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

Linked to FindBook

Google Book

Amazon

博客來

Geothermal AI : = An Artificial Intelligence for Early Stage Geothermal Exploration.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Geothermal AI :/
Reminder of title:	An Artificial Intelligence for Early Stage Geothermal Exploration.
Author:	Moraga, Jaime F.
Description:	1 online resource (119 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-05, Section: B.
Contained By:	Dissertations Abstracts International84-05B.
Subject:	Computer engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29260425click for full text (PQDT)
ISBN:	9798357536075

Geothermal AI : = An Artificial Intelligence for Early Stage Geothermal Exploration.
Moraga, Jaime F.

Geothermal AI :An Artificial Intelligence for Early Stage Geothermal Exploration. - 1 online resource (119 pages)

Source: Dissertations Abstracts International, Volume: 84-05, Section: B.

Thesis (Ph.D.)--Colorado School of Mines, 2022.

Includes bibliographical references

Exploration of geothermal resources involves analysis and management of a large number of uncertainties, which makes investment and operations decisions challenging. Remote Sensing (RS), Machine Learning (ML) and Artificial Intelligence (AI) have potential in managing the challenges of geothermal exploration. This thesis presents a methodology that integrates RS, ML and AI to create an initial assessment of geothermal potential, by resorting to known indicators of geothermal areas - namely mineral markers, surface temperature, faults and deformation. The method introduced in this thesis was implemented in two sites (Brady and Desert Peak geothermal sites) that are close to each other but have different characteristics (Brady having clear surface manifestations and Desert Peak being a blind site). Various satellite images and geospatial data were processed for mineral markers, temperature, faults and deformation and then ML methods were implemented to obtain patterns of surface manifestation related to geothermal sites. The resulting Geothermal AI uses these patterns from surface manifestations to predict geothermal potential of each pixel. The Geothermal AI was tested using independent data sets obtaining accuracy of 92-95%. The Geothermal AI was also tested by training on one site and executing it for the other site to predict the geothermal / non-geothermal delineation; in this task, which requires generalization, the Geothermal AI performed quite well in prediction with 72-76% accuracy.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798357536075Subjects--Topical Terms:

621879
Computer engineering.
Subjects--Index Terms:

Artificial intelligenceIndex Terms--Genre/Form:

542853
Electronic books.

Geothermal AI : = An Artificial Intelligence for Early Stage Geothermal Exploration.
LDR:02959nmm a2200421K 4500 001 2355023
005 20230515064548.5
006 m o d
007 cr mn ---uuuuu
008 241011s2022 xx obm 000 0 eng d
020 $a 9798357536075
035 $a (MiAaPQ)AAI29260425
035 $a AAI29260425
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Moraga, Jaime F. $3 3695411
245 1 0 $a Geothermal AI : $b An Artificial Intelligence for Early Stage Geothermal Exploration.
264 0 $c 2022
300 $a 1 online resource (119 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: 84-05, Section: B.
500 $a Advisor: Duzgun, Sebnem.
502 $a Thesis (Ph.D.)--Colorado School of Mines, 2022.
504 $a Includes bibliographical references
520 $a Exploration of geothermal resources involves analysis and management of a large number of uncertainties, which makes investment and operations decisions challenging. Remote Sensing (RS), Machine Learning (ML) and Artificial Intelligence (AI) have potential in managing the challenges of geothermal exploration. This thesis presents a methodology that integrates RS, ML and AI to create an initial assessment of geothermal potential, by resorting to known indicators of geothermal areas - namely mineral markers, surface temperature, faults and deformation. The method introduced in this thesis was implemented in two sites (Brady and Desert Peak geothermal sites) that are close to each other but have different characteristics (Brady having clear surface manifestations and Desert Peak being a blind site). Various satellite images and geospatial data were processed for mineral markers, temperature, faults and deformation and then ML methods were implemented to obtain patterns of surface manifestation related to geothermal sites. The resulting Geothermal AI uses these patterns from surface manifestations to predict geothermal potential of each pixel. The Geothermal AI was tested using independent data sets obtaining accuracy of 92-95%. The Geothermal AI was also tested by training on one site and executing it for the other site to predict the geothermal / non-geothermal delineation; in this task, which requires generalization, the Geothermal AI performed quite well in prediction with 72-76% accuracy.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Computer engineering. $3 621879
650 4 $a Energy. $3 876794
650 4 $a Remote sensing. $3 535394
653 $a Artificial intelligence
653 $a Data science
653 $a Geothermal energy
653 $a Geothermal exploration
653 $a Machine learning
653 $a Remote sensing
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0464
690 $a 0791
690 $a 0799
690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a Colorado School of Mines. $b Mining Engineering. $3 3341046
773 0 $t Dissertations Abstracts International $g 84-05B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29260425 $z click for full text (PQDT)