One of the most sophisticated and significant stages of basin modelling is simulation of hydrocarbon (HC) migration. At this stage, the attempt is made to estimate the probability of HCs existence in the region of interest. This can be done by utilizing readily available numerical reservoir simulation tools. Such tools are typically based on the Darcy flow model and are more frequently used for less

Data sharing benefits the researcher, the scientific community, and the public by allowing the impact of data to be generalized beyond one project and by making science more transparent. However, many scientific communities have not developed protocols or standards for publishing, citing, and versioning datasets. One community that lags in data management is that of low-temperature geochemistry (LTG)

The background in image of remote sensing is often complicated and changeable, and the edge of cloud and its shadow is irregular. In the traditional method, the bright part of the background is easy to be misjudged as cloud, while the dark part is easy to be misjudged as cloud shadow. Moreover, the edge information of the extracted cloud and its shadow is rough, and it is easy to miss the judgment

The spatial modeling of geo-domains has become ubiquitous in many geoscientific fields. However, geo-domains’ spatial modeling poses real challenges, including the uncertainty assessment of geo-domain boundaries. Geo-domain models are subject to uncertainties due mainly to the inherent lack of knowledge in areas with little or no data. Because they are often used for impactful decision-making, they

Through research and analysis of the existing remote sensing image sharing and distribution systems, remote sensing image recommendation mode can be divided into subscription recommendation and active recommendation. The first mode provides data query retrieval and subscription distribution services. However, retrieval and subscription services are based on query and subscription keywords, which are

Reconstructions of sea level prior to the satellite altimeter era are usually derived from tide gauge records; however most algorithms for this assume that modes of sea level variability are stationary which is not true over several decades. Here we suggest a method that is based on optimized data-dependent triangulations of the network of gauge stations. Data-dependent triangulations are triangulations

Real-time characterization of refractivity within the marine atmospheric boundary layer can provide valuable information that can potentially be used to mitigate the effects of atmospheric ducting on radar performance. Many duct characterization models are successful at predicting parameters from a specific refractivity profile associated with a given type of duct; however, the ability to classify

Airborne LiDAR point clouds classification has been a challenging task due to the characteristics of point clouds and the complexity of the urban environment. Recently, methods that directly act on unordered point set have achieved satisfactory results in point clouds classification. However, the existing methods that directly consume point clouds pay little attention to the interaction between the

Multiple-point geostatistical (MPS) simulation methods have attracted an enormous amount of attention in earth and environmental sciences due to their ability to enhance extraction and synthesis of heterogeneous patterns. To characterize the subtle features of heterogeneous structures and phenomena, large-scale and high-resolution simulations are often required. Accordingly, the size of simulation

Application of machine learning (ML) or deep learning (DL) to geophysical data inversion is a growing topic of interest. Opportunities are in the areas of enhanced efficiency, resolution, and uniqueness for the inversion of geophysical ill-posed problems. Direct application of standard data-driven ML approaches to inversion have quickly shown limitations in the practical implementation. Some of the

The computational complexity of distributed groundwater models poses a significant challenge in the conjunctive management of surface and groundwater resources. Here, we propose a multi-model framework to predict groundwater recharge using a simple water balance model. We compare and contrast two lumped parsimonious conceptual models of groundwater recharge, one based on P only and another, additionally

A large amount of raw echo data is collected by NMR logging, especially in multi-dimensional NMR logging, and a direct inversion for the raw echo data is time-consuming, so the echo data collected need to be compressed before inversion to improve inversion speed. This paper proposes a multi-dimensional NMR data compression method based on Discrete Cosine Transform (DCT). Utilizing the energy concentration

Deep learning is an effective way to improve the classification accuracy of coal images for the machine vision-based coal sorting. However, the related research on deep learning-based mineral image classification has not systematically considered the models for multi-coal and multi-class sorting. Additionally, the universal CNNs model for multi-coal image classification has not been proposed. Given

The failure of uranium mine tailings dams results in the infiltration and spreading of tailings in the subsurface. The fate and transport of radionuclides in the subsurface depends on several confounding, complex interdependent factors that describe the elements of the integrated system (i.e., meteorological; hydrological; hydrogeological; and, soil, groundwater, and mine tailings chemistry). The factors

The broad dissemination of open data policies for big Earth data leads to a diversification of users. The literacy in data and data handling differs for each user group, resulting in different needs and requirements. In addition, the development of cloud-based data systems challenges traditional workflows of all users. In order to tailor new cloud-based data systems for Earth data users, it is of utmost

In the last decade, machine learning algorithms have shown their superior performance in the spatial interpolation of environmental properties compared to classical interpolation models. In particular, the random forest ensemble model has provided the best adjustment. In this work, we compare the performance of support vector machines (SVM), simple trees (ST), random forests (RF) and extremely random

Detecting detritus from the polarized microscopic images of river sands is the first step in the tasks of sediment source analysis, tectonic evolution, and lithofacies paleogeography. Traditional detritus detection mainly relies on professionals to identify and detect manually, which is both time-consuming and labor-intensive facing large volumes of microscopic images of river sands. Currently, deep

Multichannel Analysis of Surface Waves (MASW) is a technique frequently used in geotechnical engineering and engineering geophysics to infer 1D layered models of seismic shear wave velocities in the top tens to hundreds of meters of the subsurface. We aim to accelerate MASW calculations by capitalizing on modern computer hardware available in the workstations of most engineers: multiple cores and graphics

Hidden Markov models (HMMs) are general purpose models for time-series data widely used across the sciences because of their flexibility and elegance. Fitting HMMs can often be computationally demanding and time consuming, particularly when the number of hidden states is large or the Markov chain itself is long. Here we introduce a new Graphical Processing Unit (GPU)-based algorithm designed to fit

Seismic data denoising is an important mean to extract useful information from seismic data, remove interference, and improve the SNR(signal-to-noise ratio) of seismic data. Therefore, the research on denoising methods of seismic data has always been a hot topic. At present, most convolutional neural networks for desert seismic data denoising use single scale convolutional kernels to extract feature

In the last two decades, several geostatistical simulation techniques have appeared that allow taking into account complex structural information, by quantifying a multiple-point statistical (MPS) model. The higher-order statistics are typically informed from a training image, sample model, or geological exposure. Such MPS models can represent the subsurface and are then used to simulate subsurface

The Finite Element method can be implemented to model geophysical electromagnetic data using one of two methodologies called Nodal and Vector Finite Elements. This paper presents a comparison between the two approaches, emphasizing memory usage and processing time, when simulating Marine Controlled Source Electromagnetic (MCSEM) data in three-dimensional models. The study is carried out using unstructured

The paper deals with geoid modeling using the least-squares modification of the Stokes' and Hotine's formulae using gridded and non-gridded terrestrial gravity data over the Auvergne test area (France). Differently from the conventional way of using gridded gravity data, this study utilizes non-gridded data as the input to the geoid modeling and compare the differences between the computational approaches

One area of intense scientific interest for the study of sandstones, carbonates, and shale at the pore scale is the use of limited image and petrophysical data to generate multiple realizations of a rock’s pore structure. Such images aid efforts to quantify uncertainty in petrophysical properties, including porosity–permeability transforms. We develop and evaluate a deep learning-based method to synthesize

Due to the physical constraints of X-ray Fluorescence (XRF) scanners, all but the shortest sedimentary cores are typically scanned in a piecewise fashion. Pieces are either extracted a section at a time (e.g. Russian peat corer) or are cut and sectioned after extraction (e.g. piston and gravity cores). The XRF core scanner generates an optical line-scan image, an X-ray radiograph, and a table of downcore

As the utilization of LiDAR (Light Detection and Ranging) is getting more affordable and available for a wider audience, the analysis of point clouds constructed by laser scanning is earning more attention. Airborne LiDAR is especially useful in the analysis and classification of land objects. We are able to determine if they are natural or artificial objects and what changes occurred to them throughout

Flow characterization using more accurate and robust numerical engine in the natural domains is a demanding subject in underground related surveys for an optimized performance. Primarily, we employed a more precise insight into constructing a proper three-dimensional rock model, the XCT. Representative samples were scanned with a synchrotron Micro-Tomography beamline. The reconstructed volumes were

Landslide susceptibility assessment has become the focus of geological disaster research to strengthen disaster prevention and mitigation. Landslide disasters frequently occur in the Hubei section of the Three Gorges Reservoir Area (TGRA), with some potential landslides located along the highway, which brings risks to highway engineering, maintenance and transportation. In this paper, a comprehensive

Social media data have been widely used to enrich human-centric information for situational awareness and disaster assessment. Owing to the granularity of topics detected from disaster-related contents, the effectiveness of social media in reflecting disaster losses is still limited. To address this limitation, this study developed a methodology for assessing disaster losses using social media data

An image processing workflow is presented for the characterization of pore and grain size distributions in porous geological samples from X-ray microcomputed tomography (μCT) and scanning electron microscopy (SEM) images. The pore and grain size distributions of five sandstone samples including Berea, Buff Berea, Nugget, Castlegate, and Bentheimer, and one carbonate sample, Indiana limestone, are extracted

Drainage network analysis is fundamental to understanding the characteristics of surface hydrology. Based on elevation data, drainage network analysis is often used to extract key hydrological features like drainage networks and streamlines. Limited by raster-based data models, conventional drainage network algorithms typically allow water to flow in 4 or 8 directions (surrounding grids) from a raster

Many experimental calibration data linking the variation of Raman spectral parameters (i.e., peak position, peak area/intensity ratio …) with the PVX properties of pure gases or mixtures of CO2–CH4–N2 or CH4–H2O–NaCl systems have been published in literature by different laboratories. However, there is a significant discrepancy between these calibrations, leading possibly to inaccurate results when

Pore scale modelling in porous media is a field of research where interfaces such as the fluid – mineral boundary are explicitly represented and avoids volume-averaged properties such as porosity, permeability and reaction rates as required in a continuum scale approach. Setting up, running and extracting data from a pore scale model can be complex involving multiple software packages and coding for

The spatial structural features and compositional relationships of multivariate geochemicals are influenced by complex geological processes (e.g., diagenesis and mineralization), and can help identify geochemical anomalies and provide key references for mineral resource exploration. However, previous machine-learning-based models often treat spatial structural features or compositional relationships

Transient electromagnetic method (TEM) inversion is significantly nonlinear. To eliminate the multicollinearity problem faced by the extreme learning machine (ELM) algorithm for TEM inversion, an improved ELM algorithm (F-ELM) based on fractal dimension technology is proposed. By reducing the dimension of the hidden layer output matrix (H) based on fractal dimension theory without losing the main statistical

Minerals exhibit compositional zoning patterns that can be related to changes in the environment in which they grew. Using statistical methods that have been designed to segment optical images, we have developed a procedure to segment zoned crystals using elemental maps. For a single mineral phase, compositional zones in individual crystals are correlated between multiple crystals. This allows us to

X-ray computed tomography (CT) is a non-destructive imaging technique that provides three-dimensional (3D) visualisation and high-resolution quantitative data in the form of CT numbers. CT numbers are derived from the X-ray energy, effective atomic number and density of the analysed material. The sensitivity of the CT number to changes in material density means that it can be used to identify facies

A global optimization technique using particle swarm optimization (PSO) is presented to estimate the depth to the basement of a heterogeneous sedimentary basin from the vertical component of the residual gravity anomalies. Inversion of basement relief for known density distributions has particular importance in many real applications such as mineral exploration, geothermal exploration, etc. Generally

Microseismic events generally contain strong noise-polluting and unobvious P-phase oscillating channel waveforms. The automatic P-phase arrival picking accuracy of these channel waveforms tends to be low, or even are false. Currently, unusable P-picks are not screened out automatically before geophysics inversions in most microseismic data processing software. Therefore, manual interventions are needed

Hyperpycnal flows are observed when the density of a fluid entering into a quiescent environment is greater than that of the ambient fluid. This difference can be due to salinity, temperature, concentration, turbidity, or a combination of them. Over a sloping bottom, the inflowing momentum decreases progressively until a critical stage is reached where the inflow plunges underneath the ambient and

The aim of this paper is to analyse the seismic activity of the Iberian Peninsula and a wide area of the Republic of Croatia. To do so, two incidence-magnitude seismic parameters have been defined. First, the areas have been divided into several ellipsoidal clusters using Mahalanobis clustering. Four generalised indexes (Mahalanobis Calinski Harabasz, Mahalanobis Davies–Bouldin, Mahalanobis Simplified

Partitioning the total non-water absorption coefficient (anw(λ), m-1) into subcomponent Inherent Optical Properties or absorption subcomponents (IOPs), i.e., absorption due to various optically active substances like phytoplankton, (aph(λ),m-1), detrital material, (adm(λ),m-1) and colored dissolved organic matter (CDOM), (ag(λ),m-1) provide information about light interaction with various materials

Sensor errors are inevitable when measuring the ocean; thus, a reliable dataset of observations requires a quality control (QC) procedure capable of detecting spurious measurements. While manual QC by human experts minimizes errors, it is inefficient to handle large datasets and vulnerable to inconsistencies between different experts. Although automatic QC addresses some of these issues, the traditional

Groundwater and surface water interaction is a common process of the hydrologic cycle but still challenging to be addressed in a general context. A special case of groundwater-surface water interaction is the seepage from a river/stream or canal into an adjacent phreatic aquifer, where the groundwater flow in the vicinity of the stream is crucial while the aquifer is supposed to extend to infinity

The Laplace Tidal Equations (LTEs) play a basic role for investigating the dissipation of long-term orbital energy as heat in surface oceans of icy satellites. Here we address solving LTEs by means of finite elements using PDE2D, a general-purpose finite element program for solving multidimensional PDEs. We use PDE2D to solve the equations for eccentricity and obliquity gravitational potentials for

The Gravity Recovery Object Oriented Programming System (GROOPS) is a software toolkit written in C++ that enables the user to perform core geodetic tasks. Key features of the software include gravity field recovery from satellite and terrestrial data, the determination of satellite orbits from global navigation satellite system (GNSS) measurements, and the computation of GNSS constellations and ground

Borehole resistivity measurements are routinely inverted in real-time during geosteering operations. The inversion process can be efficiently performed with the help of advanced artificial intelligence algorithms such as deep learning. These methods require a massive dataset that relates multiple Earth models with the corresponding borehole resistivity measurements. In here, we propose to use an advanced

A three-axis accelerometer was combined with large-scale particle image velocimetry (LSPIV) to obtain nonintrusive and safe surface velocity measurements. Dual cameras were established at the Yu-Feng gauging station of Shimen Reservoir to capture near-field and far-field images and analyze the surface velocity of rivers. Surface velocity measurements were obtained with a flow meter, LPSIV with ground

Groundwater prospecting with reasonable accuracy is often a challenging task. The integration of geographic information system (GIS) with Machine Learning techniques has proven very reliable to delineate the nonlinear behaviour of groundwater occurrence. The weighted overlay analysis is one of the many essential tools used for groundwater potential zonation, based on remote sensing technology and has

Objective: Efficient and flexible data integration platforms are important to apply various geoscience data for deep learning applications. Recently, deep learning techniques have been applied to analyze and predict natural phenomena in Earth sciences using geoscience data. Because geoscience data are considered as “big data”, data driven approaches such as deep learning are promising tools for understanding

This study presents a software tool Grav3CH_inv developed to estimate the three-dimensional depth structure of sedimentary basins from their gravity data through an iterative process. The algorithm linked to the developed code operates recursively both in the wavenumber domain and in the space domain based on a triple method combination. The modelling strategy allows considerations of the model space

In recent years, the concept of the Internet of Things (IoT) has been attracting attention from various fields as IoT devices can continuously monitor various environmental properties. While the number of IoT devices increases rapidly, managing large volume of IoT data faces a serious scalability issue. To address this issue, many studies have shown that the performance of key-value storages is better

The visual characteristics of landslide susceptibility have not yet been fully explored. Professional or trained technicians have to take much time and effort to interpret remote sensing images and locate landslides accordingly. Although conventional machine learning methods based on hand-crafted features for landslide susceptibility prediction (LSP) have acquired remarkable performance, they have

Seismic attenuation is usually described by a constant-Q (CQ) model that assumes the seismic quality factor (Q) is independent of the frequency. To simulate the attenuation behaviors of seismic waves, we develop a pseudo-spectral time-domain (PSTD) method to solve a CQ viscoacoustic wave equation. This method is nearly fourth-order accurate in time. Compared to the conventional temporal second-order

First-arrival picking is a necessary step in seismic data processing. Existing algorithms are either inaccurate or inefficient when the number of geophone groups is large and signal-to-noise ratio is low. In this paper, we propose the automatic first-arrival picking through convolution kernel construction and particle swarm optimization (FPCO) algorithm. First, abrupt energy fluctuations are detected