Peer-reviewedCC BY-NC-ND 4.0Scientific Reports
Integrating high-resolution magnetic surveys and metaheuristic optimization for quantitative characterization of subsurface magnetic sources in a municipal landfill
Related
- A novel deep learning prediction method for PM2.5 integrating improved MSTL decomposition and FATA optimization algorithm
- Integrating Observational Datasets and CMIP6 Projections for Multi-Scale Drought Characterization: Evidence from Western Türkiye
- Characterization of newly identified N–S shear zones in the Egyptian Nubian Shield by integrating geophysical, remote sensing and field data
- Waste-to-resource: utilizing soot as a performing adsorbent for rhodamine B. structural characterization and process optimization
- Integrating Molecular Docking, Dynamics, and AI-Based ADMET Prediction to Decipher the Toxicity Profile of Isometamidium Chloride Against Animal Trypanosomiasis
BibTeX
@article{minarto2026integrating,
title = {{Integrating high-resolution magnetic surveys and metaheuristic optimization for quantitative characterization of subsurface magnetic sources in a municipal landfill}},
author = {Eko Minarto and Z. R. Almiranda Lesar},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-76581-y},
url = {https://www.nature.com/articles/s41598-026-76581-y},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Integrating high-resolution magnetic surveys and metaheuristic optimization for quantitative characterization of subsurface magnetic sources in a municipal landfill
AU - Eko Minarto
AU - Z. R. Almiranda Lesar
PY - 2026
DA - 2026/10/19
JO - Scientific Reports
DO - 10.1038/s41598-026-76581-y
UR - https://www.nature.com/articles/s41598-026-76581-y
SN - 2045-2322
ER -