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All Journal IJOG : Indonesian Journal on Geoscience Buletin Ilmiah Sarjana Teknik Elektro
Ibrahim, Khalil
Unknown Affiliation
Earth & Planetary Sciences Electrical & Electronics Engineering

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Magnetic Basement Depth from Marine Magnetic Data in Cendrawasih Bay Hydrocarbon Prospect Area, Bird Head, Papua, Indonesia Ibrahim, Khalil; Kawab, Gracia Abigail Paraskah; Bijaksana, Satria; Fajar, Silvia Jannatul; Sapiie, Benyamin; Ngkoimani, La Ode; Suryanata, Putu Billy; Harlianti, Ulvienin; Kurniawan, Syaiful Apri; Wibisono, Salsabila Nadhifa
Indonesian Journal on Geoscience Vol. 12 No. 3 (2025)
Publisher : Geological Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17014/ijog.12.3.383-400

Abstract

The magnetic basement and structural segmentation of the eastern Bird Head region, Papua, were investigated using marine magnetic data and frequency-domain inversion (MagB_Inv). The studied area includes The Cendrawasih and Yapen–Biak Basins, both were influenced by the Yapen strike-slip fault and transtensional tectonics. Processing involved reducing to the pole, spectral depth estimation, and 3D magnetic inversion to delineate basement geometry, and to infer the sediment thickness. Three structurally bounded subbasins were identified: (1) between Cendrawasih Bay and Num Island, (2) between Cendrawasih Bay and Yapen Island, and (3) between Yapen and Biak Islands. These subbasins exhibit magnetic basement depths ranging from 0.4 to 7 km and sediment thicknesses exceeding 3 km. Magnetic highs around Yapen Island correlate with Miocene volcanic and ultramafic outcrops, interpreted as shallow high-magnetization crustal blocks. The subbasins are bounded by ridges and faults, including the Yapen Fault Zone and fold-thrust systems, which deform both basement and sedimentary cover. The basement morphology controls sediment distribution, and defines fault-bound sedimentary zones, consistent with regional tectonic trends. Seismic cross-sections and shallow earthquake hypocentres, and further supports this structural segmentation. These results provide a structural framework to understand the basin structure, and to support preliminary hydrocarbon evaluations in this underexplored region. Despite these insights, interpretations are constrained by the non-uniqueness of magnetic inversion prosess and the absence of well and high-resolution seismic data.
A Systematic Review of Machine Learning and Deep Learning Approaches in MRI-Based Brain Tumour Analysis, Detection and Classification Omran, Hanan M.; Ibrahim, Khalil; Abdel-Jaber, Gamal T.; Sharkawy, Abdel-Nasser
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 1 (2026): February
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i1.14673

Abstract

A brain tumour develops when abnormal cell growth happens in or near the brain. These tumours can grow slowly and not be cancerous, or they can grow quickly and spread, which is known as malignancy. Brain tumours put pressure on the surrounding brain tissues, causing symptoms like memory loss, migraines, movement dysfunction, and vision impairment. Brain tumours are often divided into two groups: primary tumours, which start in the brain, and secondary tumours, which are caused by cancers that spread to other regions of the body. Although brain tumours provide a significant medical challenge, patient outcomes have improved thanks to recent advancements in diagnostic and treatment methods. Because of its better soft-tissue contrast and noninvasive nature, magnetic resonance imaging (MRI) is one of the most important medical imaging modalities for the early identification and precise localization of brain tumours. Clinical practice also makes use of other imaging methods such as PET-CT and functional MRI (fMRI). Artificial intelligence and deep learning techniques have demonstrated significant promise in automated brain cancer analysis in recent years. These methods enable precise cancer diagnosis, classification, and segmentation by identifying intricate patterns from MRI data that are challenging to recognize through manual examination. A thorough study of current deep learning and machine learning techniques for MRI-based brain tumour analysis is provided in this paper. The current thorough literature search includes papers released between 2019 and 2024. 67 pertinent articles are chosen for in-depth analysis after predetermined inclusion and exclusion criteria is used. Many of these studies make use of publicly accessible datasets like Figshare, TCIA, and BraTS. The results show that deep learning models frequently outperform traditional machine learning methods in terms of accuracy and robustness, especially convolutional neural network-based designs. However, there are still issues with clinical generalisation, model interpretability, and data heterogeneity.
Co-Authors Abdel-Jaber, Gamal T. Benyamin Sapiie Fajar, Silvia Jannatul Harlianti, Ulvienin Kawab, Gracia Abigail Paraskah Kurniawan, Syaiful Apri La Ode Ngkoimani, La Ode Omran, Hanan M. Satria Bijaksana Sharkawy, Abdel-Nasser Suryanata, Putu Billy Wibisono, Salsabila Nadhifa