Ambient seismic noise suppression in COST action G2Net

2020 ◽  
Author(s):  
Velimir Ilić ◽  
Alessandro Bertolini ◽  
Fabio Bonsignorio ◽  
Dario Jozinović ◽  
Tomasz Bulik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gravitational Waves ◽  
Seismic Noise ◽  
Noise Suppression ◽  
Real Life ◽  
Low Frequency ◽  
Ambient Seismic Noise ◽  
Cost Action ◽  
The Cost ◽  
Life Challenge

<p>The analysis of low-frequency gravitational waves (GW) data is a crucial mission of GW science and the performance of Earth-based GW detectors is largely influenced by ability of combating the low-frequency ambient seismic noise and other seismic influences. This tasks require multidisciplinary research in the fields of seismic sensing, signal processing, robotics, machine learning and mathematical modeling.<br><br>In practice, this kind of research is conducted by large teams of researchers with different expertise, so that project management emerges as an important real life challenge in the projects for acquisition, processing and interpretation of seismic data from GW detector site. A prominent example that successfully deals with this aspect could be observed in the COST Action G2Net (CA17137 - A network for Gravitational Waves, Geophysics and Machine Learning) and its seismic research group, which counts more than 30 members. </p><div>In this talk we will review the structure of the group, present the goals and recent activities of the group, and present new methods for combating the seismic influences at GW detector site that will be developed and applied within this collaboration.</div><div> <p> </p> <p>This publication is based upon work from CA17137 - A network for Gravitational Waves, Geophysics and Machine Learning, supported by COST (European Cooperation in Science and Technology).</p> </div>

scholarly journals Classifying Multilingual Text in Quality Assurance

2021 ◽  
Vol 12 (3) ◽  
pp. 1987-1992
Author(s):  
Sze Pei Tan Et.al
Keyword(s):  
Machine Learning ◽  
Life Science ◽  
Real Life ◽  
Learning Systems ◽  
Daily Activities ◽  
Multinational Company ◽  
Failure Investigation ◽  
Machine Learning Model ◽  
Multilingual Text ◽  
Life Challenge

Machine learning systems play an important role in helping and assisting engineers in their daily activities. Many jobs can now be automated, and one of them is in handling and processing customers’ complaints before they could proceed with failure investigation. In this paper, we discuss a real-life challenge faced by the manufacturing engineers in a life science multinational company. This paper presents a step by step methodology of multilingual translation and multiclassification of Repair Codes. This solution will allow manufacturing engineers to take advantage of machine learning model to reduce the time taken to manually translate row by row and verify the Repair Codes in the file.

Hybrid Algorithm Applied to the Identification of Risk Factors on the Health of Newly Born in Mexico

2012 ◽  
pp. 83-112 ◽  
Author(s):  
María Dolores Torres ◽  
Aurora Torres Soto ◽  
Carlos Alberto Ochoa Ortiz Zezzatti ◽  
Eunice E. Ponce de León Sentí ◽  
Elva Díaz Díaz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Genetic Algorithm ◽  
Risk Factor ◽  
Hybrid Algorithm ◽  
Real Life ◽  
Neonatal Health ◽  
Health State ◽  
Original Size ◽  
The Cost

This chapter presents the implementation of a Genetic Algorithm into a framework for machine learning that deals with the problem of identifying the factors that impact the health state of newborns in Mexico. Experimental results show a percentage of correct clustering for unsupervised learning of 89%, a real life training matrix of 46 variables, was reduced to only 25 that represent 54% of its original size. Moreover execution time is about one and a half minutes. Each risk factor (of neonatal health) found by the algorithm was validated by medical experts. The contribution to the medical field is invaluable, since the cost of monitoring these features is minimal and it can reduce neonatal mortality in our country.

Extracting subsurface information from ambient seismic noise — A case study from Germany

Geophysics ◽  
2012 ◽  
Vol 77 (4) ◽  
pp. KS13-KS31 ◽  
Author(s):  
Alexander Goertz ◽  
Barbara Schechinger ◽  
Benjamin Witten ◽  
Matthias Koerbe ◽  
Paul Krajewski
Keyword(s):  
Seismic Noise ◽  
Body Wave ◽  
Source Region ◽  
Low Frequency ◽  
Spectral Ratio ◽  
Velocity Model ◽  
Body Waves ◽  
Seismic Survey ◽  
Ambient Seismic Noise ◽  
Oil Reservoir

We analyzed ambient seismic noise from a broadband passive seismic survey acquired in an urban area in Germany. Despite a high level of anthropogenic noise, we observe lateral variations in the quasi-stationary spectra that are of natural origin and indicative of the subsurface in the survey area. The best diagnostic is the ellipticity spectrum which is the spectral ratio of the vertical over the horizontal components. Deviations of the observed spectra from a pure Rayleigh-wave ellipticity allow an approximate separation of surface-wave from body-wave components in the analyzed frequency range, distinguishing shallow (upper tens of meters) from deeper (upper three kilometers) subsurface effects. We observe an increase of vertically polarized body waves between 1 and 4 Hz that is correlated to a subsurface structure that contains an oil reservoir at about 2-km depth. We located the source of the observed body wave microtremor in depth by applying an elastic wavefield back projection and imaging technique. The method includes normalization by the impulse response of the velocity model, effects of the receiver geometry, and lateral variation of incoherent noise. The source region of the low-frequency body wave microtremor is centered above the location of the oil reservoir. Two possible explanations for the deep microtremor are elastic body-wave scattering due to the impedance contrast of the structural trap, and viscoelastic scattering due to poroelastic effects in the partially saturated reservoir.

scholarly journals Carry-Propagation-Adder-Factored Gemmini Systolic Array for Machine Learning Acceleration

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 652
Author(s):  
Kashif Inayat ◽  
Jaeyong Chung
Keyword(s):  
Machine Learning ◽  
Systolic Array ◽  
Substantial Reduction ◽  
Real Life ◽  
Systolic Arrays ◽  
Processing Elements ◽  
Self Driving Cars ◽  
The Cost ◽  
Reduction In Area ◽  
Base Design

Systolic arrays are the primary part of modern deep learning accelerators and are being used widely in real-life applications such as self-driving cars. This paper presents a novel factored systolic array, where the carry propagation adder for accumulation and the rounding logic are extracted out from each processing element, which reduces the area, power and delay of the processing elements substantially. The factoring is performed in the column-wise manner and the cost of the factored logic, placed at each column output, is amortized by the processing elements in a column. We demonstrate the proposed factoring in an open source systolic array, Gemmini. The factoring technique does not change the functionality of the base design and is transparent to applications. We show that the proposed technique leads to substantial reduction in area and delay up to 45.3% and 23.7%, respectively, compared to the Gemmini baseline.

Very low frequency Ocean Bottom ambient seismic noise and coupling on the shallow continental shelf

1989 ◽  
Vol 11 (2) ◽  
pp. 129-152 ◽  
Author(s):  
Mark V. Trevorrow ◽  
Tokuo Yamamoto ◽  
Altan Turgut ◽  
Dean Goodman ◽  
Mohsen Badiey
Keyword(s):  
Continental Shelf ◽  
Seismic Noise ◽  
Low Frequency ◽  
Ambient Seismic Noise ◽  
Ocean Bottom ◽  
Very Low Frequency

Retrieval of reflectivity images from ambient seismic noise correlations using machine learning as a noise-panel classification tool

2020 ◽  
Author(s):  
Boris Boullenger ◽  
Merijn de Bakker ◽  
Arie Verdel ◽  
Stefan Carpentier
Keyword(s):  
Machine Learning ◽  
Seismic Noise ◽  
Body Wave ◽  
The Body ◽  
Body Waves ◽  
Ambient Seismic Noise ◽  
Depth Range ◽  
Classification Models ◽  
Coherent Noise ◽  
Noise Interferometry

<p>The theory of ambient seismic noise interferometry offers techniques to retrieve estimates of inter-receiver responses from continuously recorded ambient seismic noise. This is usually achieved by correlating and stacking successive noise panels over sufficiently long periods of time. If the noise panels contain significant body-wave energy, the stacked correlations expected to result in retrieved estimates of the body-wave responses, including reflections. Such application combined with a dense surface seismic array is promising for imaging the subsurface structures at lower cost and lower environmental impact as compared to with controlled seismic sources. Subsequently, this technique can be an alternative to active-source surveys in a range of challenging scenarios and locations, and can also be used to perform time-lapse subsurface characterization.</p><p>In this study, we apply seismic body-wave noise interferometry to 30-days of continuous records from a surface line of 31 receivers spaced by 25 meters in the South of the Netherlands with the aim to image subsurface reflectors, at depths from a few hundreds of meters to a few kilometers. As a first step, we compute stacked auto-correlations and compare the retrieved zero-offset section with a co-located stacked section from a past active reflection survey on the site.</p><p>Yet, the retrieval of reflectivity estimates relies on the identification and collection of a sufficient number of noise panels with recorded body waves that have travelled from the subsurface towards the array. Even in the case of favorable body-wave noise conditions, the panels are most often contaminated with stronger anthropogenic coherent seismic noise, mainly in the form of surface waves, which in turn prevents the stacked correlations to reveal reflectivity. Because of the limited effect of frequency filtering, the application of seismic body-wave noise interferometry requires in fact extensive effort to identify noise panels without prominent coherent noise from the surface activity. Typically, this leads to disregard a significant amount of actually useful data.</p><p>For this reason, we designed, trained and tested a deep convolutional neural network to perform this classification task more efficiently and facilitate the repetition of the retrieval method over long periods of time. We tested several supervised learning schemes to classify the panels, where two classes are defined, according to the presence or absence of prominent coherent noise. The retained classification models achieved close to 90% of prediction accuracy on the test set.</p><p>We used the trained classification models to correlate and stack panels which were predicted in the class with coherent noise absent. The resulting stacked correlations exhibit potential reflectors in a larger depth range than previously achieved. The results show the benefits of using machine learning to collect efficiently a maximum amount of favorable noise panels and a way forward to the upscaling of seismic body-wave noise interferometry for reflectivity imaging.</p>

scholarly journals Characteristics of Low-frequency Ambient Seismic Noise in South Korea

2016 ◽  
Vol 19 (2) ◽  
pp. 67-75
Author(s):  
Iseul Park ◽  
Ki Young Kim ◽  
Joongmoo Byu
Keyword(s):  
South Korea ◽  
Seismic Noise ◽  
Low Frequency ◽  
Ambient Seismic Noise

scholarly journals Use of Machine Learning in the Analysis of Indoor ELF MF Exposure in Children

2019 ◽  
Vol 16 (7) ◽  
pp. 1230 ◽  
Author(s):  
Gabriella Tognola ◽  
Marta Bonato ◽  
Emma Chiaramello ◽  
Serena Fiocchi ◽  
Isabelle Magne ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cluster Analysis ◽  
Real Life ◽  
Low Frequency ◽  
Personal Exposure ◽  
Epidemiological Studies ◽  
Electric Networks ◽  
High Exposure ◽  
Overhead Lines ◽  
The Impact

Characterization of children exposure to extremely low frequency (ELF) magnetic fields is an important issue because of the possible correlation of leukemia onset with ELF exposure. Cluster analysis—a Machine Learning approach—was applied on personal exposure measurements from 977 children in France to characterize real-life ELF exposure scenarios. Electric networks near the child’s home or school were considered as environmental factors characterizing the exposure scenarios. The following clusters were identified: children with the highest exposure living 120–200 m from 225 kV/400 kV overhead lines; children with mid-to-high exposure living 70–100 m from 63 kV/150 kV overhead lines; children with mid-to-low exposure living 40 m from 400 V/20 kV substations and underground networks; children with the lowest exposure and the lowest number of electric networks in the vicinity. 63–225 kV underground networks within 20 m and 400 V/20 kV overhead lines within 40 m played a marginal role in differentiating exposure clusters. Cluster analysis is a viable approach to discovering variables best characterizing the exposure scenarios and thus it might be potentially useful to better tailor epidemiological studies. The present study did not assess the impact of indoor sources of exposure, which should be addressed in a further study.

scholarly journals Study of acceleration measurement in gravitational wave detection

2022 ◽  
Author(s):  
Junlang Li ◽  
Teng Zhang
Keyword(s):  
Gravitational Waves ◽  
Radiation Pressure ◽  
Noise Suppression ◽  
Low Frequency ◽  
Quantum Limit ◽  
Acceleration Measurement ◽  
Standard Quantum ◽  
Standard Quantum Limit ◽  
Wave Detection ◽  
Speed Measurement

Abstract Position-meter and speed-meter interferometers have been analysed for detecting gravitational waves. Speed-meter is proposed to reduce the radiation pressure noise, which is dominant at low frequency. We introduce the concept of acceleration measurement in comparison with position and speed measurement. In this paper, we describe a general acceleration measurement and derive its standard quantum limit. We provide an example of an acceleration-meter interferometer configuration. We show that shot noise dominates at low frequency following a frequency dependence of $1/\Omega^2$, while radiation pressure noise is constant. The acceleration-meter has even a stronger radiation pressure noise suppression than speed-meter.

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