Digital elevation model (DEM) is used as a source of elevation data in wide range of applications. The applications involving larger area than an individual DEM scene often need a mosaic of DEM scenes. A simple stitching exhibits seams along the borders of DEM scenes in the mosaic output. The presence of seam lines varies with the number of scenes in an overlapping region and their overlap extents

The CryoSat-2 radar altimetry mission, launched in 2010, provides key measurements of Earth's cryosphere. CryoSat-2's primary instrument, the Synthetic Aperture Interferometric Radar Altimeter (SIRAL), allows accurate height measurements of sloped ice-surfaces including the highly crevassed Bering-Bagley Glacier System (BBGS) in southeast Alaska. The recent surge of the BBGS in 2011–2013, which resulted

The total organic carbon (TOC) content is of great significance to reflect the hydrocarbon-generation potential in shale reservoirs. The well logs were always used to predict the TOC content, but some linear regression methods do not match well with complex data. The neural network method can improve prediction accuracy, but it always generates unstable prediction models. A static committee machine

The Three-Dimension visualization effect is limited by the performance of equipment and algorithm when dealing with high-dimensional and large-scale geological data. So it is very difficult to graph the data accurately in real-time. In this paper, an accurate and efficient real-time visualization method is studied, which combines the distribution characteristics of geological data in space and the

With provision of Positioning, Navigation, and Timing (PNT) services, satellite navigation systems have become a pillar of modern society. These services lay the foundations of a growing number of technological and socio-economic systems and constitute a key enabling technology for transportation systems, services and components. Mitigation of disruptions and degradation of Global Navigation Satellite

We have attempted to accelerate a microphysics scheme embedded within a next generation climate/weather numerical model, the Model for Prediction Across Scales (MPAS), using OpenACC directives. As one of the most time-consuming physics parameterization schemes, we have focused on parallelizing the Weather Research and Forecasting (WRF) single-moment 6-class microphysics scheme (WSM6) onto a Graphics

The thermo-poroelastic model for the fluid migration and heat transport around reservoir may be employed to deal with many geophysical problems including underground resources and bounding rock system, etc. In this paper, interactions between multiple ellipsoidal inhomogeneities with arbitrary orientations and eigenstrains caused by the change of pore fluid pressure, localized heating and cooling are

Seismic anisotropy of polycrystalline materials depends on the characteristics of microcracks as well as the crystallographic orientations of minerals. Here, we present a MATLAB-based software, AnisEulerSC (Anisotropy from Euler angles using Self-Consistent approximation), for modeling the anisotropic seismic properties of a polycrystalline aggregate with microcracks using the self-consistent approximation

This paper presents a new hybrid model, called ENN-SA, for spatiotemporal drought prediction. In ENN-SA, an Elman neural network (ENN) is conjugated with simulated annealing (SA) optimization and support vector machine (SVM) classification algorithms for the standardized precipitation index (SPI) modeling at multiple stations. The proposed model could be applied to predict SPI at different time scales

The isometric logratio transformation, in the form of a so-called “balance”, has been promoted as a way to contrast two groups of parts in a compositional data set by forming ratios of their respective geometric means. This transformation has attractive theoretical properties and hence provides a useful reference, but geometric means are highly affected by parts with small relative values. When a comparison

Ontologies and vocabularies are an effective way to promote data interoperability in open data and open science. The deep time knowledge graph is one of the most discussed and studied topics in geoscience ontologies and vocabularies. The continuous evolution of deep time concepts calls for a mechanism of version control and organization to reduce the semantic ambiguity. In this paper we propose a new

Spatial undersampling is a common problem in actual seismic data due to limitations in seismic survey environments, which can be satisfactorily solved by data regularization. The convolution-based deep-learning reconstruction methods require fewer assumptions than the conventional reconstruction methods (e.g., Curvelet-domain and F-X domain data regularization methods). However, the traditional convolution

The interpretation of seismic images faces challenges due to the presence of several uncertainty sources. Uncertainties exist in data measurements, source positioning, and subsurface geophysical properties. Understanding uncertainties’ role and how they influence the outcome is fundamental in the earth sciences and essential in the oil and gas industry decision-making process. Geophysical imaging is

The isotropic Gaussian filter is frequently used as a post-processing method for mitigating errors in Gravity Recovery and Climate Experiment (GRACE) time-variable gravity field solutions. It is known that the recurrent calculation of isotropic Gaussian filter coefficients in the spherical harmonic domain results in numerical instabilities. This issue has been recently resolved by Piretzidis & Sideris

Water saturation is imperative in the evaluation of hydrocarbon reserves available. However, it is challenging to accurately determine the water saturation of complex reservoirs using conventional techniques. This is due to the fact that the conventional models are unable to fully account for the heterogeneity of the reservoir and their results are highly influenced by factors such as type of data

Full-waveform inversion (FWI) has emerged as a powerful technique to obtain high-resolution subsurface elastic properties. However, several complicating features, including interparameter trade-offs, cycle-skipping, and high computational cost, motivate a careful assessment and validation of candidate versions of FWI prior to use. Field data application of elastic FWI remains a challenging task, and

Zircon U–Pb dating is one of the most effective and widely used methods due to the widespread existence of zircon as an accessory mineral in rocks and its high closure temperature. Throughout history, each region of the earth has experienced its own crustal evolution. Using zircon U–Pb geochronology data, researchers can reveal the crustal growth history and regional discrimination. In this study,

A machine learning based image processing workflow is presented to enhance shale source rock microscopic images obtained using diverse imaging platforms. Images were acquired from a 30μm diameter cylindrical Vaca Muerta shale sample using both nondestructive Transmission X-ray Microscopy (TXM, alternately referred to as nano computed tomography) and destructive Focused Ion Beam-Scanning Electron Microscopy

The data storage burden resulting from large climate model simulations continues to grow. While lossy data compression methods can alleviate this burden, they introduce the possibility that key climate variables could be altered to the point of affecting scientific conclusions. Therefore, developing a detailed understanding of how compressed model output differs from the original is important. Here

Investigations on the origin of natural gases traditionally involve manual plotting of values for various geochemical parameters on binary gas genetic diagrams and the comparison of these values with empirically defined gas genetic fields. However, these fields considerably overlap, and the accuracy and uncertainty on the derived origin are not quantified. To overcome these issues, we developed a web-based

The primary aim of this research paper is to develop an easy-to-use tool package called Landslide Susceptibility Mapping Tool Pack (LSM Tool Pack) for producing landslide susceptibility maps based on integrating R with ArcMap Software. The proposed tool contains 5 main modules namely: (1) Data Preparation (DP), (2) Feature (Factor) Selection (FS), (3) Logistic Regression (LR), (4) Random Forest (RF)

This paper presents an application of the global-local orthogonal mapping (GLO-MAP) algorithm to derive data-driven models for magnetic field variations of the Earth. The GLO-MAP algorithm rigorously merges different independent local approximations that are based upon measured data to obtain a desired order, globally continuous approximation. We show the local magnetic field data acquired by ground-based

The sizes of microstructural features determine the mechanical behaviors of rocks, but the complex nature of their structures frequently hinders the precise quantification of the feature sizes. This study presents a digital-image-based quantification for evaluating the representative sizes of the microstructures in rocks. This method, using a madogram function, allows obtaining a single length value

QUIDDIT is a free Python software-package designed to process Fourier Transform Infrared (FTIR) spectra of diamonds automatically and efficiently. Core capabilities include baseline correction, determination of nitrogen concentration, nitrogen aggregation state and model temperature and fitting of both the 3107 cm-1 and platelet (B’) peaks. These capabilities have allowed the authors to study platelet

The decision of stationarity is a fundamental prerequisite to geostatistical estimation and uncertainty characterization of natural resources. Great effort is given to delineate relevant spatial-statistical populations for modeling. Recently developed spatial clustering methodologies claim improvements over traditional methodologies for stationary decision making. However, these novel methods are generally

Reproducible science requires a shift in thinking and application for how data, code and analysis are shared. Now, scientists must act more like software engineers to design models and perform analysis that use principles and techniques pioneered by software developers. Creating reproducible models that are easy to use and understand is in the best interest for the snow and hydrology community, enabling

In recent years, the adaptive finite element method has been used for direct current resistivity forward modeling to improve the accuracy of numerical solutions. The accuracy of adaptive finite element solutions is mainly affected by two factors: the cell size (h) and the order of the shape function (p). To further improve the accuracy of the adaptive finite element solutions and keep low computational

Large mineral grains that have undergone shock metamorphism or tectonic stress can exhibit plastic deformation. Using in situ micro X-Ray diffraction, such deformation is revealed in a 2D detector image by distortion of diffraction spots into an angular spread or ‘streaking’ along Debye ring or chi (χ) dimension; this can provide a quantitative measurement of plastic deformation via full width at half

Monitoring arid areas could effectively improve economic, ecological and humanity benefits. It is an effective monitoring approach to recognize the land cover or land use of arid areas through machine learning methods using satellite images. However, there is no public classified dataset for arid areas currently, and hence remote sensing image monitoring in desert areas is restricted. Existing classification

Borehole lithology discrimination is the foundation of formation evaluation and reservoir characterization. Due to the limitation of costing or accuracy, direct discrimination methods such as borehole core and drilling cutting analysis are unable to be deployed to every well, while logging lithology interpretation provides an alternative solution for this. Recently, several machine learning algorithms

Fast computation of three-dimensional gravity and magnetic forward models is considered. When the measurement data is assumed to be obtained on a uniform grid which is staggered with respect to the discretization of the parameter volume, the resulting kernel sensitivity matrices exhibit block-Toeplitz–Toeplitz-block (BTTB) structure. These matrices are symmetric for the gravity problem but unsymmetric

Supcrt and Phreeqc are two software packages widely used for thermodynamic calculations and geochemical modeling of gas-water-mineral reactions, respectively. The versatility and easy-to-use functions of Phreeqc have earned its popularity, but the software is limited by the default thermodynamic datasets it is distributed with, which has limited Phreeqc applications to mostly at ambient temperature

Direct forecasting has recently been proposed as a method for uncertainty quantification in non-linear inverse problems. The method directly forecasts a desired future response without the need for full model inversion. The method has been successfully applied to a range of subsurface field cases, such as groundwater, shallow and deep geothermal and oil/gas predictions. In this paper, we extend the

With the rapid development of remote sensing technology, the amount of remote sensing data is increasing, service objects are increasingly extensive, and requests from users to query the coverage of remote sensing images under specific conditions have also increased. These conditions have increased efficiency and precision requirements for concurrent access and response. Remote sensing images provide

A tile map in image format is one of the most important tools that people can use to acquire multiscale geographic information on the Internet. Traditional methods of typification in map generalization are used to handle traditional vector-based buildings and linear drainages such as rivers and ditches. In this paper, a new raster-tile-based method called artificial pond typification (APT) is developed

Displaying 3D geological models and performing corresponding analyses in web browsers are trends in "smart city" development, but these tasks are often limited by the web browser and network transmission; therefore, large quantities of data cannot be quickly displayed or analysed, thereby constraining 3D geological models implemented in web browsers. The data redundancy and computational complexity

Field surveys are an important source of data for several scientific studies. Hydrological optics investigations require a large amount of optical data acquired using sensors from distinct manufacturers built with specific technologies. Consequently, rapid and integrated processing can be difficult due to several challenges, such as non-standard outputs, multidimensional resolutions of data (spatial

Sand dunes are commonly regarded as a challenge to traditional photogrammetry due their homogeneous texture and spectral response. In this work we present an evaluation of Structure from Motion-Multi View Stereo (SfM-MVS) to obtain high-resolution elevation data of coastal sand dunes based on images acquired by Remotely Piloted Aircraft (RPA). A Digital Elevation Model (DEM) of a dunefield in Southern

Data streams have become an integral part of the rapidly evolving modern information landscape in various application domains. Stream clustering, and in particular density-based clustering, has emerged as one of the most commonly used data stream analysis tasks. Several density-based stream clustering methods have been proposed, chief among them is DenStream. Existing DenStream clustering methods usually

Bundle adjustment with very large scale datasets has drew much concern recently in both photogrammetry and computer vision communities. Different from the existing out-of-core and distributed methods for large scale datasets, we propose a fast and accurate bundle adjustment method which still uses the framework of the traditional Levenberg Marquardt (LM) algorithm while adopting preconditioned conjugate

Frequency domain electromagnetic induction (EMI) methods have had a long history of qualitative mapping for environmental applications. More recently, the development of multi-coil and multi-frequency instruments is such that the focus has shifted to inverting data to obtain quantitative models of electrical conductivity. Whilst collection of EMI data is relatively straightforward, the inverse modeling

In many geomorphic applications, it is necessary to reduce a polygon to a set of axial lines. For example, consider a fluvial channel network. On the one hand, this network has a polygonal footprint with reaches of finite width organized into a branching network. On the other hand, measuring the length or sinuosity of these reaches, as examples, requires the reduction of their polygonal footprints

Accurate pore-pressure prediction is of essential importance to hydrocarbon exploration and development. A multivariate prediction model of multiple petrophysical data is required to adequately reflect variations in pore pressure. However, the parametric multivariate models with assumptions on lithology, predominantly sand or shale, are theoretically inaccurate for mixed lithologies and require a tedious

Over the last decade machine learning has become increasingly popular for the analysis and characterization of volcano-seismic data. One of the requirements for the application of machine learning methods to the problem of classifying seismic time series is the availability of a training dataset; that is a suite of reference signals, with known classification used for initial validation of the machine

Accurately modeling and constructing a geologically realistic subsurface model remains an outstanding problem as the morphology controls the flow behaviors. Particularly, one of the pattern-based methods, namely cross-correlation based simulation, has been proved to be an effective way to reconstruct a realistic model, at both small and large scales. However, conditioning to point data in the large-scale

This paper1 presents two new direct symbolic-numerical algorithms for the transformation of Cartesian coordinates into geodetic coordinates considering the general case of a triaxial reference ellipsoid. The problem in both algorithms is reduced to finding a real positive root of a sixth degree polynomial. The first approach consists of algebraic manipulations of the equations describing the geometry

Preliminary interpretation of geological processes during field measurement campaigns require fast data analysis to adapt ongoing target strategies. It is the case of soil investigations where coupling geochemical and geophysical records favors a better understanding of subsurface processes. This task requires (i) statistical analysis to identify areas of interest during spatial surveys and (ii) signal

Petrographic analysis is based on the microscopic description and classification of rocks and is a crucial technique for sedimentary and diagenetic studies. When compared to hand specimens, thin sections provide better and more accurate means for analysis of mineral proportion, distribution, texture, pore space analysis, and cement composition. Most petrographic analysis relies on visual inspection

Accurate prediction of the spatial distribution of hydrocarbon accumulations underlies risk reduction and improvement of exploration work. Determining the location of oil and gas reserves is a comprehensive and complex problem riddled with uncertainty. This paper applies Bayesian network, which is an effective tool for uncertainty knowledge expression and reasoning, to predict the spatial distribution

We describe a data structure and associated algorithm called Fast Zonal Statistics (FZS) for the retrieval of the summary characteristics of an arbitrary polygon derived from a regular grid. The FZS algorithm can return numerical (e.g., mean, sum, and count) attributes for a polygonal object over a regular grid (e.g., raster data model). The computational complexity of the FZS algorithm is constant

Reliable and accurate landslide displacement prediction is the key to early warning system. Most studies focus only on accuracy of landslide displacement estimation and ignore catastrophic consequences caused by underestimated landslide displacement. This paper investigates a landslide displacement prediction method with risk-averse adaptation. In this methodology, double exponential smoothing method

Finding geospatial data has been a big challenge regarding the data size and heterogeneity across various domains. Previous work has explored using machine learning to improve geospatial data search ranking, but it usually relies on training data labelled by subject matter experts, which makes it laborious and costly to apply to scenarios in which data relevancy to a query can change over time. When

In recent years the exponential growth in digital data and the expansion of machine learning have fostered the development of new applications in geosciences. Natural Language Processing (NLP) tackles various issues that arise from using human language data. In this study, NLP is applied to classify and map lithological descriptions in a three dimensional space. The data originates from the Australian

Waiwera is a new reservoir simulator designed to address the current and future challenges posed by modelling geothermal systems. It incorporates several innovations aimed at improving the robustness of phase transitions and steady-state model convergence, including a modified treatment of the multi-phase gravity term. The Waiwera code is parallelised and makes extensive use of the PETSc scientific

Understanding the dynamics of glaciers is essential for the knowledge of global sea-level rise, local freshwater resources in high mountain and arid regions, and the potential glacial hazards. In this paper, we present a two-dimensional thermomechanically coupled ice flow model named PoLIM (Polythermal Land Ice Model). The velocity solver of PoLIM is developed based on the Blatter–Pattyn approximation