Machine Learning for Onshore Oil Seeps Detection: A Case Study in Kirkuk-Sulaimaniyah Area, Northeastern Iraq DOI
Arsalan Ahmed Othman, Hiwa Sidiq, Salahalddin S. Ali

et al.

SPE Journal, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 13

Published: April 1, 2025

Summary Oil seeps pose significant environmental hazards to both terrestrial and aquatic ecosystems. Traditional mapping techniques encounter logistical political challenges, particularly in complex regions, such as Kirkuk, an area rich oil gas fields. These fields contribute the proliferation of through natural processes industrial activities, underscoring need for efficient detection methods. This study introduces a novel hybrid algorithm, SAM-DT, which combines spectral angle (SAM) with decision tree (DT) classification enhance seep detection. By leveraging remote sensing data, including Sentinel-2A imagery, Landsat OLI thermal band, geomorphic physical characteristics seeps, we demonstrated utility integrating multisource data this purpose. The SAM-DT algorithm’s performance was evaluated against standard SAM using validation from 369 sites verified field surveys, Google Earth, PlanetScope, QuickBird data. results reveal that algorithm achieved accuracy 64%, outperforming 35%. findings highlight effectiveness approach across mountainous, semiarid, plain regions. underscores potential robust tool can be conducted by testing more nodes improve onshore detection, paving way future research aimed at refining incorporating additional further accuracy.

Language: Английский

Machine Learning for Onshore Oil Seeps Detection: A Case Study in Kirkuk-Sulaimaniyah Area, Northeastern Iraq DOI
Arsalan Ahmed Othman, Hiwa Sidiq, Salahalddin S. Ali

et al.

SPE Journal, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 13

Published: April 1, 2025

Summary Oil seeps pose significant environmental hazards to both terrestrial and aquatic ecosystems. Traditional mapping techniques encounter logistical political challenges, particularly in complex regions, such as Kirkuk, an area rich oil gas fields. These fields contribute the proliferation of through natural processes industrial activities, underscoring need for efficient detection methods. This study introduces a novel hybrid algorithm, SAM-DT, which combines spectral angle (SAM) with decision tree (DT) classification enhance seep detection. By leveraging remote sensing data, including Sentinel-2A imagery, Landsat OLI thermal band, geomorphic physical characteristics seeps, we demonstrated utility integrating multisource data this purpose. The SAM-DT algorithm’s performance was evaluated against standard SAM using validation from 369 sites verified field surveys, Google Earth, PlanetScope, QuickBird data. results reveal that algorithm achieved accuracy 64%, outperforming 35%. findings highlight effectiveness approach across mountainous, semiarid, plain regions. underscores potential robust tool can be conducted by testing more nodes improve onshore detection, paving way future research aimed at refining incorporating additional further accuracy.

Language: Английский

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