Understanding Seismic Wave Propagation

Onajite, Enwenode

doi:10.1016/b978-0-12-420023-4.00002-2

Cited by 2 publications

(2 citation statements)

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“…The closer r is to 1, the stronger the direct relationship between the variables, the closer r is to -1the inverse. When r = 0, there is no significant relationship between the two variables [15][16].…”

Section: Methodsmentioning

confidence: 98%

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VEVCC program for concatenation of volcanic events based on cross-correlation analysis

Dairoh,

Fauzi Masykuri,

Setyo Yuliatmoko

et al. 2023

E3S Web Conf.

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Volcanic eruptions pose a significant risk to communities located near active volcanoes. Disaster mitigation and risk reduction efforts rely on detecting and monitoring volcanic activity as early as possible. This article introduces VEVCC, a MATLAB-based application designed to precisely identify and extract volcanic seismic events from continuous data streams. VEVCC's primary objective is to facilitate the creation of an Excel file containing the arrival times of detected events, which can then be used for various purposes, such as early warning disaster mitigation and automated event identification via machine learning techniques. VEVCC utilizes cross-correlation algorithms to identify volcanic seismic events. It separates these events from background noise and other sources of seismicity, allowing for the construction of a clean and informative dataset. The extracted data is a valuable resource for estimating the frequency of volcanic events and evaluating patterns of volcanic activity. VEVCC's time-stamped event data is indispensable for improving early warning systems, real-time surveillance, and automated event identification. We tested the program on the Merapi volcano datasets during a 1998 campaign for a broadband experiment with the capability to extract the events automatically. Further machine-learning models and algorithms enhance the automatic recognition of volcanic events.

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Section: Methodsmentioning

confidence: 98%

“…The cross-correlation of the two signals xp and yp can be represented in the following form. The similarity between the events xp(t) and yp(t) is quantified using the cross-correlation function rxy(τ) [15]:…”

Section: Methodsmentioning

confidence: 99%

VEVCC program for concatenation of volcanic events based on cross-correlation analysis

Dairoh,

Fauzi Masykuri,

Setyo Yuliatmoko

et al. 2023

E3S Web Conf.

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Shear Wave Measurements of a Gelatin’s Young’s Modulus

Pansino

Taisne

2020

Front. Earth Sci.

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Gelatin is a commonly used material for analog experiments in geophysics, investigating fluid-filled fracture propagation (e.g., magmatic dikes), as well as fault slip. Quantification of its physical properties, such as the Young's modulus, is important for scaling experimental results to nature. Traditional methods to do so are either time consuming or destructive and cannot be performed in situ. We present an optical measurement technique, using shear waves. Polarizing filters enable visualization of the deviatoric stresses in a block of gelatin, so shear wave propagation can be observed. We demonstrate how the wave velocity can be measured and related to the Young's modulus, show how the results are comparable to another methodology and discuss processing techniques that maximize the measurement precision. This methodology is useful for experimentalist, as it is simple to implement into a laboratory setting, can make precise, time-efficient estimates of the material strength and additionally is non-destructive and can be performed in situ.

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Understanding Seismic Wave Propagation

Cited by 2 publications

References 0 publications

VEVCC program for concatenation of volcanic events based on cross-correlation analysis

VEVCC program for concatenation of volcanic events based on cross-correlation analysis

Shear Wave Measurements of a Gelatin’s Young’s Modulus

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