@inbook{MTMT:33315458,
	title = {Joint Optimization of Factor Scores and Loadings by Particle Swarm Optimization},
	url = {https://m2.mtmt.hu/api/publication/33315458},
	author = {Abordán, Armand and Szabó, Norbert Péter},
	booktitle = {Új eredmények a műszaki föld- és környezettudományban 2022},
	unique-id = {33315458},
	year = {2022},
	pages = {139-148}
}

@article{MTMT:32966199,
	title = {Permeability extraction from multiple well logs using particle swarm optimization based factor analysis},
	url = {https://m2.mtmt.hu/api/publication/32966199},
	author = {Szabó, Norbert Péter and Abordán, Armand and Dobróka, Mihály},
	doi = {10.1007/s13137-022-00200-x},
	journal-iso = {GEM - INT J GEOMATHEMATICS},
	journal = {GEM - INTERNATIONAL JOURNAL ON GEOMATHEMATICS},
	volume = {13},
	unique-id = {32966199},
	issn = {1869-2672},
	abstract = {In this paper, we present an innovative factor analysis algorithm for hydrocarbon exploration to estimate the intrinsic permeability of reservoir rocks from well logs. Unlike conventional evaluation methods that employ a single or a limited number of data types, we process simultaneously all available data to derive the first statistical factor and relate it to permeability by regression analysis. For solving the problem of factor analysis, we introduce an improved particle swarm optimization method, which searches for the global minimum of the distance between the observed and calculated data and gives a quick estimation for the factor scores. The learning factors of the intelligent computational technique such as the cognitive and social constants are specified as hyperparameters and calculated by using simulated annealing algorithm as heuristic hyperparameter estimator. Instead of the arbitrary fixation of these hyperparameters, we refine them in an iterative process to give reliable estimation both for the statistical factors and formation permeability. The estimated learning parameters are consistent with literature recommendations. We demonstrate the feasibility of the proposed well-log analysis method by a Hungarian oilfield study involving open-hole wireline logs and core data. We determine the spatial distribution of permeability both along a borehole and between more wells using the factor analysis approach, which serves as efficient and reliable multivariate statistical tool for advanced formation evaluation and reservoir modeling.},
	keywords = {Factor analysis; Particle Swarm Optimization; hydrocarbon reservoir; Intrinsic permeability; hyperparameter},
	year = {2022},
	eissn = {1869-2680},
	orcid-numbers = {Dobróka, Mihály/0000-0003-3956-2070}
}

@article{MTMT:32147491,
	title = {Machine learning based approach for the interpretation of engineering geophysical sounding logs},
	url = {https://m2.mtmt.hu/api/publication/32147491},
	author = {Abordán, Armand and Szabó, Norbert Péter},
	doi = {10.1007/s40328-021-00354-4},
	journal-iso = {ACTA GEOD GEOPHYS},
	journal = {ACTA GEODAETICA ET GEOPHYSICA},
	volume = {56},
	unique-id = {32147491},
	issn = {2213-5812},
	abstract = {In this paper, a set of machine learning (ML) tools is applied to estimate the water saturation of shallow unconsolidated sediments at the Bátaapáti site in Hungary. Water saturation is directly calculated from the first factor extracted from a set of direct push logs by factor analysis. The dataset observed by engineering geophysical sounding tools as special variants of direct-push probes contains data from a total of 12 shallow penetration holes. Both one- and two-dimensional applications of the suggested method are presented. To improve the performance of factor analysis, particle swarm optimization (PSO) is applied to give a globally optimized estimate for the factor scores. Furthermore, by a hyperparameter estimation approach, some control parameters of the utilized PSO algorithm are automatically estimated by simulated annealing (SA) to ensure the convergence of the procedure. The result of the suggested ML-based log analysis method is compared and verified by an independent inversion estimate. The study shows that the PSO-based factor analysis aided by hyperparameter estimation provides reliable in situ estimates of water saturation, which may improve the solution of environmental end engineering problems in shallow unconsolidated heterogeneous formations.},
	year = {2021},
	eissn = {2213-5820},
	pages = {681-696}
}

@misc{MTMT:31970938,
	title = {Tárolókőzetek áteresztőképességének meghatározása hiperparaméter becsléssel támogatott PSO eljárással},
	url = {https://m2.mtmt.hu/api/publication/31970938},
	author = {Abordán, Armand and Szabó, Norbert Péter},
	unique-id = {31970938},
	year = {2020}
}

@CONFERENCE{MTMT:31847576,
	title = {Characteristic Pressure Spectrum Produced with a New Multi-Exponential Model Describing Quality Factor-Pressure Relationship},
	url = {https://m2.mtmt.hu/api/publication/31847576},
	author = {Somogyiné Molnár, Judit and Abordán, Armand and Dobróka, Tünde Edit and Ormos, Tamás and Dobróka, Mihály},
	booktitle = {NSG2020 26th European Meeting of Environmental and Engineering Geophysics},
	doi = {10.3997/2214-4609.202020074},
	unique-id = {31847576},
	year = {2020},
	orcid-numbers = {Dobróka, Mihály/0000-0003-3956-2070}
}

@mastersthesis{MTMT:31605657,
	title = {Global Optimization-based Data Processing Methods for Advanced Well Logging Applications},
	url = {https://m2.mtmt.hu/api/publication/31605657},
	author = {Abordán, Armand},
	doi = {10.14750/ME.2020.007},
	unique-id = {31605657},
	year = {2020}
}

@article{MTMT:31203354,
	title = {Uncertainty reduction of interval inversion estimation results using a factor analysis approach},
	url = {https://m2.mtmt.hu/api/publication/31203354},
	author = {Abordán, Armand and Szabó, Norbert Péter},
	doi = {10.1007/s13137-020-0144-4},
	journal-iso = {GEM - INT J GEOMATHEMATICS},
	journal = {GEM - INTERNATIONAL JOURNAL ON GEOMATHEMATICS},
	volume = {11},
	unique-id = {31203354},
	issn = {1869-2672},
	year = {2020},
	eissn = {1869-2680}
}

@{MTMT:32079439,
	title = {Reducing the uncertainty of parameter estimation for the interval inversion method using factor analysis},
	url = {https://m2.mtmt.hu/api/publication/32079439},
	author = {Abordán, Armand},
	booktitle = {GEOMATES 2019. International Congress on Geomathematics in Earth-& Environmental Sciences},
	unique-id = {32079439},
	year = {2019},
	pages = {49}
}

@article{MTMT:30774076,
	title = {Selecting control parameters for the practicle swarm optimization based factor analysis},
	url = {https://m2.mtmt.hu/api/publication/30774076},
	author = {Abordán, Armand and Szabó, Norbert Péter},
	journal-iso = {MŰSZAKI FÖLDTUDOMÁNYI KÖZLEMÉNYEK},
	journal = {MŰSZAKI FÖLDTUDOMÁNYI KÖZLEMÉNYEK},
	volume = {88},
	unique-id = {30774076},
	issn = {2063-5508},
	year = {2019},
	pages = {134-140}
}

@inproceedings{MTMT:30754476,
	title = {Local inversion of direct push logging data by invasive weed optimization},
	url = {https://m2.mtmt.hu/api/publication/30754476},
	author = {Abordán, Armand},
	booktitle = {Doktoranduszok Fóruma},
	unique-id = {30754476},
	abstract = {Műszaki Földtudományi Kar Szekciókiadványa},
	year = {2019},
	pages = {3-9}
}