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template matching

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Journal Article
Published: 10 June 2024
Seismological Research Letters (2024) 95 (5): 2611–2621.
... introduces a novel methodology that extends an earlier time‐series feature engineering approach to include template matching against prior eruptions. We aim to identify subtle signals within seismic data to enhance our understanding of volcanic activity and future hazards. To do this, we analyze...
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Journal Article
Published: 03 April 2024
Seismological Research Letters (2024) 95 (4): 2316–2327.
...Hao Lv; Xiangfang Zeng; Gongbo Zhang; Zhenghong Song Abstract Distributed acoustic sensing (DAS) technology, combined with existing telecom fiber‐optic cable, has shown great potential in earthquake monitoring. The template matching algorithm (TMA) shows good detection capabilities but depends...
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Journal Article
Published: 17 January 2024
Seismological Research Letters (2024) 95 (3): 1949–1960.
... spatial resolution, broadband sensitivity, and the ubiquitous presence of unused telecommunication fibers in many areas of the world. In this study, we explore the feasibility of dark‐fiber array deployed in a noisy environment for detecting small explosions. We test the effectiveness of template matching...
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Journal Article
Published: 13 December 2022
Bulletin of the Seismological Society of America (2023) 113 (1): 115–130.
...‐station template matching technique to two temporary seismic networks and investigate microseismicity in southern Hispaniola. We detect a total of 6065 and 1366 new events for the 2010 and 2013–2014 datasets, respectively, using templates from pre‐existing catalogs. The magnitude of completeness ( M c...
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Journal Article
Published: 23 June 2020
Bulletin of the Seismological Society of America (2020) 110 (4): 1832–1844.
...En‐Jui Lee; Dawei Mu; Wei Wang; Po Chen ABSTRACT The templatematching algorithm (TMA) has become an important tool for detecting small and/or unconventional earthquakes, and newly detected seismic events have improved our understanding of earthquake physics, regional tectonics, and geological...
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Journal Article
Published: 04 September 2018
Bulletin of the Seismological Society of America (2018) 108 (5A): 2556–2564.
...Anthony Massari; Robert W. Clayton; Monica Kohler Abstract A method based on template matching is presented to detect and locate damage in buildings following severe shaking by an earthquake. The templates are constructed by finite‐element simulations of a suite of damage scenarios...
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Journal Article
Published: 24 January 2017
Bulletin of the Seismological Society of America (2017) 107 (2): 660–673.
... coda that a simple exponential single‐scattering formulation in a homogeneous space does not model well. Our empirical full‐envelope templatematching approach produces close fits to complicated short‐duration and high‐frequency waveforms observed at near‐field distances. Moreover, because the envelope...
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Journal Article
Journal: AAPG Bulletin
Published: 01 February 1985
AAPG Bulletin (1985) 69 (2): 287.
... by analysis of particle shapes. Template (shape) matching can be accomplished only after the digitized shapes have been normalized to a unit-sized circle and registered. Registration involves the simple computation of shape-specific points within, on, or near the 2-dimensional contour of the sand grain...
Journal Article
Published: 29 May 2019
Seismological Research Letters (2019) 90 (4): 1535–1543.
...Robert J. Skoumal; J. Ole Kaven; Jacob I. Walter ABSTRACT Oklahoma is one of the most seismically active places in the United States as a result of industry activities. To characterize the fault networks responsible for these earthquakes in Oklahoma, we relocated a large‐scale templatematching...
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Scheme of template matching by using a template derived from the d days pre‐eruptive features from an eruption (all eruptions are tested as template). (a) Example of template, which is a short time series, considered pre‐eruptive. We explore multiple template lengths ranging from 1 to 60 days. (b) Running correlation of the template over the whole feature time series. The template matching process is based on comparing the template with the long time series. In this way, when it is repeated over time, we can say that the underlying physical process in the volcano is repeated, and that would be the future element to control. The color version of this figure is available only in the electronic edition.
Published: 10 June 2024
Figure 1. Scheme of template matching by using a template derived from the d days pre‐eruptive features from an eruption (all eruptions are tested as template). (a) Example of template, which is a short time series, considered pre‐eruptive. We explore multiple template lengths ranging from 1
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Template matching using DTW distance. (a) Time series X (top), template Y (middle), and their dissimilarity measured with DTW distance (bottom). (b) The same as panel (a) but with template Y at a different time location. The color version of this figure is available only in the electronic edition.
Published: 28 March 2022
Figure 2. Template matching using DTW distance. (a) Time series X (top), template Y (middle), and their dissimilarity measured with DTW distance (bottom). (b) The same as panel (a) but with template Y at a different time location. The color version of this figure is available only
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Example of a successful template‐matching detection. Template waveforms (red) overlay continuous records (black) for three stations EAZ, LBZ, and ODZ. Both vertical and horizontal components are shown. Blue lines below each trace are cross‐correlation functions (CCFs) aligned with the start time of the template arrivals. The instantaneous cross‐correlation values are marked by the gray dash lines and specified on each trace’s left side. The individual CCFs are then shifted by the arrival‐time difference and stacked to obtain the stacked CCF shown in the bottom panel. Gray block indicates the detection threshold of 12 median absolute deviations (MADs), which equals ∼2.5 in this example. The color version of this figure is available only in the electronic edition.
Published: 18 May 2021
Figure 3. Example of a successful templatematching detection. Template waveforms (red) overlay continuous records (black) for three stations EAZ, LBZ, and ODZ. Both vertical and horizontal components are shown. Blue lines below each trace are cross‐correlation functions (CCFs) aligned
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Magnitude–frequency distribution (MFD) of the (a) template matching (TM) and (b) RESTORE catalogs (black dots represent cumulative counts and gray dots represent noncumulative counts). Vertical lines indicate Mc estimated by four different methods: Lilliefors test, max curvature, goodness‐of‐fit test (GFT), and Mc by b‐value stability (MBS). Note that events in the TM catalog falling outside the region filled by RESTORE are not included. (c) MFD of the TM catalog and related Mc estimates, only for magnitudes M≥Mc*. The color version of this figure is available only in the electronic edition.
Published: 09 April 2025
Figure 2. Magnitude–frequency distribution (MFD) of the (a) template matching (TM) and (b) RESTORE catalogs (black dots represent cumulative counts and gray dots represent noncumulative counts). Vertical lines indicate M c estimated by four different methods: Lilliefors test, max
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Template matching between a 20 s signal (Fig. 2) and day‐long continuous data from H10N1 hydrophone in 2024. Upper panels show waveform and lower panels show CC for (a) 10, (b) 11, (c) 12, (d) 13, and (e) 14 March. Magenta line indicates detection threshold, with detections exceeding threshold highlighted in orange (e.g., self‐detection on 12 March). The color version of this figure is available only in the electronic edition.
Published: 26 March 2025
Figure 4. Template matching between a 20 s signal (Fig.  2 ) and day‐long continuous data from H10N1 hydrophone in 2024. Upper panels show waveform and lower panels show CC for (a) 10, (b) 11, (c) 12, (d) 13, and (e) 14 March. Magenta line indicates detection threshold, with detections exceeding
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Located events in the template matching catalog (Shelly, 2020) used in this study and the percentage of the events reproduced by the Dice model and the BCE model, both trained with the box kernel, after applying different thresholds on the phase arrival prediction scores. An equivalent plot for the models trained with the truncated Gaussian kernel is provided in Figure S10. Note that using very low thresholds for the BCE model will likely result in many false positive detections.
Published: 24 January 2025
Figure 4. Located events in the template matching catalog ( Shelly, 2020 ) used in this study and the percentage of the events reproduced by the Dice model and the BCE model, both trained with the box kernel, after applying different thresholds on the phase arrival prediction scores
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(a) The template‐matching (TM) catalog of the 2010–2014 Pollino seismic sequence used in this work (pink circles), seismic stations used to record the earthquakes (blue diamonds), and the two main events of the sequence (ML 4.3 on the western cluster and ML 5.0 on the eastern cluster). (b) The historical earthquakes of ML 6 (Rovida et al., 2020, 2022) are shown with green squares. The Mt. Pollino area, shown in panel (a), is shown is with a red rectangle. (c) The area shown in panels (a) and (b) are shown as pink circle and blue square, respectively, compared with the Italian framework. (d) Local magnitude ML as a function of the time in the TM catalog. The stars symbol refers to the two main event of the sequence. The color version of this figure is available only in the electronic edition.
Published: 27 June 2024
Figure 1. (a) The templatematching (TM) catalog of the 2010–2014 Pollino seismic sequence used in this work (pink circles), seismic stations used to record the earthquakes (blue diamonds), and the two main events of the sequence ( M L  4.3 on the western cluster and M L  5.0
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Scheme of performance evaluation of the template matching using receiver operating characteristic (ROC) curves. (a) The resulting correlation coefficient time series (template matching output) for a template with an arbitrary. (b) Illustration of how the correlation coefficient time series, is used to determine true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN) using a threshold and a pre‐eruption eruption window. TP represents correctly predicted eruptions when the model output surpasses the threshold within the pre‐eruptive window. FN indicates missed eruptions when the output fails to reach the threshold during this window. FP signifies incorrectly classified eruptions when the threshold is surpassed outside the pre‐eruptive window, whereas TN denotes correctly classified noneruptive data. The choice of threshold greatly influences the counts of TP, FP, FN, and TN. (c) A hundred threshold values between 0 and 1 are used to count TP and FP rates for computing receiver operator curves (ROC) and their area under the curve (AUC). AUC is used as metric to evaluate the model performance when classifying as a whole (both pre‐eruptive and noneruptive data) across all possible thresholds. The color version of this figure is available only in the electronic edition.
Published: 10 June 2024
Figure 2. Scheme of performance evaluation of the template matching using receiver operating characteristic (ROC) curves. (a) The resulting correlation coefficient time series (template matching output) for a template with an arbitrary. (b) Illustration of how the correlation coefficient time
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Template matching process overview. The flowchart details the individual steps undertaken within the script to convert the portable document file (PDF) to portable network graphics (PNG), carry out the template matching process and generate the output in the form of an image overlay. DPI = dots per inch.
Published: 01 March 2024
Figure 5. Template matching process overview. The flowchart details the individual steps undertaken within the script to convert the portable document file (PDF) to portable network graphics (PNG), carry out the template matching process and generate the output in the form of an image overlay
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Template matching process consists of (A) a library of symbols (in this case, sedimentary structures) and (B) output generated by the script showing the matches where each color is representative of a matched symbol.
Published: 01 March 2024
Figure 4. Template matching process consists of (A) a library of symbols (in this case, sedimentary structures) and (B) output generated by the script showing the matches where each color is representative of a matched symbol.
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Template matching detections sorted chronologically. Waveforms are shown for the vertical component of station PRO. They are aligned on the P‐wave arrival, normalized, and filtered between 8 and 32 Hz with a fourth‐order Butterworth filter. The color version of this figure is available only in the electronic edition.
Published: 16 February 2024
Figure 4. Template matching detections sorted chronologically. Waveforms are shown for the vertical component of station PRO. They are aligned on the P ‐wave arrival, normalized, and filtered between 8 and 32 Hz with a fourth‐order Butterworth filter. The color version of this figure