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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 Electrical & Electronics Engineering Energy Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics Social Sciences

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Comparison Between Seismic Inversion and Seismic Inversion with Bayesian Inference in Acoustic Impedance Raharjo, Wiji; Palupi, Indriati Retno; Alfiani, Oktavia Dewi
Journal of Physics and Its Applications Vol 7, No 3 (2025): August 2025
Publisher : Diponegoro University Semarang Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jpa.v7i3.25867

Abstract

Finding reflection coefficient of seismic trace data is very important to be analyzed in some geological features. Reflection coefficient describes the medium of the subsurface based on Acoustic Impedance (AI) data. Model based seismic inversion is one way that can be used to find reflection coefficient of trace seismic. It needs several steps, like generating calculated trace seismic due to the original one before inversion. Unfortunately, the process is very complicated to reach a best result indicated by error value tends to be zero. While Bayesian MCMC offers the easier way, by setting mean and standard deviation values, it will generate calculated seismic trace data automatically with high similarity to the original one.  In other words, Bayesian MCMC helping the inversion process to be shorter. Finally, we have proven that Bayesian MCMC gives the better result of reflection coefficient of model based seismic inversion method.
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.
Subsurface S-type Granitoid Identification Based on Gravity and Seismic Tomography Models in Pacitan, East Java Soesilo, Joko; Palupi, Indriati Retno; Raharjo, Wiji; Sutanto Sutanto; Sulistyohariyanto, Faris Ahad; Ekaristi, Kevin Gardo Bangkit; Stiawan, Fandi Budi
EKSPLORIUM Vol. 39 No. 2 (2018): NOVEMBER 2018
Publisher : BRIN Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17146/eksplorium.2018.39.2.4954

Abstract

Granitoid outcrop has been observed in Montongan, Tulakan Subdistrict, Pacitan District, East Java. Geochemically, granitoid shows peralluminous S-type granitoid which consists of comparable plagioclase and potassium feldspar leading to adamelite and granodiorite variety with andalusite, fine size corundum and cordierite inside. These modal minerals are consistent with its bulk chemical analysis result that shows alumina rich rock. Highly weathered spotted pinkish soil with remaining quartz gravels characterizes its surface. Lateritic pink soil up to more than 25 meters thick covers the granitoid body and this feature is indicative to locate its surface distribution, while its subsurface distribution is remain uncertain. The research aimed to identify granitoid subsurface distribution. To identify the subsurface body, gravity and seismic tomography models were used. According gravity model, the pluton body is 5 km wide which is rootless downward and seems extends eastward. Meanwhile, the north-south seismic tomographic model across Pacitan Region indicates dense solid body override the recent Java subduction zone. The body is assumed to have correlation with surface granitic rock. It supports an idea that there is a micro continent trapped beneath Southern Mountain of East Java.
Studi Automatic Picking Waktu Tiba Gelombang P dan S dengan Menggunakan Spektogram pada Obspy Python Indriati Retno Palupi; Wiji Raharjo
Jurnal Teori dan Aplikasi Fisika Vol. 8 No. 1 (2020): Jurnal Teori dan Aplikasi Fisika
Publisher : Department of Physics, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtaf.v8i1.239

Abstract

Estimate Mass Density Value as A Priori Information for Gravity by using Bayesian Markov Chain Monte Carlo (MCMC) Palupi, Indriati Retno; Raharjo, Wiji
INDONESIAN JOURNAL OF APPLIED PHYSICS Vol 14, No 2 (2024): October
Publisher : Department of Physics, Sebelas Maret University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijap.v14i2.77622

Abstract

In the gravity method, information about mass density value is very important because it will influence the characteristic of the 1-D gravity acceleration graph. However, it is quite difficult to guess the mass density value so that is suitable to the 1-D acceleration graph. This is called “a priori” information. Trial and error way is one way to solve this problem. It is a very random value guess also. To make sure that the initial guess of mass density is a good parameter, Bayesian Markov Chain Monte Carlo (MCMC) can be used. It generates many possibilities from the guess value and then these possibilities will be selected to the best one by likelihood way. The validation is expressed by the random graph as a consequence of the iteration number step of the possibilities. This research is started by using certain values of mass density to create a synthetic model for the field data in Banggai Sula because the area has a complex geology. The synthetic model is used because the gravity forward modelling equation has the sinusoidal form. After Bayesian MCMC is applied to the initial mass density value, it will produce a new mass density value or the estimation value with its response to the 1-D gravity acceleration synthetic graph. Finally, this information will be very useful to create the 2D or 3D inverse modelling in Gravity.
Pemodelan Tsunami Sederhana dengan Menggunakan Persamaan Differensial Parsial Palupi, Indriati Retno; Raharjo, Wiji; Wibowo, Eko; Hamdalah, Hafiz
INDONESIAN JOURNAL OF APPLIED PHYSICS Vol 8, No 1 (2018): April
Publisher : Department of Physics, Sebelas Maret University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijap.v8i1.16284

Abstract

One way to solve fluid dynamics problem is using partial differential equation. By using Taylor expansion, fluid dynamics can be applied simply. For the example is tsunami wave. It is include to hyperbolic partial differential equation, tsunami wave propagation can describe in space and time function by using Euler FTCS (Forward Time Central Space) formula.
Estimate The Focal Mechanism of Earthquake in Indonesia By Using 1-D Convolutional Neural Network (CNN) Palupi, Indriati Retno; Raharjo, Wiji; Alfiani, Oktavia Dewi; Apriyanti, Dessy; Wahyuningrum, Dwi
INDONESIAN JOURNAL OF APPLIED PHYSICS Vol 15, No 2 (2025): October
Publisher : Department of Physics, Sebelas Maret University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijap.v15i2.84593

Abstract

Indonesia is located between three collisions of active plate tectonics (Pacific, Eurasia, and Australia), resulting in a high seismicity zone, especially along the subduction zone. Besides the subduction zone, there are also many faults as a result of these collisions. As the earthquake source, both are controlled by focal mechanisms. Focal mechanism is the geometry of fault movements. Unfortunately, Indonesia's earthquake catalog data is not complete. There is missing information in some focal mechanism data, especially the data with more than 6 Magnitudes between January 1st, 1973, and February 1st, 2023. To complete the focal mechanism data, 1-D Convolutional Neural Network (CNN) is applied as the common and powerful method of Machine Learning. Started by grouping the earthquake catalog data with clear focal mechanism information as the training data with its training label and otherwise as the test data with the unknown label, then applied these training and label data to convolutional layer with some neurons, CNN can estimate focal mechanism (label) of the test data. This process is done iteratively, and a good model is observed with little loss value in the L curve.
Co-Authors Afriliani M Handini Agus Santoso Ajimas Pascaning Setiohadiwibowo Alfiani, Oktavia Dewi Ananda Achlaqul Karimah APRIYANTI, DESSY Arif Nor Aminah Dianti Yunia Sari Dwi Wahyuningrum Edoarddo Tobing Ekaristi, Kevin Gardo Bangkit Eko Wibowo Faid Muhlis Fandi Budi Stiawan Fanny Rizkiyani Faris Ahad Sulistyohariyanto Fitrianingtyas, Rahmawati Giamboro, Wrego Seno hamdalah, hafiz 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