Correlation between Ambient Seismic Noises and Economic Growth

2020 ◽  
Vol 91 (4) ◽  
pp. 2343-2354 ◽  
Author(s):  
Tae-Kyung Hong ◽  
Jeongin Lee ◽  
Giha Lee ◽  
Junhyung Lee ◽  
Seongjun Park
Keyword(s):  
Economic Growth ◽  
High Frequency ◽  
Noise Level ◽  
Seismic Noise ◽  
Economic Conditions ◽  
Seasonal Effects ◽  
Ambient Seismic Noise ◽  
Frequency Noise ◽  
Noise Levels ◽  
Wide Range

Abstract Human activity is a major source of high-frequency seismic noise. Long-term ambient seismic noise levels and their influencing factors are investigated. The diurnal seismic noise level in 5–15 Hz display high correlation with human activities including traffic and industrial operations that are related to economic conditions. The temporal noise-level variations are consistent among three components. Analysis with seismic noises in three consecutive months of each year enables us to estimate the noise levels without seasonal effects. The daytime seismic noise-level changes in major cities of 11 countries are assessed using the 3 month records for decades. The annual seismic noise levels present strong correlations with gross domestic product (GDP), particularly with manufacturing and industrial GDP. The seismic noise levels increase quickly with GDP in low-GDP regions but slowly in high-GDP regions. This is because high-GDP regions already have large volumes of existing noise-inducing sources and because added sources contribute weakly. The seismic noise levels increased by 14%–111% for 5–23 yr depending on the economic conditions. The correlation between ambient seismic noise level and economy growth is a global feature. The high-frequency noise level may be a proxy to present the economic condition. Economic growth affects the Earth environment in a wide range of aspects.

scholarly journals Seismological assessment of human activity levels during the COVID-19 pandemic

2020 ◽  
Author(s):  
Jeongin Lee ◽  
Tae-Kyung Hong
Keyword(s):  
Human Activities ◽  
High Frequency ◽  
Noise Level ◽  
Seismic Noise ◽  
Activity Levels ◽  
Noise Levels ◽  
Mobility Data ◽  
Community Mobility ◽  
High Infection ◽  
Virus Spreading

Abstract. The COVID-19 virus has a high infection rate, spreading fast in the world. Lockdown and stay-at-home actions have been taken in many countries to reduce the rate of the virus spreading. The daytime ambient seismic noises in 11 major cities of 7 countries are assessed. Daytime seismic noises in 10 am to 6 pm at frequencies ≥ 2 Hz are assessed. The seismic noise levels are compared with the community mobility data that represent the human activities. The high-frequency seismic noise levels present high correlation with the human activities. The human activities decrease with the number of daily confirmed cases. The peak noise-level reductions in lockdown periods were as high as 42–96 %. The noise levels generally started to decrease since the days when the daily confirmed cases reached ~500. The noise level variation presents the lockdown progress. The noise level recovers with time since the end of lockdown. The high correlation between seismic noise level and community mobility suggests possible utilization of seismic noises for anonymous monitoring of human activities.

SEISMIC NOISE MEASUREMENTS IN YELLOWSTONE NATIONAL PARK

Geophysics ◽  
1974 ◽  
Vol 39 (4) ◽  
pp. 389-400 ◽  
Author(s):  
H. M. Iyer ◽  
Tim Hitchcock
Keyword(s):  
Yellowstone National Park ◽  
Seismic Noise ◽  
National Park ◽  
Hot Springs ◽  
Spectral Band ◽  
Spectral Characteristics ◽  
Frequency Noise ◽  
Noise Levels ◽  
High Noise ◽  
Geothermal Activity

In September and October, 1972 the U. S. Geological Survey made an investigation of seismic noise associated with the known geothermal phenomena in Yellowstone National Park. Eighty‐four stations, each recording for at least 48 hours, were operated. All major geyser basins were covered by the experiment. L-shaped three‐element arrays, three‐component stations, and single vertical component stations were operated. Four eight‐element mobile arrays were operated to study propagation characteristics of the noise. Preliminary analysis of data shows that high noise levels are associated with all the major thermal areas in the park. An elongated band of high noise envelops Lower and Upper Geyser Basins; noise levels are high around Norris Basin, Mammoth Hot Springs, Sulphur Mountain, and Mud Volcano; and a strong noise field exists around Lower and Upper Falls of the Yellowstone River. The seismic waves generated by the waterfalls have very different spectral characteristics from the waves associated with geothermal activity. The geothermal noise is predominantly in the spectral band of 2–8 hz, whereas the waterfall noise is predominantly around 2 hz. A mobile array operated near Norris Basin showed coherent wave trains radiating from seismic sources in the basin. Seismic noise measured around 50 m from Old Faithful Geyser showed amplitude fluctuations that followed the eruption cycles of the geyser. A few minutes after each eruption, the noise level starts rising slowly in ramplike fashion. Twenty to thirty minutes before the next eruption, sharp bursts of noise activity occur with increasing rapidity and continue for a few minutes after the eruption. The predominant energy of seismic noise generated by Old Faithful is at frequencies well above 8 hz. We postulate that only such high frequency noise is generated by the surface activity of geysers and hot springs and that the lower frequency noise found in and around the geyser basins is generated by a deeper convection system associated with the geothermal activity.

scholarly journals A Non-spiking Neuron Model With Dynamic Leak to Avoid Instability in Recurrent Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Udaya B. Rongala ◽  
Jonas M. D. Enander ◽  
Matthias Kohler ◽  
Gerald E. Loeb ◽  
Henrik Jörntell
Keyword(s):  
High Frequency ◽  
Neuron Model ◽  
Network Activity ◽  
Membrane Properties ◽  
Frequency Noise ◽  
Recurrent Networks ◽  
Spiking Neuron ◽  
Wide Range ◽  
Spiking Neuron Model ◽  
The Brain

Recurrent circuitry components are distributed widely within the brain, including both excitatory and inhibitory synaptic connections. Recurrent neuronal networks have potential stability problems, perhaps a predisposition to epilepsy. More generally, instability risks making internal representations of information unreliable. To assess the inherent stability properties of such recurrent networks, we tested a linear summation, non-spiking neuron model with and without a “dynamic leak”, corresponding to the low-pass filtering of synaptic input current by the RC circuit of the biological membrane. We first show that the output of this neuron model, in either of its two forms, follows its input at a higher fidelity than a wide range of spiking neuron models across a range of input frequencies. Then we constructed fully connected recurrent networks with equal numbers of excitatory and inhibitory neurons and randomly distributed weights across all synapses. When the networks were driven by pseudorandom sensory inputs with varying frequency, the recurrent network activity tended to induce high frequency self-amplifying components, sometimes evident as distinct transients, which were not present in the input data. The addition of a dynamic leak based on known membrane properties consistently removed such spurious high frequency noise across all networks. Furthermore, we found that the neuron model with dynamic leak imparts a network stability that seamlessly scales with the size of the network, conduction delays, the input density of the sensory signal and a wide range of synaptic weight distributions. Our findings suggest that neuronal dynamic leak serves the beneficial function of protecting recurrent neuronal circuitry from the self-induction of spurious high frequency signals, thereby permitting the brain to utilize this architectural circuitry component regardless of network size or recurrency.

Evidence for the existence of locally-generated body waves in the short-period noise at the Large Aperture Seismic Array, Montana

1972 ◽  
Vol 62 (1) ◽  
pp. 13-29 ◽  
Author(s):  
H. M. Iyer ◽  
John H. Healy
Keyword(s):  
Statistical Analysis ◽  
Phase Velocity ◽  
High Frequency ◽  
Seismic Noise ◽  
Average Velocity ◽  
Low Frequency ◽  
Body Waves ◽  
Frequency Noise ◽  
High Frequency Noise ◽  
Short Period

Abstract The approximate hexagonal configuration of LASA subarrays enables their use as omnidirectional arrays. This property is used to study the phase velocity of short-period seismic noise at different frequencies. It is found that the noise in the low-frequency band consists mainly of surface waves traveling with average velocities in the range 3.0 to 3.5 km/sec. The high-frequency noise, in the band 0.45 to 1.0 Hz, has an average velocity of about 6.0 km/sec. It is quite likely that the high-frequency noise has the nature of locally-generated body waves. Statistical analysis of Pg velocities observed during a crustal refraction experiment at LASA lends support to this hypothesis.

Burial Process of a Seismic Station by Moving Dunes in Tarim Basin

2020 ◽  
Vol 91 (5) ◽  
pp. 2936-2941
Author(s):  
Xiaofeng Liang ◽  
Sicheng Zuo ◽  
Shilin Li ◽  
Yongge Feng
Keyword(s):  
High Frequency ◽  
Noise Level ◽  
Seismic Station ◽  
Low Frequency ◽  
Seismic Signal ◽  
Frequency Noise ◽  
Temperature Record ◽  
Environment Change ◽  
Broadband Seismometer ◽  
High Frequency Noise

Abstract A temporary seismometer vault was buried by a moving sand dune in the Taklimakan Desert at northwestern China in October 2019. The dune gradually covered the solar panel and the power supply to the seismic station was subsequently cut off. Here, we show that the burial process can be diagnosed according to the temperature record from the thermometer in the data-logger, an ultra-low-frequency seismic signal, and the change of high-frequency noise level from the continuous seismograms recorded by the broadband seismometer. The ultra-low-frequency seismic signal reflects the thermoelastic effect of the suspension spring in the seismometer corresponding to the temperature gradient in the sensor vault. At the same time, the variation of high-frequency noise level correlates well with the temperature profile and the ultra-low-frequency seismic signal, indicating the ground wind intensity. The peak frequency shifts and their different responses on three-component waveforms for the high-frequency noise might reflect the distance from the moving dunes to the station and their moving directions. This observation shows a potential usage of continuous seismograms to study rapid environment change around a temporary seismic station.

Research on Cavitation Induced Noise in a Centrifugal Pump

2011 ◽  
Author(s):  
Bo Gao ◽  
Minguan Yang ◽  
Zhong Li ◽  
Can Kang
Keyword(s):  
Flow Field ◽  
Centrifugal Pump ◽  
High Frequency ◽  
Noise Level ◽  
Volume Fraction ◽  
Operating Conditions ◽  
Frequency Noise ◽  
Cavitation Flow ◽  
Cavitation Noise ◽  
Cavitation Bubbles

To study the cavitation flow field and cavitation induced noise features in a centrifugal pump, a model pump is chosen as the research object. Cavitation flow field at design and off-design operating conditions is visualized by high speed camera. The cavitation bubbles spatial distribution changing with pump net positive suction head (NPSH) value have been captured. Meanwhile, cavitation noise signals from the pump at the corresponding operating conditions have been acquired in the frequency band from 10 up to 8kHz. Noise levels at broadband frequency and discrete frequency, such as rotating frequency (RF) and blade pass frequency (BPF), are discussed. It is of help to recognize the relationship between cavitation bubbles and emitted noise spectrum characteristics. Experimental results indicate that the total noise level is unlikely to raise before and in the cavitation inception period. But sound pressure level (SPL) over high frequency broadband increases obviously, as well as SPL at BPF and half of that. It is hard to change at RF. When the NPSH goes down until to the onset of cavitation damage, cavitation cloud appears. The volume fraction of bubbles in every impeller passage is different. The total and high frequency noise level reach peak values near the NPSH critical point. The discrete tone at half of BPF also raises steeply. Cavitation bubbles are filled both on suction and pressure side of the blades in fully developed cavitation stage. Emitted noise energy fluctuates due to the unsteady features of internal flow in the pump.

Effect of Perforated Tabs on the Noise Field of an Axisymmetric Jet

2013 ◽  
Vol 307 ◽  
pp. 250-256
Author(s):  
G. Fayaaz Hussain ◽  
Afthab Shaban Nasser ◽  
Mohammad Mohiudeen Nawaz ◽  
Bikash Kumar Mondal ◽  
N. Karthikeyan
Keyword(s):  
High Frequency ◽  
Supersonic Jet ◽  
Low Frequency ◽  
Frequency Noise ◽  
Noise Levels ◽  
Convergent Nozzle ◽  
High Frequency Noise ◽  
Axisymmetric Jet ◽  
Underexpanded Jets ◽  
Mach Numbers

Effect of triangular tabs with circular perforations on the acoustic far-field of an axisymmetric jet issued from a convergent nozzle of exit diameter of 30.16 mm was studied for both subsonic and sonic underexpanded cases. It was found that the noise in the low frequency range (Strouhal number < 0.29) reduced in both subsonic and supersonic jet mach numbers with a penalty in high frequency noise. OASPL plots showed that overall noise levels in subsonic jets increased due to the introduction of tabs except for far downstream angles where the noise levels reduced by 2 dB. Overall noise levels in underexpanded jets decreased in all directions and at all jet mach numbers without the penalty of high frequency noise. Comparison between tabs without perforation and perforated tabs showed that both the tabs were equally effective.

scholarly journals A non-spiking neuron model with dynamic leak to avoid instability in recurrent networks

2020 ◽  
Author(s):  
Udaya B. Rongala ◽  
Jonas M.D. Enander ◽  
Matthias Kohler ◽  
Gerald E. Loeb ◽  
Henrik Jörntell
Keyword(s):  
High Frequency ◽  
Neuron Model ◽  
Frequency Noise ◽  
Recurrent Networks ◽  
Spiking Neuron ◽  
Neuron Models ◽  
Linear Summation ◽  
Wide Range ◽  
Spiking Neuron Model ◽  
The Brain

AbstractRecurrent circuitry components are distributed widely within the brain, including both excitatory and inhibitory synaptic connections. Recurrent neuronal networks have potential stability problems, perhaps a predisposition to epilepsy. More generally, instability risks making internal representations of information unreliable. To assess the inherent stability properties of such recurrent networks, we tested a linear summation, non-spiking neuron model with and without a ‘dynamic leak’, corresponding to the low-pass filtering of synaptic input current by the RC circuit of the biological membrane. We first show that the output of this neuron model, in either of its two forms, follows its input at a higher fidelity than a wide range of spiking neuron models across a range of input frequencies. Then we constructed fully connected recurrent networks with equal numbers of excitatory and inhibitory neurons and randomly distributed weights across all synapses. When the networks were driven by pseudorandom sensory inputs with varying frequency, the recurrent network activity tended to induce high frequency self-amplifying components, sometimes evident as distinct transients, which were not present in the input data. The addition of a dynamic leak based on known membrane properties consistently removed such spurious high frequency noise across all networks. Furthermore, we found that the neuron model with dynamic leak imparts a network stability that seamlessly scales with the size of the network, conduction delays, the input density of the sensory signal and a wide range of synaptic weight distributions. Our findings suggest that neuronal dynamic leak serves the beneficial function of protecting recurrent neuronal circuitry from the self-induction of spurious high frequency signals, thereby permitting the brain to utilize this architectural circuitry component regardless of network size or recurrency.Author SummaryIt is known that neurons of the brain are extensively interconnected, which can result in many recurrent loops within its neuronal network. Such loops are prone to instability. Here we wanted to explore the potential noise and instability that could result in recurrently connected neuronal networks across a range of conditions. To facilitate such simulations, we developed a non-spiking neuron model that captures the main characteristics of conductance-based neuron models of Hodgkin-Huxley type, but is more computationally efficient. We found that a so-called dynamic leak, which is a natural consequence of the way the membrane of the neuron is constructed and how the neuron integrates synaptic inputs, provided protection against spurious, high frequency noise that tended to arise in our recurrent networks of varying size. We propose that this linear summation model provides a stable and useful tool for exploring the computational behavior of recurrent neural networks.

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