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

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

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

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

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

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

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

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

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

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. Also, most computational intelligence methods developed to estimate water saturation have neglected the relationship that can exist between input variables and their impact on model performance

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

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

This work proposes a new technique for multiple-point statistics simulation based on a recursive convolutional neural network approach coined RCNN. The work focuses on methodology and implementation rather than performance to demonstrate the potential of deep learning techniques in geosciences. Two and three dimensional case studies are carried out. A sensitivity analysis is presented over the main

Stratigraphic forward modelling (SFM) provides the means to produce geologically coherent and realistic models. In this paper, we demonstrate the possibility of matching lithological variability simulated with a basin-scale advection-diffusion SFM to a data-rich real-world setting, i.e. the Holocene Rhine-Meuse fluvio-deltaic system in the Netherlands. SFM model calibration to real-world data in general

The transformation of Cartesian coordinates (xp, yp, zp) of a point (P) into their geodetic equivalent (ϕ, λ, h) in reference to the geodetic ellipsoid, is an essential requirement in geodesy. There are many well-known algorithms solving this transformation in closed-form, approximate or iterative approaches. This paper presents a new algorithm named “Trilateration Algorithm” for this transformation

This paper presents a novel perspective for modelling alluvial stratigraphy. It integrates the spatial geological information, geological maps and well-log descriptions, with the rules describing the hierarchy and relative chronology of the geological entities. As geological modelling tools are moving fast forward, the urgent need for expert geological input, codified as modelling rules, persists.

The recognition of opaque minerals by polarized reflected-light microscopy is a challenging task due to the use of certain qualitative properties that may lead to ambiguities in their identification. The use of a decision tree may simplify and guide the evaluation of these properties for reaching a proper mineral identification. Improvements of such classification trees can contribute to employ fewer

A pore network modeling (PNM) framework1 for the simulation of transport of charged species, such as ions, in porous media is presented. It includes the Nernst–Planck (NP) equations for each charged species in the electrolytic solution in addition to a charge conservation equation which relates the species concentration to each other. Moreover, momentum and mass conservation equations are adopted and

We adopt a pipeline approach to accelerate 3D time-domain finite-difference waveform inversion codes using graphics cards, thus avoiding domain decomposition. The key designs include streaming through the volume one block at a time and propagating this block as many time steps as possible while it is on the device. This approach allows us to process an arbitrarily large volume with a single GPU, which

Well log classification, the process of mapping well log measurements to lithofacies identified from core samples, is a common procedure in the oil and gas industry. Manually assigning lithofacies to the wire-line log measurements without core can be time consuming, and can also introduce a bias. Supervised machine learning algorithms are commonly used to automate this process, but they are prone to

The use of methods using waveform stacking are nowadays more common in microseismic monitoring applications because they avoid manual or automatic phase picking. The Source Scanning Algorithm (SSA) is a widely known technique in which the source location is estimated using a brightness function obtained from stacking the normalized absolute amplitude seismograms recorded at several stations. The SSA

While text classification can classify tweets, assessing whether a tweet is related to an ongoing flood event or not, based on its text, remains difficult. Inclusion of contextual hydrological information could improve the performance of such algorithms. Here, a multilingual multimodal neural network is designed that can effectively use both textual and hydrological information. The classification

Infrasound data are used by scientists and monitoring observatories to track shifts in eruptive behavior, identify signs of unrest, and ultimately help forecast major eruptions. However, infrasound analyses are often limited to a catalog of discrete or high-amplitude transient events, which can leave lower-amplitude emergent or continuous signals within the datastream unexplored. This study classifies

The recognition of multivariate geochemical anomalies is important for mineral exploration. Big data analytics, which involves the whole data and variables, is an alternative manner to delineate multivariate geochemical anomalies in support of machine learning algorithms due to their strong ability to capture the complex intrinsic and diverse links between geochemical characteristics and mineralization

Accurate delineation of geobodies is one of the major tasks of hydrocarbon exploration from three-dimensional seismic data. Salt bodies, in particular, are one of the toughest geobodies to model. Their natural process of formation leads to their complex geometry and topology. With the increasing size of acquisition data every year, traditional manual delineation methods become increasingly labor-intensive

Attempts using geochemical data to classify quarry sources which provided reactive rock aggregate, composed of Carboniferous aged pyritic mudrocks and limestones, which has caused structural damage to over 12, 500 homes across Ireland have not yet succeeded. In this paper, a possible solution to this problem is found by performing machine learning models, such as Logistic regression and Random Forest

Quantifying suspended-sediment transport is critical for a variety of disciplines related to the management of water resources. However, the number of gauging stations and monitoring networks in most rivers around the world is insufficient to improve understanding of river dynamics and support water resource management decisions. This is mainly due to the high operational costs and intensive labor

Most of the geophysical inverse problems in geophysical exploration consist in detecting, locating and outlining the shape of geophysical anomalous bodies imbedded into a quasi-homogeneous background by analyzing their effect in the geophysical signature. The inversion algorithm that is currently used creates a very fine mesh in the model space to approximate the shapes and the values of the anomalous

Pollen grains are valuable paleoclimate and paleovegetation proxies which require extensive knowledge of morphotypes and long acquisition time under the microscope. The abundance of damaged, folded, and broken pollen grains in the fossil register and sometimes also in modern soil and sediment samples, has so far prevented automation of pollen identification. Recent improvements in machine learning

Landslides are regarded as one of the most common geological hazards in a wide range of geo-environment. The aim of this study is to assess landslide susceptibility by integrating convolutional neural network (CNN) with three conventional machine learning classifiers of support vector machine (SVM), random forest (RF) and logistic regression (LR) in the case of Yongxin Country, China. To this end,

This paper presents an ontology-driven representation of knowledge for geological maps. The ontological formal language allows for a machine-readable encoding of the Earth scientist's interpretation through semantic categories and properties and is credited to support knowledge sharing and interoperability. We introduce an ontology-driven method for the interpretation and the encoding of the map data

Long, continuously-gaged precipitation records are needed for characterizing extreme rainfall events over sub-daily durations, as well as detecting potential historical effects of climate change. However, most such records predating the 1980s are in the form of paper strip charts (pluviograms), from which it is notoriously difficult to extract extreme precipitation depths for short durations (from

Geostatistical interpolation methods are used in diverse disciplines, such as environmental science, ecology, and hydrology. With the increasing availability of areal spatial data, area-to-area and area-to-point interpolations have great application potential. In this study, based on the variogram deconvolution algorithm proposed by Goovaerts (2008), an open-source area-to-area kriging package atakrig

sen2r is a scalable and flexible R package to enable downloading and preprocessing of Sentinel-2 satellite imagery via an accessible and easy to install interface. It allows the execution of several preprocessing steps which are commonly performed by Sentinel-2 users: searching the Sentinel-2 archive for datasets available over a spatial area of interest and in a defined time window, downloading them

This paper presents the 3D modeling program GEMM3D (Geophysical ElectroMagnetic Modeling 3D), a Fortran program for modeling frequency domain electromagnetic responses of geophysical sources in three-dimensional models. The program is an implementation of the Edge Finite Element method, in an electric field formulation, to simulate any electromagnetic source of interest, at all frequencies that are

Specific computational tools assist geologists in identifying and sorting lithologies in well surveys and reducing operational costs and practical working time. This allows for the management of professional output, the efficient interpretation of data, and completion of scientific research on data collected in geologically distinct regions. Machine learning methods and applications integrate large

Since errors frequently occur in the measurement of seismometer azimuths installed in non-standard observation stations, it is necessary to estimate the potential azimuth deviations and correct the data. Another similar problem is calculating borehole seismometer azimuths. In this paper, a new method is proposed for the rapid calculation of seismometer azimuths, which differs from the current correlation

The large volume of high-resolution images acquired by the Mars Reconnaissance Orbiter has opened a new frontier for developing automated approaches to detecting landforms on the surface of Mars. However, most landform classifiers focus on crater detection, which represents only one of many geological landforms of scientific interest. In this work, we use Convolutional Neural Networks (ConvNets) to

Landslide susceptibility mapping (LSM) is making increasing use of GIS-based spatial analysis in combination with multi-criteria evaluation (MCE) methods. We have developed a new multi-criteria decision analysis (MCDA) method for LSM and applied it to the Izeh River basin in south-western Iran. Our method is based on fuzzy membership functions (FMFs) derived from GIS analysis. It makes use of nine

The internal orientation of fossil mass occurrences can be exploited as useful source of information about their primary depositional conditions. A series of studies, using different kinds of fossils, especially those with elongated shape (e.g., elongated gastropods), deal with their orientation and the subsequent reconstruction of the depositional conditions (e.g., paleocurrents and transport mechanisms)

GIS multicriteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for the prediction of future hazards, land use planning, as well as for hazard preparedness. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are open to multiple types of uncertainty. In this paper, we present a systematic approach to uncertainty