiments and a strong noisy environment experiment. Through comparing the morphology distortions, frequency spectra, and the signal-to-noise ratios (SNRs), the results showed that the proposed method can suppress noise effectively and preserve the essential morphological features from PPG signals. As a result, the proposed method can enhance the quality of PPG signals and, thus, can contribute to the improvement of the calculation accuracy of the subsequent physiological parameters. In addition, the proposed method could be a good choice to address the real-time noise reduction of portable PPG measuring instruments.

Spatio-temporal characteristics and focal mechanisms of deep low-frequency earthquakes beneath Zao volcano, Japan

2020 ◽  
Author(s):  
Takuma Ikegaya ◽  
Mare Yamamoto
Keyword(s):  
Focal Mechanism ◽  
Low Frequency ◽  
The Other ◽  
P Wave ◽  
Earthquake Catalog ◽  
Small Magnitude ◽  
Temporal Characteristics ◽  
Frequency Components ◽  
Type C ◽  
Spatio Temporal

<p>Deep Low-Frequency earthquakes (DLFs) beneath volcanoes are possible evidence for deep-seated magmatic activities in the crust and uppermost mantle. After the 2011 Tohoku Earthquake (Mw 9.0), the number of DLFs beneath Zao volcano in the northeast Japan started increasing. The hypocenters of these DLFs form two clusters at shallow (20~28 km) and deep (28~38 km) depths. The shallow and deep clusters are located central and lower part of a high Vp/Vs zone, respectively (e.g., Okada et al., 2015), and the fact suggests different fluid involvement and source processes of DLFs at two clusters. In addition, after the activation of DLFs in 2012, increase in shallow ( < 2 km depth) seismicity has been observed since 2013, which implies the interaction of shallow and deep volcanic fluids. However, the small magnitude of DLFs makes it rather difficult to discuss detailed spatio-temporal characteristics of DLFs and focal mechanism of individual DLF. Therefore, in this study, we first detected DLFs using waveform correlation and determine their hypocenter, and then classified DLFs using waveform correlation to reveal the spatio-temporal characteristics and focal mechanism of each event type.</p><p>To detect DLFs, we applied the matched filter method to the continuous three-components waveform data recorded at stations operated by Tohoku Univ., NIED, and JMA. 146 DLFs listed in the JMA unified earthquake catalog between Jan. 2012 and Sept. 2016 were selected as templates. For each newly detected DLF, we estimated the differential arrival times using cross-correlation between the detected DLF and the template having maximum correlation, and determined the relative hypocenter using the master event method. As a result, we determined hypocenters of 1202 DLFs between Jan. 2012 to May 2018, which is about 4 times the number of DLFs listed in the JMA catalog.</p><p>We then classified newly detected DLFs using the hierarchical clustering method based on the waveform correlation, and classified 939 events into seven types (Type A: 241 events, B: 222, C: 295, D: 79, E: 42, F: 37, G: 23). The characteristics of individual waveform types are summarized as follows: Type C shows high frequency components (4-8 Hz) superimposed on the P wave, while the other types only have low frequency components (1-4 Hz); S-wave/P-wave spectral ratio of type C observed at each station shows larger azimuthal variation than that of the other types, and shows maximum peaks in northeast and southwest direction; Type C occurs mainly in the deep cluster while the other types occur in the shallow cluster; The activity of type C started in 2012 and showed rapid increase in 2015, while the other types show similar temporal changes in 2013 and 2016.</p><p>These results of this study suggest fluid transportation in the crust and different dynamic processes at each depth beneath the volcano.</p>

A Signal Processing Pipeline for Noninvasive Imaging of Ventricular Preexcitation

2005 ◽  
Vol 44 (04) ◽  
pp. 508-515 ◽  
Author(s):  
F. Hanser ◽  
B. Pfeifer ◽  
M. Seger ◽  
C. Hintermüller ◽  
R. Modre ◽  
...  
Keyword(s):  
Signal Processing ◽  
Time Window ◽  
Direct Method ◽  
Statistical Significance ◽  
Noninvasive Imaging ◽  
Accessory Pathway ◽  
Automatic Detection ◽  
Peak Detector ◽  
Processing Pipeline ◽  
Ventricular Preexcitation

Summary Objectives: Noninvasive imaging of the cardiac activation sequence in humans could guide interventional curative treatment of cardiac arrhythmias by catheter ablation. Highly automated signal processing tools are desirable for clinical acceptance. The developed signal processing pipeline reduces user interactions to a minimum, which eases the operation by the staff in the catheter laboratory and increases the reproducibility of the results. Methods: A previously described R-peak detector was modified for automatic detection of all possible targets (beats) using the information of all leads in the ECG map. A direct method was applied for signal classification. The algorithm was tuned for distinguishing beats with an adenosine induced AV-nodal block from baseline morphology in Wolff-Parkinson-White (WPW) patients. Furthermore, an automatic identification of the QRS-interval borders was implemented. Results: The software was tested with data from eight patients having overt ventricular preexcitation. The R-peak detector captured all QRS-complexes with no false positive detection. The automatic classification was verified by demonstrating adenosine-induced prolongation of ventricular activation with statistical significance (p <0.001) in all patients. This also demonstrates the performance of the automatic detection of QRS-interval borders. Furthermore, all ectopic or paced beats were automatically separated from sinus rhythm. Computed activation maps are shown for one patient localizing the accessory pathway with an accuracy of 1 cm. Conclusions: The implemented signal processing pipeline is a powerful tool for selecting target beats for noninvasive activation imaging in WPW patients. It robustly identifies and classifies beats. The small beat to beat variations in the automatic QRS-interval detection indicate accurate identification of the time window of interest.

High-Accuracy Discrimination of Blasts and Earthquakes Using Neural Networks With Multiwindow Spectral Data

2020 ◽  
Vol 91 (3) ◽  
pp. 1646-1659 ◽  
Author(s):  
Fajun Miao ◽  
N. Seth Carpenter ◽  
Zhenming Wang ◽  
Andrew S. Holcomb ◽  
Edward W. Woolery
Keyword(s):  
Time Window ◽  
Time Windows ◽  
Maximum Amplitude ◽  
P Wave ◽  
Global Maximum ◽  
Data Set ◽  
Eastern Kentucky ◽  
Time Required ◽  
Mine Blasts ◽  
Regional Earthquake

Abstract The manual separation of natural earthquakes from mine blasts in data sets recorded by local or regional seismic networks can be a labor-intensive process. An artificial neural network (ANN) applied to automate discriminating earthquakes from quarry and mining blasts in eastern Kentucky suggests that the analyst effort in this task can be significantly reduced. Based on a dataset of 152 local and regional earthquake and 4192 blast recordings over a three-year period in and around eastern Kentucky, ANNs of different configurations were trained and tested on amplitude spectra parameters. The parameters were extracted from different time windows of three-component broadband seismograms to learn the general characteristics of analyst-classified regional earthquake and blast signals. There was little variation in the accuracies and precisions of various models and ANN configurations. The best result used a network with two hidden layers of 256 neurons, trained on an input set of 132 spectral amplitudes and extracted from the P-wave time window and three overlapping time windows from the global maximum amplitude on all three components through the coda. For this configuration and input feature set, 97% of all recordings were accurately classified by our trained model. Furthermore, 96.7% of earthquakes in our data set were correctly classified with mean-event probabilities greater than 0.7. Almost all blasts (98.2%) were correctly classified by mean-event probabilities of at least 0.7. Our technique should greatly reduce the time required for manual inspection of blast recordings. Additionally, our technique circumvents the need for an analyst, or automatic locator, to locate the event ahead of time, a task that is difficult due to the emergent nature of P-wave arrivals induced by delay-fire mine blasts.

Inaudible High-Frequency Sounds Affect Brain Activity: Hypersonic Effect

2000 ◽  
Vol 83 (6) ◽  
pp. 3548-3558 ◽  
Author(s):  
Tsutomu Oohashi ◽  
Emi Nishina ◽  
Manabu Honda ◽  
Yoshiharu Yonekura ◽  
Yoshitaka Fuwamoto ◽  
...  
Keyword(s):  
High Frequency ◽  
Brain Activity ◽  
Statistical Significance ◽  
Power Spectra ◽  
Low Frequency ◽  
Frequency Component ◽  
Frequency Range ◽  
Frequency Components ◽  
Left Thalamus ◽  
The Brain

Although it is generally accepted that humans cannot perceive sounds in the frequency range above 20 kHz, the question of whether the existence of such “inaudible” high-frequency components may affect the acoustic perception of audible sounds remains unanswered. In this study, we used noninvasive physiological measurements of brain responses to provide evidence that sounds containing high-frequency components (HFCs) above the audible range significantly affect the brain activity of listeners. We used the gamelan music of Bali, which is extremely rich in HFCs with a nonstationary structure, as a natural sound source, dividing it into two components: an audible low-frequency component (LFC) below 22 kHz and an HFC above 22 kHz. Brain electrical activity and regional cerebral blood flow (rCBF) were measured as markers of neuronal activity while subjects were exposed to sounds with various combinations of LFCs and HFCs. None of the subjects recognized the HFC as sound when it was presented alone. Nevertheless, the power spectra of the alpha frequency range of the spontaneous electroencephalogram (alpha-EEG) recorded from the occipital region increased with statistical significance when the subjects were exposed to sound containing both an HFC and an LFC, compared with an otherwise identical sound from which the HFC was removed (i.e., LFC alone). In contrast, compared with the baseline, no enhancement of alpha-EEG was evident when either an HFC or an LFC was presented separately. Positron emission tomography measurements revealed that, when an HFC and an LFC were presented together, the rCBF in the brain stem and the left thalamus increased significantly compared with a sound lacking the HFC above 22 kHz but that was otherwise identical. Simultaneous EEG measurements showed that the power of occipital alpha-EEGs correlated significantly with the rCBF in the left thalamus. Psychological evaluation indicated that the subjects felt the sound containing an HFC to be more pleasant than the same sound lacking an HFC. These results suggest the existence of a previously unrecognized response to complex sound containing particular types of high frequencies above the audible range. We term this phenomenon the “hypersonic effect.”

Simulations of high-resolution images of amorphous silicon films

1986 ◽  
Vol 44 ◽  
pp. 544-545
Author(s):  
G. Y. Fan ◽  
J. M. Cowley
Keyword(s):  
Electron Microscope ◽  
High Frequency ◽  
Fourier Transformation ◽  
Amorphous Materials ◽  
Low Frequency ◽  
Chromatic Aberration ◽  
Structure Information ◽  
Frequency Components ◽  
High Resolution Images ◽  
Spatial Frequency Spectrum

It is well known that the structure information on the specimen is not always faithfully transferred through the electron microscope. Firstly, the spatial frequency spectrum is modulated by the transfer function (TF) at the focal plane. Secondly, the spectrum suffers high frequency cut-off by the aperture (or effectively damping terms such as chromatic aberration). While these do not have essential effect on imaging crystal periodicity as long as the low order Bragg spots are inside the aperture, although the contrast may be reversed, they may change the appearance of images of amorphous materials completely. Because the spectrum of amorphous materials is continuous, modulation of it emphasizes some components while weakening others. Especially the cut-off of high frequency components, which contribute to amorphous image just as strongly as low frequency components can have a fundamental effect. This can be illustrated through computer simulation. Imaging of a whitenoise object with an electron microscope without TF limitation gives Fig. 1a, which is obtained by Fourier transformation of a constant amplitude combined with random phases generated by computer.

Integration of Multi-geophysical Approaches to Identify Potential Pathways of Heavy Metals Contamination - A Case Study in Zeida, Morocco

2020 ◽  
Vol 25 (3) ◽  
pp. 415-423
Author(s):  
Ahmed Lachhab ◽  
El Mehdi Benyassine ◽  
Mohamed Rouai ◽  
Abdelilah Dekayir ◽  
Jean C. Parisot ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Seismic Velocity ◽  
Seismic Refraction ◽  
Geophysical Methods ◽  
Low Frequency ◽  
Electrical Current ◽  
P Wave ◽  
Low Resistivity ◽  
Refraction Tomography ◽  
Geophysical Surveys

The tailings of Zeida's abandoned mine are found near the city of Midelt, in the middle of the high Moulouya watershed between the Middle and the High Atlas of Morocco. The tailings occupy an area of about 100 ha and are stored either in large mining pit lakes with clay-marl substratum or directly on a heavily fractured granite bedrock. The high contents of lead and arsenic in these tailings have transformed them into sources of pollution that disperse by wind, runoff, and seepage to the aquifer through faults and fractures. In this work, the main goal is to identify the pathways of contaminated water with heavy metals and arsenic to the local aquifers, water ponds, and Moulouya River. For this reason, geophysical surveys including electrical resistivity tomography (ERT), seismic refraction tomography (SRT) and very low-frequency electromagnetic (VLF-EM) methods were carried out over the tailings, and directly on the substratum outside the tailings. The result obtained from combining these methods has shown that pollutants were funneled through fractures, faults, and subsurface paleochannels and contaminated the hydrological system connecting groundwater, ponds, and the river. The ERT profiles have successfully shown the location of fractures, some of which extend throughout the upper formation to depths reaching the granite. The ERT was not successful in identifying fractures directly beneath the tailings due to their low resistivity which inhibits electrical current from propagating deeper. The seismic refraction surveys have provided valuable details on the local geology, and clearly identified the thickness of the tailings and explicitly marked the boundary between the Triassic formation and the granite. It also aided in the identification of paleochannels. The tailings materials were easily identified by both their low resistivity and low P-wave velocity values. Also, both resistivity and seismic velocity values rapidly increased beneath the tailings due to the compaction of the material and lack of moisture and have proven to be effective in identifying the upper limit of the granite. Faults were found to lie along the bottom of paleochannels, which suggest that the locations of these channels were caused by these same faults. The VLF-EM surveys have shown tilt angle anomalies over fractured areas which were also evinced by low resistivity area in ERT profiles. Finally, this study showed that the three geophysical methods were complementary and in good agreement in revealing the pathways of contamination from the tailings to the local aquifer, nearby ponds and Moulouya River.

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