@CONFERENCE{MTMT:36859096,
	title = {Tuning of low magnetic field in the focal volume of the ZBL},
	url = {https://m2.mtmt.hu/api/publication/36859096},
	author = {Lemperger, István and Erdős, Géza and Márkusné Bebesi, Zsófia and Kovács, Péter Gábor and Németh, Zoltán and Molnár, Csaba and Kuslits, Lukács and Kis, Árpád and Dwivedi, Navin Kumar and Szalai, Sándor and Wesztergom, Viktor},
	booktitle = {IAGA / IASPEI Joint Scientific Meeting 2025},
	unique-id = {36859096},
	year = {2025},
	orcid-numbers = {Németh, Zoltán/0000-0001-9527-3447; Dwivedi, Navin Kumar/0000-0002-6418-8765}
}

@CONFERENCE{MTMT:36859150,
	title = {Establishment of the Zero Magnetic Field Laboratory (ZBL) at the Széchenyi István Geophysical Observatory (SzIGO)},
	url = {https://m2.mtmt.hu/api/publication/36859150},
	author = {Lemperger, István and Erdős, Géza and Márkusné Bebesi, Zsófia and Kovács, Péter Gábor and Németh, Zoltán and Molnár, Csaba and Kis, Árpád and Dwivedi, Navin Kumar and Szalai, Sándor and Kuslits, Lukács and Wesztergom, Viktor},
	booktitle = {IAGA / IASPEI Joint Scientific Meeting 2025},
	unique-id = {36859150},
	year = {2025},
	orcid-numbers = {Németh, Zoltán/0000-0001-9527-3447; Dwivedi, Navin Kumar/0000-0002-6418-8765}
}

@CONFERENCE{MTMT:36921511,
	title = {Demonstration of clustering-based stress field analyses: insights from Vrancea zone and the 2023 Turkey earthquake sequence},
	url = {https://m2.mtmt.hu/api/publication/36921511},
	author = {Czirok, Lili and Kuslits, Lukács and Laksona, FX Anjar Tri and Bozsó, István and Gribovszki, Katalin Eszter},
	booktitle = {IAGA / IASPEI Joint Scientific Meeting 2025, 31 August - 5 September 2025, Lisbon, Portugal: Book of Abstracts},
	unique-id = {36921511},
	year = {2025},
	orcid-numbers = {Gribovszki, Katalin Eszter/0000-0003-2577-0127}
}

@misc{MTMT:37068417,
	title = {Reversal Statistics in the Non-Autonomous Rikitake Dynamo Model},
	url = {https://m2.mtmt.hu/api/publication/37068417},
	author = {Herein, Mátyás and Kuslits, Lukács and Jánosi, Dániel},
	unique-id = {37068417},
	year = {2025},
	orcid-numbers = {Herein, Mátyás/0000-0003-1903-3761; Jánosi, Dániel/0000-0002-6138-3610}
}

@CONFERENCE{MTMT:37068994,
	title = {Physics informed neural networks at the service of geodynamo research},
	url = {https://m2.mtmt.hu/api/publication/37068994},
	author = {Kuslits, Lukács and András, Horváth and Ciaran, Beggan and Rubóczki, Tibor and Prácser, Ernő and Czirok, Lili and Bozsó, István and Lemperger, István},
	booktitle = {IAGA / IASPEI Joint Scientific Meeting 2025, 31 August - 5 September 2025, Lisbon, Portugal: Book of Abstracts},
	unique-id = {37068994},
	year = {2025},
	pages = {98-98}
}

@CONFERENCE{MTMT:37070514,
	title = {Automatic fine tuning of the low magnetic field in the focal space of the ZBL},
	url = {https://m2.mtmt.hu/api/publication/37070514},
	author = {Lemperger, István and Géza, Erdős and Zsófia, Bebesi and Péter, Kovács and Németh, Zoltán and Molnár, Csaba and Kuslits, Lukács and Kis, Árpád and Dwivedi, Navin Kumar and Szalai, Sándor and Wesztergom, Viktor},
	booktitle = {46-th COSPAR Scientific Assewmbly Abstract},
	unique-id = {37070514},
	year = {2025},
	pages = {1},
	orcid-numbers = {Dwivedi, Navin Kumar/0000-0002-6418-8765}
}

@article{MTMT:34938287,
	title = {An alternate representation of the geomagnetic core field obtained using machine learning},
	url = {https://m2.mtmt.hu/api/publication/34938287},
	author = {Kuslits, Lukács and Horváth, András and Wesztergom, Viktor and Beggan, Ciaran and Rubóczki, Tibor and Prácser, Ernő and Czirok, Lili and Bozsó, István and Lemperger, István},
	doi = {10.1186/s40623-024-02024-5},
	journal-iso = {EARTH PLANETS SPACE},
	journal = {EARTH PLANETS AND SPACE},
	volume = {76},
	unique-id = {34938287},
	issn = {1343-8832},
	abstract = {Machine learning (ML) as a tool is rapidly emerging in various branches of contemporary geophysical research. To date, however, rarely has it been applied specifically for the study of Earth’s internal magnetic field and the geodynamo. Prevailing methods currently used in inferring the characteristic properties and the probable time evolution of the geodynamo are mostly based on reduced representations of magnetohydrodynamics (MHD). This study introduces a new inference method, referred to as Current Loop-based UNet Model Segmentation Inference (CLUMSI). Its long-term goal focuses on uncovering concentrations of electric current densities inside the core as the direct sources of the magnetic field itself, rather than computing the fluid motion using MHD. CLUMSI relies on simplified models in which equivalent current loops represent electric current systems emerging in turbulent geodynamo simulations. Various configurations of such loop models are utilized to produce synthetic magnetic field and secular variation (SV) maps computed at the core–mantle boundary (CMB). The resulting maps are then presented as training samples to an image-processing neural network designed specifically for solving image segmentation problems. This network essentially learns to infer the parameters and configuration of the loops in each model based on the corresponding CMB maps. In addition, with the help of the Domain Adversarial Training of Neural Networks (DANN) method during training, historical geomagnetic field data could also be considered alongside the synthetic samples. This implementation can increase the likelihood that a network trained primarily on synthetic data will appropriately handle real inputs. Our results focus mainly on the method's feasibility when applied to synthetic data and the quality of these inferences. A single evaluation of the trained network can recover the overall distribution of loop parameters with reasonable accuracy. To better represent conditions in the outer core, the study also proposes a computationally feasible process to account for magnetic diffusion and the corresponding induced currents in the loop models. However, the quality of the reconstruction of magnetic field properties is compromised by occasional poor inferences, and an inability to recover realistic SV.},
	year = {2024},
	eissn = {1880-5981},
	orcid-numbers = {Kuslits, Lukács/0000-0002-5056-1610; Horváth, András/0000-0001-5855-4186}
}

@article{MTMT:35467820,
	title = {Cluster analysis on a suite of upper mantle xenoliths from the Nógrád–Gömör Volcanic Field (Northern Pannonian Basin)},
	url = {https://m2.mtmt.hu/api/publication/35467820},
	author = {Patkó, Levente and Kuslits, Lukács and Czirok, Lili and Liptai, Nóra},
	doi = {10.23928/foldt.kozl.2024.154.3.329},
	journal-iso = {FÖLDTANI KÖZLÖNY},
	journal = {FÖLDTANI KÖZLÖNY},
	volume = {154},
	unique-id = {35467820},
	issn = {0015-542X},
	abstract = {The Nógrád–Gömör Volcanic Field is one of the most thoroughly studied upper mantle xenolith-bearing localities in the Carpathian–Pannonian region. Dozens of upper mantle xenoliths collected there have been investigated from various aspects in the past decades. Major element compositions of upper mantle xenoliths available in scientific papers on the Nógrád–Gömör xenoliths were extracted to build a database. This database includes the geochemical composition of the rock-forming minerals (olivine, pyroxenes, spinel) of 112 upper mantle xenoliths. Using the data compiled in this database, we applied the CluStress algorithm to perform cluster analysis. The results led to the division of 2 supergroups (I and II) and 17 groups within them. Supergroup I was split into two subgroups (Supergroup I/a and I/b). The xenoliths of Supergroup I/a and I/b are distinguished by their orthopyroxene/clinopyroxene modal composition ratios (mainly >1.5 and <1, respectively) as well as their olivine Mg-numbers (dominantly >0.89 and <0.89, respectively). Most of the xenoliths (~50% of all classified xenoliths) were sorted into Group 1 within Supergroup I/a, and they appear at almost all xenolith-bearing localities in the Nógrád–Gömör. The Group 1 xenoliths exhibit narrow compositional ranges regarding their major element relationships. Due to their common occurence and spatial distribution, these rocks were considered to represent an ambient lithospheric mantle under­neath the study area. The geochemical characteristics of the other groups allowed their linking to different events described in former scientific works. At least 11 out of the 17 distinguished groups (i.e. ~35% of the classified xenoliths) are strongly related to the Neogene alkali basaltic volcanism. This suggests that the pre-Neogene rootzones of volcanic fields in the Carpathian–Pannonian region were significantly modified by intensive melt-rock reactions between upward migrating basaltic melts and wall rocks, which took place in the last ~10 million years.},
	year = {2024},
	eissn = {2559-902X},
	pages = {329-346},
	orcid-numbers = {Patkó, Levente/0000-0001-6007-3103; Liptai, Nóra/0000-0002-2464-2468}
}

@misc{MTMT:35748211,
	title = {Szintetikus adatok nagy sebességű generálása és képfeldolgozó neurális hálók a földmágnesség kutatásának szolgálatában},
	url = {https://m2.mtmt.hu/api/publication/35748211},
	author = {Kuslits, Lukács and Horváth, András and Wesztergom, Viktor and Ciaran, Beggan and Rubóczki, Tibor and Prácser, Ernő and Czirok, Lili and Bozsó, István and Lemperger, István},
	unique-id = {35748211},
	abstract = {A földi mágneses mező alakulásának megértésében évtizedek óta egyre jelentősebb szerep jut a nagy teljesítményű számítási infrastruktúrán végzett numerikus szimulációknak.
		Ezt részben a mezőt bolygónkban fenntartó folyamat összetettsége, részben annak léptékbeni sajátosságai indokolják.
		Egy, a folyamat belső viszonyait rekonstruálni képes gépi tanuló algoritmus megfelelő működéséhez szükséges, nagy számosságú és jellegében is kellő változatosságot eredményező számítássorozat generálása azonban épp ezen okoknál fogva jelent komoly kihívást. Az itt bemutatott módszertani fejlesztés a direkt numerikus szimulációk megkerülésével, egy idealizált analitikus modell és egy szisztematikus modellszámítás eredményeinek közelítése révén kísérli meg a fenti problémát orvosolni.
		A fejlesztés tágabb relevanciáját adhatják például az olyan műszaki vagy orvosdiagnosztikai problémák, melyek esetében nem lehetséges vagy nem kívánatos a vizsgált fizikai folyamathoz közvetlenül hozzáférni, annak komplex modellezése pedig jelentős számításigényű.
		Az eljárás során alkalmazott tanítási módszer képessé teheti a gépi tanuló (mélytanuló) algoritmusokat arra is, hogy kezeljék a szimulációk és a valós folyamat között feszülő ellentmondásokat.},
	year = {2024},
	orcid-numbers = {Horváth, András/0000-0001-5855-4186}
}

@CONFERENCE{MTMT:33797498,
	title = {ELF-transients detected in the broadband recordings at the Hylaty station in Poland},
	url = {https://m2.mtmt.hu/api/publication/33797498},
	author = {Bozóki, Tamás and Młynarczyk, Janusz and Bór, József and Kubisz, Jerzy and Bozsó, István and Horváth, András and Kuslits, Lukács and Timkó, Máté},
	booktitle = {EGU General Assembly 2023: Abstracts},
	doi = {10.5194/egusphere-egu23-17102},
	unique-id = {33797498},
	abstract = {Lightning acts as a natural antenna radiating electromagnetic (EM) waves in a wide frequency range. In the extremely low frequency (ELF) band (3 Hz - 3 kHz), lightning-induced EM waves suffer very weak attenuation while they propagate in the waveguide formed by the Earth’s surface and the lowest part of the ionosphere. These EM waves can travel around the Earth several times before losing most of their energy. This allows ELF-transients generated by powerful lightning discharges from around the globe to be detected at any observation site. We developed an algorithm that identifies ELF-transients in the broadband recordings at Hylaty, Poland (sampling frequency: 3004.81 Hz, antenna bandwidth: 0.02 Hz to 1.1 kHz) and finds their most probable source lightning discharge in the lightning database of the Word Wide Lightning Location Network (WWLLN) based on the technique described by Bór et al. (2022).Between July 2020 and April 2021 about 270,000 ELF-transients were found in the records from Hylaty. The most probable source of 160,000 transients  was identified in the WWLLN database. Using this data set, we show that the propagation speed of broadband ELF-transients differ significantly when the propagation path is on the dayside or on the nightside of the Earth. It is also demonstrated that for lightning discharges close to Hylaty (d<2Mm), the timing and location accuracy of WWLLN has a large impact on the identification of the lightning source and on the inferred propagation speed. A convolutional neural network, trained with ELF-transients of known source location, was used to determine the distance to the lightning source in cases where the source lightning discharge could not be found in the WWLLN database. The average accuracy of the distance provided by the neural network is 700 km. No significant difference can be seen between the distribution of distances obtained by matching the source lightning stroke in the WWLLN database and that obtained using the neural network-based approach. Reference:Bór, J., Szabóné André, K., Bozóki, T., Mlynarczyk, J., Steinbach, P., Novák, A., and Lemperger, I. (2022): Estimating the Attenuation of ELF-Band Radio Waves in the Earth’s Crust by Q-Bursts. IEEE Transactions on Antennas and Propagation, 70, 8. https://doi.org/10.1109/TAP.2022.3161504},
	year = {2023},
	orcid-numbers = {Bozóki, Tamás/0000-0002-1672-1510; Horváth, András/0000-0001-5855-4186}
}