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All Journal BERKALA FISIKA Tadris: Jurnal keguruan dan Ilmu Tarbiyah IJOG : Indonesian Journal on Geoscience Eksplorium : Buletin Pusat Pengembangan Bahan Galian Nuklir UNEJ e-Proceeding Jurnal Fisika FLUX JLBG (Jurnal Lingkungan dan Bencana Geologi) (Journal of Environment and Geological Hazards) INDONESIAN JOURNAL OF APPLIED PHYSICS Jurnal Pengabdian Masyarakat MIPA dan Pendidikan MIPA Jurnal Meteorologi Klimatologi dan Geofisika Journal of Physics and Its Applications Jurnal Teori dan Aplikasi Fisika Jurnal Geosaintek Eksplorium : Buletin Pusat Pengembangan Bahan Galian Nuklir
Indriati Retno Palupi, Indriati Retno
Program Studi Teknik Geofisika, Fakultas Teknologi Mineral, Universitas Pembangunan Nasional “Veteran” Yogyakarta Jln. SWK 104 Condong Catur Sleman Yogyakarta
Astronomy Chemical Engineering, Chemistry & Bioengineering Earth & Planetary Sciences Education Energy Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics Social Sciences

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Journal : Jurnal Geosaintek

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MULTIPLE ATENUATION IN SHOT GATHER BY USING CONVOLUTIONAL NEURAL NETWORK (CNN) Raharjo, Wiji; Palupi, Indriati Retno; Alfiani, Oktavia Dewi
Jurnal Geosaintek Vol. 11 No. 2 (2025)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25023659.v11i2.5192

Abstract

Today Machine Learning is used in almost every field for human life, including geophysics. Some examples of Machine Learning utilities are classifying lithology and predict petro physical parameters based on several supported data. Especially in seismic method, Machine Learning can be used for removing or attenuate multiple from seismic image or shot gather data by using Convolutional Neural Network (CNN). It reduces the multiple from shot gather data (input) based on filtered shot gather data (called by ground truth model) as the label or target. Unfortunately, filtering process sometimes erase boundaries layer in shot gather. Then CNN works by generating several activation function in neurons and hidden layers, multiply with input data and reconcile them to labels to reinforce the boundaries. To validate the CNN result, it can be seen from L – curve as the loss function that represent the prediction error. The fewer the prediction error, the more accurate result is observed.
Co-Authors Afriliani M Handini Agus Santoso Ajimas Pascaning Setiohadiwibowo Alfiani, Oktavia Dewi Ananda Achlaqul Karimah Arif Nor Aminah Dianti Yunia Sari Edoarddo Tobing Ekaristi, Kevin Gardo Bangkit Eko Wibowo Faid Muhlis Fandi Budi Stiawan Fanny Rizkiyani Faris Ahad Sulistyohariyanto Fitrianingtyas, Rahmawati Giamboro, Wrego Seno Jayawarsa, A.A. Ketut Joko Soesilo Kevin Gardo Bangkit Ekaristi Madona, Madona Nela Elisa Dwiyanti Oktavia Dewi Alfiani Rahma, Aldila Risca Listyaningrum Soesilo, Joko Stiawan, Fandi Budi Sulistyohariyanto, Faris Ahad Supriyono Supriyono Sutanto Sutanto Sutanto Sutanto Vira Irnanda, Elleona Septi N Wiji Raharjo