Analisis Mekanisme Fokal Menggunakan Pendekatan Deep Learning Di Zona Tumbukan Busur Banda-Lempeng Australia

Afdholi, Ariya Dhani Hilal (2026) Analisis Mekanisme Fokal Menggunakan Pendekatan Deep Learning Di Zona Tumbukan Busur Banda-Lempeng Australia. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Zona tumbukan antara Busur Banda dan Lempeng Australia di Indonesia Timur merupakan salah satu wilayah tektonik paling kompleks di dunia, yang dicirikan oleh interaksi aktif antara subduksi litosfer oseanik dan tumbukan benua. Kompleksitas ini tercermin dari variasi struktur geologi, geometri slab yang tidak seragam, serta tingkat aktivitas seismik yang tinggi. Meskipun demikian, kajian mengenai karakteristik mekanisme fokal gempa bumi di wilayah ini masih relatif terbatas, terutama yang memanfaatkan pendekatan deep learning modern. Penelitian ini menerapkan metode FocoNet untuk menentukan mekanisme fokal gempa bumi berdasarkan data seismik yang direkam oleh 33 stasiun dengan jaringan YS pada periode 2014-2018. Data gelombang seismik diproses secara terintegrasi melalui tahapan pemilihan fase otomatis, pengasosiasian fase, dan inferensi mekanisme fokal berbasis pembelajaran mendalam. Hasil analisis menunjukkan dominasi mekanisme strike-slip yang sangat kuat, dengan kehadiran mekanisme reverse dan normal dalam proporsi yang lebih kecil. Pola ini mengindikasikan dominannya deformasi geser horizontal yang berkaitan dengan konvergensi oblique di zona tumbukan Busur Banda. Gempa bumi umumnya berasosiasi dengan kedalaman menengah dan bidang patahan bersudut curam, yang mencerminkan kondisi tegasan yang kompleks di dalam slab dan zona transisi tektonik. Variasi spasial orientasi mekanisme fokal menunjukkan medan tegasan transpresif yang heterogen, sejalan dengan perubahan geometri slab dan dinamika tumbukan busur dengan benua di kawasan ini.
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The collision zone between the Banda Arc and the Australian Plate in eastern Indonesia represents one of the most tectonically complex regions in the world, characterized by an active transition from oceanic lithosphere subduction to continental collision. This complexity is expressed through highly variable slab geometry, intricate geological structures, and intense seismic activity. Despite these features, focal mechanism characteristics of earthquakes in this region remain insufficiently constrained, particularly through the application of modern deep learning techniques. This study applies the FocoNet method to determine earthquake focal mechanisms using seismic data recorded by 33 stations of the YS network during the period 2014-2018. Seismic waveforms were processed in an integrated workflow involving automatic phase picking, phase association, and deep learning–based focal mechanism inference. The results reveal a strong dominance of strike-slip mechanisms, accompanied by smaller proportions of reverse and normal mechanisms. This pattern indicates prevailing horizontal shear deformation associated with oblique convergence within the Banda Arc collision zone. Earthquakes are predominantly associated with intermediate depths and steeply dipping fault planes, reflecting complex stress conditions within the subducting slab and surrounding tectonic domains. Spatial variations in focal mechanism orientations suggest a heterogeneous transpressional stress regime, consistent with along-arc changes in slab geometry and the dynamic interaction between subduction and arc–continent collision processes. These findings provide a coherent seismotectonic framework for understanding deformation patterns within the Banda Arc system.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Busur Banda, Deep learning, EQTransformer, FocoNet, PyOcto, Banda Arc, Deep learning, EQTransformer, FocoNet, PyOcto
Subjects:	Q Science > QE Geology Q Science > QE Geology > QE538.8 Earthquakes. Seismology
Divisions:	Faculty of Civil, Environmental, and Geo Engineering > Geophysics Engineering > 33201-(S1) Undergraduate Theses
Depositing User:	Ariya Dhani Hilal Afdholi
Date Deposited:	04 Feb 2026 06:31
Last Modified:	04 Feb 2026 06:31
URI:	http://repository.its.ac.id/id/eprint/132012

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