Magnetotelluric sounding is an efficient and economical method for detecting Earth's deep structure because of its large surveying depth. We use a finite element approach to solve magnetotelluric fields in three-dimensional general anisotropic media based on the vector–scalar potentials and unstructured meshes. The implementation of a three-dimensional unstructured mesh generator TetGen with high-quality

We present a new graph-Laplacian based semi-supervised clustering method. This new approach can be viewed as an extension/improvement of previously published work, both in terms of areas of applicability and computational speed. Our clustering method is capable of handling very large datasets with millions of data points using very limited amounts of labelled data. In this work, we apply our clustering

Image-based rock typing is carried out to quantitatively assess the heterogeneity of the reservoir specimen at a pore scale by classifying an image of a heterogeneous rock sample into a number of relatively homogeneous regions. Image-based rock typing can be treated as a special application of texture classification in the field of the digital core. In conventional texture classification algorithms

A Python program is presented to expedite the process of correcting raw data and propagating the related uncertainties from Radium Delayed Coincidence Counting (RaDeCC) instruments. The performance of the program was validated against an established method with real data. Excellent agreement between determinations of excess radium-223, actinium-227, excess radium-224, thorium-228 and radium-226 was

Using geophysical inversion for three-dimensional (3D) geological modeling is an effective way to model underground geological structures. In this study, we propose and investigate a 3D geological structure inversion method using convolutional neural networks (CNNs). This method can quickly predict the parameters of a geological structure for constructing a 3D model. First, we sample the geological

The process of phosphate extraction can significantly benefit from the advances in spectral analysis and Artificial Intelligence to reduce the cost of the drilling operation. The ambiguities caused by the apparent similarities between different layers and by the existing mineralogical alterations complexify the delineation of phosphate layers with conventional vision systems. In this paper, we established

This paper presents a learning-based stochastic simulation method that incorporates high-order spatial statistics at multiple scales from sources with different resolutions. Regarding the simulation of a certain spatial attribute, the high-order spatial information from different sources is encapsulated as aggregated kernel statistics in a spatial Legendre moment kernel space, and the probability distribution

This paper studies the advantages and disadvantages of different machine learning techniques in predicting ground-motion intensity measures given source characteristics, source-to-site distance, and local site conditions. Typically, linear regression-based models with predefined equations and coefficients are used in ground motion prediction. However, restrictions of the linear regression models may

El Niño can affect climate patterns, causing extreme weather events, such as floods and droughts, around the world. Accurate forecasting of El Niño events allows preparation for El Niño-related disasters. However, the performance of current methods for predicting El Niño events one year in advance is not effective. This study proposes a hybrid approach to predicting the El Niño-related Oceanic Niño

The automatic detection of passive tracers on a moving wavy surface has numerous applications in mathematics and engineering. In oceanography, among many examples, down-looking conventional optical cameras can be employed in wave tanks to investigate the mechanisms of particle dispersion induced by wave turbulence. In this context, we present a computational system to automatically track down the trajectories

Machine learning algorithms (e.g., random forest (RF)) have recently been performed in data-driven mineral prospectivity mapping. These methods are highly sensitive to hyperparameter values, since the predictive accuracy of them can significantly increase when the optimized hyperparameters are predefined and then adjusted to training procedure. The main goal of this contribution is to propose a hybrid

The standard processing of airborne gamma-ray spectrometry (AGRS) data provides useful preliminary information to interpretation in several contexts, such as environmental studies, geological mapping, and analysis of mineral deposits. For optimal results, the acquisition conditions should be nearly constant, and the flight height should be uniform. However, abrupt changes in flight height (often originated

Flow pattern reconstructions for past glaciations are based on the analysis of the spatial distribution of subglacial landforms, and streamlined subglacial landforms (oriented parallel or sub-parallel to the ice flow) are regarded as the main indicators of the previous ice flow. Manual mapping of these landforms is a time consuming and subjective process, making semi-automated mapping (SAM) methods

Inversion of electromagnetic data finds applications in many areas of geophysics. The inverse problem is commonly solved with either deterministic optimization methods (such as the nonlinear conjugate gradient or Gauss-Newton) which are prone to getting trapped in a local minimum, or probabilistic methods which are very computationally demanding. A recently emerging alternative is to employ deep neural

Characterizing urban hydrographs during rain storms, hurricanes, and river floods is important to decrease loss of lives and assist emergency responders with mapping disruptions to operation of major cities. High water marks, stream gages, and rapidly deployed instrumentation are the current state-of-practice for hydrological data during a flood event. The objective of this study was to develop technology

Inherent Optical Properties (IOPs) of oceanic and coastal waters provide useful information for studying light availability in various ocean layers, primary productivity, particulate matter, and aquatic photochemistry. The total spectral absorption coefficient, a(λ) and spectral particulate absorption coefficient, ap(λ) are the IOPs that are some of the extensively measured IOPs with the existing absorption

The key available information to choose new locations for drilling are the estimated ore grade values and the corresponding uncertainties at the tentative locations. These pieces of information are combined to generate a single objective function. The mathematical form of the objective function should reflect the effect of these values and their relative importance. Traditional objective function use

As large point cloud datasets become ubiquitous in the Earth science community, open source libraries and software dedicated to manipulating these data are valuable tools for geospatial scientists and practitioners. We highlight an open source library called the Point Data Abstraction Library, more commonly referred to by its acronym: PDAL. PDAL provides a standalone application for point cloud processing

Geological parameterization enables the representation of geomodels in terms of a relatively small set of variables. Parameterization is therefore very useful in the context of data assimilation and uncertainty quantification. In this study, a deep-learning-based geological parameterization algorithm, CNN-PCA, is developed for complex 3D geomodels. CNN-PCA entails the use of convolutional neural networks

Stereo 3D reconstruction is continuously increasing its popularity in the study of mid-to small-scale sea waves. In the recent past, different approaches have been proposed to reconstruct the space-time sea surface elevation field from synchronized stereo frames. Usually, the reconstruction is performed by first recovering a dense and sparse 3D point cloud from stereo pairs and then by interpolating

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

Several applications in geoscience require the generation of multiple realizations of random fields of physical properties to mimic their spatial distribution and quantify the model uncertainty. Some modeling problems present complex multivariate distributions with heteroscedasticity and non-linear relations among the variables. We propose a new algorithm, namely Direct Multivariate Simulation, for

X-ray powder diffraction (XRPD) is consistently found to be the most accurate analytical technique for qualitative and quantitative mineralogy. For environmental samples such as rocks, sediments and soils that can contain a range of crystalline, disordered and amorphous components, quantitative XRPD methods implemented in Excel-based programmes such as FULLPAT and RockJock have proved particularly

Many of the most widely used deep geothermal resource maps for the UK are produced by contouring around sparsely distributed and often unreliable data points. We thus present a MATLAB-based 3D finite difference temperature modelling methodology, which provides a means for producing more resolute and geologically realistic versions of these maps. Our case study area in northern England represents an

Crater cataloging is an important yet time-consuming part of geological mapping. We present an automated Crater Detection Algorithm (CDA) that is competitive with expert-human researchers and hundreds of times faster. The CDA uses multiple neural networks to process digital terrain model and thermal infra-red imagery to identify and locate craters across the surface of Mars. We use additional post-processing

Application of supervised machine learning algorithms for mineral prospectivity mapping (MPM) requires positive and negative training samples. Typically, known mineral deposits are considered as positive training samples. However, the selection of negative training samples in the process of MPM is challenging. The one-class classification methods require positive and unlabeled samples or only positive

We report a new, open-source, MATLAB-based 3D code for numerically simulating the self-potential (SP) in subsurface reservoirs. The code works as a post-processor, using outputs from existing reservoir flow and transport simulators at a selected timestep to calculate the SP throughout the reservoir model. The material properties required to calculate the SP are user defined and may be constant or vary

In this study, we accelerated a microphysics scheme embedded within the Model for Prediction Across Scales (MPAS), using OpenACC directives. As one of the most time-consuming physics parameterization schemes, we focused on parallelizing the Weather Research and Forecasting (WRF) single-moment 6-class microphysics scheme (WSM6) onto a graphics processing unit (GPU). We applied several essential methodologies

Ordinary Kriging (OK) is a popular geostatistical algorithm for spatial interpolation and estimation. The computational complexity of OK changes quadratically and cubically for memory and speed, respectively, given the number of data. Therefore, it is computationally intensive and also challenging to process a large set of data, especially in three-dimensional (3D) cases. This paper develops a geostatistics-informed

The evolution of the volume fraction of solid phase, basal entrainment and interactions between the solid and fluid phase have significant impacts on rheological behavior, dynamic characteristics, and volume amplification in some earth-surface flows, including debris flows, hyper-concentration flows, dam-break flows. In this study, we propose a new two-phase flow dynamic model based on coupling of

In this paper GravPSO2D, a Matlab tool for two-dimensional gravity inversion in sedimentary basins using the Particle Swarm Optimization (PSO) algorithm, is presented. The package consists of a collection of functions and scripts that cover the main three parts of the process: (1) the model definition based on the observations, (2) the inversion itself, where the PSO is employed, and (3) the results

The development of morphodynamic models to simulate sediment transport accurately is a challenging process that is becoming ever more important because of our increasing exploitation of the coastal zone, as well as sea-level rise and the potential increase in strength and frequency of storms due to a changing climate. Morphodynamic models are highly complex given the non-linear and coupled nature of

Deep learning has become an area of interest in most scientific areas, including physical sciences. Modern networks apply real-valued transformations on the data. Particularly, convolutions in convolutional neural networks discard phase information entirely. Many deterministic signals, such as seismic data or electrical signals, contain significant information in the phase of the signal. We explore

Precipitation-induced erosion and orographic precipitation are thought to be coupled during mountain building, potentially serving as a negative feedback on mountain uplift. However, the strength and spatial uniformity of this coupling are not well understood due to limitations in correlating past climate and orogenic events through proxy records, as well as the substantial differences in temporal

Geographers, ecologists, and other environmental scientists are typically required to utilise non-continuous measurements from various types of sensors as part of their research activities. However, data management and processing require advanced computer skills and specific knowledge of the measurement sensors. Here, we present the Tube Database (TubeDB), an easy-to-operate software system to archive

Automatic detection of seismic events is a useful tool for routine data processing. Effective detection saves time and effort in phase picking and events’ location, especially in areas with moderate seismicity at regional and local scales. The Lai Chau area in northern Vietnam is a good example of such a region. An additional difficulty in detection is the anthropogenic origin of reservoir-triggered

Estimating reservoir properties, such as the skin factor, is an essential role of injectivity tests. Nevertheless, determining individual layer properties in multilayer systems remains a difficult task. Some techniques have been proposed to compute layer permeabilities and skin factors based on the pressure response of reservoirs under single-phase flow. Regardless, determining layer skin zone permeability

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

This paper presents work done towards porting of FWM2DA, an open source program, on multi-core CPU and GPU architectures. FWM2DA is a Fortran90 sequential program which performs acoustic wave propagation of single source location for the 2D subsurface earth model using finite difference time domain modelling. We have reproduced this program using C programming language and upgraded its functionality

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 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

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