Ditapis dengan
Enhancing reservoir porosity prediction from acoustic impedance and lithofaci…
Inferring underground porosity and evaluating its spatial distribution is of great significance in a wide range of Earth sciences and engineering, including hydrocarbon reservoir characterization and geothermal energy exploitation. Popular methods are largely based on the analysis of lithological cores, well logs, and seismic inversion. These methods are reliable, but they are still time-consum…
- Edisi
- Vol.16, December 2022
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 10 hlm PDF, 7.361 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Contributions of machine learning to quantitative and real-time mud gas data …
The current utility of mud gas data is typically limited to geological and petrophysical correlation, formation evaluation, and fluid typing. A critical and comprehensive review of the literature on mud gas data revealed that the mud gas data is abundantly acquired during drilling but not sufficiently utilized in real time. There is the need to leverage the current advances in machine learning …
- Edisi
- Vol.16, December 2022
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 9 hlm PDF, 1.691 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Applying machine learning methods to predict geology using soil sample geoche…
In this study we compared various machine learning techniques that used soil geochemistry to aid in geologic mapping. We tested six different sampling methods (undersample, oversample, Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN), SMOTE and Edited Nearest Neighbor (SMOTEENN), and SMOTE and Tomek links (SMOTETomek)). SMOTE performed best with ADASYN and…
- Edisi
- Vol.16, December 2022
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 13 hlm PDF, 13.315 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Adaptive Proxy-based Robust Production Optimization with Multilayer Perceptron
Machine learning (ML) has been a technique employed to build data-driven models that can map the relationship between the input and output data provided. ML-based data-driven models offer an alternative path to solving optimization problems, which are conventionally resolved by applying simulation models. Higher computational cost is induced if the simulation model is computationally intensive.…
- Edisi
- Vol.16, December 2022
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 15 hlm PDF, 8.103 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
A hybrid framework for modelling domains using quantitative covariates
Domains define the boundaries of mineralisation zones, within which the grade distribution of the target minerals can be quantified via an established mineral resource estimation procedure. Available domain modelling techniques include manual interpretation, implicit modelling and advanced geostatistical approaches. In mining applications, the most commonly used method is manual domaining, whic…
- Edisi
- Vol.16, December 2022
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 21 hlm PDF, 26.736 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
A framework for constructing machine learning models with feature set optimis…
A deeper understanding of the drivers of evapotranspiration and the modelling of its constituent parts (evaporation and transpiration) may be of significant importance to the monitoring and management of water resources globally over the coming decades. In this work a framework was developed to identify the best performing machine learning algorithm from a candidate set, select optimal predicti…
- Edisi
- Vol.16, December 2022
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 9 hlm PDF, 1.736 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Geoscience language models and their intrinsic evaluation
Geoscientists use observations and descriptions of the rock record to study the origins and history of our planet, which has resulted in a vast volume of scientific literature. Recent progress in natural language processing (NLP) has the potential to parse through and extract knowledge from unstructured text, but there has, so far, been only limited work on the concepts and vocabularies that ar…
- Edisi
- Vol.14, June 2022
- ISBN/ISSN
- -
- Deskripsi Fisik
- 10 hlm PDF, 1.163 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Advanced wind speed prediction using convective weather variables through mac…
High precision and reliable wind speed forecasting is a challenge for meteorologists. We used multiple nonparametric tree-based machine learning techniques, for predicting the maximum wind speed at 10 m using selected convective weather variables. Analysis is based on 127 convective storms from 2005 to 2013. The study evaluated two error models - the Bayesian Additive Regression Trees (BART) …
- Edisi
- Vol.1, October 2019
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 9 hlm PDF, 1.584 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
A competitive ensemble model for permeability prediction in heterogeneous oil…
One important property of oil and gas reservoirs is permeability, which has proven to be difficult to predict. Empirical and regression models are the current industrial practice for predicting permeability due to high cost and time consumption associated with laboratory measurement. In recent times, machine learning algorithms have been employed for the prediction of permeability due to their …
- Edisi
- Vol.1, October 2019
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 13 hlm PDF, 3.657 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Robust marker detection and identification using deep learning in underwater …
The progressing industrialization of oceans mandates reliable, accurate and automatable subsea survey methods. Close-range photogrammetry is a promising discipline, which is frequently applied by archaeologists, fish-farmers, and the offshore energy industry. This paper presents a robust approach for the reliable detection and identification of photogrammetric markers in subsea images. The prop…
- Edisi
- Vol.13, August 2024
- ISBN/ISSN
- 1872-8235
- Deskripsi Fisik
- 15 hlm PDF, 13,522 KB
- Judul Seri
- ISPRS Open Journal of Photogrammetry and Remote Sensing
- No. Panggil
- 621.3678
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