Taraprasad Bhowmick, Yong Wang, Jonas Latt, Gholamhossein Bagheri
Every solid particle in the atmosphere, from ice crystals and pollen to dust, ash, and microplastics, is non-spherical. These particles play significant roles in Earth's climate system, influencing temperature, weather patterns, natural ecosystems, human health, and pollution levels. However, our understanding of these particles is largely based on the theories for extremely small particles and experiments conducted in liquid mediums. In this study, we used an innovative experimental setup and particle-resolved numerical simulations to investigate the behaviour of sub-millimetre ellipsoids of varying shapes in the air. Our results revealed complex decaying oscillation patterns involving numerous twists and turns in these particles, starkly contrasting their dynamics in liquid mediums. We found that the frequency and decay rate of these oscillations have a strong dependence on the particle shape. Interestingly, disk-shaped particles oscillated at nearly twice the frequency of rod-shaped particles, though their oscillations also decayed more rapidly. During oscillation, even subtly non-spherical particles can drift laterally up to ten times their volume-equivalent spherical diameter. This behaviour enables particles to sweep through four times more air both vertically and laterally compared to a volume-equivalent sphere, significantly increasing their encounter rate and aggregation possibility. Our findings provide an explanation for the long-range transport and naturally occurring aggregate formation of highly non-spherical particles such as snowflakes and volcanic ash.
{"title":"Twist, turn and encounter: the trajectories of small atmospheric particles unravelled","authors":"Taraprasad Bhowmick, Yong Wang, Jonas Latt, Gholamhossein Bagheri","doi":"arxiv-2408.11487","DOIUrl":"https://doi.org/arxiv-2408.11487","url":null,"abstract":"Every solid particle in the atmosphere, from ice crystals and pollen to dust,\u0000ash, and microplastics, is non-spherical. These particles play significant\u0000roles in Earth's climate system, influencing temperature, weather patterns,\u0000natural ecosystems, human health, and pollution levels. However, our\u0000understanding of these particles is largely based on the theories for extremely\u0000small particles and experiments conducted in liquid mediums. In this study, we\u0000used an innovative experimental setup and particle-resolved numerical\u0000simulations to investigate the behaviour of sub-millimetre ellipsoids of\u0000varying shapes in the air. Our results revealed complex decaying oscillation\u0000patterns involving numerous twists and turns in these particles, starkly\u0000contrasting their dynamics in liquid mediums. We found that the frequency and\u0000decay rate of these oscillations have a strong dependence on the particle\u0000shape. Interestingly, disk-shaped particles oscillated at nearly twice the\u0000frequency of rod-shaped particles, though their oscillations also decayed more\u0000rapidly. During oscillation, even subtly non-spherical particles can drift\u0000laterally up to ten times their volume-equivalent spherical diameter. This\u0000behaviour enables particles to sweep through four times more air both\u0000vertically and laterally compared to a volume-equivalent sphere, significantly\u0000increasing their encounter rate and aggregation possibility. Our findings\u0000provide an explanation for the long-range transport and naturally occurring\u0000aggregate formation of highly non-spherical particles such as snowflakes and\u0000volcanic ash.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"256 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Goulven Monnier, Benjamin Camus, Yann-Hervé Hellouvry
In this paper, we detail the high-performance implementation of our spaceborne radar simulator for satellite oceanography. Our software simulates the sea surface and the signal to imitate, as far as possible, the measurement process, starting from its lowest level mechanisms. In this perspective, raw data are computed as the sum of many illuminated scatterers, whose time-evolving properties are related to the surface roughness, topography, and kinematics. To achieve efficient performance, we intensively use GPU computing. Moreover, we propose a fast simulation mode based on the assumption that the instantaneous Doppler spectrum within a range gate varies on a timescale significantly larger than the PRI. The sea surface can then be updated at a frequency much smaller than the PRF, drastically reducing the computational cost. When the surface is updated, Doppler spectra are computed for all range gates. Signals segments are then obtained through 1D inverse Fourier transforms and pondered to ensure a smooth time evolution between surface updates. We validate this fast simulation mode with a radar altimeter simulation case of the Sentinel-3 SRAL instrument, showing that simulated raw data can be focused and retrieved using state-of-the-art algorithms. Finally, we show that, using a modest hardware configuration, our simulator can generate enough data in one day to compute the SWH and SSH spectra of a scene. This demonstrate that we achieved an important state-of-the-art speed-up.
{"title":"High Performance Simulation of Spaceborne Radar for Remote-Sensing Oceanography: Application to an Altimetry Scenario","authors":"Goulven Monnier, Benjamin Camus, Yann-Hervé Hellouvry","doi":"arxiv-2408.11472","DOIUrl":"https://doi.org/arxiv-2408.11472","url":null,"abstract":"In this paper, we detail the high-performance implementation of our\u0000spaceborne radar simulator for satellite oceanography. Our software simulates\u0000the sea surface and the signal to imitate, as far as possible, the measurement\u0000process, starting from its lowest level mechanisms. In this perspective, raw\u0000data are computed as the sum of many illuminated scatterers, whose\u0000time-evolving properties are related to the surface roughness, topography, and\u0000kinematics. To achieve efficient performance, we intensively use GPU computing.\u0000Moreover, we propose a fast simulation mode based on the assumption that the\u0000instantaneous Doppler spectrum within a range gate varies on a timescale\u0000significantly larger than the PRI. The sea surface can then be updated at a\u0000frequency much smaller than the PRF, drastically reducing the computational\u0000cost. When the surface is updated, Doppler spectra are computed for all range\u0000gates. Signals segments are then obtained through 1D inverse Fourier transforms\u0000and pondered to ensure a smooth time evolution between surface updates. We\u0000validate this fast simulation mode with a radar altimeter simulation case of\u0000the Sentinel-3 SRAL instrument, showing that simulated raw data can be focused\u0000and retrieved using state-of-the-art algorithms. Finally, we show that, using a\u0000modest hardware configuration, our simulator can generate enough data in one\u0000day to compute the SWH and SSH spectra of a scene. This demonstrate that we\u0000achieved an important state-of-the-art speed-up.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"71 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Duccio Fanelli, Luca Bindi, Lorenzo Chicchi, Claudio Pereti, Roberta Sessoli, Simone Tommasini
Neural networks are gaining widespread relevance for their versatility, holding the promise to yield a significant methodological shift in different domain of applied research. Here, we provide a simple pedagogical account of the basic functioning of a feedforward neural network. Then we move forward to reviewing two recent applications of machine learning to Earth and Materials Science. We will in particular begin by discussing a neural network based geothermobarometer, which returns reliable predictions of the pressure/temperature conditions of magma storage. Further, we will turn to illustrate how machine learning tools, tested on the list of minerals from the International Mineralogical Association, can help in the search for novel superconducting materials.
{"title":"A short introduction to Neural Networks and their application to Earth and Materials Science Science","authors":"Duccio Fanelli, Luca Bindi, Lorenzo Chicchi, Claudio Pereti, Roberta Sessoli, Simone Tommasini","doi":"arxiv-2408.11395","DOIUrl":"https://doi.org/arxiv-2408.11395","url":null,"abstract":"Neural networks are gaining widespread relevance for their versatility,\u0000holding the promise to yield a significant methodological shift in different\u0000domain of applied research. Here, we provide a simple pedagogical account of\u0000the basic functioning of a feedforward neural network. Then we move forward to\u0000reviewing two recent applications of machine learning to Earth and Materials\u0000Science. We will in particular begin by discussing a neural network based\u0000geothermobarometer, which returns reliable predictions of the\u0000pressure/temperature conditions of magma storage. Further, we will turn to\u0000illustrate how machine learning tools, tested on the list of minerals from the\u0000International Mineralogical Association, can help in the search for novel\u0000superconducting materials.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alireza Jafari, Geoffrey Fox, John B. Rundle, Andrea Donnellan, Lisa Grant Ludwig
Advancing the capabilities of earthquake nowcasting, the real-time forecasting of seismic activities remains a crucial and enduring objective aimed at reducing casualties. This multifaceted challenge has recently gained attention within the deep learning domain, facilitated by the availability of extensive, long-term earthquake datasets. Despite significant advancements, existing literature on earthquake nowcasting lacks comprehensive evaluations of pre-trained foundation models and modern deep learning architectures. These architectures, such as transformers or graph neural networks, uniquely focus on different aspects of data, including spatial relationships, temporal patterns, and multi-scale dependencies. This paper addresses the mentioned gap by analyzing different architectures and introducing two innovation approaches called MultiFoundationQuake and GNNCoder. We formulate earthquake nowcasting as a time series forecasting problem for the next 14 days within 0.1-degree spatial bins in Southern California, spanning from 1986 to 2024. Earthquake time series is forecasted as a function of logarithm energy released by quakes. Our comprehensive evaluation employs several key performance metrics, notably Nash-Sutcliffe Efficiency and Mean Squared Error, over time in each spatial region. The results demonstrate that our introduced models outperform other custom architectures by effectively capturing temporal-spatial relationships inherent in seismic data. The performance of existing foundation models varies significantly based on the pre-training datasets, emphasizing the need for careful dataset selection. However, we introduce a new general approach termed MultiFoundationPattern that combines a bespoke pattern with foundation model results handled as auxiliary streams. In the earthquake case, the resultant MultiFoundationQuake model achieves the best overall performance.
{"title":"Time Series Foundation Models and Deep Learning Architectures for Earthquake Temporal and Spatial Nowcasting","authors":"Alireza Jafari, Geoffrey Fox, John B. Rundle, Andrea Donnellan, Lisa Grant Ludwig","doi":"arxiv-2408.11990","DOIUrl":"https://doi.org/arxiv-2408.11990","url":null,"abstract":"Advancing the capabilities of earthquake nowcasting, the real-time\u0000forecasting of seismic activities remains a crucial and enduring objective\u0000aimed at reducing casualties. This multifaceted challenge has recently gained\u0000attention within the deep learning domain, facilitated by the availability of\u0000extensive, long-term earthquake datasets. Despite significant advancements,\u0000existing literature on earthquake nowcasting lacks comprehensive evaluations of\u0000pre-trained foundation models and modern deep learning architectures. These\u0000architectures, such as transformers or graph neural networks, uniquely focus on\u0000different aspects of data, including spatial relationships, temporal patterns,\u0000and multi-scale dependencies. This paper addresses the mentioned gap by\u0000analyzing different architectures and introducing two innovation approaches\u0000called MultiFoundationQuake and GNNCoder. We formulate earthquake nowcasting as\u0000a time series forecasting problem for the next 14 days within 0.1-degree\u0000spatial bins in Southern California, spanning from 1986 to 2024. Earthquake\u0000time series is forecasted as a function of logarithm energy released by quakes.\u0000Our comprehensive evaluation employs several key performance metrics, notably\u0000Nash-Sutcliffe Efficiency and Mean Squared Error, over time in each spatial\u0000region. The results demonstrate that our introduced models outperform other\u0000custom architectures by effectively capturing temporal-spatial relationships\u0000inherent in seismic data. The performance of existing foundation models varies\u0000significantly based on the pre-training datasets, emphasizing the need for\u0000careful dataset selection. However, we introduce a new general approach termed\u0000MultiFoundationPattern that combines a bespoke pattern with foundation model\u0000results handled as auxiliary streams. In the earthquake case, the resultant\u0000MultiFoundationQuake model achieves the best overall performance.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tobias G. Meier, Dan J. Bower, Tim Lichtenberg, Mark Hammond, Paul J. Tackley, Raymond T. Pierrehumbert, José A. Caballero, Shang-Min Tsai, Megan Weiner Mansfield, Nicola Tosi, Philipp Baumeister
Many super-Earths are on very short orbits around their host star and, therefore, more likely to be tidally locked. Because this locking can lead to a strong contrast between the dayside and nightside surface temperatures, these super-Earths could exhibit mantle convection patterns and tectonics that could differ significantly from those observed in the present-day solar system. The presence of an atmosphere, however, would allow transport of heat from the dayside towards the nightside and thereby reduce the surface temperature contrast between the two hemispheres. On rocky planets, atmospheric and geodynamic regimes are closely linked, which directly connects the question of atmospheric thickness to the potential interior dynamics of the planet. Here, we study the interior dynamics of super-Earth GJ 486b ($R=1.34$ $R_{oplus}$, $M=3.0$ $M_{oplus}$, T$_mathrm{eq}approx700$ K), which is one of the most suitable M-dwarf super-Earth candidates for retaining an atmosphere produced by degassing from the mantle and magma ocean. We investigate how the geodynamic regime of GJ 486b is influenced by different surface temperature contrasts by varying possible atmospheric circulation regimes. We also investigate how the strength of the lithosphere affects the convection pattern. We find that hemispheric tectonics, the surface expression of degree-1 convection with downwellings forming on one hemisphere and upwelling material rising on the opposite hemisphere, is a consequence of the strong lithosphere rather than surface temperature contrast. Anchored hemispheric tectonics, where downwellings und upwellings have a preferred (day/night) hemisphere, is favoured for strong temperature contrasts between the dayside and nightside and higher surface temperatures.
{"title":"Geodynamics of super-Earth GJ 486b","authors":"Tobias G. Meier, Dan J. Bower, Tim Lichtenberg, Mark Hammond, Paul J. Tackley, Raymond T. Pierrehumbert, José A. Caballero, Shang-Min Tsai, Megan Weiner Mansfield, Nicola Tosi, Philipp Baumeister","doi":"arxiv-2408.10851","DOIUrl":"https://doi.org/arxiv-2408.10851","url":null,"abstract":"Many super-Earths are on very short orbits around their host star and,\u0000therefore, more likely to be tidally locked. Because this locking can lead to a\u0000strong contrast between the dayside and nightside surface temperatures, these\u0000super-Earths could exhibit mantle convection patterns and tectonics that could\u0000differ significantly from those observed in the present-day solar system. The\u0000presence of an atmosphere, however, would allow transport of heat from the\u0000dayside towards the nightside and thereby reduce the surface temperature\u0000contrast between the two hemispheres. On rocky planets, atmospheric and\u0000geodynamic regimes are closely linked, which directly connects the question of\u0000atmospheric thickness to the potential interior dynamics of the planet. Here,\u0000we study the interior dynamics of super-Earth GJ 486b ($R=1.34$ $R_{oplus}$,\u0000$M=3.0$ $M_{oplus}$, T$_mathrm{eq}approx700$ K), which is one of the most\u0000suitable M-dwarf super-Earth candidates for retaining an atmosphere produced by\u0000degassing from the mantle and magma ocean. We investigate how the geodynamic\u0000regime of GJ 486b is influenced by different surface temperature contrasts by\u0000varying possible atmospheric circulation regimes. We also investigate how the\u0000strength of the lithosphere affects the convection pattern. We find that\u0000hemispheric tectonics, the surface expression of degree-1 convection with\u0000downwellings forming on one hemisphere and upwelling material rising on the\u0000opposite hemisphere, is a consequence of the strong lithosphere rather than\u0000surface temperature contrast. Anchored hemispheric tectonics, where\u0000downwellings und upwellings have a preferred (day/night) hemisphere, is\u0000favoured for strong temperature contrasts between the dayside and nightside and\u0000higher surface temperatures.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"63 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142226703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jiawei Li, Didier Sornette, Zhongliang Wu, Hangwei Li
A systematic quantitative investigation into whether the mechanisms of large earthquakes are unique could significantly deepen our understanding of fault rupture and seismicity patterns. This research holds the potential to advance our ability to predict large earthquakes and enhance the effectiveness of disaster prevention and mitigation strategies. In 2009, one of us introduced the dragon-king theory, offering a quantitative framework for identifying and testing extreme outliers-referred to as dragon-king events-that are endogenously generated. This theory provides valuable tools for explaining, predicting, and managing the risks associated with these rare but highly impactful events. The present paper discusses the feasibility of applying this theory to seismology, proposing that dragon-king earthquake events can be identified as outliers to the Gutenberg-Richter law. It also examines several seismological mechanisms that may contribute to the occurrence of these extraordinary events. Although applying the dragon-king theory to seismology presents practical challenges, it offers the potential to significantly enrich statistical seismology. By reexamining the classification of earthquake rupture types through a statistical testing lens and integrating these insights with underlying physical mechanisms, this approach can greatly enhance the analytical tools and depth of research in the field of statistical seismology.
{"title":"New horizon in the statistical physics of earthquakes: Dragon-king theory and dragon-king earthquakes","authors":"Jiawei Li, Didier Sornette, Zhongliang Wu, Hangwei Li","doi":"arxiv-2408.10857","DOIUrl":"https://doi.org/arxiv-2408.10857","url":null,"abstract":"A systematic quantitative investigation into whether the mechanisms of large\u0000earthquakes are unique could significantly deepen our understanding of fault\u0000rupture and seismicity patterns. This research holds the potential to advance\u0000our ability to predict large earthquakes and enhance the effectiveness of\u0000disaster prevention and mitigation strategies. In 2009, one of us introduced\u0000the dragon-king theory, offering a quantitative framework for identifying and\u0000testing extreme outliers-referred to as dragon-king events-that are\u0000endogenously generated. This theory provides valuable tools for explaining,\u0000predicting, and managing the risks associated with these rare but highly\u0000impactful events. The present paper discusses the feasibility of applying this\u0000theory to seismology, proposing that dragon-king earthquake events can be\u0000identified as outliers to the Gutenberg-Richter law. It also examines several\u0000seismological mechanisms that may contribute to the occurrence of these\u0000extraordinary events. Although applying the dragon-king theory to seismology\u0000presents practical challenges, it offers the potential to significantly enrich\u0000statistical seismology. By reexamining the classification of earthquake rupture\u0000types through a statistical testing lens and integrating these insights with\u0000underlying physical mechanisms, this approach can greatly enhance the\u0000analytical tools and depth of research in the field of statistical seismology.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. R. Fuentes, Bradley W. Hindman, Adrian E. Fraser, Evan H. Anders
Recent observational constraints on the internal structure of Jupiter and Saturn suggest that these planets have ``fuzzy" cores, i.e., radial gradients of the concentration of heavy elements that might span $50%$ to $70%$ of each planet's radius. These cores could be composed of a semi-convective staircase, i.e., multiple convective layers separated by diffusive interfaces arising from double-diffusive instabilities. However, to date, no study has demonstrated how such staircases can avoid layer mergers and persist over evolutionary time scales. In fact, previous work has found that these mergers occur rapidly, quickly leading to only a single convective layer. Using 3D simulations of convective staircases in non-rotating and rotating flows, we demonstrate that rotation prolongs the lifetime of a convective staircase by increasing the timescale for both layer merger and erosion of the interface between the final two layers. We present an analytic model for the erosion phase, predicting that rotation increases the erosion time by a factor of approximately $mathrm{Ro}^{-1/2}$, where $mathrm{Ro}$ is the Rossby number of the convective flows (the ratio of the rotation period to the convective turnover time). For Jovian conditions at early times after formation (when convection is vigorous enough to mix a large fraction of the planet), we find the erosion time to be roughly $10^{9}~mathrm{yrs}$ in the non-rotating case and $10^{11}~mathrm{yrs}$ in the rotating case. Thus, the current existence of convective staircases within the deep interiors of giant planets is a strong possibility, and rotation could be an important factor in the preservation of their fuzzy cores.
{"title":"Evolution of Semi-convective Staircases in Rotating Flows: Consequences for Fuzzy Cores in Giant Planets","authors":"J. R. Fuentes, Bradley W. Hindman, Adrian E. Fraser, Evan H. Anders","doi":"arxiv-2408.10833","DOIUrl":"https://doi.org/arxiv-2408.10833","url":null,"abstract":"Recent observational constraints on the internal structure of Jupiter and\u0000Saturn suggest that these planets have ``fuzzy\" cores, i.e., radial gradients\u0000of the concentration of heavy elements that might span $50%$ to $70%$ of each\u0000planet's radius. These cores could be composed of a semi-convective staircase,\u0000i.e., multiple convective layers separated by diffusive interfaces arising from\u0000double-diffusive instabilities. However, to date, no study has demonstrated how\u0000such staircases can avoid layer mergers and persist over evolutionary time\u0000scales. In fact, previous work has found that these mergers occur rapidly,\u0000quickly leading to only a single convective layer. Using 3D simulations of\u0000convective staircases in non-rotating and rotating flows, we demonstrate that\u0000rotation prolongs the lifetime of a convective staircase by increasing the\u0000timescale for both layer merger and erosion of the interface between the final\u0000two layers. We present an analytic model for the erosion phase, predicting that\u0000rotation increases the erosion time by a factor of approximately\u0000$mathrm{Ro}^{-1/2}$, where $mathrm{Ro}$ is the Rossby number of the\u0000convective flows (the ratio of the rotation period to the convective turnover\u0000time). For Jovian conditions at early times after formation (when convection is\u0000vigorous enough to mix a large fraction of the planet), we find the erosion\u0000time to be roughly $10^{9}~mathrm{yrs}$ in the non-rotating case and\u0000$10^{11}~mathrm{yrs}$ in the rotating case. Thus, the current existence of\u0000convective staircases within the deep interiors of giant planets is a strong\u0000possibility, and rotation could be an important factor in the preservation of\u0000their fuzzy cores.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
At least two global "Snowball Earth" glaciations occurred during the Neoproterozoic Era (1000-538.8 million years ago). Post-glacial surface environments during this time are recorded in cap carbonates: layers of limestone or dolostone that directly overlie glacial deposits. Postulated environmental conditions that created the cap carbonates lack consensus largely because single hypotheses fail to explain the cap carbonates' global mass, depositional timescales, and geochemistry of parent waters. Here, we present a global geologic carbon cycle model before, during, and after the second glaciation (i.e. the Marinoan) that explains cap carbonate characteristics. We find a three-stage process for cap carbonate formation: (1) low-temperature seafloor weathering during glaciation generates deep-sea alkalinity; (2) vigorous post-glacial continental weathering supplies alkalinity to a carbonate-saturated freshwater layer, rapidly precipitating cap carbonates; (3) mixing of post-glacial meltwater with deep-sea alkalinity prolongs cap carbonate deposition. We suggest how future geochemical data and modeling refinements could further assess our hypothesis.
{"title":"Three-stage Formation of Cap Carbonates after Marinoan Snowball Glaciation Consistent with Depositional Timescales and Geochemistry","authors":"Trent B. Thomas, David C. Catling","doi":"arxiv-2408.10179","DOIUrl":"https://doi.org/arxiv-2408.10179","url":null,"abstract":"At least two global \"Snowball Earth\" glaciations occurred during the\u0000Neoproterozoic Era (1000-538.8 million years ago). Post-glacial surface\u0000environments during this time are recorded in cap carbonates: layers of\u0000limestone or dolostone that directly overlie glacial deposits. Postulated\u0000environmental conditions that created the cap carbonates lack consensus largely\u0000because single hypotheses fail to explain the cap carbonates' global mass,\u0000depositional timescales, and geochemistry of parent waters. Here, we present a\u0000global geologic carbon cycle model before, during, and after the second\u0000glaciation (i.e. the Marinoan) that explains cap carbonate characteristics. We\u0000find a three-stage process for cap carbonate formation: (1) low-temperature\u0000seafloor weathering during glaciation generates deep-sea alkalinity; (2)\u0000vigorous post-glacial continental weathering supplies alkalinity to a\u0000carbonate-saturated freshwater layer, rapidly precipitating cap carbonates; (3)\u0000mixing of post-glacial meltwater with deep-sea alkalinity prolongs cap\u0000carbonate deposition. We suggest how future geochemical data and modeling\u0000refinements could further assess our hypothesis.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"63 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
While magnetic monopoles have extensive theoretical justification for their existence, but have proved elusive to observe, ball lightning is both relatively frequently observed and largely unexplained theoretically. It was first proposed in 1990 that ball lightning might result from the catalysis of nuclear fission by a magnetic monopole. The observed frequency of ball lightning does not conflict with current upper theoretical or observational bounds for magnetic monopole flux. Some possible mechanisms to account for the association of magnetic-monopole-caused ball lightning with thunderstorms are described, and proposals for further observational and theoretical research are made.
{"title":"Could Ball Lightning Be Magnetic Monopoles?","authors":"Karl D. StephanTexas State University","doi":"arxiv-2408.10289","DOIUrl":"https://doi.org/arxiv-2408.10289","url":null,"abstract":"While magnetic monopoles have extensive theoretical justification for their\u0000existence, but have proved elusive to observe, ball lightning is both\u0000relatively frequently observed and largely unexplained theoretically. It was\u0000first proposed in 1990 that ball lightning might result from the catalysis of\u0000nuclear fission by a magnetic monopole. The observed frequency of ball\u0000lightning does not conflict with current upper theoretical or observational\u0000bounds for magnetic monopole flux. Some possible mechanisms to account for the\u0000association of magnetic-monopole-caused ball lightning with thunderstorms are\u0000described, and proposals for further observational and theoretical research are\u0000made.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lingyun Yang, Omar M. Saad, Guochen Wu, Tariq Alkhalifah
Full Waveform Inversion (FWI) is a technique employed to attain a high resolution subsurface velocity model. However, FWI results are effected by the limited illumination of the model domain and the quality of that illumination, which is related to the quality of the data. Additionally, the high computational cost of FWI, compounded by the high dimensional nature of the model space, complicates the evaluation of model uncertainties. Recent work on applying neural networks to represent the velocity model for FWI demonstrated the network's ability to capture the salient features of the velocity model. The question we ask here is how reliable are these features in representing the observed data contribution within the model space (the posterior distribution). To address this question, we propose leveraging a conditional Convolutional Neural Network (CNN) as image prior to quantify the neural network uncertainties. Specifically, we add to the deep image prior concept a conditional channel, enabling the generation of various models corresponding to the specified condition. We initially train the conditional CNN to learn (store) samples from the prior distribution given by Gaussian Random Fields (GRF) based perturbations of the current velocity model. Subsequently, we use FWI to update the CNN model representation of the priors so that it can generate samples from the posterior distribution. These samples can be used to measure the approximate mean and standard deviation of the posterior distribution, as well as draw samples representing the posterior distribution. We demonstrate the effectiveness of the proposed approach on the Marmousi model and in a field data application.
{"title":"Conditional Image Prior for Uncertainty Quantification in Full Waveform Inversion","authors":"Lingyun Yang, Omar M. Saad, Guochen Wu, Tariq Alkhalifah","doi":"arxiv-2408.09975","DOIUrl":"https://doi.org/arxiv-2408.09975","url":null,"abstract":"Full Waveform Inversion (FWI) is a technique employed to attain a high\u0000resolution subsurface velocity model. However, FWI results are effected by the\u0000limited illumination of the model domain and the quality of that illumination,\u0000which is related to the quality of the data. Additionally, the high\u0000computational cost of FWI, compounded by the high dimensional nature of the\u0000model space, complicates the evaluation of model uncertainties. Recent work on\u0000applying neural networks to represent the velocity model for FWI demonstrated\u0000the network's ability to capture the salient features of the velocity model.\u0000The question we ask here is how reliable are these features in representing the\u0000observed data contribution within the model space (the posterior distribution).\u0000To address this question, we propose leveraging a conditional Convolutional\u0000Neural Network (CNN) as image prior to quantify the neural network\u0000uncertainties. Specifically, we add to the deep image prior concept a\u0000conditional channel, enabling the generation of various models corresponding to\u0000the specified condition. We initially train the conditional CNN to learn\u0000(store) samples from the prior distribution given by Gaussian Random Fields\u0000(GRF) based perturbations of the current velocity model. Subsequently, we use\u0000FWI to update the CNN model representation of the priors so that it can\u0000generate samples from the posterior distribution. These samples can be used to\u0000measure the approximate mean and standard deviation of the posterior\u0000distribution, as well as draw samples representing the posterior distribution.\u0000We demonstrate the effectiveness of the proposed approach on the Marmousi model\u0000and in a field data application.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142226705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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