# Preprints

Filtering by Subject: Statistical Models

## Sedimentary structures discriminations with hyperspectral imaging on sediment cores

**Published**: 2020-07-17

**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 [...]

## GANSim: Conditional Facies Simulation Using an Improved Progressive Growing of Generative Adversarial Networks (GANs)

**Published**: 2020-07-05

**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 [...]

## Probabilistic soil moisture dynamics of water- and energy-limited ecosystems

**Published**: 2020-05-17

**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 [...]

## Evaluating the INLA-SPDE approach for Bayesian modeling of earthquake damages from geolocated cluster data

**Published**: 2020-01-27

**Subjects**: Physical Sciences and Mathematics, Statistical Models, Statistics and Probability

Modeled damage estimates are an important source of information in the hours to weeks following major earthquake disasters, but often lack sufficient spatial resolution for highlighting specific areas of need. Using damage assessment data from the 2015 Gorkha, Nepal Earthquake, this paper evaluates a Bayesian spatial model (INLA-SPDE) for interpolating geolocated damage survey data onto 1 km2 [...]

## Probabilistic space- and time-interaction modeling of main-shock earthquake rupture occurrence

**Published**: 2019-04-28

**Subjects**: Civil and Environmental Engineering, Earth Sciences, Engineering, Geophysics and Seismology, Physical Sciences and Mathematics, Statistical Models, Statistics and Probability, Structural Engineering

This paper presents a probabilistic formulation for modeling earthquake rupture processes of mainshocks. A correlated multivariate Bernoulli distribution is used to model rupture occurrence. The model captures time interaction through the use of Brownian passage-time (BPT) distributions to assess rupture interarrival in multiple sections of the fault, and it also considers spatial interaction [...]

## High-resolution prediction of organic matter concentration with hyperspectral imaging on a sediment core

**Published**: 2019-02-21

**Subjects**: Analytical Chemistry, Chemistry, Earth Sciences, Environmental Chemistry, Environmental Monitoring, Environmental Sciences, Optics, Physical Sciences and Mathematics, Physics, Sedimentology, Statistical Models, Statistics and Probability

In the case of environmental samples, the use of a chemometrics-based prediction model is highly challenging because of the difficulty in experimentally creating a well-ranged reference sample set. In this study, we present a methodology using short wave infrared hyperspectral imaging to create a partial least squares regression model on a cored sediment sample. It was applied to a sediment core [...]

## A statistics-based reconstruction of high-resolution global terrestrial climate for the last 800,000 years

**Published**: 2019-01-18

**Subjects**: Climate, Earth Sciences, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Statistical Models, Statistics and Probability

Curated global climate data have been generated from climate model outputs for the last 120,000 years, whereas reconstructions going back even further have been lacking due to the high computational cost of climate simulations. Here, we present a statistically-derived global terrestrial climate dataset for every 1,000 years of the last 800,000 years. It is based on a set of linear regressions [...]

## Implications of ambiguity in Antarctic ice sheet dynamics for future coastal erosion estimates: a probabilistic assessment

**Published**: 2018-11-02

**Subjects**: Earth Sciences, Engineering, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Probability, Statistical Models, Statistics and Probability

Sea-level rise (SLR) can amplify the episodic erosion from storms and drive chronic erosion on sandy shorelines, threatening many coastal communities. One of the major uncertainties in SLR projections is the potential rapid disintegration of large fractions of the Antarctic ice sheet (AIS). Quantifying this uncertainty is essential to support sound risk management of coastal areas, although it is [...]

## SSPipeline: A pipeline for estimating and characterizing uncertainty in coastal storm surge levels

**Published**: 2018-10-22

**Subjects**: Computer Sciences, Engineering, Numerical Analysis and Scientific Computing, Physical Sciences and Mathematics, Statistical Models, Statistics and Probability

Effective management of coastal risks demands projections of flood hazards that account for a wide variety of potential sources of uncertainty. Two typical approaches for estimating flood hazards include (1) direct physical process-based modeling of the storms themselves and (2) statistical modeling of the distributions and relevant characteristics of extreme sea level events. Recently, flexible [...]

## Uncertainty in sea level rise projections due to the dependence between contributors

**Published**: 2018-03-08

**Subjects**: Applied Statistics, Climate, Earth Sciences, Environmental Sciences, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Probability, Statistical Models, Statistics and Probability

Sea level rises at an accelerating pace threatening coastal communities all over the world. In this context sea level projections are key tools to help risk mitigation and adaptation. Sea level projections are often made using models of the main contributors to sea level rise (e.g. thermal expansion, glaciers, ice sheets...). To obtain the total sea level these contributions are added, therefore [...]

## Non-crossing nonlinear regression quantiles by monotone composite quantile regression neural network, with application to rainfall extremes

**Published**: 2017-12-04

**Subjects**: Artificial Intelligence and Robotics, Climate, Computer Sciences, Earth Sciences, Hydrology, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Statistical Models, Statistics and Probability

The goal of quantile regression is to estimate conditional quantiles for specified values of quantile probability using linear or nonlinear regression equations. These estimates are prone to "quantile crossing", where regression predictions for different quantile probabilities do not increase as probability increases. In the context of the environmental sciences, this could, for example, lead to [...]

## Compositional Signatures in Acoustic Backscatter Over Vegetated and Unvegetated Mixed Sand-Gravel Riverbeds

**Published**: 2017-11-01

**Subjects**: Civil and Environmental Engineering, Earth Sciences, Engineering, Environmental Monitoring, Environmental Sciences, Geomorphology, Hydraulic Engineering, Physical Sciences and Mathematics, Statistical Models, Statistics and Probability

Multibeam acoustic backscatter has considerable utility for remote characterization of spatially heterogeneous bed sediment composition over vegetated and unvegetated riverbeds of mixed sand and gravel. However, the use of high-frequency, decimeter-resolution acoustic backscatter for sediment classification in shallow water is hampered by significant topographic contamination of the signal. In [...]