Sporadic Noise Research Articles

CONTROLLED-SOURCE ELECTROMAGNETIC (CSEM) DATA PROCESSING WITH HIGH ELECTROMAGNETIC NOISE LEVELS

The Controlled-Source Electromagnetic (CSEM) method is one of the electromagnetic methods utilized in geophysical exploration. This method provides a subsurface image through the resistivity anomalies of materials encountered by electromagnetic waves. The research area is located near a major city, resulting in high electromagnetic noise. Electromagnetic noise can be categorized into two types of the noise namely periodic noise and sporadic noise. Eliminating noise is a crucial objective to enhance data quality, as it can introduce uncertainty into interpretations. Three noise removal techniques are employed: pre-stack to filter the harmonic noise, stacking to remove the sporadic noise, and post-stack for smoothing. The CSEM data used consists of signals in the time domain with a 10-second period and a 50% duty cycle. The results of applying these noise removal techniques indicate that all three methods are highly effective in noise reduction. The pre-stack technique can remove periodic noise, while sporadic noise is addressed by the stacking technique, and signal smoothing can be achieved using the poststack technique.

Snowmobile noise alters bird vocalization patterns during winter and pre‐breeding season

Abstract Noise pollution poses a significant threat to ecosystems worldwide, disrupting animal communication and causing cascading effects on biodiversity. In this study, we focus on the impact of snowmobile noise on avian vocalizations during the non‐breeding winter season, a less‐studied area in soundscape ecology. We developed a pipeline relying on deep learning methods to detect snowmobile noise and applied it to a large acoustic monitoring dataset collected in Yellowstone National Park. Our results demonstrate the effectiveness of the snowmobile detection model in identifying snowmobile noise and reveal an association between snowmobile passage and changes in avian vocalization patterns. Snowmobile noise led to a decrease in the frequency of bird vocalizations during mornings and evenings, potentially affecting winter and pre‐breeding behaviours such as foraging, predator avoidance and successfully finding a mate. However, we observed a recovery in avian vocalizations after detection of snowmobiles during mornings and afternoons, indicating some resilience to sporadic noise events. Synthesis and applications: Our findings emphasize the need to consider noise impacts in the non‐breeding season and provide valuable insights for natural resource managers to minimize disturbance and protect critical avian habitats. The deep learning approach presented in this study offers an efficient and accurate means of analysing large‐scale acoustic monitoring data and contributes to a comprehensive understanding of the cumulative impacts of multiple stressors on avian communities.

Improved cluster analysis of Werner solutions for geologic depth estimation using unsupervised machine learning

Werner deconvolution is a widely used method for computer-aided solution estimations using magnetic field data. Because of the inherent unpredictability of potential field data, the techniques frequently yield unpredictable results and may not always be able to predict the full extent of the geological structures depicted. The unsupervised clustering algorithm was then used to improve the detection of geological structures generated from magnetic field data. A synthetic magnetic model was created by combining two models that resembled dikes. To add a little more complexity to the model, sporadic noise was applied to the synthetic model. The synthetic model was subjected to Werner deconvolution to produce solutions. The developed solutions resulting from the synthetic data were applied to an unsupervised machine-learning algorithm. The algorithm was able to detect two clusters from the dataset and the centroids generated from the number of clusters formed indicated the depth of the two models, which was 5 m and 8 m respectively. The algorithm was then subjected to a real case dataset from the Banke complex, which is part of the Nigerian Younger Granite region. Three clusters were formed, having centroids of 536 m, 635 m, and 530 m. These depths were validated with previous surveys carried out in the area. This algorithm proved successful in determining the exact position in terms of the depth of the geologic bodies. This proposed approach is fully data-driven and is successful even when there is noise.

Supervised Machine Learning of High Rate GNSS Velocities for Earthquake Strong Motion Signals

AbstractHigh rate Global Navigation Satellite System (GNSS) processed time series capture a broad spectrum of earthquake strong motion signals, but experience regular sporadic noise that can be difficult to distinguish from true seismic signals. The range of possible seismic signal frequencies amidst a high, location‐varying noise floor makes filtering difficult to generalize. Existing methods for automatic detection rely on external inputs to mitigate false alerts, which limit their usefulness. For these reasons, geodetic seismic signal detection makes for a compelling candidate for data‐driven machine learning classification. In this study we generated high rate GNSS time differenced carrier phase (TDCP) velocity time series concurrent in space and time with expected signals from 77 earthquakes occurring over nearly 20 years. TDCP velocity processing has increased sensitivity relative to traditional geodetic displacement processing without requiring sophisticated corrections. We trained, validated and tested a random forest classifier to differentiate seismic events from noise. We find our supervised random forest classifier outperforms the existing detection methods in stand‐alone mode by combining frequency and time domain features into decision criteria. The classifier achieves a 90% true positive rate of seismic event detection within the data set of events ranging from MW4.8–8.2, with typical detection latencies seconds behind S‐wave arrivals. We conclude the performance of this model provides sufficient confidence to enable these valuable ground motion measurements to run in stand‐alone mode for development of edge processing, geodetic infrastructure monitoring and inclusion in operational ground motion observations and models.

Fault Feature-Extraction Method of Aviation Bearing Based on Maximum Correlation Re’nyi Entropy and Phase-Space Reconstruction Technology

To address the difficulty of extracting the features of composite-fault signals under a low signal-to-noise ratio and complex noise conditions, a feature-extraction method based on phase-space reconstruction and maximum correlation Re’nyi entropy deconvolution is proposed. Using the Re’nyi entropy as the performance index, which allows for a favorable trade-off between sporadic noise stability and fault sensitivity, the noise-suppression and decomposition characteristics of singular-value decomposition are fully utilized and integrated into the feature extraction of composite-fault signals by the maximum correlation Re’nyi entropy deconvolution. Verification based on simulation, experimental data, and a bench test proves that the proposed method is superior to the existing methods regarding the extraction of composite-fault signal features.

Novel Signal Noise Reduction Method through Cluster Analysis, Applied to Photoplethysmography.

Physiological signals can often become contaminated by noise from a variety of origins. In this paper, an algorithm is described for the reduction of sporadic noise from a continuous periodic signal. The design can be used where a sample of a periodic signal is required, for example, when an average pulse is needed for pulse wave analysis and characterization. The algorithm is based on cluster analysis for selecting similar repetitions or pulses from a periodic single. This method selects individual pulses without noise, returns a clean pulse signal, and terminates when a sufficiently clean and representative signal is received. The algorithm is designed to be sufficiently compact to be implemented on a microcontroller embedded within a medical device. It has been validated through the removal of noise from an exemplar photoplethysmography (PPG) signal, showing increasing benefit as the noise contamination of the signal increases. The algorithm design is generalised to be applicable for a wide range of physiological (physical) signals.

A novel method to precisely detect apnea and hypopnea events by airflow and oximetry signals

Sleep apnea hypopnea syndrome (SAHS) affects people's quality of life. The apnea hypopnea index (AHI) is the key indicator for diagnosing SAHS. The determination of the AHI is based on accurate detection of apnea and hypopnea events. This paper provides a novel method to detect apnea and hypopnea events based on the respiratory nasal airflow signal and the oximetry signal. The method uses sliding window and short time slice methods to eliminate systematic and sporadic noise of the airflow signal for improving the detection precision. Using this algorithm, the sleep data of 30 subjects from the Huaxi Sleep Center of Sichuan University (HSCSU) and the Teaching Hospital of Chengdu University of Traditional Chinese Medicine (THCUTCM) were auto-analyzed for detecting the apnea and hypopnea events. The total predicted apnea and hypopnea events were 8470. By manual investigation, the sensitivity and positive predictive value (PPV) of detecting apnea and hypopnea events were 97.6% and 95.7%, respectively. The sleep data of 28 subjects form HSCSU were auto-diagnosed SAHS according to the AHI. The sensitivity and PPV were 92.3% and 92.3%, respectively. This is an effective and precise method to diagnose SAHS. It can fit the home care SAHS screener.

Latin America and Democracy. UN (United Nations Organization)

Whether due to ineptitude, or pressured from within and outside their respective countries, the military dictatorships that ruled the Latin American continent in the last three decades have had to go through the witness to civil governments and return to the barracks (from where some they still guard the good work of politicians with veiled threats and sporadic noise of sabers). However, these civil governments are often not representative of the values and concerns of popular majorities. They are often cosmetically parliamentary democracies in which hegemonic power groups continue to ensure national political and economic control.

GAn improved interface for universal acoustic flame detection in modified supercritical fluid chromatography

A novel method of interfacing the acoustic flame detector (AFD) with modified supercritical fluid chromatography (SFC) is presented. By applying resistive heating directly to the burner region between the restrictor outlet and the acoustic flame, infrequent severe noise, baseline drifting, and peak deformations that can occasionally be observed with the AFD are eliminated. For example, by increasing the interface temperature only a few hundred degrees Celsius, such sporadic noise in the detector can be reduced nearly ten-fold resulting in smooth stable operation of the AFD. Further, for various levels of methanol modified supercritical carbon dioxide mobile phase examined, the interface was observed to reduce detector noise in each to a common minimal range near 10-25 Hz when an appropriate temperature was achieved. The method is simply assembled, inexpensive to construct, and robust in its daily operation. Overall, the heated interface developed and presented facilitates reliable AFD operation in modified SFC, and supports further exploration and implementation of this sensor as an alternative universal detector in separations requiring an organic cosolvent in the mobile phase.

The impact of Rayleigh backscatter induced noise on QPSK transmission with Fabry-Perot lasers

The use of uncooled Fabry-Perot (FP) lasers for upstream data in cable television (CATV) networks has recently been proposed. The merits of this choice, from use in telecommunications networks, include low cost and proven reliability. Indeed, it has been shown that these devices can transmit multiple quadrature phase shift keying (QPSK) signals in an error-free manner. However, there is a bit-error-rate (BER) penalty associated with these lasers, that impacts the useable error free range. We show this degradation is caused by laser sporadic noise generated by Rayleigh backscatter into the laser from the fiber plant. In addition, we show the noise to be impulsive and that a 14 dB CNR penalty, at 1/spl times/10/sup -8/ BER, versus theoretical performance is possible.

LOTEM data processing for areas with high cultural noise levels

Long Offset Transient Electromagnetic (LOTEM) soundings conducted in industrialized areas demand complex approaches to data processing for the removal of unwanted cultural noise. When sporadic, aperiodic noise (from pumps, electric fences, etc.) or periodic noise from power lines is present, as is the case in the F.R.G., standard data processing commonly outputs averaged transients which are contaminated by the noise influence(s), and any processing done after stacking usually fails to improve the data resolution. A new approach to the problem of periodic noise is to process each individual transient before averaging, which greatly improves the signal-to-noise ratios and provides a more detailed geoelectric image of the subsurface. An improved prestack, digital-recursive, true-amplitude notch filter is applied to synthetic and real data. Sporadic noise is best handled with either a symmetric or an area-defined rejection selective stack, whereby the signal amplitudes are selectively kept and the noise is statistically rejected. Data show that for LOTEM soundings the symmetric rejection algorithm improves the signal-to-noise ratios significantly. After selective stacking, the system response is deconvolved and the data smoothed with digital recursive filters. The output transients are then converted to apparent resistivities and inverted to a horizontally layered Earth model. A comparison of prestack and poststack data processing for one profile shows that with prestack data processing the section correlates well with the known geology of the area, whereas poststack processing outputs an interpreted geoelectric section that is inconsistent with the geology.

Simulations of noise rejection and mantissa‐only recording: An experiment in high‐amplitude noise reduction with COCORP data

Vertical stacking of Vibroseis® sweeps for the purposes of reflection data enhancement and data compression often smears sporadic noise bursts into summed traces with degrading effects. One noisy record can easily nullify the signal enhancement benefits of stacking the remaining good sweeps (e.g., Gimlin and Smith, 1980). This paper compares different methods of noise editing before vertical stacking, as applied to deep seismic data collected by COCORP (Consortium for Continental Reflection Profiling) in conditions of heavy traffic noise. Noise editing is of particular concern in crustal seismic work where weak reflections from great depths are often critical to interpretations. In our test, individual sweeps rather than the field sum of these sweeps were recorded separately on tape. This procedure, single‐sweep recording, allowed four computer simulations: (A) recording with no noise editing; (B) recording with a field noise‐rejection (NRJ) system; (C) using a more optimal NRJ system which was computed using the characteristics of the recorded sweeps and applied after recording; and (D) recording using mantissa‐only (MO) format. In this initial, limited experiment, we found significantly greater noise reduction with both tailored, postrecording NRJ systems and MO format than with the simple field NRJ currently employed by COCORP. Normally, these alternative recording methods are not used, either because they entail excessive tape handling and preprocessing, as in the case of single‐sweep recording and application of postrecording NRJ, or because they entail loss of dynamic range, as with MO format recording. However, this study suggests that in the presence of severe traffic noise the benefits of these alternative techniques may outweigh their drawbacks.