Legacy Datasets Research Articles

Inversion of downhole resistivity properties through infrared spectroscopy and whole‐rock geochemistry using machine‐learning

AbstractThe electrical properties of rocks are widely used in the geophysical exploration of natural resources, such as minerals, hydrocarbons and groundwater. In mining exploration, the primary goal is to map electrically anomalous geological features associated with different mineralization styles, such as clay alteration haloes, metal oxides and sulphides, weathered crystalline rocks or fractured zones. As such, the reconciliation of geophysical data with geological information (geochemistry, mineralogy, texture and lithology) is a critical step and can be performed based on petrophysical properties collected either on core samples or as downhole measurements. Based on data from 189 diamond drill cores collected for uranium exploration in the Athabasca Basin (Saskatchewan, Canada), this paper presents a case study of reconciliation of downhole resistivity probing with core sample geochemistry and short‐wave infrared spectroscopy (350–2500 nm) through three successive steps: (i) multivariate analysis of resistivity and other petrophysical properties (porosity, density) against geochemical and infrared spectroscopy information to characterize electrical properties of rocks with respect to other physical parameters, (ii) a machine‐learning workflow integrating geochemistry and spectral signatures in order to infer synthetic resistivity logs along with uncertainties. The best model in the basin was Light Gradient‐Boosting Machine with pairwise log‐ratio, which yielded a coefficient of determination R2 = 0.80 (root mean square error = 0.16), and in the basement, support vector regression with data fusion of infrared spectroscopy and pairwise log‐ratios on geochemistry yielded R2 = 0.82 (root mean square error = 0.35); (iii) the best model was then fitted on an area that was excluded from the original dataset (Getty Russell property) in order to infer synthetic resistivity logs for that zone. Software code is publicly available. This workflow can be re‐used for the valorization of legacy datasets.

Anatomy of the late Pennsylvanian to early Triassic failed rift system of the Cooper Basin, eastern Australia

The onshore intracratonic Cooper Basin of central Australia developed during the Late Pennsylvanian to Middle Triassic periods at paleolatitudes of approximately 50°S within the Gondwanan sector of the Pangea. Despite the wealth of data available, including the drilling of over 4,800 boreholes, there is limited knowledge about the Cooper Basin’s origins and evolution. To better understand the basin’s geological history, legacy data sets, including composite 2D seismic sections, well logs, measured sections, and 1D burial history models from the west of the basin, are integrated to reinterpret the basin’s tectonic and sedimentary evolution. Interpretation of the seismic sections and calculated subsidence rates indicates an earlier active rift phase with grabens and half-grabens that transitioned, in the latest Permian, into a regional sag phase. The evolution of tectonic styles heavily influenced the paleogeographic evolution of the basin fill and resulting depositional architecture. The basin sediments are entirely terrestrial in nature and facies reflect a transition from glacial environments in the late Pennsylvanian to warmer and drier conditions in the early Triassic. During much of the Permian the basin was underfilled and the relative low influx of fluvial sediment did not keep pace with creation of accommodation, allowing the development of extensive mire and lake systems. Coal beds are up to 30 m thick. By contrast, the basin appears to have been overfilled during the latest Permian to Triassic with rivers flowing along the central axis of the basin. The synchroneity of commencement of rifting, termination of rifting, and commencement of a sag phase within the failed rift systems of the Cooper Basin, the East Gondwana Interior Rift, and the East Australian Rift strongly suggests a continent-wide period of extension related to significant changes in plate motions during the Late Pennsylvanian to Middle Triassic.

The 17 January 1994 Northridge, California, Earthquake: A Retrospective Analysis

Abstract The 17 January 1994 Northridge, California, earthquake was a watershed event with far-reaching societal and scientific impacts. The earthquake, which occurred in the early days of both broadband seismic networks and the Internet, spurred advances in seismic monitoring, real-time systems, and development of data products. Motivated by the 30th anniversary of the earthquake, we present a brief retrospective of the earthquake and its impact, and reconsider both ground motions and the aftershock distribution using modern tools and the best-available data. With improvements in instrumentation and analysis methodology, recent earthquakes continue to reveal the increasing complexity of ground motions, fault systems, and earthquake ruptures. Even in the absence of data from state-of-the-art instrumentation, a retrospective consideration of ground-motion data from the Northridge earthquake reveals complexities beyond what could be characterized (and modeled) 30 yr ago. Aftershock relocations for both the 1971 Sylmar and 1994 Northridge earthquakes also reveal an updated view of fault complexity. Our study does provide a cautionary tale regarding legacy data sets and research results that are not easily accessible, which can result in discrepancies between catalog data and products from the best available science. We also briefly describe outreach products produced as a part of the anniversary commemoration.

Be FAIR to Pedestrian Dynamics Data

For improving the safety of people in large crowds, it is of great importance to understand the basic mechanisms of pedestrian dynamics, e.g. with help of experiments. The number of openly shared datasets of these experiments has increased in the last years also due to stricter requirements from journals and funders. We share our own experimental data by an open access data archive which data is widely used in the community. However, our data and also data of other researchers in the field of pedestrian dynamics is not annotated in a systematic or semantically harmonized way, which impairs FAIRness in general and interoperability specifically. In this paper, we propose a standardized extensible metadata schema and key data structures for trajectories and geometry. The proposed metadata schema and data structures hopefully support the interoperability within the community and will assist to make data reutilization more efficient. Our own legacy datasets are continuously annotated with essential information using this metadata schema steadily. This metadata is provided beside the converted data on our data archive and thus enhance its findability and reusability.

EMERGING EMPIRES AND OPPORTUNISTIC LAND-USE LEGACIES: TESTING A NEW MODEL ON ROME'S CONQUEST AND COLONIZATION THROUGH ARCHAEOLOGICAL SURVEY DATA IN THE TERRITORIES OF COSA AND TARRACO

This paper tests a new model to study the Roman conquest and colonization of the Western Mediterranean. This recent model depicts Roman expansion as a more sustainable process than previously assumed, which tapped into, reinforced and integrated wider Mediterranean settlement trends. Contrary to what is assumed by traditional narratives, colonization did not entail the immediate destruction and restructuring of native landscapes — but rather the integration, opportunistic reuse, appropriation and development of previous land-uses and settlements. Two legacy datasets collected through pedestrian survey in the colonial territories of Cosa (Italy) and Tarraco (Spain) were used to test this model on a supranational scale. The analysis indicated that certain portions of the native landscape were possibly integrated into the Roman Empire without initial drastic changes being reflected in the settlement patterns or the landscape.

Pattern Augmented Lightweight Convolutional Neural Network for Intrusion Detection System

As the world increasingly becomes more interconnected, the demand for safety and security is ever-increasing, particularly for industrial networks. This has prompted numerous researchers to investigate different methodologies and techniques suitable for intrusion detection systems (IDS) requirements. Over the years, many studies have proposed various solutions in this regard, including signature-based and machine learning (ML)-based systems. More recently, researchers are considering deep learning (DL)-based anomaly detection approaches. Most proposed works in this research field aim to achieve either one or a combination of high accuracy, considerably low false alarm rates (FARs), high classification specificity and detection sensitivity, lightweight DL models, or other ML and DL-related performance measurement metrics. In this study, we propose a novel method to convert a raw dataset to an image dataset to magnify patterns by utilizing the Short-Term Fourier transform (STFT). The resulting high-quality image dataset allowed us to devise an anomaly detection system for IDS using a simple lightweight convolutional neural network (CNN) that classifies denial of service and distributed denial of service. The proposed methods were evaluated using a modern dataset, CSE-CIC-IDS2018, and a legacy dataset, NSLKDD. We have also applied a combined dataset to assess the generalization of the proposed model across various datasets. Our experimental results have demonstrated that the proposed methods achieved high accuracy and considerably low FARs with high specificity and sensitivity. The resulting loss and accuracy curves have demonstrated the efficacy of our raw dataset to image dataset conversion methodology, which is evident as an excellent generalization of the proposed lightweight CNN model was observed, effectively avoiding overfitting. This holds for both the modern and legacy datasets, including their mixed versions.

Seismic geomorphology: subsurface analyses, data integration and palaeoenvironment reconstructions – an introduction

Abstract Since the last Special Publication on seismic geomorphology, the application of seismic data has grown substantially, revolutionizing our understanding of basin evolution in the process. The papers presented here provide an insight into the direction of travel for seismic geomorphological analyses and how the science has evolved since 2007. New methods of data collection, new methods of processing and visualization, and the integration of new types of complementary data, all have played a role in maximizing the potential palaeo-environmental insights that can be derived from such studies. The submissions range across different geological settings, consisting of glacial, fluvial, volcanic, deltaic and slope settings. Many of these studies integrate different methods, showing what can be achieved by combining multiple datasets to understand the subsurface. As more legacy datasets become available, the observed acceleration in seismic data availability and the associated publications will likely continue. Newer methods and greater knowledge of the subsurface are yielding a greater understanding of not just the palaeoenvironments, but also what generates seismic reflectivity in the subsurface. The study of seismic geomorphology remains in its infancy, and much exciting research potential is yet to be realized.

BCFtools/liftover: an accurate and comprehensive tool to convert genetic variants across genome assemblies.

Many genetics studies report results tied to genomic coordinates of a legacy genome assembly. However, as assemblies are updated and improved, researchers are faced with either realigning raw sequence data using the updated coordinate system or converting legacy datasets to the updated coordinate system to be able to combine results with newer datasets. Currently available tools to perform the conversion of genetic variants have numerous shortcomings, including poor support for indels and multi-allelic variants, that lead to a higher rate of variants being dropped or incorrectly converted. As a result, many researchers continue to work with and publish using legacy genomic coordinates. Here we present BCFtools/liftover, a tool to convert genomic coordinates across genome assemblies for variants encoded in the variant call format with improved support for indels represented by different reference alleles across genome assemblies and full support for multi-allelic variants. It further supports variant annotation fields updates whenever the reference allele changes across genome assemblies. The tool has the lowest rate of variants being dropped with an order of magnitude less indels dropped or incorrectly converted and is an order of magnitude faster than other tools typically used for the same task. It is particularly suited for converting variant callsets from large cohorts to novel telomere-to-telomere assemblies as well as summary statistics from genome-wide association studies tied to legacy genome assemblies. The tool is written in C and freely available under the MIT open source license as a BCFtools plugin available at http://github.com/freeseek/score.

Leveraging museum specimens, genomics and legacy datasets to unravel the phylogeny and biogeography of cryptin wasps (Hymenoptera, Ichneumonidae, Cryptini)

AbstractModern genomic techniques have enabled the generation of phylogenetic datasets of unprecedented scale. However, there are also troves of molecular data accumulated from past studies using Sanger sequencing, often at fine taxonomic scales. Combining both sources of data is an obviously appealing possibility, but it can also lead to inconsistency due to high levels of missing data, disparities in the scale of Sanger versus genomic datasets, and little overlap in sequences across terminals. To provide an empirical investigation of the potential of such ‘hybrid’ datasets, we combined data from ultraconserved elements (UCEs) for 183 species of Cryptini (Ichneumonidae, Hymenoptera) with a previously existing dataset of 7 loci and morphological data including 308 species plus outgroup taxa. Bioinformatics pipelines allowed recovery of ‘legacy’ markers from the bycatch of UCE sequencing, reducing the problem of limited character overlap. The resulting tree combining Sanger and UCE data is highly supported and includes dense taxon sampling of the group, allowing for a better understanding of the global radiation of Cryptini. The Neotropical region had the highest phylogenetic diversity but the lowest level of phylogenetic dispersion when corrected for standardized effect size, while the Oriental fauna showed the highest level of phylogenetic dispersion. Our results highlight the potential of hybrid datasets to produce a more complete picture of the Tree of Life combining affordability, robust support and deep taxonomic sampling.

A statistical method for image-mediated association studies discovers genes and pathways associated with four brain disorders

Brain imaging and genomics are critical tools enabling characterization of the genetic basis of brain disorders. However, imaging large cohorts is expensive and may be unavailable for legacy datasets used for genome-wide association studies (GWASs). Using an integrated feature selection/aggregation model, we developed an image-mediated association study (IMAS), which utilizes borrowed imaging/genomics data to conduct association mapping in legacy GWAS cohorts. By leveraging the UK Biobank image-derived phenotypes (IDPs), the IMAS discovered genetic bases underlying four neuropsychiatric disorders and verified them by analyzing annotations, pathways, and expression quantitative trait loci (eQTLs). A cerebellar-mediated mechanism was identified to be common to the four disorders. Simulations show that, if the goal is identifying genetic risk, our IMAS is more powerful than a hypothetical protocol in which the imaging results were available in the GWAS dataset. This implies the feasibility of reanalyzing legacy GWAS datasets without conducting additional imaging, yielding cost savings for integrated analysis of genetics and imaging.

Framework to develop an open‐source forage data network to improve primary productivity and enhance system resiliency

AbstractData repositories using legacy data are needed to minimize research redundancy, enable knowledge synthesis, and optimize productivity and resiliency of agricultural systems. However, a framework is needed to develop an online searchable database to identify optimum systems for sustainable agricultural intensification and diversification, particularly for forages. Therefore, this paper outlines the development of a community‐driven forage database (Forage Data Hub) using legacy datasets encompassing multiple temporal and spatial scales and species to analyze system functionality and resiliency. Specific steps outlined included (1) developing minimum and preferred data requirements; (2) data standardization, structuring, and compilation; (3) creating a data thesaurus, data shapes, and data model; and (4) creating a web‐based system interface to facilitate database access. We demonstrate utility of curating these diverse datasets by quantifying forage system resiliency during extreme weather occurrences (relationship between standardized yields and yields during years receiving 25th percentile of the 30‐year normal precipitation) for three systems (monocrop annual, monocrop perennial, and mixed perennial) spanning 52,997 data entries (108 unique locations and 51 years). Overall, during low‐precipitation years, monocrop annual systems achieved 67% of their expected yield, while monocrop perennial systems achieved 93% and mixed perennial systems achieved 112% of potential yields. Therefore, diverse perennial systems were more resilient to climate‐related stresses compared to annual forage systems. Development of the US Forage Data Hub underscores the benefits of community‐driven data sharing and curation for a given commodity to provide system‐level sustainability assessments for identifying practices that promote ecological intensification and resiliency to climatic stochasticity.

Legacy community science data suggest reduced beached litter in response to a container deposit scheme at a local scale

Marine debris is causing significant environmental harm. Legislation is being implemented to reduce litter, including schemes like container deposit legislation that incentivize the return of commonly littered items for recycling. While there is a suggestion that these schemes reduce litter, no study has examined the long-term impact on the local environment before and after implementation. This study analyses community science data from 8 years prior to the implementation of a container deposit scheme, paired with 3 years of data afterwards, to assess the scheme's effectiveness at a local scale. Although using legacy datasets limits the generalizability of the conclusions compared to dedicated studies, the findings strongly indicate that container deposit schemes effectively manage targeted containers but have little impact on overall waste abundances. Long-term datasets like these are invaluable for assessing the impact of management efforts.

Indexing legacy data-sets for global access and processing in multi-cloud environments

Global data access and processing for multi-cloud applications proves challenging where there is a need for efficient access to large pre-existing, legacy data-sets. To address this problem, we created an indexing subsystem, allowing us to index and gather the metadata of legacy data-sets. The gathered information is later incorporated into a multi-cloud data management system to achieve global integration of legacy storage systems containing large data-sets. The solution is based on metadata management, organization and periodic monitoring of legacy data-sets, which makes possible scheme-less co-existence of a legacy storage system and a multi-cloud data management system for managing the same data collections. The approach has been initially evaluated in a multi-cloud deployment scenario. A large data collection consisting of legacy cultural data-sets was exposed to public and commercial infrastructure for data access and processing.

2.F. Skills building seminar: Health Data Pipelines: moving away from Excel to scalable, insightful and future-proof infostructure

Abstract Public Health Data Pipelines are critical in the implementation of scalable Digital Public Health projects as part of the reporting, evaluation and monitoring aspects of any public health intervention. The WHO Europe Programme of Work covering the period 2020 to 2025 puts an emphasis on “developing big-data capacity in surveillance, modelling and policy monitoring”. This workshop will provide an initial insight into the world of Health Data Pipelines and will give you the necessary ingredients to get closer to establishing processes and systems that provide you the right information at the right time and context. Key messages • Data Linking is critical in the transition between legacy systems and data sets based on decades old technology to reliable and robust information infrastructure (infostructure). • Kicking off the necessary internal discussions to consolidate data and processes into health data pipelines do not require advanced technical knowledge.

Corrigendum

Geostandards and Geoanalytical ResearchEarly View CORRIGENDUMFree Access Corrigendum First published: 11 October 2022 https://doi.org/10.1111/ggr.12462AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat The referenced article, Boone et al. (2022), was published with errors: 1. A reference has been added to the reference section:- Jones, S.L., Anderson, J.R., Fraser, G.L., Lewis, C.J., McLennan, S.M. (2018) A U–Pb geochronology compilation for northern Australia: Version 2, 2018. Record 2018/49. Geoscience Australia, Canberra. https://doi.org/10.11636/Record.2018.049 2. The citation for this reference should appear at the end of the caption for Figure 9:- AusGeochem map view geospatially displaying SIMS U-Pb data from the Northern Territory of Australia. Data from Jones et al. (2018). 3. The third sentence in the ‘Legacy Datasets as test cases" section was also incorrect and has been amended to: Legacy datasets to date include the McNaughton sample and SIMS U-Pb data compilation (2765 samples, 701 with corresponding U-Pb results), the Geological Survey of Western Australia SIMS U-Pb data compilation (1404 analyses), the Geoscience Australia SIMS U-Pb data compilation (3413 analyses, Jones et al., 2018) and the Australian-wide apatite fission-track survey (2594 samples) of Kohn et al. (2002) and Gleadow et al. (2002), the analytical results of which will soon follow when the corresponding fission track model is released in early 2022. Reference Boone S. C., Dalton H., Prent A., Kohlmann F., Theile M., Greau Y., Florin G., Noble W., Hodgekiss S.-A., Ware B., Phillips D., Kohn B., O’Reilly S., Gleadow A., McInnes B., Rawling T. (2022) AusGeochem: An Open Platform for Geochemical Data Preservation, Dissemination and Synthesis. Geostandards and Geoanalytical Research, 46, 245- 259. Wiley Online LibraryWeb of Science®Google Scholar Early ViewOnline Version of Record before inclusion in an issue ReferencesRelatedInformation

Machine learning models for predicting soil particle size fractions from routine soil analyses in Quebec

AbstractSoil texture and particle size distribution are important soil properties for understanding and interpreting the multiple processes and interactions in soil agrosystems. However, soil particle size analysis is rarely included in routine soil laboratory analyses due to cost but can be derived from other soil physico‐chemical properties and from more powerful regressions using machine learning techniques. The performance of multiple linear regression and four machine learning algorithms (MLAs)—K‐nearest neighbor (KNN), Random Forest, extra‐gradient boosting (XGBoost), and multilayer neural network (NeuralNetwork)—was compared to predict particle size fractions of clay, sand, and silt using routine analyses of soil pH, cation exchange capacity, Mehlich‐3 extracted elements, and density of dried sieved soil from 8,364 soil samples distributed across Quebec. Particle size fractions were predicted as compositional data using isometric log ratio transformation. The XGBoost model performed best, with RMSE values of 7, 10, and 12% and R2 values of .77, .57, and .73 for the prediction of clay, silt, and sand fractions, respectively. Feature importance classification varied from one model to another, but the best predictors remained the same regardless of the model used. Sieved soil density measured in the laboratory and Mehlich‐3 Mg, K, Fe, and Zn ranked as better predictors of particle size fractions. Predicting soil particle sizes using routine soil physico‐chemical properties and MLAs appears an option for incomplete legacy datasets and a promising economic alternative to current methods carried out in commercial laboratories.

Soil degradations in the IGP of central Haryana, India-a spatial assessment for reclamation and management.

Natural and anthropogenic processes are primary drivers for soil degradations in the Gangetic plain of India. Indian Remote Sensing (IRS) data was used to assess soil degradations and study spatial changes comparing with the legacy datasets. Based on the nature, extent, and degrees of limitations, these soils were evaluated to suggest suitable reclamation and management options. Typical strong signature (white tones) from a barren highly salinized soil (P3, P6, P9, P11, and P15) and higher energy absorption (blue-black tones) from a permanent waterlogged surface (P13, P14, P10) in the irrigated areas have favored delineation of salt-affected, strong, and permanent waterlogged areas. Moderate and slightly salt-affected soils (P1, P2, P4, P5, P7, P8, and P12) and the areas with high water table depths (P13 and P14) have shown mixed spectral signatures for associations of soil, water, and crops and were mapped using ground truth and soil chemical analysis data. Irrigation with poor quality ground water (RSC: 1.2 to 12.3 me L-1) has prompted developing salinity and sodicity in P2, P5, and P11, while irrigation in poorly drained areas caused appearance of waterlogging (WTD < 1.5m) and salinization (P3, P5, P7, P9, P13, P14). The gypsum application and sub-surface drainage (SSD) techniques have enhanced soil reclamation by (i) neutralizing CO32 - and HCO3 - in P12 (pH 9.2, ESP 24) and (ii) controlling soil salinity (EC 3.0 dS m-1) and waterlogging (WTD > 1.5m) in P14. Soils with CaCO3 layers at shallow depth in P3 (0.7-4.2%), P6 (1.2-7.1%), P9 (8-16%), P11 (4-15%), and P13 (0.9-9.4%) were managed using forest plantations. Ground water with high SAR in T1 to T7 can be applied for agriculture using mixing or cyclic mode. Water with high RSC in T4, T5, T7, T8, T9, and T10 needs treatment with gypsum. A significant area (~ 24.87%) of salt-affected soils was reclaimed since 1971. Prominent changes (3.27 to 17.71%) were shown in Panipat and Karnal districts. Small areas of brick kiln (P11), industrial effluent (P10), riverine sand, partially stabilized dunes, and mining have appeared due to anthropogenic activities.

A Multivariate Approach to Investigate Metallurgical Technology: The Case of the Chinese Ritual Bronzes

Research into ancient Chinese metallurgy has flourished over recent years with the accumulation of analytical data reflecting the needs of so many archaeological finds. However, the relationship between technology and society is unlikely to be revealed simply by analysing more artefacts. This is particularly evident in the debates over the sources of metals used to manufacture the Chinese ritual bronzes of the Shang (c. 1500-1046 BCE), Western Zhou (c. 1046–771 BCE) and Eastern Zhou (c. 771–256 BCE) dynasties. This article recognises that approaches to analytical data often fail to provide robust platforms from which to investigate metallurgical technology within its wider social and cultural contexts. To address this issue, a recently developed multivariate approach is applied to over 300 Chinese ritual bronzes from legacy data sets and nearly 100 unearthed copper-based objects from Anyang and Hanzhong. Unlike previous investigations that have relied predominantly on interpreting lead isotope signatures, the compositional analyses presented here indicate that copper and lead used to manufacture the bronzes are derived from mining progressively deeper ores in the same deposits rather than seeking out new sources. It is proposed that interpretations of social, cultural and technological change predicated on the acquisition of metals from disparate regions during the Chinese Bronze Age may need to be revised.

'Boundary Objects' in Archaeological Finds and Environmental Research: Using Bowker and Star's Concept of the 'Boundary Object' to Analyse and Interpret Disparate Archaeological Legacy Data

Recently the concept of big data has been employed in archaeology to push forward research on very large-scale legacy datasets, often with a developer-funded component; however, relatively little of this effort has focused on artefact and ecofact data. This paper explores the possibility of using Bowker and Star’s concept of the boundary object to manage the issues caused by data scale, complexity, diversity, and variable information standards when attempting to carry out large-scale research on artefacts and ecofacts. The critique of archaeological data standards as it impacts research into artefacts and ecofacts is reviewed. A methodology for the construction and use of a large database of legacy data is described, and a case study on the regional histories of food production/consumption in southern Britain, using datasets derived from the author’s PhD as part of the English Landscapes and Identities (EngLaId) Project (Gosden et al. 2021) at Oxford University is presented.

Digitalization of Legacy Datasets and Machine Learning Regression Yields Insights for Reservoir Property Prediction and Submarine-Fan Evolution: A Subsurface Example From the Lewis Shale, Wyoming

Machine-learning algorithms have long aided in geologic property prediction from well-log data, but are primarily used to classify lithology, facies, formation, and rock types. However, more detailed properties (e.g., porosity, grain size) that are important for evaluating hydrocarbon exploration and development activities, as well as subsurface geothermal, CO2 sequestration, and hydrological studies have not been a focus of machine-learning predictions. This study focuses on improving machine-learning regression-based workflows for quantitative geological property prediction (porosity, grain size, XRF geochemistry), using a robust dataset from the Dad Sandstone Member of the Lewis Shale in the Green River Basin, Wyoming. Twelve slabbed cores collected from wells targeting turbiditic sandstones and mudstones of the Dad Sandstone member provide 1212.2 ft. of well-log and core data to test the efficacy of five machine-learning models, ranging in complexity from multivariate linear regression to deep neural networks. Our results demonstrate that gradient-boosted decision-tree models (e.g., CatBoost, XGBoost) are flexible in terms of input data completeness, do not require scaled data, and are reliably accurate, with the lowest or second lowest root mean squared error (RMSE) for every test. Deep neural networks, while used commonly for these applications, never achieved lowest error for any of the testing. We also utilize newly collected XRF geochemistry and grain-size data to constrain spatiotemporal sediment routing, sand-mud partitioning, and paleo-oceanographic redox conditions in the Green River Basin. Test-train dataset splitting traditionally uses randomized inter-well data, but a blind well testing strategy is more applicable to most geoscience applications that aim to predict properties of new, unseen well locations. We find that using inter-well training datasets are more optimistic when applied to blind wells, with a median difference of 0.58 RMSE when predicting grain size in phi units. Using these data and results, we establish a baseline workflow for applying machine-learning regression algorithms to core-based reservoir properties from well-log and core-image data. We hope that our findings and open-source code and datasets released with this paper will serve as a baseline for further research to improve geological property prediction for sustainable earth-resource modeling.