Jurnal Geosains Terapan

Penilaian Potensi Risiko Likuefaksi Berdasarkan Metode Decision Tree dan Random Forest Berdasarkan Data Pengukuran dan Sejarah di Kota Palu.

irfan alfarisy — Tue, 16 Apr 2024 00:00:00 +0000

Machine learning atau pembelajaran mesin merupakan sebuah metode yang sudah tidak asing lagi didengar. Akhir-akhir ini pembelajaran mesin sering digunakan untuk menyelesaikan masalah kebencanaan, khususnya pada pembuatan peta likuefaksi. Pembelajaran mesin akan memprediksi daerah-daerah yang memiliki potensi likuefaksi dari rendah hingga ke tinggi. Pada penelitian ini, metode pembelajaran mesin yang akan digunakan adalah Decision Tree dan Random Forest, dan beberapa algoritma lain sebagai pembanding dari 2 algoritma sebelumnya. Likuefaksi dipengaruhi oleh seismisitas atau magnitudo terjadinya gempa pada suatu wilayah. Pada penelitian ini digunakan 2 magnitudo sebagai pembanding yaitu magnitudo 6 dan magnitudo 7.5. Selain itu di penelitian ini digunakan 4 peta prediktor sebagai fitur-fitur input yaitu PGA (Peak Ground Acceleration), MAT (muka air tanah), Slope (kemiringan lereng) dan Vs30 (kecepatan gelombang geser). Penelitian ini mengambil 33 titik pengambilan sampel untuk melatih model pembelajaran mesin ini. Untuk nilai akurasi dari masing-masing algoritma yaitu menggunakan confusion matrix untuk membandingkan performa dari model DT dan RF.

Subsurface Natural Fracture Modelling and Prediction on Igneous Rocks of "U" Geothermal Field

M. Farel Bagaskara, Felix M. H. Sihombing, Agus Riyanto — Tue, 16 Apr 2024 00:00:00 +0000

United States of America has a promising geothermal energy potential, especially in Roosevelt Hot Springs area in Utah. Geothermal system needs fractures as a considerable aspect in geothermal system evaluation. Fracture formed by the geological condition in the area, so it can affect the characteristic of the fractures. This research aims to analyze the structural geology condition, fracture characteristic, fracture prediction accuracy, and the comparison of the fracture prediction result with the fracture model. To achieve it, there are some data processing steps, such as seismic data interpretation, building seismic attributes, building implicit fracture model, and predicting fracture occurrence using Support Vector Machine (SVM) method which is a machine learning method. The research shows the structural geology condition in the study area consists of east – west trending normal faults and north – south trending reverse faults. The fracture in the study area has a dominant trend of north – south with the intensity ranging from 0 to 3. High fracture intensity zone can be found around faults and curvatures. The fracture prediction using SVM method produces an accuracy value of 73%. Overall, the fracture prediction result is good enough, although there are some zones which have a poor result when it compared to the implicit fracture model.

Integrasi Metode Seismik Inversi dan Spektral Dekomposisi Untuk Memetakan Penyebaran Reservoir dan Kandungan Fluida

Herdiana Prasetyaningrum — Tue, 16 Apr 2024 00:00:00 +0000

Untuk memetakan penyebaran reservoir sekaligus memperkirakan keberadaan fluida pada Lapangan D digunakan metode inversi seismik yang diintegrasikan dengan metode dekomposisi spektral.

Daerah target yang diteliti merupakan reservoir batupasir yang berada di antara formasi Lower Sihapas dan Transisi. Dari analisis seismik inversi dan croosplot log pada sumur menunjukkan bahwa porositas daerah penelitian semakin besar dari arah utara ke selatan dan terdapat perselingan tipis batulempung di antara batu pasir.

Dari integrasi sebaran AI dan amplitudo pada Top A, terdapat tiga zona yang merupakan oil well. Pada Top B, terdapat dua zona yang merupakan oil well. Pada lima zona tersebut, sebaran AI nya rendah dan amplitudonya tinggi yang mengindikasikan bahwa zona tersebut merupakan zona reservoir yang mengandung hidrokarbon. Pada Top A direkomendasikan empat zona sebagai zona prospek dan pada Top B direkomendasikan tiga zona sebagai zona prospek.

Penggunaan kedua metode, yaitu seismik inversi dan dekomposisi spektral ini dapat saling menguatkan untuk menunjukkan sebaran reservoir dan kandungan fluida di Lapangan D.

Identification of Shallow Aquifer Zone Using Vertical Electrical Sounding (VES) Method with Schlumberger Array. Case Study: Universitas Indonesia

Yuannisa Rustriandayani, Ayunda Aulia Valencia, Tsabita Rasyda Fadia — Tue, 16 Apr 2024 00:00:00 +0000

This study aims to identify shallow aquifers in Universitas Indonesia (UI). Fifteen vertical electrical sounding (VES) surveys were conducted using the Schlumberger array. The apparent resistivity from VES measurements was interpreted using curve matching inversion to obtain depth, thickness, and resistivity of subsurface layers, which is then compared with the literature and lithology log from boreholes. The result shows that subsurface layers in the study area consist of clay, sand, and silt. Sand layers are interpreted as aquifers because they have higher porosity and permeability than clay and silt layers. In addition, sand layers exhibit a resistivity range (< 300 Ωm) similar to other studies. Sand aquifers can be found at shallow depths (min 0.5 m) along the Northwest and Southeast of the study area. The sand aquifers are thicker within the campus than in the forest area (up to 12 m). This information is important in decision-making regarding groundwater search and utilisation in the UI area.

The Limestone Diagenesis in The Bojongmanik Formation Based on Petrographic Analysis

Yogie Sani, Tri Rani Puji Astuti, Tito Latif Indra — Tue, 16 Apr 2024 00:00:00 +0000

The Bojongmanik Formation is a deposit belonging to the Banten Block, has an age range from Middle Miocene to
early Pliocene, and is interspersed with sandstone, marl, shale claystone, and limestone (Sudana and Santosa, 1992).
The limestone members of the Bojongmanik Formation are limestones containing mollusks with age equivalent to the
Middle Miocene (Efendi, 1998). This research aims to know the microscopic labeling. This diagenetic process works
on the limestone in the Bojongmanik Formation to know the stages of diagenesis and the relationship between the
diagenetic process and the porosity of the limestone. The method used in this research is petrographic analysis. Based
on the results of research from 15 samples of thin incisions of the Bojongmanik Formation, there are three types of
limestone: packstone limestone, floatstone limestone, and wackestone limestone, which undergo a process of
diagenetic cementation, dissolution, neomorphism, microbial micritization, and compaction. The depositional
environment of the limestone diagenesis of the Bojongmanik Formation is in the marine phreatic, burial, meteoric
phreatic, and meteoric vadose environments. The dominant porosity was found to be vuggy and intraparticle types.
One factor that influences the process of diagenesis is the formation of secondary porosity in limestone. The lower
porosity value indicates that there is a little dissolving effect. The higher porosity value indicates much dissolving in
the phreatic zone.
Keyword: The Bojongmanik Formation, Petrography Analysis, Porosity, Limestone Diagenesis