{"help": "https://data.gov.au/data/en/api/3/action/help_show?name=package_show", "success": true, "result": {"archived": false, "author_email": null, "contact_point": "clientservices@ga.gov.au", "creator_user_id": "c2fbbe4a-4ba0-4945-808b-67454605a4cf", "duplicate_score": 2, "geospatial_topic": [], "id": "b79f9ed2-f824-4585-b25b-0fcb8e42fffc", "isopen": false, "language": "eng", "license_id": "notspecified", "license_title": "notspecified", "maintainer": null, "maintainer_email": null, "metadata_created": "2025-11-18T21:41:10.173377", "metadata_modified": "2025-11-18T21:41:10.173385", "name": "stochastic-modelling-of-mineral-exploration-targets1", "notes": "Rapid, efficient, and accurate prediction of mineral occurrence that takes uncertainty into 20 account is essential to optimise defining exploration targets. Traditional approaches to mineral 21 potential mapping often fail to fully appreciate spatial uncertainties of input predictors and their 22 spatial cross-correlation. In this study a stochastic technique based on multivariate 23 geostatistical simulations and ensemble tree-based learners is introduced for predicting and 24 uncertainty quantification of mineral exploration targets. The technique is tested on a synthetic 25 case inspired by the characteristics of a hydrothermal mineral system model and a real-world 26 dataset from the Yilgarn Craton in Western Australia. Results from the two cases proved the 27 superior performance and robustness of the proposed stochastic technique, especially when 28 dealing with high dimensional and large data sets.\nCitation: Talebi, H., Mueller, U., Peeters, L.J.M. et al. Stochastic Modelling of Mineral Exploration Targets. Math Geosci 54, 593\u2013621 (2022). https://doi.org/10.1007/s11004-021-09989-z", "num_resources": 0, "num_tags": 9, "organization": {"id": "91f054ec-d0c3-4d42-a89a-5daa2c7a6818", "name": "geoscience-australia-data", "title": "Geoscience Australia Data", "type": "organization", "description": "Harvester for Geoscience Australia Data", "image_url": "", "created": "2025-06-23T12:29:08.024111", "is_organization": true, "approval_status": "approved", "state": "active"}, "original_harvest_source": {"site_url": "https://ecat.ga.gov.au", "href": "https://ecat.ga.gov.au/geonetwork/srv/eng/csw/dataset/stochastic-modelling-of-mineral-exploration-targets1", "title": "Geoscience Australia"}, "owner_org": "91f054ec-d0c3-4d42-a89a-5daa2c7a6818", "private": false, "promotion_level": "0", "spatial": "{\"type\": \"Polygon\", \"coordinates\": [[[112.0, -44.0], [154.0, -44.0], [154.0, -9.0], [112.0, -9.0], [112.0, -44.0]]]}", "spatial_coverage": "{\"type\": \"Polygon\", \"coordinates\": [[[112.0, -44.0], [154.0, -44.0], [154.0, -9.0], [112.0, -9.0], [112.0, -44.0]]]}", "state": "active", "temporal_coverage_from": "2019-04-08 01:55:29", "title": "Stochastic Modelling of Mineral Exploration Targets", "type": "dataset", "unpublished": false, "url": null, "version": null, "extras": [{"key": "harvest_object_id", "value": "0c9ba16c-0bd8-493c-9d64-138149dd0a2d"}, {"key": "harvest_source_id", "value": "00080910-39e7-408f-882c-e6e1eb6baadb"}, {"key": "harvest_source_title", "value": "Geoscience Australia"}], "tags": [{"display_name": "EARTH SCIENCES", "id": "927af2a7-7457-45c2-bd55-10000fd09c14", "name": "EARTH SCIENCES", "state": "active", "vocabulary_id": null}, {"display_name": "Exploration Geochemistry", "id": "f1e37d08-73e2-457a-965c-f98c7a6c344d", "name": "Exploration Geochemistry", "state": "active", "vocabulary_id": null}, {"display_name": "Published_External", "id": "5178775c-8044-4b7f-881f-5428a4e2d925", "name": "Published_External", "state": "active", "vocabulary_id": null}, {"display_name": "Stochastic Analysis and Modelling", "id": "d2c8b1e5-5173-40c0-8fdc-fcac3f932a4b", "name": "Stochastic Analysis and Modelling", "state": "active", "vocabulary_id": null}, {"display_name": "machine Learning", "id": "1bc279c8-929b-423d-a2dd-d14c80c18a8c", "name": "machine Learning", "state": "active", "vocabulary_id": null}, {"display_name": "mineral potential mapping", "id": "2eac06bc-9ed4-43c4-b069-a22911f23c21", "name": "mineral potential mapping", "state": "active", "vocabulary_id": null}, {"display_name": "multivariate geostatistical simulation", "id": "45d3ed93-72bd-490c-9555-4f69af3fae7e", "name": "multivariate geostatistical simulation", "state": "active", "vocabulary_id": null}, {"display_name": "predictive model", "id": "f80f3287-8d80-4603-93f1-acb6bed45517", "name": "predictive model", "state": "active", "vocabulary_id": null}, {"display_name": "spatial data", "id": "81aa7e9f-a1a5-4bd2-814d-1c2eada01755", "name": "spatial data", "state": "active", "vocabulary_id": null}], "resources": [], "groups": [], "relationships_as_subject": [], "relationships_as_object": []}}