Geoscience Research Research Articles

Mindat.org – the open access mineralogy database to accelerate data-intensive geoscience research

Abstract The mindat.org website (Mindat) has been operating since October 2000 as a free, crowd-sourced, and expert-curated database particularly focused on mineral species and their occurrences worldwide. The project has transformed from a hobbyist site in the beginning into a resource that has found use in various scientific research projects and educational programs. Together with other open data resources, Mindat has helped accelerate scientific discoveries in many fields, such as mineral evolution, mineral ecology, and the co-evolution of the geosphere and biosphere. Recently, through open data efforts, machine interfaces and software packages have been established to enable flexible data discovery and download from Mindat. We assume the data access and usage will further scale up in the next years. Although Mindat is curated by a team of geoscience and database experts across the world, the crowd-sourced records in Mindat still pose some bias. In this paper we first present an overview of the primary data subjects in Mindat, and then we give extensive details about the characteristics and bias of three most popular data subjects: locality, mineral species, and mineral occurrence. In the discussion we also give an outlook on appropriate data usage and future extension of data records. We hope users can obtain a more comprehensive view of the Mindat database through this paper and thus better plan their data use. We also hope more people will be inspired to contribute to the data curation work to make Mindat a sustained data ecosystem for geoscience research.

Multidisciplinarity in Geoscientific Research

The multidisciplinary approach of geoscientific research in the academic setting underscores the increasing necessity of integrating diverse fields of knowledge to tackle complex interconnected issues. This multidisciplinary field seeks to uncover the complex operations of the Earth, encompassing the analysis of its past movements of tectonic plates, historical climates, and valuable natural resources like minerals and hydrocarbons. In this context, geospatial intelligence plays a crucial role by enabling the effective analysis and interpretation of extensive geographic datasets, thus enhancing our understanding of environmental dynamics. Nevertheless, advancements in technology, globalization, and the difficulties posed by environmental and climatic transformations demand an even more profound collaboration among various disciplines, facilitating an expansive viewpoint and yielding more holistic solutions to contemporary predicaments.

Can AI Get a Degree in Geoscience? Performance Analysis of a GPT-Based Artificial Intelligence System Trained for Earth Science (GeologyOracle)

Generative artificial intelligence systems based on the GPT model have shown groundbreaking capabilities in producing scientific texts, revolutionising how geoscientists research and teach. However, there is a noticeable absence of generative AI systems tailored specifically for geoscience, and the efficacy of GPT technology has not been examined within the Earth Science realm. To fill these gaps, we propose a new artificial intelligence system (GeologyOracle) built upon the GPT-4o model and trained on Earth Science data. It is designed to simulate a conversation with a geoscientist, having the capabilities to analyse geologic datasets, suggest new geoscience hypotheses, explain Earth-Science concepts, and interpret geosites. To evaluate the performance of GeologyOracle, the system was tested with 152 geoscience questions provided and evaluated by a panel of geoscience academics on a scale of 0–10. Performance analysis revealed that 79.6% of the answers scored equal to or above the passing mark of 5, with a tendency to deliver high-quality responses (mean: 6.5; median = 7; interquartile range: 5–8). The results demonstrate that GeologyOracle is effective in performing complex geoscience tasks, such as identifying rocks, fossils, and minerals, and interpreting outcrop, core data, and quantitative datasets. The new AI system has exhibited noteworthy ability in the interpretation of internationally-acknowledged geosites situated within geoparks and nature reserves. The performance of the AI system is comparable to that of trained geoscience specialists, suggesting its potential as an assistant in various geoscience fields, including structural geology, palaeontology, geomorphology, sedimentology, and economic geology. While AI does not render geoscientists obsolete, it offers significant potential for accelerating scientific discovery, automating geoscience research, and assisting educators, students, and geotourists.

Synergistic application of digital outcrop characterization techniques and deep learning algorithms in geological exploration

In order to meet the needs of geologists for the analysis of data characterizing field outcrops (rock sections or formations exposed on the ground surface), this study developed a field digital outcrop visualization platform based on Cesium (a 3D geospatial visualization technology) digital outcrop characterization technology. The platform was developed based on WebGL (a protocol for rendering interactions on web pages), which overcame the shortcomings of traditional software in terms of visualization, cross-device, cross-platform, and ease of use. Firstly, UAV inclined photography is used for data collection, which transforms a large amount of geological data into an intuitive 3D geological model, while the visualization platform provides rich measurement and mapping tools for the identified features, which more intuitively displays the outcrop information, helps geological explorers to understand the geological conditions in the field more quickly and comprehensively, and improves the analysis efficiency and ease-of-use of outcrop characterization data. Combined with the improved VGG19 (a deep convolutional neural network architecture) algorithm model, it has excellent performance in dealing with the fine texture and complex structure of rocks, which significantly improves the accuracy of lithology identification. The synergistic application of this technology provides geologists with a faster and more comprehensive means to understand the geological conditions in the field. The reliability of combining the Cesium digital outcrop characterization technology with the VGG19 lithology identification algorithm in geological exploration is verified through case studies. The synergistic application of this technology will greatly enhance the efficiency and ease of analysis of outcrop characterization in the field, and provide new perspectives for future research in geosciences.

Three-Dimensional Geological Modelling in Earth Science Research: An In-Depth Review and Perspective Analysis

This study examines the development trajectory and current trends of three-dimensional (3D) geological modelling. In recent years, due to the rising global energy demand and the increasing frequency of regional geological disasters, significant progress has been made in this field. The purpose of this study is to clarify the potential complexity of 3D geological modelling, identify persistent challenges, and propose potential avenues for improvement. The main objectives include simplifying the modelling process, improving model accuracy, integrating different data sources, and quantitatively evaluating model parameters. This study integrates global research in this field, focusing on the latest breakthroughs and applications in mineral exploration, engineering geology, geological disaster assessment, and military geosciences. For example, unmanned aerial vehicle (UAV) tilt photography technology, multisource data fusion, 3D geological modelling method based on machine learning, etc. By identifying areas for improvement and making recommendations, this work aims to provide valuable insights to guide the future development of geological modelling toward a more comprehensive and accurate “Transparent Earth”. This review underscores the global applications of 3D geological modelling, highlighting its crucial role across various sectors such as mineral exploration, the oil and gas industry, urban planning, geological hazard assessment, and geoscientific research. The review emphasizes the sector-specific importance of this technology in enhancing modelling accuracy and efficiency, optimizing resource management, driving technological innovation, and improving disaster response capabilities. These insights provide a comprehensive understanding of how 3D geological modelling can significantly impact and benefit multiple industries worldwide.

Novel definition of local baseline values for potentially toxic elements in Czech farmland using adaptive spatial weighting

Exploratory data analysis is commonly used in geoscientific research to identify various data populations within datasets. Frequently, the distribution-wise metrics for the statistical centre and spread are combined to define normal (background) ranges of topsoil contents of potentially toxic elements. When decreasing the geographical scale, the survey areas turn too heterogeneous for the statistical definition of a single background range. The traditional solution is the domain approach wherein various data populations (and their statistical parameters) can be attributed to contextual (geological, ecological) information. Nevertheless, summarising the entire data set from large areas as a single statistical entity would provide too much data reduction which would decrease sensitivity of detecting localised anthropogenic contamination and work wrong in areas of geogenic anomalies spatially larger than contamination. In this paper, we tested a novel numerical solution for deriving local distribution-wise baseline values via spatially limited sliding window combined with geographical weighting. Considering environmental variables (soil and topographical properties) at every analysed soil sample point, we extended the geographical kernel weighting approach which considers only spatial dimension (given by geographical coordinates). The advanced version combines two similarity modes to assign highest weights to the nearest points expected to share similar environmental contexts within the user-defined moving kernel. The method was implemented for data-mining in the Czech high-density monitoring data for agricultural soils which had to be firstly regressed to achieve analytical harmony between two distinct extraction methods employed in that monitoring, in particular cold diluted nitric acid and hot aqua regia. After the reliable harmonisation, local baseline values were delivered as the localised outer limits of variation using the proposed double-weighted kernel approach. We compared the estimated localised background ranges for 10 potentially toxic elements (As, Be, Cd, Co, Cr, Cu, Ni, Pb, V, and Zn) with those based on the conventional substrate-wise domain approach and single nationwide legislation thresholds. The comparison was also efficient in identifying an inappropriate aggregation of some geological units. Finally, the kernel approach delivered regional outer limits of variability sensitive to subtle regional variations of topsoil geochemistry.

Geoscientific research's influence on renewable energy policies and ecological balancing

Geoscientific research plays a crucial role in shaping renewable energy policies and promoting ecological balance. This review explores the impact of geoscientific research on renewable energy policies and its role in ecological balancing. Geoscientific research provides valuable insights into the geological and environmental factors that influence the feasibility and sustainability of renewable energy projects. By studying the earth's processes, geoscientists can identify suitable locations for renewable energy installations, assess potential risks, and develop strategies to mitigate environmental impacts. One of the key ways in which geoscientific research influences renewable energy policies is through its contribution to resource assessment. Geoscientists use various techniques, such as geological mapping, geophysical surveys, and remote sensing, to identify and quantify renewable energy resources, such as solar, wind, and geothermal energy. This information is essential for policymakers to develop effective policies that promote the development and utilization of renewable energy sources. Furthermore, geoscientific research plays a crucial role in ecological balancing by providing insights into the environmental impacts of renewable energy projects. By studying the interactions between renewable energy installations and the surrounding environment, geoscientists can identify potential ecological risks and develop strategies to minimize them. This information is essential for policymakers to develop policies that ensure the sustainable development of renewable energy projects while protecting the environment. In conclusion, geoscientific research plays a crucial role in shaping renewable energy policies and promoting ecological balance. By providing valuable insights into renewable energy resources and their environmental impacts, geoscientists help policymakers develop effective policies that promote the sustainable development of renewable energy projects.

Quantitative regularization in robust vision transformer for remote sensing image classification

AbstractVision Transformers (ViTs) are exceptional at vision tasks. However, when applied to remote sensing images (RSIs), existing methods often necessitate extensive modifications of ViTs to rival convolutional neural networks (CNNs). This requirement significantly impedes the application of ViTs in geosciences, particularly for researchers who lack the time for comprehensive model redesign. To address this issue, we introduce the concept of quantitative regularization (QR), designed to enhance the performance of ViTs in RSI classification. QR represents an effective algorithm that adeptly manages domain discrepancies in RSIs and can be integrated with any ViTs in transfer learning. We evaluated the effectiveness of QR using three ViT architectures: vanilla ViT, Swin‐ViT and Next‐ViT, on four datasets: AID30, NWPU45, AFGR50 and UCM21. The results reveal that our Next‐ViT model surpasses 39 other advanced methods published in the past 3 years, maintaining robust performance even with a limited number of training samples. We also discovered that our ViT and Swin‐ViT achieve significantly higher accuracy and robustness compared to other methods using the same backbone. Our findings confirm that ViTs can be as effective as CNNs for RSI classification, regardless of the dataset size. Our approach exclusively employs open‐source ViTs and easily accessible training strategies. Consequently, we believe that our method can significantly lower the barriers for geoscience researchers intending to use ViT for RSI applications.

Review of Triassic Sherwood Sandstone Group reservoirs of Ireland and Great Britain and their future role in geoenergy applications

As the world progressively shifts to a low/neutral carbon economy over the course of the twenty-first century, geoscience will continue to play a vital role in the energy sector and beyond. Static and dynamic characterization of the subsurface, a key component of the petroleum industry for decades, will be required to evaluate and unlock a variety of different initiatives including carbon, capture and storage (CCS), hydrogen (or any other gas) storage, thermal energy storage and geothermal energy development. Onshore and offshore Ireland and Great Britain, Lower Triassic sandstones are likely to represent one of the primary geological targets for future geoenergy applications. This paper provides a review of the Triassic Sherwood Sandstone Group depositional system across the British and Irish region of NW Europe including its overall stratigraphic context, palaeogeography, sediment provenance and transport directions, and present-day distribution. In addition to geological outcrop data, there is a wealth of subsurface data available from historical petroleum wells. Looking forward, one important focus of geoscience research will be to extrapolate away from these well-defined control points to predict sandstone facies distribution, diagenetic history and hence overall reservoir quality in new undrilled areas that will be of interest to the geothermal, CCS and gas storage sectors.

GeoKnowledgeFusion: A Platform for Multimodal Data Compilation from Geoscience Literature

With the advent of big data science, the field of geoscience has undergone a paradigm shift toward data-driven scientific discovery. However, the abundance of geoscience data distributed across multiple sources poses significant challenges to researchers in terms of data compilation, which includes data collection, collation, and database construction. To streamline the data compilation process, we present GeoKnowledgeFusion, a publicly accessible platform for the fusion of text, visual, and tabular knowledge extracted from the geoscience literature. GeoKnowledgeFusion leverages a powerful network of models that provide a joint multimodal understanding of text, image, and tabular data, enabling researchers to efficiently curate and continuously update their databases. To demonstrate the practical applications of GeoKnowledgeFusion, we present two scenarios: the compilation of Sm-Nd isotope data for constructing a domain-specific database and geographic analysis, and the data extraction process for debris flow disasters. The data compilation process for these use cases encompasses various tasks, including PDF pre-processing, target element recognition, human-in-the-loop annotation, and joint multimodal knowledge understanding. The findings consistently reveal patterns that align with manually compiled data, thus affirming the credibility and dependability of our automated data processing tool. To date, GeoKnowledgeFusion has supported forty geoscience research teams within the program by processing over 40,000 documents uploaded by geoscientists.

Status of geo-scientific research at Wadia Institute of Himalayan Geology, Dehradun during 2020–2023

Status of geo-scientific research at Wadia Institute of Himalayan Geology, Dehradun during 2020–2023

Geoscience for Cities: Delivering Europe’s Sustainable Urban Future

European Union (EU) policy is clear in its ambition to deliver a sustainable urban future for Europe. In this paper, we consider the role of urban geoscience to help achieve these ambitions. We highlight the relevance of geology to urban subsurface planning and wider EU policy and strategy. Despite the lack of explicit mention of urban underground space in key policy documents, we identify a significant number of priority urban issues for which geological characterisation is a pre-requisite and for which the geological system forms part of the solution, such as mitigation of climate impacts, delivering net zero energy, and implementing nature-based solutions. We reflect on the paradigm shift of urban geoscience as a geological discipline, rooted initially in engineering geology but which has moved towards an interdisciplinary, solution-focused science operating at the inter-section of environmental–social–built systems. In this regard, we highlight cutting-edge urban geoscience research aligned to current urban challenges and note, in particular, the significance of digital technologies to enable 3D urban characterisation, support data-driven decision-making for planning and development, and serve as a means to communicate geology to urban practitioners. The role of the urban geoscientist as an agent of change to enhance integrated science, improve the accessibility of geological issues, and accelerate the translation of national–regional geology to local settings and to urban policy drivers should not be underestimated.

Quantifying the spatial associations among terrain parameters from digital elevation models

AbstractTerrain parameters describe the morphological characteristics of a surface and are an important part of the application of geoscience. At present, there is still a lack of scientific and systematic research on the associations among terrain parameters. In this article, 14 representative terrain parameters are selected to explore their spatial association relationships by using the quantification methods of linear correlation degree, distribution similarity and influence degree. Then, we discuss the effects of the digital elevation model (DEM) cell size and landform region on the associations among terrain parameters. Furthermore, the mechanism of the association relationships among terrain parameters is also analyzed to achieve a better understanding of terrain analysis. This study revealed that the similarity in calculation methods, the derivation of terrain parameters from other parameters, the geographic significance of representations, and the characteristics and appearances of landforms are significant factors contributing to the associations of terrain parameters. The terrain parameters with strong associations remained stable with changes in cell size and landform region. However, terrain parameters with weak associations significantly respond to this variety of factors. We quantify the associations among terrain parameters, provide a new perspective for examining the associations among terrain parameters, and provide guidance for the selection of terrain parameters in geoscience research.

Research on fault-controlled hydrocarbon accumulation in rifted lacustrine basins based on structural geological modeling: a case study of the Banqiao area in Bohai Bay Basin, China

The Banqiao area in the Bohai Bay Basin has experienced three stages of extensional deformation, leading to the formation of numerous fault-bound traps. Faults, acting as boundary conditions for these traps, play a crucial role in hydrocarbon accumulation. In this study, we conducted a 3D structural modeling of the area using high-resolution 3D seismic data and established a fault-reservoir database based on previous research. Our findings reveal four levels of faults in the Banqiao area: basin-controlling faults, boundary faults, derivative master faults, and secondary adjusting faults. The structural units can be categorized into subsag areas, slope areas, stress tran-sition zones, bifurcation and main incised fault zones, and southern block areas. The segmented growth of the main boundary faults controls the evolution of the subsags, with the subsidence center gradually shifting eastward from Rift Phase I to Rift Phase II, aligning with the distribution of source rocks. Fault-bound traps in the Banqiao area include single faults, intersecting faults, and side faults. Faults primarily act as barriers to lateral hydrocarbon migration during the process of hydrocarbon accumulation, while also providing pathways to a lesser extent. By integrating the fault-reservoir database with the fault system classification, we identified four types of fault-controlled hydro-carbon accumulation models: like-dipping fault barrier model, oppositely-dipping fault barrier model, intersecting fault barrier model, and reactivation-controlled secondary hydrocarbon ac-cumulation model. This structural geological model effectively demonstrates the spatial configura-tion of faults and their role in hydrocarbon accumulation in the Banqiao area. The fault control mechanisms presented in the model can also be applied to other blocks in the Bohai Bay Basin, laying a foundation for future petroleum exploration in continental rifted basins and facilitating the ap-plication of big data algorithms in various geoscientific research fields.

High School and Undergraduate Student Volunteers as an Imperfect Solution to Machine Learning Geoscience Research Needs

AbstractThe machine learning revolution presents geoscientists with exciting new opportunities for research, but is constrained by the need for large, high quality training data sets. Simultaneously, undergraduate and graduate program admissions have become increasingly competitive, pressuring high school and undergraduate students to differentiate themselves through involvement in research at earlier stages. Aligning these two interests provides mutually beneficial opportunities for both geoscientists and early‐stage students. We describe our experiences working with 20 early‐stage students to build a large training data set digitized from satellite images of meltwater drainage patterns on ice sheets. The intent of this Perspective is to share our experience and lessons learned with other machine learning researchers who, like us, may have minimal experience mentoring young volunteer researchers but may seek such partnerships for the first time in response to their machine learning training data set needs. These partnerships enabled creation of a powerful new machine learning model that would have otherwise been infeasible. Student benefits varied with their commitment and proactiveness, ranging from exposure to geoscience research and a resume line item to strong letters of recommendation and ongoing connections with geoscience researchers at an elite university lab. Many students were attracted to the project solely out of interest in machine learning, so the opportunity reached students who would not otherwise have conducted research in geoscience. Still, without incentives for researchers to engage less‐privileged students, our experience suggests that mutually beneficial partnerships between researchers and early‐stage students may exacerbate issues of inequality and lack of diversity within the geosciences.

Co-RISK: a tool to co-create impactful university–industry projects for natural hazard risk mitigation

Abstract. Translation of geoscience research into tangible changes, such as modified decisions, processes, or policy, in the wider world is an important yet notably difficult process. Illustratively, university-based scientists and professionals work on different timescales, seek different insights, and may have a substantial cognitive distance between them. The work on Co-RISK reported in this paper is motivated by an ongoing need for mechanisms to aid this translation process. Co-RISK is an accessible (i.e. open access, paper based, zero cost) toolkit for use by stakeholder groups within workshops. Co-RISK has been developed to aid the co-creation of collaborative inter-organisational projects to translate risk-related science into modified actions. It is shaped to avoid adding to a proliferation in increasingly complex frameworks for assessing natural hazard risk and is given a robust basis by incorporating paradox theory from organisation studies, which deal with navigating the genuine tensions between industry and research organisations that stem from their differing roles. Specifically designed to ameliorate the organisational paradox, a Co-RISK workshop draws up “maps” including key stakeholders (e.g. regulator, insurer, university) and their positionality (e.g. barriers, concerns, motivations) and identifies exactly the points where science might modify actions. Ultimately a Co-RISK workshop drafts simple and tailored project-specific frameworks that span from climate to hazard, to risk, to implications of that risk (e.g. solvency). The action research approach used to design Co-RISK, its implementation in a trial session for the insurance sector, and its intellectual contribution are described and evaluated. The initial Co-RISK workshop was well received so it is envisaged to be applicable to other sectors (i.e. transport infrastructure, utilities, government). Joint endeavours enabled by Co-RISK could fulfil the genuine need to quickly convert the latest insights from environmental research into real-world climate change adaptation strategies.

Understanding Human Dynamic Sampling Objectives to Enable Robot-assisted Scientific Decision Making

Truly collaborative scientific field data collection between human scientists and autonomous robot systems requires a shared understanding of the search objectives and tradeoffs faced when making decisions. Therefore, critical to developing intelligent robots to aid human experts is an understanding of how scientists make such decisions and how they adapt their data collection strategies when presented with new information in situ . In this study, we examined the dynamic data collection decisions of 108 expert geoscience researchers using a simulated field scenario. Human data collection behaviors suggested two distinct objectives: an information-based objective to maximize information coverage and a discrepancy-based objective to maximize hypothesis verification. We developed a highly simplified quantitative decision model that allows the robot to predict potential human data collection locations based on the two observed human data collection objectives. Predictions from the simple model revealed a transition from information-based to discrepancy-based objective as the level of information increased. The findings will allow robotic teammates to connect experts’ dynamic science objectives with the adaptation of their sampling behaviors and, in the long term, enable the development of more cognitively compatible robotic field assistants.

Lithium: critical, or not so critical?

Some metals necessary to deliver renewable energy are considered critical. Metal criticality is a major factor in achieving energy decarbonization, leading to efforts to make metals uncritical . Among the most critical is lithium which, like many critical metals, represents a small-scale market experiencing significant demand increase causing price and supply volatility, thereby hindering necessary transformative investment. Global lithium demand is soaring, with current supply now dominated by pegmatite-sourced lithium hydroxide. Clay extraction has yet to be industrially proven, thus there remains uncertainty from where and in what quantity future lithium supply will come, and whether lithium remains critical, however geoscience research is best focused on pegmatite and clay-sourced lithium to improve discovery and extraction. Of five lithium criticality scenarios (business as usual; clays onstream; everything plus recycling; shift away from lithium; black swan event), only two project a longer-term criticality reduction. However, few metals will be critical over the very long term as techno-economic and environmental, social, and governance challenges can be overcome and/or metal demand will be structurally adjusted by substitution. Although criticality may be a short to medium term barrier to the energy transition, effective research and overall market forces will reduce the majority of mineral criticality over the longer term. Thematic collection: This article is part of The energy-critical metals for a low carbon transition collection available at: https://www.lyellcollection.org/topic/collections/critical-metals

SwinMin: A mineral recognition model incorporating convolution and multi-scale contexts into swin transformer

Mineral recognition plays a pivotal role in advancing geological survey methodologies and exploration techniques, serving as a cornerstone of contemporary geoscience research. Recently, Transformer-based neural networks have outperformed ConvNets and have become increasingly prominent in vision models. However, adapting Transformer models to mineral photograph recognition presents two significant challenges. Firstly, mineral photograph recognition heavily relies on low-level features such as color, texture, and edges, which Transformers are not intrinsically optimized for. Secondly, the accurate recognition of small-scale objects within mineral images often poses difficulties. To tackle these challenges, we introduce the SwinMin model, specifically designed for mineral photograph recognition. This model incorporates convolutional information into Transformer sequences, thereby enriching the global representation with finer details. Furthermore, we propose a dynamic feature fusion module, which effectively exploits multi-scale contexts, ensuring a more comprehensive representation. Extensive experiments on the mineral photograph datasets demonstrated that SwinMin achieves state-of-the-art performance compared to existing mineral image recognition methods, underlining its potential for reliable and precise mineral image identification.

Shallow vs. Deep Geoethics: Moving Beyond Anthropocentric Views

At its inception, geoethics was envisioned as a type of professional ethics concerned with the moral implications of geoscientific research, applications, and practices. More recently, however, some scholars have proposed versions of geoethics as public and global ethics. To better understand these developments, this article considers the relationship between geoethics and environmental ethics by exploring different aspects of the human-nature relation (i.e., the moral status and role of humans in relation to the non-human world). We start by noting that the main strains of geoethical thought elaborated so far represent examples of environmental virtue ethics and defend moral weak anthropocentric positions (e.g., “ethical”, “responsible” or “enlightened” anthropocentrism). Some scholars propose that such weak anthropocentric geoethics can synthesize the different positions in environmental ethics and move beyond them toward a novel and distinct approach. We compare the meaning and the use of the term “anthropocentrism” in both environmental ethics and geoethics, stressing that although geoethics is inevitably epistemically anthropocentric (i.e., anthropogenic), it does not need to be morally anthropocentric. We consider the compatibility of non-anthropocentric stances with current geoethical theory and argue for the integration of normative non-anthropocentric accounts (e.g., ecocentric) into geoethical debates and geoscience education.