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

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

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

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

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

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

The first 3D visualisation of a heteromorph cephalopod species from the Southern Alps (Dolomites, northern Italy) is presented. Computed tomography, palaeontological data and 3D reconstructions were included in the production of a movie, which shows a life reconstruction of the extinct organism. This detailed reconstruction is according to the current knowledge of the shape and mode of life as well

The grade estimation is a quite important and money/time-consuming stage in a mine project, which is considered as a challenge for the geologists and mining engineers due to the structural complexities in mineral ore deposits. To overcome this problem, several artificial intelligence techniques such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) have recently been employed with various architectures

Indicator kriging provides a flexible interpolation approach that is well suited for datasets where: 1) many observations are below the detection limit, 2) the histogram is strongly skewed, or 3) specific classes of attribute values are better connected in space than others (e.g. low pollutant concentrations). To apply indicator kriging at its full potential requires, however, the tedious inference

Increases in the availability of reliable health data are widely recognised as essential for efforts to strengthen health-care systems in resource-poor settings worldwide. Effective health-system planning requires comprehensive and up-to-date information on a range of health metrics and this requirement is generally addressed by a Health Management Information System (HMIS) that coordinates the routine

This paper describes the development of a census data-mapping package for the Sirius microcomputer. The major objective was to display data from the 1981 United Kingdom Census Small Area Statistics onto various map bases for use in published reports and for interactive comparisons among census variables