Geological Entities Research Articles

Research on 3D Geological and Numerical Unified Model of in Mining Slope Based on Multi-Source Data

As mining engineering progresses into the deep excavation phase, the intensification of high pressure, high temperature, strong disturbances, and complex geological conditions becomes increasingly prominent. Researchers perform stability analysis on the excavation area to reduce potential safety hazards during the extraction process. Developing a detailed numerical calculation model that accurately reflects the true geological structure is essential for numerical simulation analysis in mining engineering. Based on the excellent 3D geological modeling capabilities of 3D Mine software, this paper introduces a new 3D geological and numerical unified modeling method (3DMine-Rhino-HyperMesh) involving multi-software coupling and details the specific steps and concepts of this modeling approach. Subsequently, using a certain open-pit mine in Panzhihua as a backdrop, a detailed geological and numerical unified model is established, reflecting the true geological structure of the mining area, and the potential failure mechanisms of the mine slope are analyzed. The results indicate that the modeling method aligns well with the actual geological conditions, enhancing the grid quality of the numerical model and offering a new modeling approach for simulating and analyzing large complex geological entities in mining operations.

BIOSTRATIGRAPHY, PALEOENVIRONMENT AND HYDROCARBON POTENTIAL OF FIKA MEMBER OF PINDIGA FORMATION, GONGOLA SUB-BASIN, NORTHERN BENUE TROUGH, NE NIGERIA

The Fika Member, a crucial stratigraphic unit within the Pindiga Formation, represents a significant geological entity with substantial implications for understanding the regional paleoenvironmental evolution and hydrocarbon prospectivity. This study integrates biostratigraphic analyses, sedimentological investigations, and hydrocarbon potential assessments to elucidate the depositional history, paleoenvironmental conditions, and source rock prospect of the Fika Member. Facies and their stratigraphical distribution analyses were conducted on the Fika Shale exposed mainly at Gabukka Stream 1 and 2. Twenty (20) samples collected were subjected for standard palynological and foraminifera analyses. A standard method of palynomorphs extraction that involves the use of Hydrofluoric and Hydrochloric acid was employed. For foraminifera analysis, washing and sieving method was used. It was discovered that agglutinated foraminifera are the dominance taxa in ten (10) out the twenty (20) samples studied. Based on the colours of the agglutinated foraminiferal and Foraminiferal Color Index (FCI) values suggest low thermal maturity for both oil and gas generation. The foraminiferal assemblages indicate marginal marine (fluvio marine) to shallow inner neritic environments. This is supported by the sedimentological data which indicates a shallow marine depositional environment. The biostratigraphic data suggest Santonian age for the Fika Member.

Combined geochronological U-Pb, Hf isotopes and trace element zircon studies from Piedra Alta Terrane, Rio de la Plata Craton (West Uruguay, South America): A geodynamic model

This work delves into the intricate Paleoproterozoic geological setting of Uruguay, focusing on the Rio de la Plata Craton (RPC) and its prominent constituent Piedra Alta Terrane (PAT). The PAT constitutes the core of the RPC, characterized as a complex geological entity that encompasses a granite-migmatite basement, metamorphic belts, granite intrusions, and Proterozoic magmatism. To the west, it interfaces with the Transbrasiliano-Kandi tectonic corridor, bounded by the Late-Neoproterozoic Pampia Terrane. The PAT s southeastern boundary extends to Sierra de Tandil and perhaps to Sierra de La Ventana, demarcating its limits with Phanerozoic rocks of the North Patagonian Massif. The Sarandí del Yí shear zone defines the eastern limit of the PAT. Despite varied interpretations of the Piedra Alta Terrane’s significance, it is important to note that consensus prevails regarding its four major associations: a granite-gneiss-migmatitic basement complex, three volcanosedimentary belts of low to medium metamorphic grade, diverse late igneous intrusions, and extensional Statherian magmatism (1.79 Ga), represented by tholeiitic gabbroic dikes. In this study, a novel tectonic model for the PAT evolution emerges, supported by an extensive zircon dataset comprising new U-Pb ages, Hf isotopes, and trace element geochemistry. The analytical core involves U-Pb analyses on zircon for dating igneous rocks and establishing the provenance and maximum age of deposition of metavolcanosedimentary sequences. Lu-Hf isotopic analyses on the same zircon crystals provide petrogenetic insights, revealing episodes of crustal growth and minor recycling. Trace element geochemistry and zircon εHf(t) data further accentuate these findings, hinting at mantle source enrichment due to subduction within an intraoceanic magmatic-arc arc setting.

Paleoproterozoic Rocks of the Belcher Islands, Nunavut: A Review of Their Remarkable Geology and Relevance to Inuit-led Conservation Efforts

The Paleoproterozoic Belcher Group (ca. 2.0 to 1.83 Ga) occurs on the remote Belcher Islands of Hudson Bay in Nunavut, Canada. It includes nearly nine kilometres of well-preserved siliciclastic and carbonate sedimentary rocks, deposited initially in a marginal to shallow marine setting representing one of the first true continental shelf environments on the proto-Canadian Shield. A wide variety of depositional facies exists within the Belcher Group, and it is particularly well known for its spectacular stromatolites in dolostone. In addition to these macroscopic features, two of its formations (Kasegalik and McLeary) contain intact microfossils of Eoentophysalis belcherensis, the oldest known occurrence of cyanobacteria in the geological record. The uppermost part of the Belcher Group contains sedimentary rocks of very different character that represent a younger foreland basin that developed in response to accretionary and collisional processes of the Trans-Hudson orogen. These younger formations (Omarolluk and Loaf) consist of a thick sequence of turbidites, overlain by arkose and other immature clastic sedimentary rocks. A defining characteristic of the Omarolluk Formation is the presence of calcareous concretions. The Omarolluk Formation shares attributes with “omars”, which are glacially transported clasts that occur both locally and further afield throughout parts of Canada and the northern United States and have helped characterize Pleistocene ice-flow trends across the continent. The Belcher Group also includes two formations dominated by spectacular mafic volcanic rocks. The earlier episode, represented by the Eskimo Formation, reflects eruption of largely subaerial volcanic flows interpreted to represent flood basalt associated with the rifting of Archean basement during the establishment of the continental shelf. A later volcanic episode (the Flaherty Formation) is dominated by submarine pillowed basalt flows and has been assigned to varied tectonic settings, including volcanic arcs related to subduction and oceanic plateaus related to mantle plume activity and renewed rifting along the continental margin. This later volcanism marks the transition from shelf to foreland basin. Mafic sills and related intrusions (Haig intrusions) occur in the middle and lower part of the Belcher Group. Thermal and chemical interactions between mafic magma and calcareous shale generated unusual rocks that are well known in Nunavut as high-quality artisanal carving stone. The Belcher Group also contains Superior-type iron formations that have attracted past exploration interest. The Belcher Group is a unique geological entity defined by its wide variety of rock types, its superb exposures, and its potential to illustrate many important geological processes in a formative time in Earth’s history. It is also a unique microfossil paleontological resource, and its deposition brackets a crucial and much-debated interval of Precambrian atmospheric and oceanic evolution. It represents an important scientific resource in the context of understanding such changes. This general review paper highlights its most important features, discusses its potential for future research and contributes to wider discussions about its possible future role as a protected area within Nunavut.

Joint extraction of entity relations from geological reports based on a novel relation graph convolutional network

Geological reports house a wealth of geological domain knowledge and expert experience knowledge, and the efficient extraction of geological entity relations from these texts holds great significance in constructing a geological knowledge graph. To address challenges such as overlapping entity relations, data imbalance, and the limited incorporation of contextual features in existing deep learning-based entity relation extraction methods, this paper proposes a relational graph convolutional network pointer model based on the pre-trained model of Robustly Optimize Bidirectional Encoder Representation from Transformers Pre-training Approach (RoBERTa). Specifically, our approach tackles the issue of overlapping entity relationships by identifying the primary entity and the subordinate entity associated with a specific relationship. Additionally, by incorporating a relationship graph convolutional network, we can capture multiple relationships among entities. Moreover, we leverage the Biaffine layer to accurately handle entity annotation boundaries, thereby enhancing the precision of entity classification. To address the issue of imbalanced data, we adopt the Focal Loss function during experiments. Furthermore, we construct a geological entity relation dataset comprising 24 types of relationships, which is generated by collecting seven regional geological reports and employing semi-automatic annotation techniques. Finally, the proposed algorithm was tested and comparison with several baseline models, the results demonstrate the superior performance of the proposed model in this paper.

Semantic information extraction and search of mineral exploration data using text mining and deep learning methods

Large-scale mineral resource reports offer a wealth of information for geological knowledge mining and mineral explorers’ knowledge discovery. The geological conditions in which mineral deposits develop may be learned a great deal from these mineral exploration reports. Mineral exploration data may be queried and aggregated to effectively mitigate future exploration risks and reduce costs. However, due to the reports being presented in unstructured textual format, it becomes challenging to extract valuable geological data without manually scanning through a vast number of reports. This laborious process poses difficulties for geologists. To address this issue, this paper proposes a system that extracts a set of geologically relevant keywords/keyphrases of each chapter from each mineral exploration reposts using latent Dirichlet allocation (LDA), develops a topic graph, recognizes the geological entity and related relations for constructing a knowledge graph, and uses visualization of those graphs (e.g., topic graphs and knowledge graphs) to explore the contents of the report. The text mining and machine learning technique described here serves as the foundation for future research into incorporating semantic analysis into geological information extraction. The findings of this study show how automated text analysis may help with the quick processing of huge quantities of reports in order to identify target mineral systems and their related geological location and rock mineral composition. The suggested approaches can quickly and reliably convert mineral exploration data (e.g., text, figure, and table) into a structured form, which is a hitherto untouched field in geological knowledge mining.

Multi-Criteria Evaluation (MCE) of Groundwater Prospect and Vulnerability Index Mapping from Second-Order Geo-Electric Indices: A Case Study of Coastal Environments

Exploration, management, and conservation of groundwater resources are critical stages toward potable water supply, driven by an expanding populace and the threat of a new norm posed by the distinctive coronavirus (COVID-19) pandemic. An in-depth assessment of the potential of groundwater reserves and susceptibility, using a multi-criteria evaluation, is required to aid in the planning of exploration programs for groundwater well location. Thirty (30) vertical electrical soundings (VES) were collected in Okerenkoko, Warri-Southwest, Delta State, to assess groundwater potential and vulnerability indicators. The VES data were used to obtain the first-order geoelectric variables, which were further exploited to calculate the geo-hydraulic parameters (hydraulic conductivity and transmissivity) and the vulnerability indices of the aquifer. For aquifer vulnerability appraisal, the AVI (aquifer vulnerability index), GOD (groundwater occurrence, overlying lithology, and depth to the aquifer), and GLSI (geoelectric layer susceptibility index) models were used. The groundwater characteristics in the area were evaluated using the aquifer resistivity, thickness, transmissivity and coefficient of anisotropy values of the aquifer layers defined from VES 1-30. The results show that aquifer layers with low resistivity favor more saturation due to immense porosity and therefore have greater groundwater potential than aquifers with high resistivity. The geoelectric structures defined by VES 1, 2 and 4 were consistent in their groundwater potential and yield judging from the multi-criteria assessments. The estimation of AVI, GOD, and GLSI models for aquifer threat assessment was facilitated by the multi-criteria evaluation of vulnerability indices utilizing hydro-geophysical parameters and index-based approaches. The models depend on the symbiotic effects of geologic array and thickness as the basis for the magnitude of conservation imparted to any particular aquifer involved. The AVI model map depicts that most of the VES locations were rated high (C between 1 and 2) to extremely high (C < 1), indicating that the aquifers at these locations are vulnerable to pollution. However, the extent of vulnerability observed in the GOD model is less than in the AVI model, as GOD accords much more inclination to the inherent properties of geologic entities. The GOD model map categorized the vulnerability index ratings in the area as negligible (0.0-0.1), low (0.1-0.3) and moderate (0.3-0.5), with most VES locations ranked low to moderate, which indicates that these locations are susceptible to vulnerability. In the GLSI model, individual overlying layer thicknesses were prioritized. The GLSI model map shows that the vulnerability index ratings in the area are ranked as moderate (2.00-2.99), high (3.00-3.99) and extremely high (≥ 4.00) with most of the VES locations ranked moderate to high with the exception of VES 27, which ranked extremely high in both AVI and GLSI indices. By correlating the results of vulnerability index valuation for the AVI, GOD and GLSI models, more correlation was observed between the AVI and GLSI models. These findings validate the adoption of a multi-criteria evaluation methodology for groundwater potential and aquifer vulnerability studies and are strongly recommended as practical criteria for locating subsurface aquifers and their protective measures for groundwater prospect development planning and management.

Information extraction and knowledge linkage of geological profiles and related contextual texts from mineral exploration reports for geological knowledge graphs construction

Geological profiles serve as invaluable repositories of data for knowledge extraction and the identification of geological entities, constituting a fundamental resource in scientific investigation. By encapsulating the stratigraphic arrangement and rock characteristics, these profiles offer a visually compelling and efficacious representation of stratigraphy along the vertical axis. Within geological profiles, intricate semantic associations frequently emerge, connecting various objects such as strata, rocks, and minerals across two or more profiles. These relationships often unveil inherent correlations or evolutionary attributes among geological entities. Understanding the general semantic relations between geological objects by manually scrutinizing vast quantities of profiles proves arduous, if not impracticable. In response, this study proposes an innovative framework capable of automated comprehension of extensive geological profiles and their associated contextual textual information. Built upon a natural language processing model and employing image vectorization techniques, this framework enables extraction of semantic information encompassing geological profiles, semantic relationships, and properties. In this article, we propose the comprehensive architecture of the framework, delineating its individual modules and subsequently applying it within a case study. Moreover, we conduct exploratory visualizations utilizing a knowledge graph to depict the extracted semantic information concerning diverse geological objects. The proposed framework presents a promising approach for supporting large-scale content analysis in the realm of stratigraphic research, facilitating advancements in our understanding of the evolution of stratigraphy.

GeoFault: A well-founded fault ontology for interoperability in geological modeling

Geological modeling currently uses various computer-based applications. Data harmonization at the semantic level using ontologies is essential to make these applications interoperable. Since geo-modeling is part of several multidisciplinary projects, interoperability requires semantic harmonization to exchange information between geological applications and integrate other domain knowledge at a general level. Therefore, domain ontologies that describe geological knowledge must be based on a sound ontological background to ensure this knowledge is integrable. Faults are essential for understanding and solving structural problems but are complex to model because the concept of fault includes a group of geological entities with a distinct ontological nature. A fault can correspond to thin, deformed rock volumes or spatial arrangements resulting from the displacement of geological blocks, but at a broader scale, geologists describe faults as surfaces or components of complex fault arrays. Our work intends to harmonize these views by presenting a domain ontology, GeoFault, resting on the Basic Formal Ontology (BFO) and the GeoCore ontology. GeoCore and GeoFault support the parametric description of geological sites as a preliminary step for quantitative and qualitative analysis. We have proposed GeoFault after systematically revising the literature and several knowledge-acquisition sessions with expert structural geologists. The ontology formalizes a vocabulary for fault “sensu stricto,” excluding ductile shear deformations. It covers the regional to outcrop scales, excluding structures at the microscopic, orogenic, and tectonics scales, and it avoids interpretive language associated with geological processes as far as possible. Extending the BFO and GeoCore ontologies allows the fault concept to be related to formal ontological classes in a consistent semantic-rich framework. The ontology artifact is implemented in OWL 2, validated by competency questions with two use cases, and tested using an in-house ontology-driven data entry application. The GeoFault ontology is publicly available and provides a solid framework for clarifying fault knowledge and a foundation for many applications.

A deep neural network model for coreference resolution in geological domain

Coreference resolution of geological entities is an important task in geological information mining. Although the existing generic coreference resolution models can handle geological texts, a dramatic decline in their performance can occur without sufficient domain knowledge. Due to the high diversity of geological terminology, coreference is intricately governed by the semantic and expressive structure of geological terms. In this paper, a framework CorefRoCNN based on RoBERTa and convolutional neural network (CNN) for end-to-end coreference resolution of geological entities is proposed. Firstly, the fine-tuned RoBERTa language model is used to transform words into dynamic vector representations with contextual semantic information. Second, a CNN-based multi-scale structure feature extraction module for geological terms is designed to capture the invariance of geological terms in length, internal structure, and distribution. Thirdly, we incorporate the structural feature and word embedding for further determinations of coreference relations. In addition, attention mechanisms are used to improve the ability of the model to capture valid information in geological texts with long sentence lengths. To validate the effectiveness of the model, we compared it with several state-of-the-art models on the constructed dataset. The results show that our model has the optimal performance with an average F1 value of 79.78%, which is a 1.22% improvement compared to the second-ranked method.

Geological profile-text information association model of mineral exploration reports for fast analysis of geological content

Mineral exploration reports include not only a large number of geological profiles but also geological text by offering valuable information and knowledge about the geological environments in which mineral deposits form. Extracting and understanding historical data can assist in the fast analysis of geological content and support 3D model construction. However, geological texts are written in unstructured form and have many correlations with geological profiles. It is a challenging task to derive meaningful geological information without manually reading through a large collection of reports, which is a formidable task for geologists. This paper proposes a geological profile-text association framework for constructing a knowledge graph, and it aims to understand the contents of the geological profile, transform a larger amount of textual data into structure form, and link the geological profile and text to a graph-based knowledge representation that assists further analysis of knowledge discovery. The concept of constructing vector geological profile rock layer objectification is proposed to make each rock layer with geometric features and attribute information for the geological profile, and the geological entity relationship is extracted to form a triple by deep learning and stored and expressed in the form of a graph structure for geological text. Finally, a geological profile and text association model is established by word vector similarity. The proposed approach is capable of rapidly and robustly understanding geological profiles, extracting geological texts, establishing correlations between them, and performing geological knowledge mining.

Heat flow in the Main Karoo Basin, South Africa

Abstract The Main Karoo Basin, and its Late Carboniferous to Early Jurassic stratified fill, the Karoo Supergroup, is the largest geological entity in South Africa and one of the economically most strategic because of its coal resources and potential shale gas resources. It also has a potential future in renewable energy in the form of geothermal energy. Although the basin has not received sufficient dedicated attention from a thermal perspective, numerous heat flow studies have resulted in the gradual accumulation of a substantial data base of the thermal conductivity of the constituent rocks, geothermal gradients and heat flow data. The main purpose of this paper is to collate this information, as well as new observations and thus generate a reference for future geothermal investigations. Approximately 900 conductivity measurements have resulted in well-established average conductivities for most stratified rock types, which vary by an order of magnitude from less than 0.3 W m-1 K-1 for coal to more than 5.0 W m-1 K-1 for sandstone. The maximum recorded temperature in the deepest parts of the basin (greater than 5 km) is 160°C and the overall average thermal gradient is approximately 29 K/km, which is greater than most other geological environments in southern Africa. The heat flow at 74 localities varies in the range 41 to 83 mW m-2 and the average for the central and southern parts of the basin is 62 ± 11 mW m-2 (34 values); this is approximately equal to the average heat flow for the mid-Proterozoic Namaqua tectonic province that underlies the southern half of the basin. The heat flow decreases to less than 50 mW m-2 in the northern and north-western parts of the basin, which values are typical of the underlying Archaean Kaapvaal Craton. Simple models illustrate the application of the heat flow and thermal conductivity data for calculating crustal temperature in the Karoo Basin.

Named entity annotation schema for geological literature mining in the domain of porphyry copper deposits

Owing to the development of natural language processing and deep learning models, geological text data have become a vital resource for knowledge discovery and have attracted the attention of publishers, academic organizations, and domain scientists. However, the extraction of information from unstructured literature still remains a challenge, in which a fundamental issue is the categories and the type of discipline-specific information. This paper presents an effective workflow of building and applying ontologies in geoscience text mining, which includes a use case-driven method for building an ontology model of porphyry copper deposits, an entity annotation schema for text mining, and implementation of them to tackle real-world data. First, the Dexing porphyry copper deposit was selected as a case study to guide the construction of the ontology model. Text data in this study provided a series of entity instances. By analyzing both domain knowledge of mineral deposit models and the instance data, we built classes in the ontology. Second, with the established ontology, a named entity annotation schema comprising 21 entity tokens was designed to scale up the text mining tasks. Third, based on the annotation schema, a draft corpus with more than 200,000 words and a finely corrected corpus of 53,339 words were built for training a geological entity recognizer for porphyry copper deposits. The performance of the geological entity recognizer and the statistical distribution of entities in the corpus prove that the workflow presented in this study is effective for designing entity annotation schemas and facilitating large-scale text data mining in geoscience.

Chinese Fine‐Grained Geological Named Entity Recognition With Rules and FLAT

AbstractGeological named entity recognition (NER) is an essential prerequisite to realizing geological information extraction and information retrieval and is an actual means for accomplishing structured reconstruction of unstructured geological data. Existing geological NER methods mainly focus on coarse‐grained geological entity recognition, but geological entities are fine‐grained. To solve this problem, a Chinese fine‐grained geological entity corpus encompassing 21 types of fine‐grained labels is constructed. In addition, in this article, a fine‐grained geological entity recognition model based on Bidirectional Encoder Representations from Transformer (BERT)‐Flat‐Lattice Transformer is designed. This paper names this method FGNER (Fine‐grained Geological Named Entity Recognition) which adds geological naming rules to revise the model results to improve the recognition of complex geological entities. The fine‐grained geological entity recognition method is evaluated using regional geological literature reports as experimental data. The experimental results show that the precision, recall, and F1‐score of the FGNER model are 95.73%, 89.26%, and 92.05%, respectively, thus achieving better performance than baseline models, such as BERT‐Conditional Random Field.

Construction and Application of a Knowledge Graph for Gold Deposits in the Jiapigou Gold Metallogenic Belt, Jilin Province, China

Over the years, many geological exploration reports and considerable geological data have been accumulated during the prospecting and exploration of the Jiapigou gold metallogenic belt (JGMB). It is very important to fully utilize these geological and mineralogical big data to guide future gold exploration. This work collects the original textual data of different gold deposits in JGMB and constructs a knowledge graph (KG) for deposits based on deep learning (DL) and natural language processing (NLP). Based on the metallogenic geological characteristics of deposits, a visual construction method of a KG for deposits and a calculation of the similarity between deposits are proposed. In this paper, 20 geological entities and 24 relationship categories are considered. By condensing the key KG information, the metallogenic geological conditions and factors controlling the ore in 14 typical deposits in the JGMB are systematically analyzed, and the metallogenic regularity is summarized. By calculating the deposits’ cosine similarities based on the KG, the mineralization types of deposits can be divided into two categories according to the industrial types of ore bodies. The results also show that the KG is a cutting-edge technology that can extract the rich information of ore-forming regularity and prospecting criteria contained in the textual data to help researchers quickly analyze the mineralization information.

Understanding geological reports based on knowledge graphs using a deep learning approach

Geological reports aid in understanding exploration by providing valuable information on rock formation ,evolution and the geological environment in which deposits formed. Querying and extracting the critical information from these enormous historical geological report data helps in understanding the exploration risks in different geological settings. However, large amounts of unstructured text data occur in geological reports; therefore, it is challenging to obtain valuable information from them without performing information extraction and processing. This study proposed an automated method for extracting information from geological reports through triple extraction, then automatically constructs a geological knowledge graph from the extracted entities and relations. Simultaneously, due to the lack of samples, this study used multiple geological reports to construct a corpus of jointly extracted geological entities and relations. The proposed model reached an F1-score of 90.05% in the experimental results on the constructed corpus. Finally, a knowledge graph was constructed based on the extracted results to demonstrate the application value of the proposed method. The results showed that the structured information helps better represent the content of the source report and matches well with the geological domain knowledge. The proposed method can quickly and robustly convert textual data into a structured form that is convenient for reasoning and querying geological entities and relations.

The Astrobiological Significance of Caves on Earth and on Mars

Caves are geologic entities that can be frequently found around the globe. Cave-like features have been documented on Mars by satellite imagery and special detection devices. On Earth Subterranean habitats like caves might host microbial growth because of their relatively stable physicochemical conditions and mineral rich content. Moreover, caves have also been isolated from UV radiation and other present environmental conditions which actually make them ideal for searching for unique microbial life. Mars is an arid planet with thin atmosphere and quite weak magnetosphere. Therefore Mars as we know is inunhabitable. Research shows that Mars might have been a wet planet in the past, having streams of running water. Earth like subterranean cavities on Mars might provide protection from these environmental hazards. This makes Earth caves important astrobiological sites as Mars analogues for the investigation of the possibility of life on Mars. Researching caves both on Earth and Mars will provide us insight into extreme life conditions and important astrobiological questions. In this review, we are suggesting that geobiological significance of Earth caves plays an important role in searching for life on Mars and defining Mars analogues on Earth.

Remote sensing data processing and analysis for the identification of geological entities

Remote sensing data processing and analysis for the identification of geological entities

Integrating geological data in Europe to foster multidisciplinary research

The European Plate Observing System (EPOS, www.epos-eu.org) is a multidisciplinary pan-European research infrastructure for solid Earth science. It integrates a series of domain-specific service hubs (Thematic Core Service, TCS) such as the Geological Information and Modelling, which provides access to data, data products and services on European boreholes, geological maps, mineral occurrences, mines and 3D models. TCS GIM services are hosted by a group of European Geological Surveys and a couple of national research organizations. This paper presents novel data discovery and integration, facilitated using borehole logging information with on-demand web services to produce 3D geological structures. This domain interoperability across EPOS was created for the purpose of research, but it is also highly relevant for the response to societal grand challenges such as natural hazards and climate change. European and international interoperability implementation frameworks are well described and used (e.g., INSPIRE, ISO, OGC, and IUGS/CGI). It can be difficult for data providers to deploy web services that support the full semantic data definition (e.g., OGC Complex Feature) to expose several millions of geological entities through web-enabled data portals as required by pan-European projects. The TCS GIM group implemented and innovatively extended two standardized descriptions, i.e. GeoSciML-Lite and EarthResourceML-Lite, with an important reuse of content from Linked Data Registries. This approach was applied to design and implement the European Borehole Index and associated web services (View-WMS and Discovery-WFS), extended to 3D models, geological maps as well as mineral occurrences and mines. Results presented here apply the Linked Data approach ensuring optimal semantic description and enriching the data graphs, with complex descriptions and contents. In this way, it is now possible to traverse from one Borehole Index instance to linked richer information such as the borehole geological log, groundwater levels, rock sample description, analyses, etc. All this detailed information is served following international interoperability standards (Observations & Measurements, GroundWaterML 2.0, GeoSciML4, amongst others).

About this title - Cross-Border Themes in Petroleum Geology I: The North Sea

A cross-border approach to exploration, appraisal and development is important in mature basins such as the North Sea, where the ‘low hanging fruit’ have long gone. This approach emphasizes the need to see the basin as one geological entity, in order to maximize economic recovery and prepare the area for the energy transition. This volume offers an up-to-date, ‘geology-without-borders’ view of the stratigraphy, sedimentology, tectonics and oil-and-gas exploration trends of the entire North Sea basin, along with the challenges associated with differences in data continuity and nomenclature across median lines. This volume includes overviews of cross-border play statistics, lithostratigraphic naming conventions and exploration performance through to new facies models for cross-border areas. As such, this volume will be a valuable reference for every geoscientist working in the North Sea for years to come.