For example, machine learning (ML) can be used to create estimation domains with stationary grade populations, and to represent consistent geological volumes. This work is conventionally done as a manual wireframing task undertaken by resource geologists. However, automated ML tasks can ideally be ...
Geographical information system (GIS) is gaining its popularity beyond geography and information technology (IT) with its strong power in managing and analysing spatial data. In medical geology, GIS provides two main useful functions: (a) mapping and (b) spatial analysis. It contains specialised co...
Geographical information system (GIS) is gaining its popularity beyond geography and information technology (IT) with its strong power in managing and analysing spatial data. In medical geology, GIS provides two main useful functions: (a) mapping and (b)
To test if machine learning could help them narrow in on interesting geology sites, Donn and Beach focused on an area in northwestern Belize that was heavily vegetated and difficult to access. They concentrated on finding cave entrances deep in the forest that had yet to be been uncovered. Mik...
(Zheng W, Tian F, Di Q, Xin W, Cheng F and ShanX. Electrofacies classification of deeply buried carbonate strata using machinelearning methods: A case study on ordovician paleokarst reservoirs in TarimBasin[J]. Marine...
An investigation of global trace-element data suggests that the parental melts of hotspot lavas are uniform in their elemental composition, consistent with derivation from a common depleted and outgassed mantle reservoir. Matthijs A. Smit & Ellen Kooijman Research Briefing | 02 September 2024 Grap...
This research introduces robust machine learning (ML) approaches to predict rock mass quality conditions in complex geological environments, leveraging a large database of TBM parameters and rock mass rating (RMR) values. To do so, a total of 6879 stable phase TBM cycle data were collected from...
AREA OF EXPERTISE: Subsurface/Spatial Data Analytics, Geostatistics, and Machine Learning Share this: Click to share on Facebook (Opens in new window) Click to share on Twitter (Opens in new window) Click to share on LinkedIn (Opens in new window) ...
List of resources for mineral exploration and machine learning, generally with useful code and examples. nlpdata-sciencedatamachine-learningdeep-learninggeoscienceartificial-intelligencegeophysicscoppernickelmodellinggeologyrocksstratigraphygeochemistrymineral-explorationspectral-unmixinglithologymineralsprospectivity ...
Python client for the Earthchem REST and OGC APIs open-sourceopen-dataopen-sciencegeologygeochemistryfair-data Updatedon Jul 3, 2018 Python SKBEL - Bayesian Evidential Learning framework built on top of scikit-learn. machine-learningsklearnbayesian-inferencegeologygaussian-processesgroundwaterpfamultivariate...