Dynamic Time Warping as an Alternative to Windowed Cross Correlation in Seismological Applications

Kumar, Utpal; Legendre, C. P.; Zhao, Li; Chao, B. F.

doi:10.1785/0220210288

Cited by 13 publications

(4 citation statements)

References 86 publications

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“…The signals are analyzed and compared using Pearson correlation, dynamic time warping (DTW), and time-frequency representation. Phantom 1, for instance, has a normalized DTW distance of 0.13 and a Pearson correlation coefficient of 0.99, which indicates that the two signals are reasonably well-matched Girault, Kouame, Ouahabi and Patat (2000); Niethammer, Jacobs, Qu and Jarzynski (2001); Kumar, Legendre, Zhao and Chao (2022); Bhardwaj and Maini (2022). According to an examination of the PIP data, gelatin percentage and 𝜂1 in the first layer have a positive relationship, as increasing gelatin percentage causes an increase in 𝜂1 value Nguyen et al (2014); Ismail et al (2017).…”

Section: Discussionmentioning

confidence: 99%

Using Torsional Wave Elastography to Evaluate Spring Pot Parameters In Skin Tumor Mimicking Phantoms

Almashakbeh,

Shamimi,

Callejas

et al. 2023

Preprint

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Estimating the tissue parameters of skin tumors is crucial for diagnosis and effective therapy in dermatology and related fields. However, identifying the most sensitive biomarkers require an optimal rheological model for simulating skin behavior this remains an ongoing research endeavor. Additionally, the multi-layered structure of the skin introduces further complexity to this task. In order to surmount these challenges, an inverse problem methodology, in conjunction with signal analysis techniques, is being employed. In this study, a fractional rheological model is presented to enhance the precision of skin tissue parameter estimation from the acquired signal from torsional wave elastography technique (TWE) on skin tumor-mimicking phantoms for lab validation and the estimation of the thickness of the cancerous layer. An exhaustive analysis of the spring-pot model (SP) solved by the finite difference time domain (FDTD) is conducted. The results of experiments performed using a TWE probe designed and prototyped in the laboratory were validated against ultrafast imaging carried out by the Verasonics Research System. Twelve tissue-mimicking phantoms, which precisely simulated the characteristics of skin tissue, were prepared for our experimental setting. The experimental data from these bi-layer phantoms were measured using a TWE probe, and the parameters of the skin tissue were estimated using inverse problem-solving. The agreement between the two datasets was evaluated by comparing the experimental data obtained from the TWE technique with simulated data from the spring-pot FDTD model using Pearson correlation, dynamic time warping (DTW), and time-frequency representation. Our findings show that the spring-pot FDTD model and TWE are capable of determining the mechanical properties of both layers in a bilayer phantom, using a single signal and an inverse problem approach. The ultrafast imaging and the validation of TWE results further demonstrate the robustness and reliability of our technology for a realistic range of phantoms. This fusion of the spring-pot FDTD model and TWE, as well as inverse problem-solving methods has the potential to have a considerable impact on diagnoses and treatments in dermatology and related fields.

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Section: Discussionmentioning

confidence: 99%

Using Torsional Wave Elastography to Evaluate Spring Pot Parameters In Skin Tumor Mimicking Phantoms

Almashakbeh,

Shamimi,

Callejas

et al. 2023

Preprint

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“…We have tested the accuracy of this approach using synthetic waveform datasets with waveforms of different lengths. Recent studies have shown that in many cases, DTW, which has a higher computational cost, is a superior distance metric for time series clustering when compared to cross-correlation or direct distance measures between unstretched waveforms ( 42 ).…”

Section: Methodsmentioning

confidence: 99%

Microscopic defect dynamics during a brittle-to-ductile transition

O’Ghaffari,

Peč,

Mittal

et al. 2023

Proc. Natl. Acad. Sci. U.S.A.

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Deformation of all materials necessitates the collective propagation of various microscopic defects. On Earth, fracturing gives way to crystal-plastic deformation with increasing depth resulting in a “brittle-to-ductile” transition (BDT) region that is key for estimating the integrated strength of tectonic plates, constraining the earthquake cycle, and utilizing deep geothermal resources. Here, we show that the crossing of a BDT in marble during deformation experiments in the laboratory is accompanied by systematic increase in the frequency of acoustic emissions suggesting a profound change in the mean size and propagation velocity of the active defects. We further identify dominant classes of emitted waveforms using unsupervised learning methods and show that their relative activity systematically changes as the rocks cross the brittle–ductile transition. As pressure increases, long-period signals are suppressed and short-period signals become dominant. At higher pressures, signals frequently come in avalanche-like patterns. We propose that these classes of waveforms correlate with individual dominant defect types. Complex mixed-mode events indicate that interactions between the defects are common over the whole pressure range, in agreement with postmortem microstructural observations. Our measurements provide unique, real-time data of microscale dynamics over a broad range of pressures (10 to 200 MPa) and can inform micromechanical models for semi-brittle deformation.

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“…Ganter et al (2018) proposed a similar waveform search based on ML to generate templates satisfying the alternate null hypothesis, which incorporates the possibility of false positives. Kumar et al (2022) suggested that using the dynamic time warping technique instead of cross-correlations possibly improves the performance of similar waveform searching, including template matching analysis and unsupervised waveform clustering.…”

Section: Similar Waveform Searchingmentioning

confidence: 99%

Recent advances in earthquake seismology using machine learning

Kubo,

Naoi,

Kano

2024

Earth Planets Space

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Given the recent developments in machine-learning technology, its application has rapidly progressed in various fields of earthquake seismology, achieving great success. Here, we review the recent advances, focusing on catalog development, seismicity analysis, ground-motion prediction, and crustal deformation analysis. First, we explore studies on the development of earthquake catalogs, including their elemental processes such as event detection/classification, arrival time picking, similar waveform searching, focal mechanism analysis, and paleoseismic record analysis. We then introduce studies related to earthquake risk evaluation and seismicity analysis. Additionally, we review studies on ground-motion prediction, which are categorized into four groups depending on whether the output is ground-motion intensity or ground-motion time series and the input is features (individual measurable properties) or time series. We discuss the effect of imbalanced ground-motion data on machine-learning models and the approaches taken to address the problem. Finally, we summarize the analysis of geodetic data related to crustal deformation, focusing on clustering analysis and detection of geodetic signals caused by seismic/aseismic phenomena. Graphical Abstract

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Dynamic Time Warping as an Alternative to Windowed Cross Correlation in Seismological Applications

Cited by 13 publications

References 86 publications

Using Torsional Wave Elastography to Evaluate Spring Pot Parameters In Skin Tumor Mimicking Phantoms

Using Torsional Wave Elastography to Evaluate Spring Pot Parameters In Skin Tumor Mimicking Phantoms

Microscopic defect dynamics during a brittle-to-ductile transition

Recent advances in earthquake seismology using machine learning

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