Filtering by Subject: Statistics and Probability

**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 [...]

**Published**: 2019-02-21

**Subjects**: Applied Statistics, Earth Sciences, Geophysics and Seismology, Physical Sciences and Mathematics, Statistics and Probability

In this work we present a deep neural network inversion on map-based 4D seismic data for pressure and saturation. We present a novel neural network architecture that trains on synthetic data and provides insights into observed field seismic. The network explicitly includes AVO gradient calculation within the network as physical knowledge to stabilize pressure and saturation changes separation. [...]

**Published**: 2019-01-25

**Subjects**: Applied Statistics, Earth Sciences, Environmental Education, Environmental Monitoring, Environmental Sciences, Environmental Studies, Geographic Information Sciences, Geography, Geology, Geomorphology, Oceanography, Oceanography and Atmospheric Sciences and Meteorology, Other Earth Sciences, Other Oceanography and Atmospheric Sciences and Meteorology, Other Statistics and Probability, Physical and Environmental Geography, Physical Sciences and Mathematics, Social and Behavioral Sciences, Spatial Science, Statistics and Probability, Tectonics and Structure

The geographic focus of the current study Mariana trench, the deepest point of the Earth located in the west Pacific Ocean. Mariana trench has unique structure and features formed in the complex process of the trench development. There is a range of the environmental factors affecting trench structure and functioning: bathymetry, geography, geology and tectonics. Current research aimed to study [...]

**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 [...]

**Published**: 2018-11-07

**Subjects**: Earth Sciences, Geology, Physical Sciences and Mathematics, Planetary Geology, Planetary Sciences, Planetary Sedimentology, Statistical Methodology, Statistics and Probability, Stratigraphy, Tectonics and Structure

The orientations of planar rock layers are fundamental to our understanding of structural geology and stratigraphy. Remote-sensing platforms including satellites, unmanned aerial vehicles (UAVs), and LIDAR scanners are increasingly used to build three-dimensional models of structural features on Earth and other planets. Remotely-gathered orientation measurements are straightforward to calculate [...]

**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 [...]

**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 [...]

**Published**: 2018-09-29

**Subjects**: Civil and Environmental Engineering, Civil Engineering, Climate, Computer Sciences, Earth Sciences, Engineering, Environmental Sciences, Hydrology, Numerical Analysis and Scientific Computing, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Statistics and Probability, Water Resource Management

Water resources planning requires making decisions about infrastructure development under substantial uncertainty in future regional climate conditions. However, uncertainty in climate change projections will evolve over the 100-year lifetime of a dam as new climate observations become available. Flexible strategies in which infrastructure is proactively designed to be changed in the future have [...]

**Published**: 2018-09-24

**Subjects**: Applied Mathematics, Applied Statistics, Earth Sciences, Geochemistry, Geology, Multivariate Analysis, Physical Sciences and Mathematics, Statistics and Probability, Volcanology

Primitive lavas erupted in the Cascades arc of western North America demonstrate significant patterns of along-arc heterogeneity. Such compositional diversity may be the result of differences in mantle melting processes, subduction geometry, regional tectonics, or compositions of the slab, mantle or overlying lithosphere. Previous authors have partitioned the arc into four geochemically distinct [...]

**Published**: 2018-07-18

**Subjects**: Applied Mathematics, Atmospheric Sciences, Climate, Earth Sciences, Environmental Sciences, Hydrology, Multivariate Analysis, Oceanography, Oceanography and Atmospheric Sciences and Meteorology, Other Oceanography and Atmospheric Sciences and Meteorology, Other Physical Sciences and Mathematics, Physical Sciences and Mathematics, Physics, Statistics and Probability

The published version of this article is available at https://advances.sciencemag.org/content/5/9/eaaw5531. Compound flooding (CF) is an extreme event taking place in low-lying coastal areas as a result of co-occurring high sea level and large amounts of runoff, caused by precipitation. The impact from the two hazards occurring individually can be significantly lower than the result of their [...]

**Published**: 2018-06-21

**Subjects**: Computational Engineering, Computer Sciences, Databases and Information Systems, Education, Engineering, Mathematics, Numerical Analysis and Scientific Computing, Physical Sciences and Mathematics, Science and Mathematics Education, Statistics and Probability

Data analytics is the process of examining large amounts of varying data types to uncover hidden patterns, unknown correlations and other useful information. The Earth Science Information Partners (ESIP) Earth Science Data Analytics (ESDA) Cluster was created to facilitate the co-analysis of Earth science data and information. In addition to pioneering the definition of ESDA, the cluster has [...]

**Published**: 2018-06-18

**Subjects**: Computer and Systems Architecture, Computer Engineering, Earth Sciences, Engineering, Environmental Monitoring, Environmental Sciences, Geology, Geomorphology, Other Statistics and Probability, Physical Sciences and Mathematics, Statistics and Probability

The application of deep learning, specifically deep convolutional neural networks (DCNNs), to the classification of remotely sensed imagery of natural landscapes has the potential to greatly assist in the analysis and interpretation of geomorphic processes. However, the general usefulness of deep learning applied to conventional photographic imagery at a landscape scale is, at yet, largely [...]

**Published**: 2018-04-19

**Subjects**: Categorical Data Analysis, Computer Sciences, Environmental Sciences, Geographic Information Sciences, Geography, Numerical Analysis and Scientific Computing, Physical Sciences and Mathematics, Social and Behavioral Sciences, Statistics and Probability, Theory and Algorithms

There is a keen interest in inferring spatial associations between different variables spanning the same study area. We present a method for quantitative assessment of such associations in the case where spatial variables are either in the form of regionalizations or in the form of thematic maps. The proposed index of spatial association – called the V-measure – is adapted from a measure [...]

**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 [...]

**Published**: 2018-02-25

**Subjects**: Applied Statistics, Physical Sciences and Mathematics, Probability, Statistics and Probability

This paper details team SUTD’s effort when participating in the “Prediction of extremal precipitation” challenge. We propose a framework that combines the generalized Pareto distribution, a bootstrap resampling scheme and inverse distance weights to capture spatial dependence. Our method reduces the quantile loss functions by 55.1% as compared to a naive benchmark, and shows improvement across [...]