東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Embedding Space Optimization for Enh...

Park, Min Jun,

Linked to FindBook

Google Book

Amazon

博客來

Embedding Space Optimization for Enhanced Seismic Monitoring /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Embedding Space Optimization for Enhanced Seismic Monitoring // Min Jun Park.
Author:	Park, Min Jun,
Description:	1 electronic resource (161 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
Contained By:	Dissertations Abstracts International86-12B.
Subject:	Earthquakes. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31974669
ISBN:	9798311957014

Embedding Space Optimization for Enhanced Seismic Monitoring /
Park, Min Jun,

Embedding Space Optimization for Enhanced Seismic Monitoring /Min Jun Park. - 1 electronic resource (161 pages)

Source: Dissertations Abstracts International, Volume: 86-12, Section: B.

Seismic monitoring is essential for understanding subsurface processes and ensuring the safety and efficiency of geological applications such as carbon sequestration and seismic event detection. However, it faces challenges stemming from the complexity and large volume of seismic data. This dissertation addresses these challenges by utilizing embedding space optimization, a machine learning technique that transforms high-dimensional data into a lower-dimensional space, enhancing feature representation for enhanced seismic monitoring.The first challenge involves detecting subsurface changes due to carbon dioxide (CO2) injection in geological carbon storage (GCS) projects. Obtaining accurate labels or ground truth information about the exact distribution of injected CO2in the subsurface is impractical. To overcome this, we propose an unsupervised anomaly detection approach using embedding space optimization. By learning the normal patterns of the subsurface from pre-injection seismic data, the model identifies deviations in post-injection data as anomalies, effectively detecting CO2plume signals without requiring labeled data. This method enhances the monitoring of GCS projects by providing a practical solution for identifying subsurface changes associated with CO2injection.The second challenge pertains to the detection of seismic events within datasets where seismic events are rare and noise is abundant. This rarity of seismic events poses difficulties for traditional supervised learning methods, which often fail to detect infrequent but critical seismic events. To address this, we introduce a semi-supervised clustering framework utilizing embedding space optimization. By combining a small amount of labeled data with a large volume of unlabeled data, the model effectively distinguishes between seismic events and noise. This approach enhances the detection performance of rare seismic events without the need for additional labeled data. We demonstrate the effectiveness of the proposed methods through two case studies in distributed acoustic sensing (DAS) data analysis: microseismic monitoring during hydraulic fracturing operations and earthquake detection in urban environments. In both cases, embedding space optimization significantly improves the models' ability to detect seismic events despite the scarcity of event data and without requiring additional training data.The contributions of this dissertation are twofold. First, it advances the field of GCS monitoring by providing an unsupervised anomaly detection method that effectively identifies CO2plumes without labeled data. Second, it enhances seismic event detection in datasets with rare events by using a semi-supervised clustering framework, improving detection accuracy of rare events in large volumes of noise-dominated data without the need for extensive labeled datasets. These methodologies leverage embedding space optimization to extract meaningful representations from complex seismic data, facilitating better interpretation and decision-making in geophysical applications.Overall, this work demonstrates the potential of embedding space optimization to address critical challenges in seismic monitoring. By improving data analysis and detection capabilities without the need for additional labeled data, the findings contribute to the advancement of geophysical research and offer practical solutions for real-world applications in seismic monitoring.

English

ISBN: 9798311957014Subjects--Topical Terms:

535233
Earthquakes.

Embedding Space Optimization for Enhanced Seismic Monitoring /
LDR:04738nmm a22003733i 4500 001 2438306
005 20251210114005.5
006 m o d
007 cr|nu||||||||
008 261011s2025 miu||||||m |||||||eng d
020 $a 9798311957014
035 $a (MiAaPQD)AAI31974669
035 $a (MiAaPQD)Stanfordyr424yh9008
035 $a AAI31974669
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Park, Min Jun, $e author. $3 3829564
245 1 0 $a Embedding Space Optimization for Enhanced Seismic Monitoring / $c Min Jun Park.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2025
300 $a 1 electronic resource (161 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
500 $a Advisors: Biondi, Biondo Committee members: Beroza, Greg; Clapp, Robert; Mukerji, Tapan.
502 $b Ph.D. $c Stanford University $d 2025.
520 $a Seismic monitoring is essential for understanding subsurface processes and ensuring the safety and efficiency of geological applications such as carbon sequestration and seismic event detection. However, it faces challenges stemming from the complexity and large volume of seismic data. This dissertation addresses these challenges by utilizing embedding space optimization, a machine learning technique that transforms high-dimensional data into a lower-dimensional space, enhancing feature representation for enhanced seismic monitoring.The first challenge involves detecting subsurface changes due to carbon dioxide (CO2) injection in geological carbon storage (GCS) projects. Obtaining accurate labels or ground truth information about the exact distribution of injected CO2in the subsurface is impractical. To overcome this, we propose an unsupervised anomaly detection approach using embedding space optimization. By learning the normal patterns of the subsurface from pre-injection seismic data, the model identifies deviations in post-injection data as anomalies, effectively detecting CO2plume signals without requiring labeled data. This method enhances the monitoring of GCS projects by providing a practical solution for identifying subsurface changes associated with CO2injection.The second challenge pertains to the detection of seismic events within datasets where seismic events are rare and noise is abundant. This rarity of seismic events poses difficulties for traditional supervised learning methods, which often fail to detect infrequent but critical seismic events. To address this, we introduce a semi-supervised clustering framework utilizing embedding space optimization. By combining a small amount of labeled data with a large volume of unlabeled data, the model effectively distinguishes between seismic events and noise. This approach enhances the detection performance of rare seismic events without the need for additional labeled data. We demonstrate the effectiveness of the proposed methods through two case studies in distributed acoustic sensing (DAS) data analysis: microseismic monitoring during hydraulic fracturing operations and earthquake detection in urban environments. In both cases, embedding space optimization significantly improves the models' ability to detect seismic events despite the scarcity of event data and without requiring additional training data.The contributions of this dissertation are twofold. First, it advances the field of GCS monitoring by providing an unsupervised anomaly detection method that effectively identifies CO2plumes without labeled data. Second, it enhances seismic event detection in datasets with rare events by using a semi-supervised clustering framework, improving detection accuracy of rare events in large volumes of noise-dominated data without the need for extensive labeled datasets. These methodologies leverage embedding space optimization to extract meaningful representations from complex seismic data, facilitating better interpretation and decision-making in geophysical applications.Overall, this work demonstrates the potential of embedding space optimization to address critical challenges in seismic monitoring. By improving data analysis and detection capabilities without the need for additional labeled data, the findings contribute to the advancement of geophysical research and offer practical solutions for real-world applications in seismic monitoring.
546 $a English
590 $a School code: 0212
650 4 $a Earthquakes. $3 535233
650 4 $a Carbon sequestration. $3 601998
650 4 $a Deep learning. $3 3554982
650 4 $a Neural networks. $3 677449
650 4 $a Geophysics. $3 535228
690 $a 0467
690 $a 0373
710 2 $a Stanford University. $e degree granting institution. $3 3765820
720 1 $a Biondi, Biondo $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-12B.
790 $a 0212
791 $a Ph.D.
792 $a 2025
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31974669