An intelligent lithology identification method is proposed based on the deep learning of rock images. The lithology information and position information in rock images can be predicted using the Faster R–CNN architecture through the RPN proposal generation algorithm and the Fast R–CNN detector. To obtain more rock features, the rock detection model is built on the ResNet structure, and the residual

In the paper, we review selected existing solutions of raster map calculators and propose a new approach for map calculation tools. The main criteria to select raster maps calculators was the ability to run them in batch mode and to use them in external scripts. Such a working method is common in the processing and modeling of massive datasets. We compared the following solutions: r.mapcalc from GRASS

In this paper, we propose a community detection method to find regions in spatial data with higher correlations. We construct a ‘correlation deviation graph’ that considers the influence of global correlation caused by the node's geographical location. We compare the performance of the correlation deviation graph to conventional correlation graph construction approaches for community detection, using

When sampling at offset is too coarse during seismic acquisition, spatial aliasing will appear, affecting the accuracy of subsequent processing. The receiver spacing can be reduced by interpolating one or more traces between every two traces to remove the spatial aliasing. And the seismic data with spatial aliasing can be seen as regular missing data. Deep learning is an efficient method for seismic

Hyperspectral Image analysis has gained much attention due to the presence of rich spectral information. Hyperspectral Image (HSI) classification is being utilized in a wide range of applications. Convolutional Neural Networks (CNN) are popularly used in the image classification tasks due to their capability of extracting spatial features from the raw image data. Creating an ensemble of multiple classifiers

Mineralization is a rare event. Hence, the geosciences datasets used for three-dimensional (3D) mineral potential mapping (MPM) are often imbalanced, consisting of scarce positive samples and abundant unlabeled data. Compared with selecting positive samples, it is challenging to select reliable negative samples in 3D MPM. However, the application of supervised machine learning algorithms in 3D MPM

Numerical simulations are a highly valuable tool for improving our understanding of mantle dynamics. COMSOL Multiphysics® is a commercial software suite designed to numerically model experiments featuring multiple branches of physics. This modeling approach applies to mantle convection, which can be viewed as a combination of fluid dynamics and thermodynamics. COMSOL® is of interest to the geoscience

Local binary pattern (LBP) is an extensively used method in image analysis and pattern recognition related works across various domains. In this paper, we explore cyclone cloud evolution patterns using a modified LBP. The multilayer multi-block local binary pattern (MMLBP) is an extended version of Completed LBP (CLBP) with multiple blocks and many layers. A tropical cyclone (TC) image is converted

River width is an essential parameter for studying the river’s hydrological process and has been widely used to estimate the river discharge. The existing approaches to measuring river width are based on remotely sensed imagery such as MODIS, Landsat to identify the river, and then estimate the river width. In this work, an alternate approach for river width estimation is proposed using the under-explored

Ecosystem Services Web Services (ESWS) are new web-based approaches to quantifying the benefits that humans derive from nature. Specifically, ESWS are the application of open web standards to ecosystem service assessment to facilitate creation, iteration, and dissemination in a seamless way. This integration streamlines collaboration, automation, and curation, while providing an open interface through

Radial Basis Function-generated Finite Differences (RBF-FD) is a popular variant of local strong-form meshless methods that do not require a predefined connection between the nodes, making it easier to adapt node-distribution to the problem under consideration. This paper investigates an RBF-FD solution of time-domain acoustic wave propagation in the context of seismic modeling in the Earth's subsurface

The surface-wave analysis method is widely adopted to build near-surface shear-wave velocity structure. Automatic picking of surface-wave dispersion curves is a problem that needs to be solved, especially when dealing with a large amount of data. In this paper, we proposed an automatic picking method of multi-mode surface-wave dispersion curves based on unsupervised machine learning methods. Firstly

The recent technological advancements and emergence of the open data in environmental and life sciences are opening new research opportunities while creating new challenges around data management. They make available an unprecedented amount of data that can be exploited for studying complex phenomena. However, new challenges related to data management need to be addressed to ensure effective data sharing

The Hankel matrix’s low-rank property derived from the noise-free seismic data describing a few linear events and has been successively leveraged in many low-rank seismic data de-noising approaches. In such rank reduction methods, the typical scheme is to determine the best low-rank estimation of the formulated Hankel matrix, and then obtain the de-noised data. However, if the noisy data has been rearranged

When performing spatial-temporal investigations of multiple lake systems, geoscientists face the challenge of dealing with complex and heterogeneous data of different types, structure, and format. To support comparability, it is necessary to transform such data into a uniform format that ensures syntactic and semantic comparability. This paper presents a data science approach for transforming research

This paper numerically studies the interaction of a flowing granular material with an entrainable granular bed, while materials are mixed at the interface of two materials. The rheological behavior of this granular mixture is characterized by a generalized viscoplastic model that includes local volume fraction of materials as well as their physical properties, i.e. size, density, and friction angle

Simulating spatial Gaussian realizations is one of the core components of geostatistics and numerous other fields involving uncertainty, risk, and reliability. The use of decorrelation methods and truncated Gaussian algorithms further promotes the use of Gaussian realizations. In multivariate cases, geological variables may represent different scales and exhibit different spatial structures and anisotropy

The emergence of high resolution LiDAR measurements raises the challenge of extracting accurate geometric information from 3D point cloud data. The standard approach is to generate digital elevation models in the form of regular grids and to compute numerical descriptors such as slope and curvatures. An alternative is the use of triangular representations, which preserve many details that might be

With the explosive growth in seismic data acquisition and the successful application of deep convolutional neural networks (DCNN) to various image processing tasks within multidisciplinary fields, many researchers have begun to research DCNN based automatic seismic interpretation techniques. Due to the vast number of parameters considered in deep neural networks, deep learning methods usually require

Apparent resistivity is a critical parameter for providing information about underground structures. Since the fields of the transient electromagnetic method (TEM) are an implicit function of resistivity, full-time apparent resistivity definition has always been one of the difficult issues. Besides, the definition of TEM apparent resistivity was always based on the magnetic field. However, the multi-channel

Climate change is deeply impacting the society on different scales. Decision making becomes a complex task when, in adverse weather conditions, the meteorological records show missing data due to failures of measuring instruments. Several investigations have proposed optimized regression methods, K-nearest-neighbor imputation, and multiple imputations for the treatment of missing data; however, there

Sedimentary petrology is the basis for most mineral and textural identification in sandstones. Automating mineralogical interpretation of an entire thin section image has many practical applications, including improved geological understanding, input of spatial distribution of mineralogy and grain size for petrophysical evaluations, and integration with 3D imaging modalities (micro-CT, nano-CT). We

This study presents experimental results from assessing fault orientation using triangulation and a combinatorial algorithm. We constructed two geological surfaces with vertical and inclined faults. These surfaces were documented by boreholes and represented by triangulated surfaces. We first calculated orientations for a small sample of triangles genetically related to faults that were also members

Few deep wells have been drilled in the Cheshire Basin, resulting in high geological and financial risk of geothermal developments. Although the geothermal gradient in the basin can be predicted, the transmissivity of aquifers at depth are unknown. This has led to an investigation of lower risk strategies such as deep coaxial borehole heat exchangers (BHEs) for spatial heating, rather than traditional

Meandering is one of the unique processes in Earth surface dynamics. Since the 1960s, fluvial geomorphologists have generalized numerous widely-acknowledged empirical or semi-empirical formulae to describe meandering rivers' characteristics. The most frequently applied one is the Kinoshita high-sinuosity curve to describe meander channel planform geometry. Combining the Kinoshita curve with the Beck

This paper presents a mixed finite element framework for coupled hydro-mechanical–chemical processes in heterogeneous porous media. The framework combines two types of locally conservative discretization schemes: (1) an enriched Galerkin method for reactive flow, and (2) a three-field mixed finite element method for coupled fluid flow and solid deformation. This combination ensures local mass conservation

Well logs are commonly used by geoscientists to infer and extrapolate physical properties of subsurface rocks. However, at some depth intervals, well log values might be missing due to operational issues in the logging process. To overcome this problem, an innovative approach to reconstruct well logs is proposed using machine learning methods. Based on other complete logging features, the missing well

The northern branch of the North Anatolian Fault in the Sea of Marmara (SoM) shows a complex seismological behavior including shallow, fluid-related seismicity, deep seismicity, and locked and creeping segments. On July 25, 2011, a Mw 5 earthquakes occurred at a depth of ~11.5 km in the western SoM and immediately followed by hundreds of triggered micro-earthquakes mostly outside the rupture area of

Inversion of sonic logging data becomes a non-trivial problem for anisotropic media, especially for the case of vertical transverse isotropic (VTI) formation, when a well is parallel to its axis of symmetry. Most modern processing techniques use only the kinematic characteristics of the wavefield. Thus, they are incapable of determining all formation elastic parameters (density, compressional and shear

When solving inverse problems in geophysical imaging, deep generative models (DGMs) may be used to enforce the solution to display highly structured spatial patterns which are supported by independent information (e.g. the geological setting) of the subsurface. In such case, inversion may be formulated in a latent space where a low-dimensional parameterization of the patterns is defined and where Markov

Imaging Earth structure or seismic sources from seismic data involves minimizing a target misfit function, and is commonly solved through gradient-based optimization. The adjoint-state method has been developed to compute the gradient efficiently; however, its implementation can be time-consuming and difficult. We develop a general seismic inversion framework to calculate gradients using reverse-mode

This contribution presents a rewritten and expanded version of the Fast Grain Boundary (FGB) program (Eiler et al., 1994) with the motivation of adding to the geochemical tools available for reconstructing temperature-time (T–t) histories that inform studies of tectonics and crustal evolution. Both the original and the new FGB programs model the oxygen-isotope compositional evolution of a rock resulting

It is essential to classify tree species accurately for the sustainable management of forest resources and effective monitoring of species diversity. Airborne hyperspectral images have high spatial and spectral resolution, and consequently, the large quantity of information on spectral and spatial structures is effective for tree species classification. In this research, Gaofeng Forest Farm in Nanning

Travel/residence time distributions (TTDs/RTDs) are important tools to evaluate the vulnerability of catchments to contamination and understand many aspects of catchment function and behavior. In recent years, the calculation of TTDs/RTDs based on the Lagrangian particle tracking approach together with the integrated hydrologic modeling has become a popular counterpart to analytical approaches and

We developed a numerical simulation algorithm to explore the nuclear magnetic resonance (NMR) response of the porous media based on the Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence and the Bloch equation. The evolution of the magnetization vector of two representative pores at different pulse properties, including the excitation angle, the refocusing angle, the phase angle, as well as the pulse

Many techniques have been developed to quantify different conceptualizations of self-interaction and patterns within spatial data. We propose a new metric and related algorithm that describes the geometric spatial disorder of geographic point sets, the “Index of Disorder” (IoD). The IoD algorithm was applied to synthetic and natural datasets and was shown to be able to differentiate between areas of

This study presents a fast and efficient vector finite element method, based on the model reduction algorithm, to simulate the 3-D magnetotelluric response. Firstly, we derive a finite element governing equation based on electric field vector from Maxewell's equations Then we express the electric field by a function of frequency parameter to get a transfer function that can be approximated by the Krylov

A stacking method was followed for the classification and prediction of rock types and for 3D geological modeling in the Laochang Sn camp, Geijiu (China). The sequence of the methodology is as follows. Initially, multi-source datasets were compiled GOCAD software, including a borehole dataset with rock types and 1:50,000 scale gravity (residual density) and magnetic susceptibility inversion interpretation

Nowadays, the advantages of Digital Rock Physics (DRP) are well known and widely applied in comprehensive core analysis. It is also known that the quality of the 3D pore scale model drastically influences the results of rock properties simulation, which makes the preprocessing stage of DRP very important. In this work, we consider the application of Deep Convolutional Neural Networks (CNNs) for the

Identifying the categories of detritus collected from river sands is an important work in geological researches, including sediment source analysis, tectonic evolution and lithofacies palaeogeography. Among deep learning techniques developed in recent years, Convolutional Neural Network (CNN) can be applied to the detritus identification problem. However, due to both data insufficiency caused by the

The calculation of flow accumulation is one of the tasks in digital terrain analysis that is not easy to parallelize. The aim of this work was to develop new, faster ways to calculate flow accumulation and achieve shorter execution times than popular software tools for this purpose. We prepared six implementations of algorithms based on both top-down and bottom-up approaches and compared their performance

Image-based rock typing is carried out to classify an image of the heterogeneous rock sample into different rock types where each rock type can be treated as a homogeneous porous medium. In this study, we propose an innovative method for rock typing of the heterogeneous rock sample via three steps. First, the target image, a segmented binary image with two phases of pore and solid, is consecutively

Coherent investigation of paleo-records relies on the interpretation of multiple time series of climate proxies which requires the application of signal matching techniques between separate records and within different proxy measurements inside a single record. However, current methodologies, such as correlation matrices or manual tuning using prominent signal features, result in considerable signal

The Spatiotemporal Weighted Regression (STWR) model is an extension of the Geographically Weighted Regression (GWR) model for exploring the heterogeneity of spatiotemporal processes. A key feature of STWR is that it utilizes the data points observed at previous time stages to make better fit and prediction at the latest time stage. Because the temporal bandwidths and a few other parameters need to

Direct sampling is an efficient information theory-based multipoint simulation method. To reduce the computational cost, many approaches have been proposed to speed up this simulation method. In this paper, an edge-based two-stage strategy is proposed to achieve speed-up. The proposed method first performs a simulation on a coarse grid and then detects edge cells in the simulation grid. Next, only

The availability of advanced Machine Learning algorithms has made the estimation process of biophysical parameters more efficient. However, the efficiency of those methods seldom compared with the efficiency of already established semi-empirical procedures. Aboveground biomass (AGB) of mangrove forests is a crucial biophysical parameter as it is positively correlated to the carbon stocks and fluxes

Seismogenic source zone models, including the delineation and the characterization, still have a role to play in seismic hazard calculations, particularly in regions with moderate or low to moderate seismicity. Seismic source zones establish areas with common tectonic and seismic characteristics, described by a unique magnitude–frequency distribution. Their definition can be addressed from different

The ensemble smoother with multiple data assimilation (ES-MDA) has gained much attention as a powerful tool for history matching problems. Previous studies showed that it could provide both a good match of data and estimates of model parameters. In the original ES-MDA formulation, the number of data assimilation and covariance inflation factors are determined in advance. Selecting them in a decreasing

In the modelling of subsurface fluid flow, faults are dominant features since they can act as fluid pathways, baffles or barriers. Special emphasis is therefore placed in representing them in a numerically efficient manner and the use of lower dimensional domains has become prevalent to simulate higher permeability features like fractures. Such features, however, only represent some of the components

This work presents a Gaussian process model (a Bayesian derivation of kriging) for the interpolation of structural field data (dip and strike measurements). The structural data are treated as the directional derivatives of a latent potential field. The latent field’s isosurfaces characterize the general structural trend in a region, and the predictive variance can be used as a measure of uncertainty

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

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

The geochemistry of basaltic rocks is widely used to investigate the tectonic setting of magmatism. The limitation of traditional two-dimensional tectonic discrimination diagrams is mainly risen from the fact that they can only simultaneously use the information of two (x-y plots) or three (ternary diagrams) elements (or element ratios) for discrimination. This obstacle can be overcome with the assistance

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

Folk's textual classification scheme which is widely used for sediment study operates with the proportions of gravel, sand, silt and clay fractions conventionally. However, dealing with data from different sources usually needs to face missing values that may make the classification difficult. To solve this problem and discover other methods of analyzing the scheme, with samples of offshore seabed

We systematically explore the effect of calibration data length on the performance of a conceptual hydrological model, GR4H, in comparison to two Artificial Neural Network (ANN) architectures: Long Short-Term Memory Networks (LSTM) and Gated Recurrent Units (GRU), which have just recently been introduced to the field of hydrology. We implemented a case study for six river basins across the contiguous

The Comparison Model Method (CMM) is a relatively simple and computationally efficient direct method for the identification of the transmissivity of a confined aquifer by solution of an inverse problem. However, it suffers some of the classical drawbacks related to ill-posedness and ill-conditioning of inverse methods. Effectiveness of the CMM can be improved by some approaches. First of all, the introduction