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Showing 6 of 6 academic articles

长白山天池火山地热系统

Jian Zhang, Yanxia Fan, Wei Chu, Yubei He • Dizhen xuebao (2024)

长白山天池火山是晚新生代中心喷发式复合型层状火山，全新世以来发生过多次大规模喷发，与深部岩浆热扰动活动相关的构造地震、地表形变、温泉气体组份等均显示该火山仍可能再次喷发，是我国东部潜在的高温地热资源区。长白山天池火山周边地下水十分丰富，具有形成高温水热活动的岩浆囊热源。为探索地壳浅层隐伏的高温地热资源，本文在野外考察基础上，利用地热学方法，计算了该区地层结构与热状态，分析了地表下的火山地热系统。结果表明：长白山天池火山区地下2 km深处的温度在66—110℃之间，12 km深...

DOAJ Open Access (DOAJ) DOI: 10.11939/jass.20220180

Particulate matter, air quality and climate: lessons learned and future needs

S. Fuzzi, U. Baltensperger, K. Carslaw, S. Decesari et al. • Atmospheric Chemistry and Physics (2015)

The literature on atmospheric particulate matter (PM), or atmospheric aerosol, has increased enormously over the last 2 decades and amounts now to some 1500–2000 papers per year in the refereed literature. This is in part due to the enormou...

DOAJ Open Access (DOAJ) DOI: 10.5194/acp-15-8217-2015

On the Dual Nature of Atmospheric Escape

Darius Modirrousta-Galian, Jun Korenaga • The Astrophysical Journal (2026)

Planetary atmospheres cannot remain hydrostatic at all altitudes because they approach finite density at infinite radius, implying infinite mass. Classical treatments address this in two directions: either retain a hydrostatic structure whi...

DOAJ Open Access (DOAJ) DOI: 10.3847/1538-4357/ae4a12

Machine Learning in Earthquake Seismology

S. Mostafa Mousavi, Gregory C. Beroza • Annual Review of Earth and Planetary Sciences (2022)

Machine learning (ML) is a collection of methods used to develop understanding and predictive capability by learning relationships embedded in data. ML methods are becoming the dominant approaches for many tasks in seismology. ML and data m...

OPENALEX cc-by DOI: https://doi.org/10.1146/annurev-earth-071822-100323