Claim Missing Document
Check
Articles

Found 16 Documents
Search

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.
Application of K-means clustering and B-value algorithms for analysis of earthquake-dangerous zones in Java Island Anggarajati, Bentang; Yatini, Y; Raharjo, Wiji
International Journal of Advances in Applied Sciences Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i4.pp907-915

Abstract

Java Island is an island with a high earthquake vulnerability. Therefore, earthquake mitigation measures are needed to reduce the impact of earthquakes. Earthquake mitigation is done by knowing the zones with a high risk of earthquakes and high levels of rock stress. The methods used to map earthquake-prone zones are K-means clustering and B-value. The K-means clustering method can provide earthquake clusters based on their characteristics and the B-value can produce rock stress conditions in the area. The results of this study are that the K-means clustering method produces 7 earthquake clusters with 5 classifications of very low, low, medium, high, and very high. In contrast, the B-value process has a high B-value with a value of 1.2-1.5 in West Java and a low B-value with a value of 0.9-1.2 in the central to the eastern part of Java.
Earthquake Hazard Mapping Based on Earthquake Intensity Model in North Maluku Islands Ningrum, Rohima Wahyu; Achmad, Rahim; Aswan, Marwis; Raharjo, Wiji
TECHNO: JURNAL PENELITIAN Vol 13, No 2 (2024): TECHNO JURNAL PENELITIAN
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/tjp.v13i2.9737

Abstract

Earthquake hazard mapping in the North Maluku region is urgent and essential because this area has a very high earthquake hazard potential. The high level of earthquake vulnerability in the North Maluku region makes it very important to focus mitigation activities to reduce the impact of earthquakes that occur. Earthquake hazard mapping that illustrates the effects of earthquakes on an area is one of the disaster mitigation efforts. In this study, earthquake intensity using the Probability Seismic Hazard Analysis (PSHA) method will be used to analyse the level of earthquake hazard in the North Maluku region. The results of the distribution of Peak Ground Acceleration (PGA) values for North Maluku range from 0.67 - 3.75 g. At the same time, the value of the MMI earthquake intensity scale is in the range of IX-XII. The most incredible earthquake intensity is in the Morotai, West Halmahera, Ternate, Tidore, Bacan, and Obi Island areas. The results of this study can have implications as a reference in safer spatial and infrastructure planning, the preparation of more effective mitigation strategies, and can be a medium for educating the public to be more prepared and responsive to earthquake risks.
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.
The Spatial Distribution of Petroleum Hydrocarbon Contamination in Groundwater Around Fuel Storage Tank Utami, Ayu; Sahetapi, Calvin Alex; Rahayu, Mey Yani Puji; Kristanto, Wisnu Aji Dwi; Raharjo, Wiji; Isnaini, RR Desi Kumala; Fahri, Ricky Al; Anifah, Eka Masrifatus
Jurnal Presipitasi : Media Komunikasi dan Pengembangan Teknik Lingkungan Vol 22, No 3 (2025): November 2025
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/presipitasi.v22i3.782-791

Abstract

Groundwater is vital for domestic, agricultural, and industrial use; however, previous studies have indicated that its quality often fails to meet drinking water standards. The sources of groundwater contaminants can be from domestic, industrial, saltwater intrusion, surface waste ponds, pipelines, mine pits, underground storage tanks, waste pits, etc. This research investigates the spatial distribution of Total Petroleum Hydrocarbons (TPH) contamination in groundwater surrounding fuel storage tanks, using the LeGrand method to assess groundwater vulnerability based on five physical environmental parameters. The study employs a quantitative approach, incorporating primary data from well measurements and secondary data from geological and land use maps. The results reveal that shallow groundwater levels significantly increase vulnerability to contamination, while the type of soil and aquifer permeability also play critical roles in contaminant transport dynamics. In the second research location, the analysis focuses on benzene contamination, with low concentrations below 0.02 ppb. Despite the low levels detected, the potential for contamination remains a concern due to the proximity of the gas station to residential areas. Statistical correlation analysis demonstrates a significant inverse relationship between TPH concentrations and vulnerability scores. The study underscores the importance of preventive measures to mitigate contamination risks, involving collaboration among stakeholders.