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Imaging the Earth's Near Surface wit...

Yang, Yan,

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Imaging the Earth's Near Surface with Dense Seismic Observation /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Imaging the Earth's Near Surface with Dense Seismic Observation // Yan Yang.
作者:	Yang, Yan,
面頁冊數:	1 electronic resource (221 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
Contained By:	Dissertations Abstracts International86-04B.
標題:	Random access memory. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31596687
ISBN:	9798342127349

Imaging the Earth's Near Surface with Dense Seismic Observation /
Yang, Yan,

Imaging the Earth's Near Surface with Dense Seismic Observation /Yan Yang. - 1 electronic resource (221 pages)

Source: Dissertations Abstracts International, Volume: 86-04, Section: B.

Understanding the Earth's near surface is critical for assessing seismic hazards and ensuring environmental sustainability. In this thesis, I explore the use of advanced observation and analysis techniques for near-surface imaging with big seismic data. Chapters 2-6 focus on the applications of Distributed Acoustic Sensing (DAS). DAS is an emerging sensing technology that transforms fiber-optic cables into dense seismic arrays. In Chapter 2, I introduce a high-performance Python tool for computing seismic ambient noise cross-correlation with large volumes of DAS data. In Chapter 3, I perform ambient noise tomography using a DAS array in Ridgecrest, California, to resolve spatial variation of the near-surface structure, revealing its correlation with earthquake ground shaking amplification. In Chapter 4, I use surface wave scattering observed in the DAS noise crosscorrelation for fault zone detection and characterization. In Chapter 5, I analyze three years of DAS noise cross-correlation to monitor seismic velocity changes, providing insights into vadose zone soil moisture dynamics and water resource management in the context of climate change. In Chapter 6, I use a DAS array at the South Pole to characterize firn structure for a better understanding of cryosphere mass balance. Chapters 7 and 8 focus on imaging geological structures in the urban Los Angeles region using dense arrays of geophones. Chapter 7 uses converted S-to-p phases recorded by a dense network of low-cost accelerometers to map the basin depth. Chapter 8 investigates shallow seismicity in the Long Beach area to illuminate complex fault structures. In Chapters 9 and 10, I apply a state-of-the-art machine learning framework known as a neural operator for solving seismic wave equations. The trained neural operator enables full seismic waveform modeling with substantial advancements over conventional numerical techniques including its fast speed, generalizability, and convenient differentiability for full waveform inversion.

English

ISBN: 9798342127349Subjects--Topical Terms:

623617
Random access memory.

Imaging the Earth's Near Surface with Dense Seismic Observation /
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