Preprints
Filtering by Subject: Statistics and Probability
Classification, segmentation and correlation of zoned minerals
Published: 2021-02-02
Subjects: Earth Sciences, Environmental Sciences, Geochemistry, Geology, Numerical Analysis and Scientific Computing, Statistics and Probability, Volcanology
Minerals exhibit zoning patterns that can be related to changes in the environment in which they grew. Using statistical methods that have been designed to segment optical images, we have developed a procedure to segment zonation within minerals and correlate these zones between multiple crystals using elemental maps. This allows us to quantify the complexity and variability of chemical zoning [...]
Assessing erosion and flood risk in the coastal zone through the application of the multilevel Monte Carlo method
Published: 2021-01-07
Subjects: Applied Mathematics, Earth Sciences, Geomorphology, Hydrology, Numerical Analysis and Computation, Risk Analysis, Statistics and Probability
The risk from erosion and flooding in the coastal zone has the potential to increase in a changing climate. The development and use of coupled hydro-morphodynamic models is therefore becoming an ever higher priority. However, their use as decision support tools suffers from the high degree of uncertainty associated with them, due to incomplete knowledge as well as natural variability in the [...]
Yield estimation of the 2020 Beirut explosion using open access waveform and remote sensing data
Published: 2020-12-22
Subjects: Applied Statistics, Earth Sciences, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Physics, Statistics and Probability
We report on a multi-technique analysis using publicly available data for investigating the huge, accidental explosion that struck the city of Beirut, Lebanon, on August 4, 2020. Its devastating shock wave led to thousands of injured with more than two hundred fatalities and caused immense damage to buildings and infrastructure. Our combined analysis of seismological, hydroacoustic, infrasonic [...]
A Physics-Based Decorrelation Phase CovarianceModel for Noise Reduction in UnwrappedInterferometric Phase Stacks
Published: 2020-10-23
Subjects: Earth Sciences, Statistics and Probability
The accuracy of geophysical parameter estimation made with Interferometric Synthetic Aperture Radar (InSAR) time-series techniques can be improved with rapidly increasing available data volumes, and with the development of noise covariance matrices applicable to joint analysis of networks of interferograms. Here we present a physics-based decorrelation phase covariance model and discuss [...]
Sedimentary structures discriminations with hyperspectral imaging on sediment cores
Published: 2020-07-18
Subjects: Analytical Chemistry, Chemistry, Earth Sciences, Environmental Chemistry, Environmental Monitoring, Environmental Sciences, Multivariate Analysis, Optics, Physical Sciences and Mathematics, Physics, Sedimentology, Statistical Models, Statistics and Probability
Hyperspectral imaging (HSI) is a non-destructive high-resolution sensor, which is currently under significant development to analyze geological areas with remote devices or natural samples in a laboratory. In both cases, the hyperspectral image provides several sedimentary structures that need to be separated to temporally and spatially describe the sample. Sediment sequences are composed of [...]
Climate has contrasting direct and indirect effects on armed conflicts
Published: 2020-07-08
Subjects: Environmental Indicators and Impact Assessment, Environmental Monitoring, Environmental Sciences, Environmental Studies, Geography, Human Geography, International and Area Studies, Library and Information Science, Life Sciences, Nature and Society Relations, Physical and Environmental Geography, Physical Sciences and Mathematics, Remote Sensing, Social and Behavioral Sciences, Statistics and Probability
There is an active debate regarding the influence that climate has on the risk of armed conflict, which stems from challenges in assembling unbiased datasets, competing hypotheses on the mechanisms of climate influence, and the difficulty of disentangling direct and indirect climate effects. We use gridded historical non-state conflict records, satellite data, and land surface models in a [...]
GANSim: Conditional Facies Simulation Using an Improved Progressive Growing of Generative Adversarial Networks (GANs)
Published: 2020-07-06
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Geology, Hydrology, Mathematics, Physical Sciences and Mathematics, Sedimentology, Statistical Models, Statistics and Probability
Conditional facies modeling combines geological spatial patterns with different types of observed data, to build earth models for predictions of subsurface resources. Recently, researchers have used generative adversarial networks (GANs) for conditional facies modeling, where an unconditional GAN is first trained to learn the geological patterns using the original GANs loss function, then [...]
Exposure and vulnerability estimation for modelling flood losses to commercial assets in Europe
Published: 2020-07-06
Subjects: Earth Sciences, Hydrology, Multivariate Analysis, Physical Sciences and Mathematics, Statistics and Probability
Commercial assets comprise buildings, machinery and equipment, which are susceptible to floods. Existing damage models and exposure estimation methods for this sector have limited transferability between flood events. In this study we introduce two methodologies aiming at broader applicability: (1) disaggregation of economic statistics to obtain detailed building-level estimates of replacement [...]
Seasonal impact-based mapping of compound hazards
Published: 2020-06-17
Subjects: Applied Mathematics, Atmospheric Sciences, Climate, Earth Sciences, Environmental Sciences, Hydrology, Mathematics, Multivariate Analysis, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Statistics and Probability
Impact-based, seasonal mapping of compound hazards is proposed. It is pragmatic, identifies phenomena to drive the research agenda, produces outputs relevant to stakeholders, and could be applied to many hazards globally. Illustratively, flooding and wind damage can co-occur, worsening their joint impact, yet where wet and windy seasons combine has not yet been systematically mapped. Here, [...]
Large model parameter and structural uncertainties in global projections of urban heat waves
Published: 2020-06-10
Subjects: Atmospheric Sciences, Civil and Environmental Engineering, Computer Sciences, Earth Sciences, Engineering, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Risk Analysis, Statistics and Probability
Urban heat waves (UHWs) are strongly associated with socioeconomic impacts. Reliable projections of these extremes are pressingly needed for local actions in the context of extreme event preparedness and mitigation. Such information, however, is not available because current multi-model projections largely lack a representation of urban areas. Here, we use a newly-developed urban climate emulator [...]
Observation-based Simulations of Humidity and Temperature Using Quantile Regression
Published: 2020-05-29
Subjects: Atmospheric Sciences, Climate, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Statistics and Probability
The human impacts of changes in heat events depend on changes in the joint behavior of temperature and humidity. Little is currently known about these complex joint changes, either in observations or projections from general circulation models (GCMs). Further, GCMs do not fully reproduce the observed joint distribution, implying a need for simulation methods that combine information from GCMs [...]
A newly reconciled data set for identifying sea level rise and variability in Dublin Bay
Published: 2020-05-28
Subjects: Applied Statistics, Climate, Earth Sciences, Life Sciences, Oceanography and Atmospheric Sciences and Meteorology, Other Earth Sciences, Other Life Sciences, Physical Sciences and Mathematics, Planetary Sciences, Statistics and Probability
We provide an updated sea level dataset for Dublin for the period 1938 to 2016 at yearly resolution. Using a newly collated sea level record for Dublin Port, as well as two nearby tide gauges at Arklow and Howth Harbour, we perform data quality checks and calibration of the Dublin Port record by adjusting the biased high water level measurements that affect the overall calculation of mean sea [...]
Probabilistic soil moisture dynamics of water- and energy-limited ecosystems
Published: 2020-05-18
Subjects: Agriculture, Agronomy and Crop Sciences Life Sciences, Civil and Environmental Engineering, Earth Sciences, Engineering, Environmental Engineering, Environmental Sciences, Forest Sciences, Hydrology, Life Sciences, Physical Sciences and Mathematics, Plant Sciences, Statistical Models, Statistics and Probability
This paper presents an extension of the stochastic ecohydrological model for soil moisture dynamics at a point of Rodriguez-Iturbe et al. (1999) and Laio et al. (2001). In the original model, evapotranspiration is a function of soil moisture and vegetation parameters, so that the model is suitable for water-limited environments. Our extension introduces a dependence on maximum evapotranspiration [...]
Three Common Statistical Missteps We Make in Reservoir Characterization
Published: 2020-05-12
Subjects: Chemical Engineering, Earth Sciences, Engineering, Geology, Petroleum Engineering, Physical Sciences and Mathematics, Statistical Methodology, Statistics and Probability
Reservoir characterization analysis resulting from incorrect applications of statistics can be found in the literature, particularly in applications where integration of various disciplines is needed. Here, we look at three misapplications of ordinary least squares linear regression (LSLR) and show how they can lead to poor results and offer better alternatives, where available. The issues are [...]
Comparison of permeability predictions on cemented sandstones with physics-based and machine learning approaches
Published: 2020-02-06
Subjects: Chemical Engineering, Earth Sciences, Engineering, Hydrology, Multivariate Analysis, Petroleum Engineering, Physical Sciences and Mathematics, Statistics and Probability
Permeability prediction has been an important problem since the time of Darcy. Most approaches to solve this problem have used either idealized physical models or empirical relations. In recent years, machine learning (ML) has led to more accurate and robust, but less interpretable empirical models. Using 211 core samples collected from 12 wells in the Garn Sandstone from the North Sea, this [...]