Geoscience Data Research Articles

Frontier of Understanding Earth's Dynamics

Frontier of Understanding Earth's Dynamics

Issue Information

Geoscience Data JournalVolume 10, Issue 2 p. 193-194 ISSUE INFORMATIONOpen Access Issue Information First published: 19 April 2023 https://doi.org/10.1002/gdj3.167AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume10, Issue2April 2023Pages 193-194 RelatedInformation

Welcome to a New Era in Geosciences Data Management

In the waning days of August 2017, Hurricane Harvey dumped more than 30 trillion gallons of water on Texas’s Gulf Coast. At least 68 people died.

Integration of geoscience data to delineate quality of the Asmari reservoir, Iranian part of the Persian Gulf basin

The Hendijan Field is an asymmetric structure located in the northwestern Persian Gulf and consists of several oil and gas-bearing intervals. Oligocene to Miocene successions in the field host large volumes of hydrocarbons. The Asmari Formation comprises the mixed siliciclastic and carbonate members formed in a shallow marine carbonate ramp depositional environment. The siliciclastic part displays the best reservoir quality within the Asmari Formation. Through petrophysical, sedimentological, and geophysical analysis, this study divides the Asmari Formation into six, third-order depositional sequences. By doing so, it clarifies the relationships between depositional sequences and reservoir characteristics, particularly porosity and water saturation. Detailed petrographical analysis, core description, well log analysis, and seismic interpretation distinguish eight microfacies, two petrofacies, and six third-order sequences within the Asmari Formation. 3D reservoir-property models are developed that incorporate elements of the sequence stratigraphical framework. Within the six identified sequences, high reservoir quality zones are associated with AS-4 and AS-5, which comprise mostly grain-supported lithologies associated with shoal facies. The supratidal, intertidal, and lagoonal facies of AS-2, which are often associated with the occurrence of anhydrite and mud-supported fabrics, reflect lower reservoir quality. Hydraulic flow units (HFU) are delineated to assist in characterizing reservoir heterogeneity and porosity/permeability prediction. Five HFU were identified by applying the flow-zone-indicator method to evaluate reservoir quality. Both the transgressive system tract (TST) of AS-2 and early TST plus late highstand system tract (HST) of AS-6 contain elements of HFU-A. The most productive units of AS-4 and AS-5 belong to HFU-E.

Heat Source Identification by Reprocessing Geoscience Data to Update Conceptual Model of Pentadio Geothermal Prospect Area

The Pentadio Geothermal Prospect Area, Gorontalo, has been studied for geothermal development in the exploration stage. The previous conceptual model that has been produced shows that magma is the heat source of the geothermal system only based on geochemistry of manifestations that had high SO4 2- concentrations. However, the presence of tertiary intrusion based on regional geological data and high SO4 2- concentration cannot be strong evidence to prove the heat source is associated with magmatic system. The purposes of this research are updating the conceptual model, defining the heat source, and producing a different perspective from the previous study for the geothermal system in Pentadio Geothermal Prospect Area by providing a further analysis based on geoscience data reprocessing that had been acquired before. The methods consist of geological structure analysis that involves defining the permeability and geochemistry analysis for interpreting the type of reservoir fluid. Gravity data were reprocessing produced the regional and residual maps, and 3D modelling analysis for determining subsurface condition and heat source possibility in Pentadio Geothermal Prospect Area. The Pentadio Geothermal System is correlated with Gorontalo Graben which controls the permeability in the system. The fluid from geothermal manifestations near Limboto Lake has alkaline pH even though it has high SO4 2- content and low magnesium content. Gravity data shows the regional fault (NW-SE) has a depth of more than 4 km and seems to have the possibility of producing heat influx to the near surface. It is suspected as a heat source of the geothermal system in this area. There is no evidence for magma intrusion to be a heat source in the subsurface by geology and geophysics model. The high concentration of SO4 − in manifestations was influenced by sediment contamination in this area.

Topological Navigation for Autonomous Underwater Vehicles in Confined Semi-Structured Environments.

In this work, we present the design, implementation, and simulation of a topology-based navigation system for the UX-series robots, a spherical underwater vehicle designed to explore and map flooded underground mines. The objective of the robot is to navigate autonomously in the 3D network of tunnels of a semi-structured but unknown environment in order to gather geoscientific data. We start from the assumption that a topological map has been generated by a low-level perception and SLAM module in the form of a labeled graph. However, the map is subject to uncertainties and reconstruction errors that the navigation system must address. First, a distance metric is defined to compute node-matching operations. This metric is then used to enable the robot to find its position on the map and navigate it. To assess the effectiveness of the proposed approach, extensive simulations have been carried out with different randomly generated topologies and various noise rates.

Integrated Diagenetic-Depositional Facies (IDDF) characterization and 3D geomodeling in carbonate reservoirs

Carbonate reservoirs are very prone to diagenetic modification of pre-existing fabrics. The process around the modification of pre-existing depositional fabrics is often interrelated with diagenetic phases that impact reservoir quality and distribution. Characterization and 3D modeling of these features have always been a challenge in the upstream industry. This paper outlines a novel workflow for characterizing and modeling the Integrated Diagenetic-Depositional Facies (IDDF) in carbonate reservoirs, from 1D IDDF classification and Neural Network (NN) estimation, to 2D IDDF trend and seismic-based probability region modeling, culminating with the 3D geo-cellular modeling processes. The workflow integrates several geoscience data sets including; linking of petrographic thin sections analyses with other scales of data from core descriptions, well-logs signatures, Routine Core Analyses (RCA), Production Logging Tool (PLT) inflow analyses, diagenetic proxies and depositional concepts. The IDDF workflow ties these features with 3D seismic attribute region extraction to benefit 3D geo-cellular property modeling. This workflow greatly improves reservoir quality prediction, 3D reservoir modeling and volume estimation.

Deriving big geochemical data from high-resolution remote sensing data via machine learning: Application to a tailing storage facility in the Witwatersrand goldfields

Remote sensing data is a cheap form of surficial geoscientific data, and in terms of veracity, velocity and volume, can sometimes be considered big data. Its spatial and spectral resolution continues to improve over time, and some modern satellites, such as the Copernicus Programme's Sentinel-2 remote sensing satellites, offer a spatial resolution of 10 m across many of their spectral bands. The abundance and quality of remote sensing data combined with accumulated primary geochemical data has provided an unprecedented opportunity to inferentially invert remote sensing data into geochemical data. The ability to derive geochemical data from remote sensing data would provide a form of secondary big geochemical data, which can be used for numerous downstream activities, particularly where data timeliness, volume and velocity are important. Major benefactors of secondary geochemical data would be environmental monitoring and applications of artificial intelligence and machine learning in geochemistry, which currently entirely relies on manually derived data that is primarily guided by scientific reduction. Furthermore, it permits the usage of well-established data analysis techniques from geochemistry to remote sensing that allows useable insights to be extracted beyond those typically associated with strictly remote sensing data analysis. Currently, no generally applicable and systematic method to derive chemical elemental concentrations from large-scale remote sensing data have been documented in geosciences. In this paper, we demonstrate that fusing geostatistically-augmented geochemical and remote sensing data produces an abundance of data that enables a more generalized machine learning-based geochemical data generation. We use gold grade data from a South African tailing storage facility (TSF) and data from both the Landsat-8 and Sentinel remote sensing satellites. We show that various machine learning algorithms can be used given the abundance of training data. Consequently, we are able to produce a high resolution (10 m grid size) gold concentration map of the TSF, which demonstrates the potential of our method to be used to guide extraction planning, online resource exploration, environmental monitoring and resource estimation.

The Intelligent Prospector v1.0: geoscientific model development and prediction by sequential data acquisition planning with application to mineral exploration

Abstract. Geoscientific models are based on geoscientific data; hence, building better models, in the sense of attaining better predictions, often means acquiring additional data. In decision theory, questions of what additional data are expected to best improve predictions and decisions is within the realm of value of information and Bayesian optimal survey design. However, these approaches often evaluate the optimality of one additional data acquisition campaign at a time. In many real settings, certainly in those related to the exploration of Earth resources, a large sequence of data acquisition campaigns possibly needs to be planned. Geoscientific data acquisition can be expensive and time-consuming, requiring effective measurement campaign planning to optimally allocate resources. Each measurement in a data acquisition sequence has the potential to inform where best to take the following measurements; however, directly optimizing a closed-loop measurement sequence requires solving an intractable combinatoric search problem. In this work, we formulate the sequential geoscientific data acquisition problem as a partially observable Markov decision process (POMDP). We then present methodologies to solve the sequential problem using Monte Carlo planning methods. We demonstrate the effectiveness of the proposed approach on a simple 2D synthetic exploration problem. Tests show that the proposed sequential approach is significantly more effective at reducing uncertainty than conventional methods. Although our approach is discussed in the context of mineral resource exploration, it likely has bearing on other types of geoscientific model questions.

The necessary optimization of the data lifecycle: Marine geosciences in the big data era

In the marine geosciences, observations are typically acquired using research vessels to understand a given phenomenon or area of interest. Despite the plateauing of ship time and active research vessels in the last decade, the rate of marine geoscience data production has continued to increase. Simultaneously, there exists large quantities of legacy data aggregated within data repositories; however, these data are rarely curated to be both discoverable and machine-readable (i.e., accessible). This results in inefficient use, or even omission, of high-quality data, that is, both increasingly important to utilize and impractical to recollect. The proliferation of newly acquired data, and increasing importance of legacy data, has only been met with incremental evolution in the methods of data integration. This paper describes some improvements at each stage of the data lifecycle (acquisition, curation, and integration) that could align the marine geosciences better with the “big data” paradigm. We have encountered several major issues coordinating these efforts which we outline here: 1) geologic anomalies are the primary focus of data acquisition and pose difficulty in understanding the dominant (i.e., baseline) marine geology, 2) marine geoscience data are rarely curated to be accessible, and 3) aforementioned issues preclude the use of efficient integration tools that can make optimal use of data. In this paper, we discuss challenges and solutions associated with these issues to overcome these concerns in future decades of marine geoscience. The successful execution of these interconnected steps will optimize the lifecycle of marine geoscience data in the “big data” era.

Ground surface temperature monitoring data analysis and applications to geothermal exploration in volcanic areas, Mount Meager, western Canada

The southern Mount Meager area has been studied for its geothermal resource potential for a half century. Although attempts to commercialize the resource were unsuccessful in the past, a large volume of surface and subsurface geoscience data have been collected and made publicly available, making the Mount Meager area a unique natural laboratory for testing new ideas or tools for geothermal exploration. This study investigates the potential of using a ground surface temperature (GST) monitoring network as a tool in geothermal resource exploration. Twenty-two temperature data loggers were deployed in, and surrounding, the core area of the geothermal prospect for nearly one year. The collected temperature time series were analyzed statistically, and machine learning methods such as hierarchical clustering and k-means classification, were applied to data interpretation. The temporal variation of measured temperature can be divided into four segments: 1) snow free, 2) snow affected, 3) snow curtain, and 4) snow melt seasons. The intensity of daily ground temperature variation in the snow free season shows clear environmental and topographic footprints and is complicated by soil properties. The sites with a short period of near zero GST during the snow curtain period is spatially coincident with high heat flow areas. The measured mean GST shows a close affinity with subsurface heat flow and shallow ground water circulation, suggesting that GST time series contain information indicative of subsurface geothermal anomalies and that a GST monitoring network could be an effective tool for geothermal resource exploration in high latitude and/or elevation areas with seasonal snow cover.

On spectral theory of random fields in the ball

The paper investigates random fields in the ball. It studies three types of such fields: restrictions of scalar random fields in the ball to the sphere, spin, and vector random fields. The review of the existing results and new spectral theory for each of these classes of random fields are given. Examples of applications to classical and new models of these three types are presented. In particular, the Matérn model is used for illustrative examples. The derived spectral representations can be utilised to further study theoretical properties of such fields and to simulate their realisations. The obtained results can also find various applications for modelling and investigating ball data in cosmology, geosciences and embryology.

Introduction to Special Issue: Geoscience Data Analytics and Machine Learning

Introduction to Special Issue: Geoscience Data Analytics and Machine Learning

The Application of Geologic Mapping, Core Logging, and 3-D Geoscientific Data Integration in the Exploration and Resource Expansion of Porphyry Copper-(Gold) Deposits: Examples from Recent Discoveries in Ecuador and Chile

Abstract The collection of accurate geologic data is critical in minerals exploration. The successful visualization, integration, and interpretation of quantitative vein abundance data, associated selvages of wall-rock alteration mineralogy, sulfide mineral proportions, and metal zoning facilitate the path to discovery and orebody delineation. The application of the Anaconda method, developed by the Anaconda Copper Mining Company geologists in the 1960s, to geologic mapping and drill core logging has facilitated the discovery and resource expansion in the giant Alpala porphyry Cu-Au-Ag deposit in northern Ecuador and the Cortadera porphyry Cu-Au-Mo deposit cluster in northern Chile. In both porphyry systems, high-grade zones have been targeted through the delineation of an intrusion and vein chronology that allows for the recognition of early-stage causal intrusions, zones of abundant porphyry-style quartz veins, and elevated chalcopyrite to pyrite ratios. Increased amounts of molybdenum, as molybdenite along fractures and in quartz veins, as well as anhydrite veins and disseminations, correlate with proximity to the deposit cores. The field-based methods presented in this article have proven useful in porphyry exploration for decades. The application of the Anaconda method of geologic mapping and core logging by geologists can assist in the targeting of high-grade cores of deposits and improved estimation of metal resources.

Multithematic Geoscience Data Integration for Identification of Mineral Potential Areas in Parts of Central Indian Precambrian Craton

Abstract The paper highlights the results of multi-thematic geoscience data integration involving geological, geophysical, geochemical and remote sensing data sets, for an area of 4000 Sq. Km in parts of Precambrian Bastar craton, Chhattisgarh. The present team has been able to identify two discrete mineral potential areas for base metal sulphides, Sn, W and REE in sheared and altered felsic volcanics in Kotra-Bhansula area and for gold in propylitically altered meta basalts in Bhursatola-Khampura area. The dataset utilized during collation and integration includes (i) high resolution aeromagnetic data (ii) 1:50000 geological data covering litho-structural and mineralisation attributes (iii) 1:50 k geochemical data for 61 elements (iv) multispectral ASTER satellite data and its derivative alteration maps. Thematic data and derivative maps were studied and interpreted for evaluation of concerned parameters for estimating their role in mineralisation. The data integration has been implemented in three steps beginning with GIS based spatial modelling using Fuzzy logic method resulting into production of mineral favourability map, followed by detailed field evaluation and characterization of the identified mineral favourability areas. Eventual identification of potential areas was based on synthesis of modelling output, field outcome and laboratory results of analyses carried out on critical samples collected from mineral favourability areas. The identified two mineral potential areas are recommended for further detailed exploration for gold, base metal, Sn, W and REE through surface and sub-surface probing.

Investigation of the main reservoir of the Tulehu geothermal system (Indonesia) using 3-D inversion of MT data

Tulehu geothermal prospect area is located in Maluku Province, Indonesia. The existence of the geothermal system in this area is recognized by the appearance of hot springs on the western side of Mt Eriwakang and the northern side of Mt. Huwe. Most of these hot springs occur along inferred geological structures. The geological conditions of this area are dominated by Quaternary volcanic rocks, limestone, alluvium, and metamorphic rocks. Several authors have conducted several studies to propose various geothermal conceptual models, from structurally controlled to volcanic-hosted geothermal systems. However, the geoscientific data supporting the hypotheses are not comprehensive yet. There are still gaps in the data coverage as well as in the interpretation of geological, geochemical, and geophysical data, so a comprehensive study should be done before deciding on further stages. Therefore, this paper proposes a new conceptual model of the Tulehu geothermal area by reducing the existing data gap. Interpretation of remote sensing data, geological field observations, age dating analysis, reanalysis of geochemical data, and additional MT data covering Mt. Eriwakang are conducted to reconstruct the conceptual model. Graben-like structures are identified based on the geological data where the product of Mt. Eriwakang is in the middle. Based on the age dating data, the product of Mt. Eriwakang erupted in the Quaternary time (413±6 kA), so it is still quite potential as a heat source. This indication is also supported by the trend of Cl, HCO3, and SiO2, which describe the possible flow from Mt. Eriwakang to the surrounding hot springs. Based on the Na-K-Mg and Silica geothermometers, the reservoir temperature is estimated at 210-240 °C. A three-dimensional (3-D) MT inversion was applied to all the existing and new MT data to obtain a 3-D subsurface resistivity model. The oceanic bathymetry data was also incorporated to mitigate the possible coastal effect from the surrounding seawater to the MT data. The new 3-D MT inversion results show a good agreement with the geological and geochemical indications. The updome-shaped pattern of the subsurface resistivity distribution beneath Mt. Eriwakang supports the geological and geochemical data analysis results concerning the indications of the prospects area around Mt. Eriwakang. These interpretations can strengthen the recommendation to focus on the area around Mt. Eriwakang for further development.

Visualising disasters

When a natural disaster strikes, rapid access to reliable, relevant and real-time geoscientific data is essential. But the datasets involved with natural disasters and their management can be vast and complex, and the people working together to interpret the data and manage the disaster-response efforts frequently come from disparate fields and may not share the […]

Estimation of Spatial Distribution Using the Gaussian Mixture Model with Multivariate Geoscience Data

Estimation of Spatial Distribution Using the Gaussian Mixture Model with Multivariate Geoscience Data

Site Selection for a Deep Geological Repository in Switzerland: The Role of Performance Assessment Modeling

In the development of deep geological repositories (DGRs), performance assessment modeling is used to evaluate the integrity and performance of the engineered and geological barriers for thousands or millions of years of evolution of the disposal system. To evaluate the suitability of a site for a DGR, geoscientific data from dedicated site investigation programs are integrated into site-specific assessments. This paper presents the development and implementation of a modeling workflow aimed at comparing three potential siting areas for a DGR in Switzerland from the viewpoint of long-term safety and technical feasibility. The workflow follows the guidelines of the national regulator addressing safety relevant criteria such as the barrier efficiency of the host rock and its mechanical and chemical integrity in response to repository-induced influences and the long-term stability of the repository site over geological scales. In the regulatory requirements, the role of parametric, conceptual, and scenario uncertainty has been identified as an issue of special importance in the site selection process. The assessment approach comprises a portfolio of numerical models for the simulation of solute, gas and heat transport in the repository nearfield. The modeling was performed with deterministic as well as probabilistic variants integrated in an indicator-based approach that allows the consistent comparison of the candidate sites using quantitative dimensionless performance indices. The model-based assessment of the sites allows a traceable, transparent, and verifiable implementation of the site selection process.

Integrated Geomatics Surveying and Data Management in the Investigation of Slope and Fluvial Dynamics

In mountain environments, slope and fluvial dynamics often interact, and their relationship can be investigated through an integrated methodological approach. Landslides are a source of supplying sediments into riverbeds and can interact or interrupt the water course. Water courses can trigger or re-activate slope movements. The complexity of investigating the interaction between the two dynamics needs a complementarity of methods and techniques, combining remote and proximal sensing, geotechnical in situ surveys, and repositories and catalogue datasets. This leads to a synergistic use of all the heterogeneous data from different fields and formats. The present paper provides a literature review on the approaches and surveying procedures adopted in the investigation of slope and fluvial dynamics and highlights the need to improve the integrated management of geospatial information complemented by quality information. In this regard, we outline a geodatabase structure capable of handling the variety of geoscientific data available at different spatial and temporal scales, with derived products that are useful in integrated monitoring tasks. Indeed, the future adoption of a shared physical structure would allow the merging and synergistic use of data provided by different surveyors as well as the effective storing and sharing of datasets from a monitoring perspective.