Noise Recordings Research Articles

One year of sound recorded by a mermaid float in the Pacific: hydroacoustic earthquake signals and infrasonic ambient noise

SUMMARY A fleet of autonomously drifting profiling floats equipped with hydrophones, known by their acronym mermaid, monitors worldwide seismic activity from inside the oceans. The instruments are programmed to detect and transmit acoustic pressure conversions from teleseismic P wave arrivals for use in mantle tomography. Reporting seismograms in near-real time, within hours or days after they were recorded, the instruments are not usually recovered, but if and when they are, their memory buffers can be read out. We present a unique 1-yr-long data set of sound recorded at frequencies between 0.1 and 20 Hz in the South Pacific around French Polynesia by a mermaid float that was, in fact, recovered. Using time-domain, frequency-domain and time-frequency-domain techniques to comb through the time-series, we identified signals from 213 global earthquakes known to published catalogues, with magnitudes 4.6–8.0, and at epicentral distances between 24° and 168°. The observed signals contain seismoacoustic conversions of compressional and shear waves travelling through crust, mantle and core, including P, S, Pdif, Sdif, PKIKP, SKIKS, surface waves and hydroacoustic T phases. Only 10 earthquake records had been automatically reported by the instrument—the others were deemed low-priority by the onboard processing algorithm. After removing all seismic signals from the record, and also those from other transient, dominantly non-seismic, sources, we are left with the infrasonic ambient noise field recorded at 1500 m depth. We relate the temporally varying noise spectral density to a time-resolved ocean-wave model, WAVEWATCH III. The noise record is extremely well explained, both in spectral shape and in temporal variability, by the interaction of oceanic surface gravity waves. These produce secondary microseisms at acoustic frequencies between 0.1 and 1 Hz according to the well-known frequency-doubling mechanism.

Partial Compensation of Thermal Noise in the Fundamental Mode of an Optical Cavity

Thermal noise of optical cavities limits the accuracy of many experiments on precision laser spectroscopy and interferometry. The study of the physical properties of this noise opens opportunities for the creation of more stable cavities, reduction of phase noise of optical radiation, and performance of accurate optical studies. The paper proposes a method of partial recording of TEM00 mode thermal noise of a Fabry-Perot cavity using two "probe" modes of higher order. The method allows partial compensation of noise in the fundamental mode. Mathematical modeling is performed, which confirms the efficiency of the method.

Machine learning powered tools for automated analysis of muscle sympathetic nerve activity recordings.

Automated analysis and quantification of physiological signals in clinical practice and medical research can reduce manual labor, increase efficiency, and provide more objective, reproducible results. To build a novel platform for the analysis of muscle sympathetic nerve activity (MSNA), we employed state‐of‐the‐art data processing and machine learning applications. Data processing methods for integrated MSNA recordings were developed to evaluate signals regarding the overall quality of the signal, the validity of individual signal peaks regarding the potential to be MSNA bursts and the timing of their occurrence. An overall probability score was derived from this flexible platform to evaluate each individual signal peak automatically. Overall, three deep neural networks were designed and trained to validate individual signal peaks randomly sampled from recordings representing only electrical noise and valid microneurography recordings. A novel data processing method for the whole signal was developed to differentiate between periods of valid MSNA signal recordings and periods in which the signal was not available or lost due to involuntary movement of the recording electrode. A probabilistic model for timing of the signal bursts was implemented as part of the system. Machine Learning algorithms and data processing tools were implemented to replicate the complex decision‐making process of manual MSNA analysis. Validation of manual MSNA analysis including intra‐ and inter‐rater validity and a comparison with automated MSNA tools is required. The developed toolbox for automated MSNA analysis can be extended in a flexible way to include algorithms based on other datasets.

Ambient noise and ERT data provide insights into the structure of co-seismic rock avalanche deposits in Sichuan (China)

The post-seismic history of the 2008 Mw7.9 Wenchuan earthquake shows that marginally stable deposits of large co-seismic landslide dams can pose persistent debris flow hazards for the downstream areas. Here, we combine analyses of single-station recordings of ambient noise with electrical resistivity tomography (ERT) surveys to explore the potential of drawing information on structure and geometry of the deposit of a large rock avalanche triggered by the Mw 7.9 2008 Wenchuan earthquake, which dammed the Yangjia stream in the Sichuan Province (China). The substantial thickness and heterogeneity of this kind of deposits limit the application of standard geophysical techniques, like active seismic surveys, which require highly energetic sources and long linear geophone arrays to reach adequate investigation depths. Passive single-station methods, relying on ambient noise recordings to determine site resonance properties, controlled by the contrast between soft surface layers and a stiffer substratum, offer the opportunity of investigating subsoil properties down to larger depths. In particular, we use a recently developed technique, which isolates the contribution of Rayleigh waves to ambient noise and draws information on sub-soil properties from the inversion of Rayleigh wave ellipticity curves plotted as function of frequency. In this framework, the ERT data can support the ellipticity curve inversion, typically affected by highly non-univocal solutions, by providing constraints for defining of the thickness of the uppermost surficial layers. The results allowed inferring the overlap of different layers within the 2008 rock avalanche deposit, as well as estimating lateral variations in their thickness and S-wave (Vs) velocities.

Polarization characteristics of Rayleigh waves to improve seismic site effects analysis by HVSR method

The horizontal-to-vertical spectral ratio (HVSR) method based on ambient noise recordings is a popular and efficient approach to estimate the seismic site effect parameters. This method is sufficiently robust in determining the predominant frequency (f0) of the site, whereas its estimate of the amplification factor lacks consistency because generation mechanisms, source locations and proportions of the wave types making up ambient noise vary locally and are not well understood, in addition to lack of clarity in the theory of the HVSR method. As the generation mechanisms, source locations and wave compositions of the single- and double-frequency (SF and DF respectively) microseisms (0.05–0.5 Hz) are better studied compared to the noise in other frequency bands, this study focuses on investigating the site effects on the fundamental Rayleigh (Rg) waves in the SF and DF microseisms recorded at 30 Transportable Array stations in the east coast of the United States. HVSRs are calculated first to obtain f0s as well as the unconsolidated sediment thicknesses (USTs). Then the polarization analyses are carried out to select the ellipsoids approximating the particle motions of the Rg waves and to estimate the HVSR of these Rg ellipsoids (HVSRR). The polarization parameters and HVSRR indicate that the Rg waves' particle motions form oblate ellipsoids in the UST range whose f0 values fall within the DF band. Comparison of the observed HVSR and HVSRR and the computed HVSR values based on the body wave and the Rayleigh wave theories shows that the observed HVSR in the DF band on bedrock and/or thin unconsolidated sediments are mainly due to the ellipticity of Rg waves, while those on thick unconsolidated sediments whose f0 values fall within the DF band result from the combined effects of the ellipticity of Rg waves and multiple reflection of S-waves. This outcome will help develop procedures to improve the consistency of HVSR's amplification estimates and hence its reliability in seismic site effects analysis.

Broad-Scale Responses of Harbor Porpoises to Pile-Driving and Vessel Activities During Offshore Windfarm Construction

Offshore windfarm developments are expanding, requiring assessment and mitigation of impacts on protected species. Typically, assessments of impacts on marine mammals have focused on pile-driving, as intense impulsive noise elicits adverse behavioral responses. However, other construction activities such as jacket and turbine installation also change acoustic habitats through increased vessel activity. To date, the contribution of construction-related vessel activity in shaping marine mammal behavioral responses at windfarm construction sites has been overlooked and no guidelines or mitigation measures have been implemented. We compared broad-scale spatio-temporal variation in harbor porpoise occurrence and foraging activity between baseline periods and different construction phases at two Scottish offshore windfarms. Following a Before-After Control-Impact design, arrays of echolocation click detectors (CPODs) were deployed in 25 km by 25 km impact and reference blocks throughout the 2017–2019 construction. Echolocation clicks and buzzes were used to investigate porpoise occurrence and foraging activity, respectively. In parallel, we characterized broadband noise levels using calibrated noise recorders (SoundTraps and SM2Ms) and vessel activities using AIS data integrated with engineering records. Following an impact gradient design, we then quantified the magnitude of porpoise responses in relation to changes in the acoustic environment and vessel activity. Compared to baseline, an 8–17% decline in porpoise occurrence was observed in the impact block during pile-driving and other construction activities. The probability of detecting porpoises and buzzing activity was positively related to the distance from vessel and construction activities, and negatively related to levels of vessel intensity and background noise. Porpoise displacement was observed at up to 12 km from pile-driving activities and up to 4 km from construction vessels. This evidence of broad-scale behavioral responses of harbor porpoises to these different construction activities highlights the importance of assessing and managing all vessel activities at offshore windfarm sites to minimize potential impacts of anthropogenic noise.

Multiclass Classification of Cardiac Rhythms on Short Single Lead ECG Recordings using Bidirectional Long Short-Term Memory Networks

The recognition of cardiac rhythms is a topic of great relevance, particularly using short single-lead ECG recordings, due to its potential to early detect cardiovascular diseases and take actions quickly to preserve peoples wellbeing. Among cardiac arrhythmias, atrial fibrillation is the most common sustained cardiac arrhythmia, with significant mortality and morbidity rates. Several approaches have been conceived to identify cardiac rhythms, from the comparison of heart rate with adaptive and fixed thresholds to the application of deep and machine learning techniques. In this work, the classification of three cardiac rhythms (normal, atrial fibrillation, and other arrhythmias), as well as the identification of noise recordings, were performed using bidirectional LSTM networks that exploit the ECG signal (a representation of the cardiac electric activity) and time series containing information about the auricular and ventricular activities. A Monte Carlo 10-fold cross-validation of 10 iterations was performed to assure the generalization of the classifiers and the replicability of the results. An average accuracy of 77.97% was obtained to recognize the four classes but increase up to 85.95% when noise recordings were left out of the classification process. Moreover, micro F1 scores of 89.96%, 79.23%, and 79.77% were obtained for normal rhythm, atrial fibrillation, and other arrhythmias, respectively. The imbalance of classes and the characteristic patterns of normal rhythm and atrial fibrillation were the main factors associated with these performances.

Different glutamate sources and endogenous co-agonists activate extrasynaptic NMDA receptors on amacrine cells of the rod pathway microcircuit.

The NMDA receptors (NMDARs) expressed by AII and A17 amacrine cells, the two main inhibitory interneurons of the rod pathway microcircuit in the mammalian retina, are exclusively extrasynaptic, activated by ambient levels of glutamate, and molecularly distinct, with AII and A17 amacrines expressing GluN2B- and GluN2A-containing receptors, respectively. This important sensory microcircuit thus provides a unique model to study the activation and function of extrasynaptic NMDARs. Here, we investigated the sources of glutamate and the endogenous co-agonists (d-serine or glycine) that activate these distinct populations of NMDARs. With acute slices from rat retina, we used whole-cell voltage-clamp recording and measurement of current noise to monitor levels of NMDAR activity. Pre-incubation of retina with bafilomycin A1 (an inhibitor of neurotransmitter uptake into synaptic vesicles) abolished NMDAR-mediated noise in AII, but not A17 amacrines, suggesting a vesicular source of glutamate activates AII NMDARs, whereas a non-vesicular source activates A17 NMDARs. Pre-incubation of retina with l-methionine sulfoximine (an inhibitor of glutamine synthetase) also abolished NMDAR-mediated noise in AII, but not A17 amacrines, suggesting a neuronal source of glutamate activates AII NMDARs, whereas a glial source activates A17 NMDARs. Enzymatic breakdown of d-serine reduced NMDAR-mediated noise in AII, but not A17 amacrines, suggesting d-serine is the endogenous co-agonist at AII, but not A17 NMDARs. Our results reveal unique characteristics of these two populations of extrasynaptic NMDARs. The differential and independent activation of these receptors is likely to provide specific contributions to the signal processing and plasticity of the cellular components of the rod pathway microcircuit.

Особенности методов проведения межлабораторных сличений при измерении виброакустических показателей

External quality control in the form of interlaboratory comparisons (ILCs) is an important criterion of the testing laboratory competence. The study was aimed to summarize the approaches to developing objects for proficiency testing (OPT) based on physical simulation of acoustic noise sources, airborne ultrasound, vibration, and the practice of their use for ILC. Analysis of the OPT effectiveness based on physical simulation of factors, the test benches (TBs), was performed based on their testing and certification results, as well as on the results of appropriate ILCs. The results of using TB as OPT are considered for the following factors: acoustic noise, airborne ultrasound, and vibration. When measuring acoustic noise, TB played back the acoustic noise record with high stability. ILC involving measurement of airborne ultrasound was performed the same way, however, the frequency of the acoustic signal being reproduced was in the range of 11–22 kHz. TBs, based on a manual mechanized tool and a platform equipped with electromechanical agitator, were developed for ILC involving the measurement of local and general vibration. Stability of vibration generated was provided by means of the automated system for maintaining the set level with feedback and proportional integral derivative (PID) controller. When arranging and performing ILCs involving measurement of noise and vibration, a crucial role is played by the methods developed specifically for ILCs, allowing one to take into account all the conditions that affect the measurement results.

Diverse ocean noise classification using deep learning

The alarming rise in the ocean noises due to the ramping up of anthropogenic activities had adversely impacted the marine fauna and altered the soundscape of the oceans. In recent times, Deep learning has gained high significance in assessing this pervasive underwater noise data. Studying the underwater soundscape is crucial in conserving the ocean health and in achieving a “quiet ocean”. As an effort to it, in this paper, we present a deep neural network architecture, Convolutional Neural Network-based ocean noise classification cum recognition system capable of classifying vocalization of cetaceans, fishes, marine invertebrates, anthropogenic sounds, natural sounds, and the unidentified ocean sounds from passive acoustic ocean noise recordings. The challenge is to classify these noises amidst the highly non-stationary sound spectrum of short low grunts to long high peaked vocals, limited sensor receiving range, and the need for a large annotated training data. The proposed method can self-learn the features from the training audio data with no need for feature extraction proving better adaptability to complex audio acoustic signals. Experimental results prove that the proposed system is befitting for classifying ocean noises with 96.1% accuracy. Classification helps in distinguishing natural acoustic systems from artificial acoustic systems.

Ambient Noise Tomography with Short-Period Stations: Case Study in the Borborema Province

The analysis of seismic ambient noise has been recently proven a viable alternative to the analysis of seismic waves generated by earthquakes. Although seismic ambient noise had traditionally been discarded from earthquake records, it has now been shown that the cross-correlation of the seismic ambient noise allows for the recovery of the Green’s function between the receivers. Furthermore, seismic noise has the ability to propagate continuously and independently of the occurrence of earthquakes, allowing for high-resolution tomographic studies in regions of low seismicity. Over the last two decades, the cross-correlation of continuous seismic noise recordings between pairs of stations has been widely utilized in surface-wave tomography studies. For the Northeast Brazil region, tomographic studies have been exclusively performed with seismic data from broadband stations; however, in addition to those stations, there exists a large volume of short-period data that might potentially improve the resolution of existing ambient noise tomographies. Thus, the goal of this work is to utilize, in addition to broadband data, short-period data recorded by 22 short-period stations in the Borborema Province. Through cross-correlation and stacking of ambient seismic noise, the emergence of the fundamental mode of the Rayleigh waves and their dispersive character was observed. Once the empirical Green's functions were retrieved, the accuracy of (phase and group) dispersion curves for periods up to 10 s and its tomographic inversion were assessed. Our results demonstrate that short-period dispersion measurements can be successfully integrated in regional tomographic studies for improved resolution.

The influence of a signal\u2019s time structure on the perceived noise annoyance of road traffic noise

PurposeRoad traffic noise is the most common source of noise in modern cities. The noise indicators used to manage noise do not take into account its temporal structure. However, in cities the traffic flow varies during the day, peaking due to congestion and more fluent periods. In this research we sought to analyze how people (giving answers on a numerical ICBEN scale) perceive noise stimuli with the same LAeqT values but different time structures (more/less noise events, different amplitude envelopes).Methods31 people with normal hearing took part in an experiment conducted in an anechoic chamber. Participants listened to 18 different noise recordings and rated each of them using the numerical ICBEN scale regarding noise annoyance.ResultsThe results showed that only sound level was a statistically significant factor. However, based on people’s remarks about noise, we can also say that the more intermittent the noise is, the more negative feelings it evokes in people.ConclusionsTime structure does not have a significant influence on people’s judgments about noise annoyance. However, people tend to have a preference for a steady noise rather than an intermittent one.

FindPorpoises: Deep learning for detection of harbor porpoise echolocation clicks

Tom Norris expressed conservation concern for the vaquita, a close relative of harbor porpoises; here we report on a harbor porpoise (Phocoena phocoena) conservation effort. Tidal energy devices are installed in high-flow estuaries that are also prime harbor porpoise habitat. To study tidal energy device impacts on porpoises, sound was recorded in Minas Passage, Bay of Fundy, Canada. Analysis aimed to distinguish harbor porpoises from noise sources. “Click candidate” sounds were detected using the ratio between the harbor porpoise frequency band and lower guard band, then reviewed by humans to label which were correct. Because more “correct” instances were needed, data were augmented by mixing porpoise clicks with known noise, producing 20,000 “click present” labeled instances. Additionally, 20 000 “non-click” instances were extracted from noise recordings. Labeled instances were made into a 0.5-s equalized spectrograms for training deep-learning networks. Of the network architectures tried, the best was a convolutional neural network with pooling and fully connected layers, achieving 99.1% accuracy. User-friendly “FindPorpoises” software was made to pre-process raw files, detect candidate clicks, use the trained network to sort candidate clicks into correct and incorrect instances, and plot and tabulate the results by time of day, tide cycle, and month. [Work supported by OERA.]

COVID-19 impact on the Northern Gulf of Mexico soundscapes

Changes in the Gulf of Mexico soundscapes due to COVID-19 are investigated. Baseline ambient noise recordings from five Environmental Acoustic Recording System buoys in the Mississippi Valley/Canyon region between May 2018 and June 2020, collected as a part of the collaborative efforts funded by BOEM, are analyzed. The statistical data processing is based on the standards developed for the previous large-scale ocean ambient noise monitoring projects; extracted acoustic metrics are directly comparable to the levels reported in the Baltic Sea, the North Atlantic, and the North Sea . The monthly medians of 1/3-octave band Sound Pressure Levels (SPLs) vary up to 15 dB re 1 μPa across sites depending on surrounding anthropogenic activities and local propagation conditions that makes it challenging to recognize the variations due to the pandemic. Overall, the deepest site appears to have the highest SPLs at the low frequency bands and the lowest SPLs at the mid-frequency bands. The mid-frequency band highest SPLs are observed at the shallow site and the site outside the canyon. Exploration surveys and industrial activities were present across all COVID-19 impacted months. More subtle changes in ambient levels due to the pandemic impact will be discussed in the presentation.

Conversational distance adaptation in noise and its effect on signal-to-noise ratio in realistic listening environments.

Everyday environments impose acoustical conditions on speech communication that require interlocutors to adapt their behavior to be able to hear and to be heard. Past research has focused mainly on the adaptation of speech level, while few studies investigated how interlocutors adapt their conversational distance as a function of noise level. Similarly, no study tested the interaction between distance and speech level adaptation in noise. In the present study, participant pairs held natural conversations while binaurally listening to identical noise recordings of different realistic environments (range of 53-92 dB sound pressure level), using acoustically transparent headphones. Conversations were in standing or sitting (at a table) conditions. Interlocutor distances were tracked using wireless motion-capture equipment, which allowed subjects to move closer or farther from each other. The results show that talkers adapt their voices mainly according to the noise conditions and much less according to distance. Distance adaptation was highest in the standing condition. Consequently, mainly in the loudest environments, listeners were able to improve the signal-to-noise ratio (SNR) at the receiver location in the standing condition compared to the sitting condition, which became less negative. Analytical approximations are provided for the conversational distance as well as the receiver-related speech and SNR.

K-complexes are a sensitive marker of noise-related sensory processing during sleep: a pilot study.

The primary aim of this study was to examine dose-response relationships between sound pressure levels (SPLs) and K-complex occurrence probability for wind farm and road traffic noise. A secondary aim was to compare K-complex dose-responses to manually scored electroencephalography arousals and awakenings. Twenty-five participants underwent polysomnography recordings and noise exposure during sleep in a laboratory. Wind farm and road traffic noise recordings of 20-sec duration were played in random order at 6 SPLs between 33 and 48 dBA during established N2 or deeper sleep. Noise periods were separated with periods of 23 dBA background noise. K-complexes were scored using a validated algorithm. K-complex occurrence probability was compared between noise types controlling for noise SPL, subjective noise sensitivity, and measured hearing acuity. Noise-induced K-complexes were observed in N2 sleep at SPLs as low as 33 dBA (Odds ratio, 33 dBA vs 23 dBA, mean (95% confidence interval); 1.75 (1.16, 2.66)) and increased with SPL. EEG arousals and awakenings were only associated with noise above 39 dBA in N2 sleep. K-complexes were 2 times more likely to occur in response to noise than EEG arousals or awakenings. Subjective noise sensitivity and hearing acuity were associated with the K-complex occurrence, but not arousal or awakening. Noise type did not detectably influence K-complexes, EEG arousals, or awakening responses. These findings support that K-complexes are a sensitive marker of sensory processing of environmental noise during sleep and that increased hearing acuity and decreased self-reported noise sensitivity increase K-complex probability.

Variable and consistent traffic noise negatively affect the sleep behavior of a free-living songbird.

Anthropogenic noise is a ubiquitous disturbance factor, which, owing to the extensive nature of transportation networks, and ability of sound waves to penetrate distances, has wide-reaching impacts on biological communities. Research effort on biological effects of anthropogenic noise is extensive, but has focused on waking behavior, and to our knowledge, no published experimental study exists on how noise affects sleep in free-living animals. Sleep plays vital functions in processes such as cellular repair and memory consolidation. Thus, understanding the potential for noise to disrupt sleep is a critical research objective. Whether different noise regimes exert distinct effects on behavior also remains poorly understood, as does intraspecific variation in noise sensitivity. To address these knowledge gaps, we used a repeated-measures field experiment involving broad-casting traffic noise recordings at great tit (Parus major) nest boxes over a series of consecutive nights. We evaluated whether increasing the temporal variability and amplitude of traffic noise increased deleterious effects on sleep behavior in free-living great tits, and whether individuals differed in the magnitude of responses. We found that traffic noise reduced sleep duration, proportion, and bout length, and induced birds to exit nest boxes earlier in the morning. There was some support for a stronger effect of more variable noise, and relative to lower amplitude noise, higher amplitude noise resulted in less and more fragmented sleep. Effects of noise on sleep duration were stronger in older adults, and substantial, repeatable variation existed in individual responses. We demonstrate for the first time that anthropogenic noise can have strong effects on sleep in free-living animals, which may have cascading effects on waking behavior, physiology and fitness. Results suggest that reducing the amplitude of traffic noise may be an effective mitigation strategy, and that differences in individual sensitivity are important to consider when evaluating effects of noise exposure. CAPSULE: Experimental exposure to temporally variable and consistent traffic noise negatively affected sleep behavior in a free-living songbird.

RaspMI: Raspberry Pi Assisted Embedded System for Monitoring and Recording of Seismic Ambient Noise

Recently, the passive seismic techniques based on the microtremor measurements have emerged as an alternative to conventional seismic and geotechnical methods used in engineering seismology and earthquake engineering, especially in the local site effect investigations. These methods are basically based on the recording of seismic ambient noise and analysis of its spectral features. Although these techniques are more practical than traditional seismic methods, the commercial instruments designed for recording the seismic ambient noise are quite expensive. Furthermore, most of these instruments require an external computer for recording and monitoring the data. In this sense, this article intends to present a low-cost and Raspberry Pi single-board microcomputer aided seismic ambient noise recording and monitoring system (RaspMI) as an alternative to the computer aided commercial seismic noise measurement systems. RaspMI is an open-source hardware and software system that is controlled by a graphical user interface developed by using the MonoDevelop cross-platform and C# programming language. This system has many features such as 72 dB dynamic range, 100 Hz sampling frequency, timer recording mode, text and SAC data file format, and latitude, longitude, date and time support with GPS module. RaspMI has subjected to the various tests in terms of the reliability and sensitivity. The obtained results have shown that RaspMI can be used as an alternative instrument for seismic ambient noise measurements.

Rapid fabrication of teflon apertures by controlled high voltage pulses for formation of free standing planar lipid bilayer membrane.

Free standing artificial lipid bilayers are widely used in the study of biological pores. In these types of studies, the free standing planar lipid bilayer is formed over a micron-sized aperture consisting of either polymer such as Polytetrafluoroethylene (PTFE, Teflon) or glass. Teflon is chemically inert, has a low dielectric constant, and has a high electrical resistance which combined allow for obtaining low noise recordings. This study investigates the reproducible generation of micropores in the range of 50-100 microns in diameter in a Teflon film using a high energy discharge set-up. The discharger set-up consists of a microprocessor, a transformer, a voltage regulator, and is controlled by a computer. We compared two approaches for pore creation: single and multi-pulse methods. The results showed that the multi-pulse method produced narrower aperture size distributions and is more convenient for lipid bilayer formation, and thus would have a higher success rate than the single-pulse method. The bilayer stability experiments showed that the lipid bilayer lasts for more than 33h. Finally, as a proof-of-concept, we show that the single and multi-channel electrophysiology experiments were successfully performed with the apertures created by using the mentioned discharger. In conclusion, the described discharger provides reproducible Teflon-pores in a cheap and easy-to-operate manner.

Seismic monitoring of the Auckland Volcanic Field during New Zealand's COVID-19 lockdown

Abstract. The city of Auckland, New Zealand (Tāmaki Makaurau, Aotearoa), sits on top of an active volcanic field. Seismic stations in and around the city monitor activity of the Auckland Volcanic Field (AVF) and provide data to image its subsurface. The seismic sensors – some positioned at the surface and others in boreholes – are generally noisier during the day than during nighttime. For most stations, weekdays are noisier than weekends, proving human activity contributes to recordings of seismic noise, even on seismographs as deep as 384 m below the surface and as far as 15 km from Auckland's Central Business District. Lockdown measures in New Zealand to battle the spread of COVID-19 allow us to separate sources of seismic energy and evaluate both the quality of the monitoring network and the level of local seismicity. A matched-filtering scheme based on template matching with known earthquakes improved the existing catalogue of five known local earthquakes to 35 for the period between 1 November 2019 and 15 June 2020. However, the Level-4 lockdown from 25 March to 27 April – with its drop in anthropogenic seismic noise above 1 Hz – did not mark an enhanced detection level. Nevertheless, it may be that wind and ocean swell mask the presence of weak local seismicity, particularly near surface-mounted seismographs in the Hauraki Gulf that show much higher levels of noise than the rest of the local network.