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All Journal JGE (Jurnal Geofisika Eksplorasi) Journal of Geoscience, Engineering, Environment, and Technology Jurnal Geofisika Jurnal Migasian
Iktri Madrinovella
Geophysical Engineering, Pertamina University
Chemical Engineering, Chemistry & Bioengineering Chemistry Earth & Planetary Sciences Energy Engineering Environmental Science Physics

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THE DIRECT-INVERSION DECONVOLUTION AND ITS APPLICATION IN SEISMIC DATA Iktri Madrinovella; Waskito Pranowo
Jurnal Geofisika Eksplorasi Vol 8, No 1 (2022)
Publisher : Engineering Faculty Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jge.v8i1.187

Abstract

Seismic traces are generated by the convolution of reflectivity and seismic wavelet. Due to limited frequency bandwidth, reflectivity can not be resolved easily. Deconvolution is a method to increase the frequency bandwidth and gives seismic data higher resolution, which makes it easier to analyze. Deconvolution is a common method in the seismic data processing. The mathematical definition of deconvolution is an inverse process of convolution, but the computation of deconvolution uses convolution in its process (Wiener deconvolution). We explained a method that is direct from the mathematical definition. We refer to it as direct-inversion deconvolution. The direct-inversion deconvolution process involves the matrix operation between seismic trace and wavelet instead of the deconvolution filter. By applying the direct-inversion deconvolution, the produced (or deconvolved) seismic trace shows a better result with higher resolution, regardless of the wavelet’s phase. Finally, we performed a phase rotation experiment, and the deconvolution result shows no seismic phase alteration. In comparison, we also perform spiking deconvolution in synthetic data experiments. This method is applied to The North Sea Volve Data Village seismic data, and more thin layers are significantly detected. Finally, it turns out that direct-inversion deconvolution gives a higher resolution to seismic data.
Hasil Awal Analisis Peak Ground Acceleration di Bali D. R. A Gandini; Y A Setiawan; I Madrinovella; A Abdullah; B Pranata; S K Suhardja; S R Aisy
Jurnal Geofisika Vol 20 No 2 (2022): Jurnal Geofisika
Publisher : Himpunan Ahli Geofisika Indonesia (HAGI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36435/jgf.v20i2.535

Abstract

Wilayah Bali merupakan daerah dengan tingkat seismisitas yang tinggi karena terletak di Zona Subduksi antara Lempeng Indo-Australia dan Eurasia (selatan) serta adanya struktur back arc thrust (utara). Gempa Bumi Seririt yang terjadi pada tanggal 14 Juli 1976 dengan magnitudo 6,2 SR merupakan salah satu gempa paling merusak di Bali. Oleh karena itu perlu dilakukan upaya mitigasi untuk melihat tingkat potensi bahaya bencana gempa bumi di wilayah Bali. Peak Ground Acceleration (PGA) merupakan pendekatan yang dapat dilakukan dengan cara menentukan intensitas seismik dari gempa bumi di suatu wilayah sehingga dapat diperkirakan risiko paling parah yang dapat dihasilkan dari suatu kejadian gempa bumi. Perhitungan nilai PGA dapat dilakukan melalui suatu pendekatan rumus empiris atenuasi khusus wilayah tertentu. Data yang digunakan berasal dari katalog gempa bumi ISC (1970-2019), USGS (1963-2019), dan data rekaman accelerograph BMKG (2012-2016) di wilayah Bali. Rumus empiris atenuasi dipilih dengan mempertimbangkan jenis gempa bumi dominan dan kondisi geologi di wilayah Bali. Terdapat 3 rumus empiris atenuasi yang digunakan yaitu oleh McGuire (1977), Donovan (1973), dan Esteva & Villaverde (1973). Rumus empiris atenuasi oleh McGuire (1977) menghasilkan nilai error terkecil berdasarkan perbandingan nilai PGA observasi dan nilai PGA kalkulasi. Analisa tingkat potensi risiko akibat gempa bumi dapat dilakukan berdasarkan peta percepatan tanah maksimum dan peta intensitas seismik maksimum di Wilayah Bali. Kabupaten Buleleng dan sekitarnya serta daerah paling timur dari Kabupaten Karangasem merupakan wilayah yang berpotensi terkena dampak paling parah jika terjadi gempa bumi bersifat merusak, sedangkan daerah paling selatan dari Pulau Bali memiliki dampak yang relatif ringan jika terjadi gempa bumi yang bersifat merusak.
Identifikasi Batuan Berdasarkan Data Well Log Menggunakan K-Means Clustering Meredita Susanty; Prinsislamsheeny Brilliantdianty Ebelaristra; Ahmad Fauzan Rahman; Ade Irawan; Ikri Madrinovella; Weny Astuti
Jurnal Migasian Vol 4 No 1 (2020): Jurnal Migasian
Publisher : LPPM Institut Teknologi Petroleum Balongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36601/jurnal-migasian.v4i1.96

Abstract

One of the stages in oil and gas exploration is a Petrophysical analysis, which aims to determine the structure of rock layers below the earth's surface. The petrophysical analysis uses physical properties in a well-log to determine the rock type below the surface. Nowadays, the software for conducting petrophysical analysis has utilized a machine-learning approach to predict rock types. Most of the software uses the supervised learning method to classify rock types. This research uses a different approach, unsupervised learning, to group rock types based on various features in a well-log. Using a publicly available well-log in Stafford, United States, and the k-means clustering algorithm, this study groups the data into 3 clusters. The result is compared with manual analysis interpretation and shows an alignment between them. From the result, it shows that the unsupervised learning method effectively predicts limestone, shale, and evaporites in the well. It classifies the dataset into useful clusters, generates useful lithologies, provides useful rock characterization, and less time-consuming.
Seismic Vulnerability Analysis Using the Horizontal to Vertical Spectral Ratio (HVSR) Method on the West Palu Bay Coastline Amirudin; Iktri Madrinovella; Sofian
Journal of Geoscience, Engineering, Environment, and Technology Vol. 8 No. 02-2 (2023): Special Issue from “The 1st International Conference on Upstream Energy Te
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jgeet.2023.8.02-2.13879

Abstract

This research was carried out to make a map of the dominant frequency (f0), amplification factor (A0), seismic susceptibility index (Kg), Vs30, Sediment Layer Thickness (H) and Peak ground acceleration (PGA). Microtremor measurements were carried out with a three-component seismometer of the TDL-303S type as many as 27 measurement points. The data was analyzed by the Horizontal to Vertical Spectral Ratio (HVSR) method. The PGA calculation was carried out using the Kanai equation with a reference to the Palu-Donggala earthquake on September 28, 2018. The results showed that the distribution of the dominant frequency value (f0) ranged from 0.4149 Hz-0.8869 Hz, the soil amplification factor (A0) ranged from 2,199–4,884, the seismic vulnerability index (Kg) ranged from 8.79 s2/cm-41.41 s2/cm, the shear wave velocity to a depth of 30 meters ( Vs30) ranged from Vs30 197.7 m/s-320.2 m/s , the thickness of the sedimentary layer ranges from 260.3 m-291.1 m and the peak ground acceleration (PGA) of Kanai ranges from 137.3 gal – 234.2 gal by using Mw 7.4 earthquakes with an intensity scale (MMI) VI to VII. The coastal area of West Palu bay has an intermediate seismic vulnerability II to a high seismic vulnerability IV so that it will be vulnerable in the event of an earthquake disaster. Areas that have a very high vulnerability index are in the upper western and easternmost regions while those with a lower level tend to have a lower vulnerability index value.
Co-Authors A Abdullah Ade Irawan Ahmad Fauzan Rahman Amirudin B Pranata D. R. A Gandini Meredita Susanty Prinsislamsheeny Brilliantdianty Ebelaristra S K Suhardja S R Aisy Sofian Waskito Pranowo Weny Astuti Y A Setiawan