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Добірка наукової літератури з теми "NCF (Noise Correlation Function)"

Автор: Grafiati

Опубліковано: 10 березня 2023

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Статті в журналах з теми "NCF (Noise Correlation Function)"

1

Peng, Hanshu, Zhongliang Wu, and Changsheng Jiang. "Pre-seismic changes of noise correlation function (NCF) before the Wenchuan earthquake?" Concurrency and Computation: Practice and Experience 22, no. 12 (2009): 1774–83. http://dx.doi.org/10.1002/cpe.1517.

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2

Kuponiyi, Ayodeji Paul, and Honn Kao. "Temporal Variation in Cultural Seismic Noise and Noise Correlation Functions during COVID-19 Lockdown in Canada." Seismological Research Letters 92, no. 5 (2021): 3024–34. http://dx.doi.org/10.1785/0220200330.

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Abstract The COVID-19 pandemic of 2020 led to a widespread lockdown that restricted human activities, particularly land, air, and maritime traffic. The “quietness” on land and ocean that followed presents an opportunity to measure an unprecedented reduction in human-related seismic activities and study its effect on the short-period range of ambient noise cross-correlation functions (NCFs). We document the variations in seismic power levels and signal quality of short-period NCFs measured by four seismographs located near Canadian cities across the pandemic-defined timeline. Significant drops

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3

Hawkins, Rhys, and Malcolm Sambridge. "An Adjoint Technique for Estimation of Interstation Phase and Group Dispersion from Ambient Noise Cross Correlations." Bulletin of the Seismological Society of America 109, no. 5 (2019): 1716–28. http://dx.doi.org/10.1785/0120190060.

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Abstract A method of extracting group and phase velocity dispersions jointly for Love‐ and Rayleigh‐wave observations is presented. This method uses a spectral element representation of a path average Earth model parameterized with density, shear‐wave velocity, radial anisotropy, and VP/VS ratio. An initial dispersion curve is automatically estimated using a heuristic approach to prevent misidentification of the phase. A second step then more accurately fits the observed noise correlation function (NCF) between interstation pairs in the frequency domain. For good quality cross correlations wit

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4

Jiang, Changsheng, and Zhongliang Wu. "Temporal variation of noise correlation function (NCF) in Beijing and surroundings: Its relation with climate events and implications." Earthquake Science 22, no. 1 (2009): 75–81. http://dx.doi.org/10.1007/s11589-009-0075-5.

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5

Ragland, John, and Shima Abadi. "Exploring surface source distributions for ocean ambient noise interferometry with airgun shots." Journal of the Acoustical Society of America 151, no. 4 (2022): A190. http://dx.doi.org/10.1121/10.0011063.

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Ambient noise interferometry utilizes the cross-correlations of ambient sound to estimate the time domain Green’s function (TDGF). We have previously shown that ambient noise interferometry can resolve multi-path arrivals between two bottom-mounted hydrophones separated by 3.2 km, at a depth of 1500 m, and located 470 km off the Oregon coast. In 2019, a seismic reflection survey was conducted directly over the two hydrophones for 28 days covering a 763 km2 area. The airgun shots occurred every 37.5 m while the ship moved at a speed of ∼4.5 knots, equivalent to a shooting interval of 16 s. The

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6

Steinmann, René, Céline Hadziioannou, and Eric Larose. "Effect of centimetric freezing of the near subsurface on Rayleigh and Love wave velocity in ambient seismic noise correlations." Geophysical Journal International 224, no. 1 (2020): 626–36. http://dx.doi.org/10.1093/gji/ggaa406.

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SUMMARY About a decade ago, noise-based monitoring became a key tool in seismology. One of the tools is passive image interferometry (PII), which uses noise correlation functions (NCF) to retrieve seismic velocity variations. Most studies apply PII to vertical components recording oceanic low-frequent ambient noise ( &lt; 1 Hz). In this work, PII is applied to high-frequent urban ambient noise ( &gt; 1 Hz) on three three-component sensors. With environmental sensors inside the subsurface and in the air, we are able to connect observed velocity variations with environmental parameters.

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7

Tian, Jinyu, Jian Lin, Fan Zhang, et al. "Time Correction of Ocean-Bottom Seismometers Using Improved Ambient Noise Cross Correlation of Multicomponents and Dual-Frequency Bands." Seismological Research Letters 92, no. 3 (2021): 2004–14. http://dx.doi.org/10.1785/0220200358.

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Abstract An effective approach was developed for identifying and correcting ocean-bottom seismometer (OBS) time errors through improving ambient noise cross-correlation function (NCCF) analysis and combination with other methods. Significant improvements were illustrated through analyzing data from a passive-source seismic experiment in the southwestern sub-basin of the South China Sea. A novel method was first developed that can effectively identify errors in the sampling frequency of the OBS instruments. The traditional NCCF method was then expanded by increasing the analyzed data spectrum f

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8

Cheng, Wei, Lintao Liu, and Guocheng Wang. "A new method for estimating the correlation of seismic waveforms based on the NTFT." Geophysical Journal International 226, no. 1 (2021): 368–76. http://dx.doi.org/10.1093/gji/ggab047.

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SUMMARY We propose a new correlation function called the similarity coefficient (SC) based on the normal time–frequency transform (NTFT) to evaluate the similarity between two non-stationary seismic signals as a function of the delay time. The SC is defined in the time–frequency spectrum of the NTFT, and the instantaneous phase and amplitude of each frequency component in a signal are used to calculate the SC. Our simulation experiments demonstrate that the SC method can effectively recognize similar signals compared to the conventional normalized cross-correlation coefficient (NCC) under high

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9

Wang, Jian, Xiaolei Lv, Zenghui Huang, and Xikai Fu. "An Epipolar HS-NCC Flow Algorithm for DSM Generation Using GaoFen-3 Stereo SAR Images." Remote Sensing 15, no. 1 (2022): 129. http://dx.doi.org/10.3390/rs15010129.

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Radargrammetry is a widely used methodology to generate the large-scale Digital Surface Model (DSM). Stereo matching is the most challenging step in radargrammetry due to the significant geometric differences and the inherent speckle noise. The speckle noise results in significant grayscale differences of the same feature points, which makes the traditional Horn–Schunck (HS) flow or multi-window zero-mean normalized cross-correlation (ZNCC) methods degrade. Therefore, this paper proposes an algorithm named Epipolar HS-NCC Flow (EHNF) for dense stereo matching, which is an improved HS flow meth

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10

Perez-Cham, Oscar E., Cesar Puente, Carlos Soubervielle-Montalvo, Gustavo Olague, Carlos A. Aguirre-Salado, and Alberto S. Nuñez-Varela. "Parallelization of the Honeybee Search Algorithm for Object Tracking." Applied Sciences 10, no. 6 (2020): 2122. http://dx.doi.org/10.3390/app10062122.

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Object tracking refers to the relocation of specific objects in consecutive frames of a video sequence. Presently, this visual task is still considered an open research issue, and the computer science community attempted solutions from the standpoint of methodologies, algorithms, criteria, benchmarks, and so on. This article introduces a GPU-parallelized swarm algorithm, called the Honeybee Search Algorithm (HSA), which is a hybrid algorithm combining swarm intelligence and evolutionary algorithm principles, and was previously designed for three-dimensional reconstruction. This heuristic inspi

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