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During the twentieth century, Iceland became a leader in geothermal energy use, with hot water and steam providing for the heating of 9 out of 10 houses and almost one-third of the electricity supply. A crucial factor in the expansion of geothermal energy between the 1940s and 1970s were indirect ‘spillovers’ from the oil industry via the applied geosciences. While fundamentally different in end use, geothermal and hydrocarbon industries employ similar geoscientific methods for exploring and extracting fluids and gases. This article traces the spillovers from oil to geothermal by examining Iceland’s Geothermal Division and its founding director, Gunnar Böðvarsson. He saw the oil industry as a model and embraced its geoscientific exploration methods, oilwell rotary drilling and petroleum reservoir engineering. Adapted to the requirements of geothermal energy, petroleum technologies greatly improved the knowledge of geothermal reservoirs and the efficiency of well drilling and hot water and steam extraction. The geoscience spillover between oil and geothermal reveals an understudied dimension of geothermal history and the crucial role of the geosciences at the intersection of academia and energy industries, which also resulted in reverse spillovers from geothermal to oil and complicates the common dichotomization of fossil fuels vs. renewable alternatives. [ABSTRACT FROM AUTHOR]

The American Geophysical Union (AGU), a scholarly society serving the Earth and space sciences, has a conventional books portfolio with their publisher, Wiley, and a new partnership with the Geological Society of London for an open access (OA) book series. The latter serves authors who need (because of funding mandates) or want to publish OA and comes with added benefits, including a production workflow to facilitate faster times to online publication. Flexibility in how the costs of individual volumes can be covered aims to encourage uptake, but challenges lie ahead in ensuring that all authors can equitably choose OA for their book project. This commentary contributes to the collection in Area about the evolving OA books landscape, addressing many of the challenges described by Gandy, and describing the start of AGU's journey towards OA books. It brings a perspective from the sciences, where books are mostly large, edited volumes with many international contributors, many of them physical geographers. Their experiences of funding, motivation and recognition for contributing to books may be quite different to human geographers more familiar with the sole‐authored monograph format. [ABSTRACT FROM AUTHOR]

Critical to the foundation of scientific research, the reproducibility of studies and their results is a fundamental element, ensuring the credibility of the findings and fostering trust. Maps play an important role here, as they are used across disciplines to visualize complex insights and outcomes from a wide range of studies. Due to their relevance in this context, it is essential to be able to reproduce maps as well, and consequently, to assess whether a map reproduction was successful. In this paper, we present our research on which aspects affect the success of map reproduction and how to assess map reproduction based on the visual differences between the original and the reproduced map. We collected data from two focus groups and an online survey to gain a deeper understanding of how geoscientists and map experts conceptualize map reproduction, which factors they consider relevant in this process, and how strong their impact is relative to each other. We analyzed this data qualitatively and quantitatively to create a conceptual model that describes the assessment of map reproduction and a set of criteria that influence the success of the reproduction. With these contributions, we aim to provide an initial toolkit for systematically assessing the success of map reproduction attempts. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has become one of the most controversial tools of recent times. Offering an extremely simple operating system, users can generate texts, images, videos and even human voices. The possibility of using such a powerful tool creates new paths and challenges in the field of environmental education: How does it influence natural heritage protection? Is it considered positive within sustainability and quality education? The reality is very different, showing algorithms trained with information of dubious quality and, on many occasions, obtained without permission from authors and artists around the world. UNESCO Global Geoparks (UGGps) are international references in education at all levels, related to territorial development and geoscience education. This article discusses if generative AI is, nowadays, an effective and applicable educational tool for the strategies developed and promoted by UGGps. This designation exists for people's opportunities. The use of these tools in their current state could make the UGGp figure change its values and fundamental pillars in the future. [ABSTRACT FROM AUTHOR]

The accumulation of sea-surface debris around the coastal waters of Malta poses significant ecological and environmental challenges, negatively affecting marine ecosystems and human activities. This issue is exacerbated due to the lack of an effective system tailored to predict surface-debris movement specifically for the Islands of Malta. To address this gap, a pipeline that combines a machine learning-based prediction system with a physics-based model is proposed. This pipeline uses data on historical sea-surface current velocities to forecast future conditions and visualise debris movement. Central to this system are two machine learning models trained to predict surface velocities for the next 24 h for a specific area. These predictions are then utilised in a Lagrangian model to simulate and visualise the debris movement, providing insights into future dispersion patterns. A comparative evaluation of both models using real-world data is made to determine which one performs best in this application. This method offers a tailored approach to addressing sea-surface debris around Malta by accurately predicting sea-surface current velocities and visualising debris movement, improving cleanup operations and marine conservation strategies. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) has emerged as a pivotal technology in geosciences, offering unparalleled insights into Earth's surface. Indeed, its ability to provide high‐resolution, all‐weather, and day‐night imaging has revolutionized our understanding of various geophysical processes. Recent advancements in SAR technology, that is, developing new satellite missions, enhancing signal processing techniques, and integrating machine learning algorithms, have significantly broadened the scope and depth of geosciences. Therefore, it is essential to summarize SAR's comprehensive applications for geosciences, especially emphasizing recent advancements in SAR technologies and applications. Moreover, current SAR‐related review papers have primarily focused on SAR technology or SAR imaging and data processing techniques. Hence, a review that integrates SAR technology with geophysical features is needed to highlight the significance of SAR in addressing challenges in geosciences, as well as to explore SAR's potential in solving complex geoscience problems. Spurred by these requirements, this review comprehensively and in‐depth reviews SAR applications for geosciences, broadly including various aspects in air‐sea dynamics, oceanography, geography, disaster and hazard monitoring, climate change, and geosciences data fusion. For each applied field, the scientific advancements produced because of SAR are demonstrated by combining the SAR techniques with characteristics of geophysical phenomena and processes. Further outlooks are also explored, such as integrating SAR data with other geophysical data and conducting interdisciplinary research to offer comprehensive insights into geosciences. With the support of deep learning, this synergy will enhance the capability to model, simulate, and forecast geophysical phenomena with greater accuracy and reliability. Plain Language Summary: Synthetic aperture radar (SAR) uses microwaves to remotely see the Earth's surface under all weather conditions, day and night. SAR has been providing high‐resolution images for many decades and they have been applied to many fields in geosciences. Several SAR sensors have been launched in recent years, significantly increasing the SAR data volume and leading to great developments in SAR technology, thereby improving our understanding of geophysical phenomena and processes. This work comprehensively overviews the application of SAR in geosciences, including oceanography, geography, geodesy, climatology, seismology, meteorology, and environmental science. Moreover, this review paper highlights the significance of SAR in various aspects of geosciences, summarizes recent advancements in SAR technology, and demonstrates unique insights and important contributions of SAR in understanding and solving geophysical questions. Future directions and outlooks include integrating SAR with other geophysical data and interdisciplinary applications for complex questions. This review serves as an up‐to‐date guide to the cutting‐edge uses of SAR technology in comprehensive geophysical studies. It is aimed at researchers and practitioners in geosciences, as well as policymakers and stakeholders interested in leveraging SAR for geosciences. Key Points: Synthetic Aperture Radar (SAR) for geosciences is comprehensively reviewed broadly including oceanography, geography, hazards, and climate changeScientific advances contributed by SAR techniques for each topic are overviewed in‐depth with recent developments and frontiers highlightedData, techniques, and scientific insights of SAR are summarized and prospected, highlighting the role of machine learning [ABSTRACT FROM AUTHOR]

This article explores the ways in which physical scientists, especially in the geosciences, are responding to calls to decolonise university curricula in current conjunctural conditions. It asserts that it is crucial not to strip decolonisation of its radical political potential and reduce it to an instrumental Equity, Diversion and Inclusion (EDI) initiative. Geoscientists in higher education who wish to decolonise their curricula must also pay attention to epistemological pluralism, politics, and colonial violence and free themselves from Eurocentric legacies of positivism, universality and objectivity. They must also make the turn to social theory, in ways that address the politics of geologic matter and the modes of violence that geoscientific practice and knowledge reproduce. Engaging with curricular decolonisation has potential not only to arrest the decline being experienced by the geosciences, but to make the forced neoliberal mergers between geography and geology less painful and more intellectually productive. This article explores the ways in which physical scientists, especially in the geosciences, are responding to calls to decolonise university curricula in current conjunctural conditions. It asserts that geoscientists who wish to decolonise their curricula must pay attention to epistemological pluralism, politics, and colonial violence and free themselves from Eurocentric legacies of positivism, universality and objectivity. Engaging with curricular decolonisation has potential not only to arrest the decline being experienced by the geosciences, but to make the forced neoliberal mergers between geography and geology less painful and more intellectually productive. [ABSTRACT FROM AUTHOR]

Interpretation of vertical electrical sounding (VES) data is inherently difficult due to its ambiguity and non-linear characteristics. Conventional least square-based methods rely on a well-defined apriori model, constrained by ground information (available borehole logs and field observations of exposed lithological sections) for meaningful interpretations. In this work, a back-propagation neural network-based model was developed to estimate layer resistivities and thickness from given apparent resistivities. The model was trained and validated on noise-infused synthetic datasets. Since the effectiveness and generalisation of any model depend on its hyperparameter settings, we investigated effective methods for estimating hyperparameters such as learning rate, momentum, model architecture and learning rate scheduling parameters. It is well known that the optimal values of hyperparameters are not entirely independent of each other. Thus, any change in one hyperparameter changes the optimal range of all other hyperparameters, and thus, tuning any hyperparameter individually is futile. This warrants a joint hyperparameter tuning along with network architecture, which was carried out using a modified version of meta-heuristic black hole algorithm. The modifications include randomly flipping one or more coordinates of the population stars (solutions) whose cost function was above a threshold value decided by a mutation rate parameter. This helped in boosting the exploration capability of the algorithm and prune trajectories with higher cost functions. It is demonstrated that with a finely tuned neural network model, reasonable resistivity model parameters which interpret the ground conditions fairly well could be obtained. The model was tested on resistivity-sounding data with associated borehole lithologs and was found to be giving reasonable results. The same model was used to estimate the overburden thickness consisting of topsoil and deposited silts in Bhadradri–Kothagudem district, India. The layer thickness was consistent with those seen in cutbanks in the area. The methods of optimal hyperparameter set estimation are not exclusive to this model and can be used to train models accomplishing other geophysical tasks. [ABSTRACT FROM AUTHOR]

The UNESCO Global Geoparks are grounded in three core principles: education, geoconservation, and geotourism. Since 2015, the Napo Sumaco Aspiring UNESCO Global Geopark (NSAUGG) in the Ecuadorian Amazon has concentrated its efforts on enhancing local capabilities through Earth Sciences education, underscoring the safeguarding of natural and cultural heritage and environmental stewardship. A comprehensive educational dissemination model was implemented, incorporating both indoor and outdoor activities, geoscience education training, geodiversity knowledge enhancement, and the creation of content and workshops that integrate classroom and field-based learning and evaluation. The concepts of geodiversity and geoparks were disseminated to adults and youth, primarily members of the native peoples' communities, particularly those employed at the community tourism centers and the geopark guides designated as "Yuyaiwa Pushak Runakuna." A total of 60 individuals from the NSAUGG populations between Tena and Archidona cantons participated in the educational activities, which involved 19 geoeducators. These initiatives facilitated an appreciation of the geopark's diverse heritages, including its geological, biological, and cultural assets. The geoeducation efforts fostered geotourism, with the Yuyaiwa Pushak Runakuna acquiring greater value through their assimilation of geological and speleological knowledge. This is evident in their use of geological vocabulary and understanding of geological processes and materials in guiding activities for both national and foreign tourists. The most significant achievement is the establishment of "Geoeducation for Sustainability" actions, which promotes collective life through knowledge dialogues based on Minka (voluntary community work) and Turkana (reciprocity and barter). These actions encouraged a deeper connection to the geopark's heritage and strengthened community cohesion. [ABSTRACT FROM AUTHOR]

The UNESCO Global Geoparks are grounded in three core principles: education, geoconservation, and geotourism. Since 2015, the Napo Sumaco Aspiring UNESCO Global Geopark (NSAUGG) in the Ecuadorian Amazon has concentrated its efforts on enhancing local capabilities through Earth Sciences education, underscoring the safeguarding of natural and cultural heritage and environmental stewardship. A comprehensive educational dissemination model was implemented, incorporating both indoor and outdoor activities, geoscience education training, geodiversity knowledge enhancement, and the creation of content and workshops that integrate classroom and field-based learning and evaluation. The concepts of geodiversity and geoparks were disseminated to adults and youth, primarily members of the native peoples' communities, particularly those employed at the community tourism centers and the geopark guides designated as “Yuyaiwa Pushak Runakuna.” A total of 60 individuals from the NSAUGG populations between Tena and Archidona cantons participated in the educational activities, which involved 19 geoeducators. These initiatives facilitated an appreciation of the geopark's diverse heritages, including its geological, biological, and cultural assets. The geoeducation efforts fostered geotourism, with the Yuyaiwa Pushak Runakuna acquiring greater value through their assimilation of geological and speleological knowledge. This is evident in their use of geological vocabulary and understanding of geological processes and materials in guiding activities for both national and foreign tourists. The most significant achievement is the establishment of “Geoeducation for Sustainability” actions, which promotes collective life through knowledge dialogues based on Minka (voluntary community work) and Turkana (reciprocity and barter). These actions encouraged a deeper connection to the geopark's heritage and strengthened community cohesion.

The accumulation of sea-surface debris around the coastal waters of Malta poses significant ecological and environmental challenges, negatively affecting marine ecosystems and human activities. This issue is exacerbated due to the lack of an effective system tailored to predict surface-debris movement specifically for the Islands of Malta. To address this gap, a pipeline that combines a machine learning-based prediction system with a physics-based model is proposed. This pipeline uses data on historical sea-surface current velocities to forecast future conditions and visualise debris movement. Central to this system are two machine learning models trained to predict surface velocities for the next 24 h for a specific area. These predictions are then utilised in a Lagrangian model to simulate and visualise the debris movement, providing insights into future dispersion patterns. A comparative evaluation of both models using real-world data is made to determine which one performs best in this application. This method offers a tailored approach to addressing sea-surface debris around Malta by accurately predicting sea-surface current velocities and visualising debris movement, improving cleanup operations and marine conservation strategies.

This article presents an in-depth analysis of global research trends in Geosciences from 2014 to 2023. By integrating bibliometric analysis with expert insights from the Deeptime Digital Earth (DDE) initiative, this article identifies key emerging themes shaping the landscape of Earth Sciences①.

This is a systematic literature review of the application of machine learning (ML) algorithms in geosciences, with a focus on environmental monitoring applications. ML algorithms, with their ability to analyze vast quantities of data, decipher complex relationships, and predict future events, and they offer promising capabilities to implement technologies based on more precise and reliable data processing. This review considers several vulnerable and particularly at-risk themes as landfills, mining activities, the protection of coastal dunes, illegal discharges into water bodies, and the pollution and degradation of soil and water matrices in large industrial complexes. These case studies about environmental monitoring provide an opportunity to better examine the impact of human activities on the environment, with a specific focus on water and soil matrices. The recent literature underscores the increasing importance of ML in these contexts, highlighting a preference for adapted classic models: random forest (RF) (the most widely used), decision trees (DTs), support vector machines (SVMs), artificial neural networks (ANNs), convolutional neural networks (CNNs), principal component analysis (PCA), and much more. In the field of environmental management, the following methodologies offer invaluable insights that can steer strategic planning and decision-making based on more accurate image classification, prediction models, object detection and recognition, map classification, data classification, and environmental variable predictions.

We examined data from the American Geophysical Union (AGU), the world's largest earth and space science society, to characterize demographics of multiple milestones in a biogeoscientists' career. Geoscientists of color and White women make up a smaller proportion of those participating in activities critical to transitioning from student to professional (submitting manuscripts, getting published, and receiving review invitations) in comparison to White men. Gender parity for biogeoscientists appears within reach at earlier career stages, with 37% of AGU Biogeosciences members and 41% of Biogeosciences attendees at the Fall Meeting identifying as women in 2020. The demographics of those successfully completing activities required for career advancement, that is, publishing and reviewing with Journal of Geophysical Research and Global Biogeochemical Cycles, two biogeoscience journals, illustrate more progress is needed. A large majority of manuscripts were submitted by men (73%), many of which have no co‐authors that identify as women or non‐binary geoscientists. Further, our communities' bias of who we suggest as reviewers, results in 85% of the reviewer invites going to White geoscientists and 63% going to men. Thus, while representation of diverse communities has improved in some areas, barriers to publishing result in authorship not reflective of society: only 25% of accepted manuscripts were led by self‐identified geoscientists of color and fewer than 5% were led by women geoscientists of color. It is critical that efforts to diversify move beyond gender, to ensure that scientists of color are also afforded the access and opportunity needed to thrive as biogeoscientists. Plain Language Summary: The geosciences remain one of the least diverse STEM fields. We use data from the American Geophysical Union (AGU), the largest scientific society for earth and space scientists, to characterize the demographics of scientists participating in scientific publication and conference activities. We used information provided on biogeoscientists' gender, race and/or ethnicity, and age to illustrate representation trends reached early in one's career (attending a scientific meeting), at career development transitions points (publishing), and in the later more established phase of one's career (invitation to peer review). We found that while the gender gap between people that identify as men and those that identify as women is closing at early career milestones, like that of AGU membership and conference attendance, we do not see this same progress at more advanced career milestones, such as publishing and reviewing. Biogeoscientists of color continue to be overlooked at all career stages; however we were limited in our ability to draw conclusions about race and ethnicity given a scarcity of representation from ethnically or racially diverse groups within the data provided. Key Points: Women make up a larger fraction of American Geophysical Union (AGU) Biogeosciences members and meeting attendees as compared to AGU and the Fall Meeting overallBiogeosciences manuscript submissions (73%) and invited reviewers (69%) are dominated by geoscientists identifying as menWhite geoscientists enjoy higher acceptance rates (66%) and received the majority (85%) of reviewer invites [ABSTRACT FROM AUTHOR]

The sedimentary sequence of the Ninama Basin is associated with the latest phase of the continental Deccan volcanism of the Indian peninsula. It occurs as inter-, intratrappeans with a 39-m thick sequence comprising Sukhbhadar Formation and Ninama Limestone. The investigation aims to reconstruct its depositional setting based on the lithofacies and inorganic geochemistry to understand the influence of paleosalinity, paleoredox, paleodepth, provenance and paleoclimate on the sedimentation pattern. It is characterized by clastic and chemically formed rocks, including shale, mudstone, siltstone, sandstone and limestone. Facies analysis has revealed seven lithofacies, amongst which four are clastic facies, shale (SH), calcareous shale (CSH), silty mudstone (SM) and lithic greywacke (LGW), and three are carbonate facies, namely, grey-black limestone (GBL), cherty limestone (CL) and marlite (ML). Using geochemical analysis of major oxides and trace elements, ratios of Ca/(Ca+Fe), Sr/Ba, V/(V+Ni), V/Cr, Fe/(Ca+Mg), Ti/Al, Na/K, K/(Fe+Mg), CIA-K and MAP were determined and used to interpret and note the changes in the depositional system. The combined results of the lithofacies and geochemistry indicate that the deposition occurred in three phases. Phase 1 is characterized by high energy conditions in the shallow to moderate depth brackish-saline lake with oxic to dysoxic conditions; Phase 2 is characterized by anoxic deep fresh to the brackish water basin; and Phase 3 is characterized by semiarid conditions, saline, anoxic waters, high evaporation leading to shallowing of the lake, and low to moderate terrestrial input. This lacustrine basin in the paleotopographic low of the Deccan Volcanic Province is characterized by distinct lithology governed by various depositional parameters. A composite depositional model is constructed for the Ninama Basin, reflecting three phases of evolution that are dominantly controlled by the paleoenvironment. Research highlights: The present study focuses on lithofacies and inorganic geochemical analysis of Ninama paleolake sediments, Saurashtra intertrappeans, to interpret their paleosalinity, paleoredox, water depth, provenance, detrital influx, and paleoclimate. Phase 1 deposition was initiated in paleotopographic low of Deccan Traps, in an oxic–dysoxic, high-energy turbulent brackish water environment, in humid conditions. Phase 2 characterizes fine-grained clastic facies deposited in anoxic, fresh-brackish, deep lake waters in humid conditions. Phase 3 marks the shallowing, evident by carbonate facies with low-moderate terrestrial input deposited in saline and anoxic paleolake in semiarid conditions with high evaporation and low-moderate precipitation. The Deccan Traps have influenced the paleolake intertrappean sequence. [ABSTRACT FROM AUTHOR]

This is a systematic literature review of the application of machine learning (ML) algorithms in geosciences, with a focus on environmental monitoring applications. ML algorithms, with their ability to analyze vast quantities of data, decipher complex relationships, and predict future events, and they offer promising capabilities to implement technologies based on more precise and reliable data processing. This review considers several vulnerable and particularly at-risk themes as landfills, mining activities, the protection of coastal dunes, illegal discharges into water bodies, and the pollution and degradation of soil and water matrices in large industrial complexes. These case studies about environmental monitoring provide an opportunity to better examine the impact of human activities on the environment, with a specific focus on water and soil matrices. The recent literature underscores the increasing importance of ML in these contexts, highlighting a preference for adapted classic models: random forest (RF) (the most widely used), decision trees (DTs), support vector machines (SVMs), artificial neural networks (ANNs), convolutional neural networks (CNNs), principal component analysis (PCA), and much more. In the field of environmental management, the following methodologies offer invaluable insights that can steer strategic planning and decision-making based on more accurate image classification, prediction models, object detection and recognition, map classification, data classification, and environmental variable predictions. [ABSTRACT FROM AUTHOR]

Human existence and progress hinge on sustainability and resilience, especially in the Anthropocene Era, where the diversity of nature plays a critical role. Central to this endeavor is the realm of geoethics, which not only reshapes the role of geosciences but also fosters the development of ethical behavior and practices in our interaction with the Earth. This paper presents a conceptual framework that integrates the seemingly disparate domains of geoethics, geoenvironmental education, and the sense of place. By conducting a systematic review using the keywords "Geoethics AND Education" and "Geoethics AND place" across global databases, including Scopus, Web of Science, ProQuest, and JSTOR, up to October 2023, we seek to uncover evidence illuminating the potential connections between these concepts. Out of n = 98 records identified, n = 22 met the eligibility criteria for inclusion in our review. Our findings reveal a growing scientific interest in the spectrum of geoethics over the last decade, with diverse perspectives related to concerns about geoeducation. We propose that future research should focus on expanding the scope of geoethics across various disciplines, emphasizing the importance of human ethos, socio-eco responsibility, and ethical considerations in geological heritage and geoconservation management. Geoenvironmental education emerges as a crucial avenue for advancing geoethics, while the multifaceted outcome of the sense of place offers a promising pathway for standardizing and correlating these fields effectively. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has become one of the most controversial tools of recent times. Offering an extremely simple operating system, users can generate texts, images, videos and even human voices. The possibility of using such a powerful tool creates new paths and challenges in the field of environmental education: How does it influence natural heritage protection? Is it considered positive within sustainability and quality education? The reality is very different, showing algorithms trained with information of dubious quality and, on many occasions, obtained without permission from authors and artists around the world. UNESCO Global Geoparks (UGGps) are international references in education at all levels, related to territorial development and geoscience education. This article discusses if generative AI is, nowadays, an effective and applicable educational tool for the strategies developed and promoted by UGGps. This designation exists for people’s opportunities. The use of these tools in their current state could make the UGGp figure change its values and fundamental pillars in the future.

This study aims to analyze sinkholes by applying QGIS, mapping and comparing the occurrences with existing natural patterns to observe the cause of the occurrence and the solution. Sinkholes are topographic depressions that appear when the underlying bedrock weathers down, commonly found in sedimentary bedrock. They typically form due to drastic changes in groundwater levels. In urban areas, sediments may also be flushed out due to leaks in underground water pipes, increasing sinkhole formation. This study collected data on 41 sinkhole incidents in South Korea. The latitude and longitude of each incident were manually obtained and cross-checked. The locations were also classified by region to indicate whether they were considered rural or urban. The data was put into a spreadsheet and mapped using QGIS, and waterways were imported from OpenStreetMap (OSM) data and refined. The resulting map provides spatial insights into the distribution of sinkhole incidents in relation to waterways and sedimentary rocks. Whereas rural sinkholes tend to occur in areas with sedimentary rock as its bedrock material during summer when precipitation levels are high, sinkholes around urban areas are related to many human disturbances. By identifying patterns of sinkhole occurrences, the aim is to predict the formation of sinkholes to prevent catastrophic losses. [ABSTRACT FROM AUTHOR]

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Correlative light and electron microscopy (CLEM) is an advanced imaging approach that faces critical challenges in the analysis of both materials and biological specimens. CLEM integrates the strengths of both light and electron microscopy, in a hardware and software correlative environment, to produce a composite image that combines the high resolution of the electron microscope with the large field of view of the light microscope. It enables a more comprehensive understanding of a sample's microstructure, texture, morphology, and elemental distribution, thereby facilitating the interpretation of its properties and characteristics. CLEM has diverse applications in the geoscience field, including mineralogy, petrography, and geochemistry. Despite its many advantages, CLEM has some limitations that need to be considered. One of its major limitations is the complexity of the imaging process. CLEM requires specialized equipment and expertise, and it can be challenging to obtain high-quality images that are suitable for analysis. In this study, we present a CLEM workflow based on an innovative sample holder design specially dedicated to the examination of thin sections and three-dimensional samples, with a particular emphasis on geosciences. [ABSTRACT FROM AUTHOR]

Reconstruction of geological images using partial measurement is one of the most important topics in geosciences. In many methods, this is done using training images and very complex models which increase the computational complexity. In the first part of the article, a simple method based on spatial domain filters such as median and mean filter has been presented to reconstruct geological images. One of the most significant characteristics of this method is that it does not need the training image; moreover, its computational complexity is less than the other advanced methods. Via this method, it is easy to reconstruct binary, continuous, and three-dimensional images. The results show that the reconstruction accuracy of the proposed method is also acceptable. In the second part of the article, to introduce quantum computing to geosciences and encourage researchers to work on this issue, a quantum median filter is proposed to reconstruct geological images. According to the results, this method has much less computational complexity than classical methods such as DS. Also, its results are acceptable in terms of reconstruction rate. Due to the high speed of quantum algorithms and the widespread use of quantum computers in the near future, researchers in this field must become more familiar with quantum computing.

Achieving a successful energy transition requires society to deploy as many technologies as possible, rather than relying on one single technology to be the ‘magic bullet’. However, there are characteristics that make this transition more challenging than previous transitions in terms of its scope. These challenges include the wide range of sustainable technologies involved and the time constraints. For this research the importance of carbon capture and storage (CCS) and hydrogen technologies for the decarbonization process was analysed, including the main challenges that their large-scale implementation is facing from a subsurface perspective. The ongoing role that fossil fuels play, as well as how the hydrocarbon industry can facilitate the current transition, must also be considered. The common denominator in the analysis is the critical position of Earth sciences in discovering, characterizing, and sustainably utilizing subsurface resources. Geoscientists are essential for providing communication and cooperation between scientists and stakeholders who use, manage and preserve the subsurface. The success of CO2 and hydrogen storage, as part of the climate change mitigation strategies, and the eventual phase-out of fossil fuels ultimately depends on the sustainable development of the subsurface.

The importance of empirical versus theoretical laws is a controversial issue in many scientific fields, the latter being generally accepted and the relevance of which is not discussed here. As in other areas, there are well-known theoretical and empirical formulas in geosciences that do not adequately represent the reality of a given phenomenon. Quantitative comparison of geophysical and petrophysical results with data from the other multiple fields that comprise the geosciences compels a high exigency to avoid discontinuities in existing relationships. However, the proposal of new empirical laws that more accurately reflect a given phenomenon is often considered insufficient to contradict existing formulas. The aim of this work is to defend the development of new empirical laws by showing that they constitute a true model of analysed behaviour if certain criteria are followed. This defence is especially needed when non-linearisable functions are required to fit the empirical data. To achieve this aim, this study shows the established algebraic function as a function of a single variable, whose main advantage is its application to phenomena of a geological nature that show two differentiated behaviours as the variable x is increased. A series of five examples of different phenomena related to geosciences is selected to demonstrate the level of accuracy that new empirical laws can reach in contrast to the widely accepted historical relationships. [ABSTRACT FROM AUTHOR]

In 2019, GeoScienceWorld was actively planning to bring a large content and data repository, that includes a significant proportion of highly valued Grey Literature, into our existing collection of 50+ peer-reviewed journals and over 2300 books in the geosciences. Due to various external situations, including the impacts of the COVID-19 pandemic, and an absence of communityaccepted standards for Grey Literature publishing, this project has stalled. GeoScienceWorld continues to investigate opportunities to bring original datasets, as well as other collections of Grey Literature, predominantly in the form of partner societies' conference proceedings and related conference materials, into our traditional research platform. We are also in the early stages of planning for a new research tool that will be truly content agnostic in bringing research and valuable insights to our primary end-user stakeholders, researchers, whether in academia or industry. As an organization, GeoScienceWorld is further implementing an Agile mindset and development philosophy to bring increasingly useful, and timely, resources to our stakeholder groups. A key ceremony of all truly Agile development processes is the Retrospective. In this paper, I review the initial aims of the project to incorporate a large grey dataset into our traditional scholarly literature platform and provide reflections on how both GeoScienceWorld and the wider Grey Literature community can move forward to bring such valuable datasets to audiences that both want and need, such content to advance their research. For each element of the initial project, I ask the following Agile Retrospective questions:  What did we do well?  What could we have done better?  What have we learned?  What are we still puzzled by? As a result of applying these questions to the initial project, I will present recommendations that both inform GeoScienceWorld's future integration and presentation of Grey Literature, as well as offer a clearer path toward greater Grey Literature acceptance within traditional scholarly platforms such as ours. [ABSTRACT FROM AUTHOR]

Efforts to diversify STEM fields have not completely eliminated higher rates of women leaving certain science, technology, engineering, and mathematics (STEM) disciplines, such as geosciences. A diverse mentorship network is linked to persistence in STEM among college students, but little is known about how it leads to persistence. In particular, does a student's sense of belonging mediate the relationship between the diversity of one's mentor network and persistence in a field? This longitudinal study investigated whether students' university sense of belonging mediates the relationship between the diversity of mentor networks and interest in geoscience. 253 college women in STEM majors from nine U.S. universities reported on the diversity of their mentor networks, university belongingness, and interest in geoscience from Spring 2018 to Spring 2019. Consistent with our hypotheses, mediation analysis revealed a statistically and practically significant indirect effect of the diversity of mentor networks on interest development through university belonging. [ABSTRACT FROM AUTHOR]

Abstract: Climate models calibrated exclusively with observations from the 19th through 21st centuries are unsuitable for assessing many important hypotheses about the future. Many systems in the modern climate are expected to cross dynamic thresholds in the near future, requiring more than the instrumental record for adequate calibration. In this paper I argue that paleoclimate analogues from earth’s past can mitigate this threshold problem, even if the modern climate exhibits features that make it historically unique. While this requires that paleoclimatologists be increasingly discriminate in the past systems they target for modern analogues, the upshot is a practice of climate model construction that improves the reliability of future projections. All of this is achieved by integrating features of past climate systems discovered by the study of earth deep in the past. My analysis synthesizes important empirical work done by climate modelers and paleoclimatologists with relevant philosophical work on analogical reasoning in science. [ABSTRACT FROM AUTHOR]

As a result of the continuous growth in the amount of geological data, machine learning (ML) offers an opportunity to contribute to solving problems in geosciences. However, digital geology applications introduce new challenges for machine learning due to the unique geoscience properties encountered in each problem, requiring novel research in ML. This paper proposes a novel machine learning method, entitled "Partial Decision Tree Forest (PART Forest)", to overcome these challenges introduced by geoscience problems and offers potential advancements in both machine learning and geoscience disciplines. The effectiveness of the proposed PART Forest method was illustrated in mineral classification. This study aims to build an intelligent ML model that automatically classifies the minerals in terms of their crystal structures (triclinic, monoclinic, orthorhombic, tetragonal, hexagonal, and trigonal) by taking into account their chemical compositions and their physical and optical properties. In the experiments, the proposed PART Forest method demonstrated its superiority over one of the well-known ensemble learning methods, random forest, in terms of accuracy, precision, recall, f-score, and AUC (area under the curve) metrics. [ABSTRACT FROM AUTHOR]

Reconstruction of geological images using partial measurement is one of the most important topics in geosciences. In many methods, this is done using training images and very complex models, which increase the computational complexity. In the first part of the article, a simple method based on spatial domain filters, such as median and mean filters is presented in order to reconstruct geological images. One of the most significant characteristics of this method is that it does not need the training image; moreover, its computational complexity is less than the other advanced methods. Via this method, it is easy to reconstruct binary, continuous, and three-dimensional images. The results show that the reconstruction accuracy of the proposed method is also acceptable. In the second part of the article, in order to introduce quantum computing to geosciences and encourage researchers to work on this issue, a quantum median filter is proposed to reconstruct geological images. According to the results, this method has much less computational complexity than classical methods, such as DS. Also, its results are acceptable in terms of reconstruction rate. Due to the high speed of quantum algorithms and the widespread use of quantum computers in the near future, it is necessary for researchers in this field to become more familiar with quantum computing. [ABSTRACT FROM AUTHOR]

As an important branch of computed tomography (CT), industrial CT is used widely in many fields, such as aerospace, military industry, and geological analysis fields, because of its advantages of high resolution, repeatability, and wide detection range. On the basis of thorough investigation and study, this paper summarizes three typical industrial CT technologies (i.e., seismic wave CT, resistivity CT, and electromagnetic wave CT) as well as the comprehensive geophysical exploration methods used in the geosciences. The current applications of industrial CT in pore structure studies, gas hydrate studies, digital core construction, and geological utilization and storage of carbon dioxide are introduced. The development trend of industrial CT in the geosciences is also discussed.

This paper explores the concept of geoethics and its application in the professional practice of geosciences. Geoethics involves research and reflection on the values and principles that underpin appropriate behaviours wherever human actions interact with the earth system. It advocates for solutions to the problems of human-Earth interaction and contributes to protecting humanity and citizens in the professional field and everyday life. After evaluating the common geoethical problem in Latin America, provide examples from three South American countries: Peru, Chile and Brazil. The synergistic action of the geoscientific community in promoting a geoethical framework is discussed. The need for transparent, quantitative, rational and repeatable analyses is highlighted to ensure high professionalism in conducting studies. The importance of providing role models for young and beginning geoscientists and correctly disseminating geoscientific information to the public is further emphasized. The ability to communicate effectively is a fundamental requirement to train engineers, geologists and geoscientists capable of addressing global issues such as environmental management, geological hazards, and energy transition. Additionally, the implementation of geoethics in geological engineering and geoscience careers is proposed to promote social and environmental responsibility among young graduates and scientists starting their careers to prevent unethical professional practices and improve the visibility of geosciences and its benefits to society. The article concludes by calling for the involvement of geoscientists in public policies for the protection of geological heritage and the promotion of geoethical values, as well as the creation of a professional group to develop and promote geoscience didactics to improve educational quality in the field. [ABSTRACT FROM AUTHOR]

The conducted review presents the possibility of using artificial neural networks in sectors related to environmental protection, agriculture, forestry, land uses, groundwater and bathymetric. Today there is a lot of research in these areas with different research methodologies. The result is the improvement of decision-making processes, design, and prediction of certain events that, with appropriate intervention, can prevent severe consequences for society. The review shows the capabilities to optimize and automate the processes of modeling urban and land dynamics. It examines the forecasts of assessment of the damage caused by natural phenomena. Detection of environmental changes via the analysis of certain time intervals and classification of objects on the basis of different images is presented. The practical aspects of this work include the ability to choose the correct artificial neural network model depending on the complexity of the problem. This factor is a novel element since previously reviewed articles did not encounter a study of the correlation between the chosen model or algorithm, depending on the use case or area of the problem. This article seeks to outline the reason for the interest in artificial intelligence. Its purpose is to find answers to the following questions: How can artificial neural networks be used for spatial analysis? What does the implementation of detailed algorithms depend on? It is proved that an artificial intelligence approach can be an effective and powerful tool in various domains where spatial aspects are important. [ABSTRACT FROM AUTHOR]

With the rapid expansion in big data and artificial intelligence (AI), Earth sciences are undergoing unprecedented advances in data processing and interpretation techniques, as well as in facilitating data-driven discoveries of complex Earth systems. This special collection explores scientific research related to data-driven discoveries in geosciences and provides a timely presentation of progress in developments and/or applications of AI and big data approaches to multiple aspects of geosciences. These include geohazards monitoring, mineral resource exploration, and environmental assessments. We hope this collection will inspire researchers and will transform the work undertaken in the field of data-driven Earth science. While many challenges remain, including the formidable tasks of transforming the deluge of geoscience data into useable information and furthering knowledge via cutting-edge AI techniques, we envision that data-driven discovery will revolutionize conventional methods of observation, analysis, modeling, and prediction in geosciences, and will further advance scientific understanding of our complex Earth system. [ABSTRACT FROM AUTHOR]

In a period in which climate change is significantly varying rainfall regimes and their intensity all over the world, river-flow prediction is a major concern of geosciences. In recent years there has been an increase in the use of deep-learning models for river-flow prediction. However, in this field we can observe two main issues: i) many case studies use similar (or the same) strategies without sharing the codes, and ii) the application of these techniques requires good computer knowledge. This work proposes to employ a Google Colab notebook called CleverRiver, which allows the application of deep-learning for river-flow predictions. CleverRiver is a dynamic software that can be upgraded and modified not only by the authors but also by the users. The main advantages of CleverRiver are the following: the software is not limited by the client hardware, operating systems, etc.; the code is open-source; the toolkit is integrated with user-friendly interfaces; updated releases with new architectures, data management, and model parameters will be progressively uploaded. The software consists of three sections: the first one enables to train the models by means of some architectures, parameters, and data; the second section allows to create predictions by using the trained models; the third section allows to send feedback and to share experiences with the authors, providing a flux of precious information able to improve scientific research. [ABSTRACT FROM AUTHOR]