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Journal Article

Detecting Repeating Earthquakes on the San Andreas Fault with Unsupervised Machine Learning of Spectrograms Open Access

Theresa Sawi, Felix Waldhauser, Benjamin K. Holtzman, Nate Groebner
Published: 22 December 2023
The Seismic Record (2023) 3 (4): 376–384.
DOI: https://doi.org/10.1785/0320230033
... ( Holtzman et al. , 2018 ) to reduce earthquake spectrograms into low‐dimensional, characteristic fingerprints, and apply hierarchical clustering to group similar fingerprints together independent of location, allowing for a global search for potential RES throughout the data set. We then relocate...
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View PDF for article title, Detecting Repeating Earthquakes on the San Andreas Fault with Unsupervised Machine Learning of <span class="search-highlight">Spectrograms</span>
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Journal Article

Applying Feature Transformation‐Based Domain Confusion to Neural Network for the Denoising of Dispersion Spectrograms Available to Purchase

Weibin Song, Shichuan Yuan, Ming Cheng, Guanchao Wang, Yilong Li, Xiaofei Chen
Published: 17 October 2023
Seismological Research Letters (2024) 95 (1): 378–396.
DOI: https://doi.org/10.1785/0220230103
...Weibin Song; Shichuan Yuan; Ming Cheng; Guanchao Wang; Yilong Li; Xiaofei Chen Abstract Ambient noise tomography has been widely used to estimate the shear‐wave velocity structure of the Earth. A key step in this method is to pick dispersions from dispersion spectrograms. Using the frequency–Bessel...
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View PDF for article title, Applying Feature Transformation‐Based Domain Confusion to Neural Network for the Denoising of Dispersion <span class="search-highlight">Spectrograms</span>
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Journal Article

Direct Image Dissimilarity Inversion of Ambient Noise Multimodal Dispersion Spectrograms Available to Purchase

Qi Liu, Xiaofei Chen, Lina Gao, Zhenjiang Yu, Juqing Chen
Published: 16 June 2023
Bulletin of the Seismological Society of America (2023) 113 (5): 1960–1981.
DOI: https://doi.org/10.1785/0120230012
...Qi Liu; Xiaofei Chen; Lina Gao; Zhenjiang Yu; Juqing Chen ABSTRACT The frequency–Bessel transform (F–J) method, which can reliably provide multimodal surface‐wave dispersion spectrograms from recorded ambient noise, has been applied in many studies of the earth’s velocity structure. However...
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View PDF for article title, Direct Image Dissimilarity Inversion of Ambient Noise Multimodal Dispersion <span class="search-highlight">Spectrograms</span>
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Journal Article

Automatically Extracting Surface‐Wave Group and Phase Velocity Dispersion Curves from Dispersion Spectrograms Using a Convolutional Neural Network Available to Purchase

Shaobo Yang, Haijiang Zhang, Ning Gu, Ji Gao, Jian Xu, Jiaqi Jin, Junlun Li, Huajian Yao
Published: 30 March 2022
Seismological Research Letters (2022) 93 (3): 1549–1563.
DOI: https://doi.org/10.1785/0220210280
... surface‐wave group and phase velocity dispersion curves from cross‐correlation functions of continuous ambient noise recordings. One traditional way is to manually pick the dispersion curves from dispersion spectrograms in the period‐velocity domains, which is very labor intensive and time consuming...
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View PDF for article title, Automatically Extracting Surface‐Wave Group and Phase Velocity Dispersion Curves from Dispersion <span class="search-highlight">Spectrograms</span> Using a Convolutional Neural Network
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Journal Article

Detection of Short Time Transients from Spectrograms Using Scan Statistics Available to Purchase

Steven R. Taylor, Stephen J. Arrowsmith, Dale N. Anderson
Published: 01 October 2010
Bulletin of the Seismological Society of America (2010) 100 (5A): 1940–1951.
DOI: https://doi.org/10.1785/0120100017
...Steven R. Taylor; Stephen J. Arrowsmith; Dale N. Anderson Abstract We present a methodology for the detection of small, impulsive signal transients using time-frequency spectrograms closely related to the emerging field of scan statistics. In local monitoring situations, single-channel detection...
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View PDF for article title, Detection of Short Time Transients from <span class="search-highlight">Spectrograms</span> Using Scan Statistics
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Journal Article

Difference Spectrograms: A New Method for Studying S -Wave Generation from Explosions Available to Purchase

Indra N. Gupta, Howard J. Patton
Published: 01 October 2008
Bulletin of the Seismological Society of America (2008) 98 (5): 2460–2468.
DOI: https://doi.org/10.1785/0120080057
...Indra N. Gupta; Howard J. Patton Abstract A new method for studying the source characteristics of regional phases including explosion-generated S waves is developed and utilizes differences between spectrograms of two closely located explosions recorded at a common station. Relative source effects...
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View PDF for article title, Difference <span class="search-highlight">Spectrograms</span>: A New Method for Studying S -Wave Generation from Explosions
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Journal Article

A Spectrogram‐Based Method of Rg Detection for Explosion Monitoring Available to Purchase

Colin T. O’Rourke, G. Eli Baker
Published: 06 December 2016
Bulletin of the Seismological Society of America (2017) 107 (1): 34–42.
DOI: https://doi.org/10.1785/0120160184
... using the spectrogram of the seismic signal to find peaks in the characteristic Rg frequency band. We tested the detector using a network of over 200 seismometers in Wyoming, which recorded dozens of nearby coal‐mining blasts and active‐source tamped borehole shots. The detector finds peaks...
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View PDF for article title, A <span class="search-highlight">Spectrogram</span>‐Based Method of Rg Detection for Explosion Monitoring
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Journal Article

Enhancing the Frequency–Bessel Spectrogram of Ambient Noise Cross‐Correlation Functions Available to Purchase

Gongheng Zhang, Qi Liu, Xiaofei Chen
Published: 06 December 2022
Bulletin of the Seismological Society of America (2023) 113 (1): 361–377.
DOI: https://doi.org/10.1785/0120220124
...Gongheng Zhang; Qi Liu; Xiaofei Chen ABSTRACT The frequency–Bessel (F–J) spectrogram has been used for the extraction of multimodal dispersion curves to constrain the fine crustal shear‐wave velocity structure. The original F–J spectrogram was contaminated with curved as well as straight crossed...
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View PDF for article title, Enhancing the Frequency–Bessel <span class="search-highlight">Spectrogram</span> of Ambient Noise Cross‐Correlation Functions
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Journal Article

Seismic spectral decomposition using deconvolutive short-time Fourier transform spectrogram Available to Purchase

Wenkai Lu, Fangyu Li
Journal: Geophysics
Published: 31 January 2013
Geophysics (2013) 78 (2): V43–V51.
DOI: https://doi.org/10.1190/geo2012-0125.1
... by applying a 2D deconvolution operation on the short-time Fourier transform (STFT) spectrogram. For seismic spectral decomposition, to reduce the computation burden caused by the 2D deconvolution operation in the DSTFT, the 2D STFT spectrogram is cropped into a smaller area, which includes the positive...
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View PDF for article title, Seismic spectral decomposition using deconvolutive short-time Fourier transform <span class="search-highlight">spectrogram</span>
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Journal Article

Classification of Local Seismic Events in the Utah Region: A Comparison of Amplitude Ratio Methods with a Spectrogram‐Based Machine Learning Approach Available to Purchase

Rigobert Tibi, Lisa Linville, Christopher Young, Ronald Brogan
Published: 22 October 2019
Bulletin of the Seismological Society of America (2019) 109 (6): 2532–2544.
DOI: https://doi.org/10.1785/0120190150
... the populations under consideration. The spectrogram‐based ML approach involves trained convolutional neural network (CNN) models. To our knowledge, this study is the first of its kind that directly compares and contrasts the performances—in terms of discrimination power—of a traditional, physics‐based (AR...
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View PDF for article title, Classification of Local Seismic Events in the Utah Region: A Comparison of Amplitude Ratio Methods with a <span class="search-highlight">Spectrogram</span>‐Based Machine Learning Approach
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F-J spectrograms from the MASW data. The blue lines are the picked dispersion curves of Rayleigh waves, where m0 and m1 denote the fundamental mode and the first higher mode, respectively. (a) The F-J spectrogram for the spatial window at a distance of 24 m, (b) the F-J spectrogram for the spatial window at a distance of 834 m, and (c) the F-J spectrogram for the spatial window at a distance of 1410 m.

F-J spectrograms from the MASW data. The blue lines are the picked dispersi... Available to Purchase

Published: 11 June 2025
Figure 10. F-J spectrograms from the MASW data. The blue lines are the picked dispersion curves of Rayleigh waves, where m0 and m1 denote the fundamental mode and the first higher mode, respectively. (a) The F-J spectrogram for the spatial window at a distance of 24 m, (b) the F-J spectrogram
Image
The F-H convergent spectrograms of synthetic OBC data in Figures 1–4. The four F-H convergent spectrograms in (a–d) are extracted from OBC geophone waveforms of Figure 1a–1d. The F-H convergent spectrograms in (e–h) are extracted from OBC hydrophone waveforms of Figure 2a–2d. The F-H convergent spectrograms in (i–l) are extracted from OBC geophone waveforms of Figure 3a–3d. The F-H convergent spectrograms in (m–p) are extracted from OBC hydrophone waveforms of Figure 4a–4d.

The F-H convergent spectrograms of synthetic OBC data in Figures  1 – 4 . T... Available to Purchase

Published: 31 March 2025
Figure 5. The F-H convergent spectrograms of synthetic OBC data in Figures  1 – 4 . The four F-H convergent spectrograms in (a–d) are extracted from OBC geophone waveforms of Figure  1a – 1d . The F-H convergent spectrograms in (e–h) are extracted from OBC hydrophone waveforms of Figure  2a – 2d
Image
The diagram illustrates the moving spatial window method. Panel (a) depicts the spatial relationship between the spatial window and the shot gathers. The spatial window is utilized to select seismic traces from the shot gathers for spectrogram extraction. The spectrograms extracted from the seismic traces selected from shot gather 2, 3, 5, and 6 are displayed as panels (b–e), respectively. Panel (f) displays the spectrogram obtained by stacking panels (b–e). Panel (g) shows the spectrogram obtained by stacking the spectrograms from all shot gathers sampled by the spatial window. The black curves in panel (g) are the multimodal dispersion curves extracted for inversion. The color version of this figure is available only in the electronic edition.

The diagram illustrates the moving spatial window method. Panel (a) depicts... Available to Purchase

Published: 17 April 2025
Figure 3. The diagram illustrates the moving spatial window method. Panel (a) depicts the spatial relationship between the spatial window and the shot gathers. The spatial window is utilized to select seismic traces from the shot gathers for spectrogram extraction. The spectrograms extracted from
Image
Stacked F-J spectrograms and picked dispersion curves of Rayleigh waves from the SSR data. The blue lines represent the picked dispersion curves of Rayleigh waves, where m0, m1, and m2 denote the fundamental mode, the first higher mode, and the second higher mode, respectively. (a) The F-J spectrogram for the spatial window at a distance of 24 m. The signal marked by the ellipse is not selected because it is difficult to determine the mode number. (b) The F-J spectrogram for the spatial window at a distance of 834 m. The signal marked by the ellipse is not selected because of the low resolution. (c) The F-J spectrogram for the spatial window at a distance of 1410 m.

Stacked F-J spectrograms and picked dispersion curves of Rayleigh waves fro... Available to Purchase

Published: 11 June 2025
Figure 5. Stacked F-J spectrograms and picked dispersion curves of Rayleigh waves from the SSR data. The blue lines represent the picked dispersion curves of Rayleigh waves, where m0, m1, and m2 denote the fundamental mode, the first higher mode, and the second higher mode, respectively
Journal Article

Constraining the shallow S-wave velocity structure by surface waves recorded in the shallow seismic reflection survey Available to Purchase

Wenbin Guo, Shichuan Yuan, Shuai Zhao, Xiaofei Chen
Journal: Geophysics
Published: 11 June 2025
Geophysics (2025) B183–B192.
DOI: https://doi.org/10.1190/geo2024-0765.1
...Figure 10. F-J spectrograms from the MASW data. The blue lines are the picked dispersion curves of Rayleigh waves, where m0 and m1 denote the fundamental mode and the first higher mode, respectively. (a) The F-J spectrogram for the spatial window at a distance of 24 m, (b) the F-J spectrogram...
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The F-H convergent spectrograms of field OBC data in Figures 7 and 8. The four F-H convergent spectrograms in (a–d) are extracted from the OBC hydrophone waveforms of Figure 7a–7d. The F-H convergent spectrograms in (e–h) are extracted from OBC hydrophone waveforms of Figure 8a–8d.

The F-H convergent spectrograms of field OBC data in Figures  7 and 8 . T... Available to Purchase

Published: 31 March 2025
Figure 9. The F-H convergent spectrograms of field OBC data in Figures  7 and 8 . The four F-H convergent spectrograms in (a–d) are extracted from the OBC hydrophone waveforms of Figure  7a – 7d . The F-H convergent spectrograms in (e–h) are extracted from OBC hydrophone waveforms of Figure  8a
Image
Individual F-J spectrograms extracted from the different shot gathers of the SSR data using the spatial window at the distance of 24 m. (a) The location of the spatial window and the seismic sources generating the spectrograms in panels (b)–(e). (b–e) The F-J spectrograms extracted from the shot gathers generated by the respective sources. The offset represents the distance between the center of the spatial window and the seismic source, where negative values indicate that the seismic source is to the left of the spatial window and positive values indicate that it is to the right.

Individual F-J spectrograms extracted from the different shot gathers of th... Available to Purchase

Published: 11 June 2025
Figure 4. Individual F-J spectrograms extracted from the different shot gathers of the SSR data using the spatial window at the distance of 24 m. (a) The location of the spatial window and the seismic sources generating the spectrograms in panels (b)–(e). (b–e) The F-J spectrograms extracted from
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DAS records of typical events recorded during our DAS development. (a–d) Sources of train, car, vibrator vehicle, and industrial factory, with each figure displaying four panels: a single channel number (CN) waveform, a time–frequency spectrogram of the waveform, a multichannel waveform and a space–frequency spectrogram panel from top to bottom. The delay time label indicates the timestamp of the space–frequency spectrogram relative to the starting time (0 s). The red line in the first panel of car represents the quasi‐static waveform after low‐pass filtering. (e) The earthquake recorded by the DAS. The earthquake panels on the left represent the single‐channel waveform and spectrogram, and the panels on the right represent the multichannel waveform and space–frequency spectrogram. The arrival times of P waves and S waves of the earthquake are marked with red and blue dots, respectively, in the figures. The short‐time Fourier transform (STFT) time windows are set to 2.56 s, with an overlap of 0.9 between adjacent time windows. The color version of this figure is available only in the electronic edition.

DAS records of typical events recorded during our DAS development. (a–d) So... Available to Purchase

Published: 17 April 2025
Figure 2. DAS records of typical events recorded during our DAS development. (a–d) Sources of train, car, vibrator vehicle, and industrial factory, with each figure displaying four panels: a single channel number (CN) waveform, a time–frequency spectrogram of the waveform, a multichannel waveform
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The observation system for recording synthetic ambient noise, and spectrograms extracted from the ambient noise. (a) Observation system: the black triangles represent receivers and the red stars denote random seismic sources. The seismic sources are inside the observation array, which is different from the situation in which the sources are outside the array in Figure 4a. (b) The F-H spectrogram, (c) the F-J spectrogram, (d) the F-H spectrogram and theoretical Rayleigh wave dispersion curve points, and (e) the F-H converging spectrogram.

The observation system for recording synthetic ambient noise, and spectrogr... Available to Purchase

Published: 14 February 2024
Figure 6. The observation system for recording synthetic ambient noise, and spectrograms extracted from the ambient noise. (a) Observation system: the black triangles represent receivers and the red stars denote random seismic sources. The seismic sources are inside the observation array, which
Image
The synthetic passive source observation system, synthetic ambient noise waveforms, and Rayleigh wave dispersion energy spectrograms extracted from the ambient noise data. (a) Observation system: the red stars represent randomly distributed vertical force sources and the black triangles denote seismic stations, (b) the vertical components of synthetic ambient noise data, (c) the F-H spectrogram, (d) the F-J spectrogram, (e) the F-H spectrogram and theoretical Rayleigh wave dispersion curve points, and (f) the F-H converging spectrogram.

The synthetic passive source observation system, synthetic ambient noise wa... Available to Purchase

Published: 14 February 2024
Figure 4. The synthetic passive source observation system, synthetic ambient noise waveforms, and Rayleigh wave dispersion energy spectrograms extracted from the ambient noise data. (a) Observation system: the red stars represent randomly distributed vertical force sources and the black triangles