# Geochemistry and Geologic Mapping

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/geochemistry-and-geologic-mapping/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on the application of machine learning, remote sensing, and compositional data analysis techniques for mineral prospectivity mapping. It explores the use of advanced technologies such as ASTER and hyperspectral imaging to identify geological features, geochemical anomalies, and hydrothermal alterations associated with mineralization. The cluster also delves into the challenges and opportunities in using support vector machines, fractal modeling, and statistical analysis for predicting undiscovered mineral deposits. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | t12157 |
| Works | 51 |

## Topic papers all

Showing 15 of 51.

- [The influence of pH and organic matter content in paddy soil on heavy metal availability and their uptake by rice plants](https://scholariq.org/papers/the-influence-of-ph-and-organic-matter-content-in-paddy-soil-on-heavy-metal/)
- [Global soil pollution by toxic metals threatens agriculture and human health](https://scholariq.org/papers/global-soil-pollution-by-toxic-metals-threatens-agriculture-and-human-health/)
- [Discarding or downweighting high-noise variables in factor analytic models](https://scholariq.org/papers/discarding-or-downweighting-high-noise-variables-in-factor-analytic-models/)
- [Multiscale Dynamic Graph Convolutional Network for Hyperspectral Image Classification](https://scholariq.org/papers/multiscale-dynamic-graph-convolutional-network-for-hyperspectral-image/)
- [An overview and comparison of machine-learning techniques for classification purposes in digital soil mapping](https://scholariq.org/papers/an-overview-and-comparison-of-machine-learning-techniques-for-classification/)
- [Spatial risk assessment and sources identification of heavy metals in surface sediments from the Dongting Lake, Middle China](https://scholariq.org/papers/spatial-risk-assessment-and-sources-identification-of-heavy-metals-in-surface/)
- [Artificial intelligence for geoscience: Progress, challenges, and perspectives](https://scholariq.org/papers/artificial-intelligence-for-geoscience-progress-challenges-and-perspectives/)
- [Normalizing XRF-scanner data: A cautionary note on the interpretation of high-resolution records from organic-rich lakes](https://scholariq.org/papers/normalizing-xrf-scanner-data-a-cautionary-note-on-the-interpretation-of-high/)
- [Estimating the distribution trend of soil heavy metals in mining area from HyMap airborne hyperspectral imagery based on ensemble learning](https://scholariq.org/papers/estimating-the-distribution-trend-of-soil-heavy-metals-in-mining-area-from-hymap/)
- [Review of deep learning methods for remote sensing satellite images classification: experimental survey and comparative analysis](https://scholariq.org/papers/review-of-deep-learning-methods-for-remote-sensing-satellite-images/)
- [Provenance and ages of the Altyn Complex in Altyn Tagh: Implications for the early Neoproterozoic evolution of northwestern China](https://scholariq.org/papers/provenance-and-ages-of-the-altyn-complex-in-altyn-tagh-implications-for-the/)
- [Non-carcinogenic risk assessment of exposure to heavy metals in underground water resources in Saraven, Iran: Spatial distribution, monte-carlo simulation, sensitive analysis](https://scholariq.org/papers/non-carcinogenic-risk-assessment-of-exposure-to-heavy-metals-in-underground/)
- [Regional groundwater productivity potential mapping using a geographic information system (GIS) based artificial neural network model](https://scholariq.org/papers/regional-groundwater-productivity-potential-mapping-using-a-geographic/)
- [Integrating hierarchical bioavailability and population distribution into potential eco-risk assessment of heavy metals in road dust: A case study in Xiandao District, Changsha city, China](https://scholariq.org/papers/integrating-hierarchical-bioavailability-and-population-distribution-into/)
- [Heavy metals in road dust from Xiandao District, Changsha City, China: characteristics, health risk assessment, and integrated source identification](https://scholariq.org/papers/heavy-metals-in-road-dust-from-xiandao-district-changsha-city-china/)

## Topic primary papers

- [Normalizing XRF-scanner data: A cautionary note on the interpretation of high-resolution records from organic-rich lakes](https://scholariq.org/papers/normalizing-xrf-scanner-data-a-cautionary-note-on-the-interpretation-of-high/)
- [Estimating the distribution trend of soil heavy metals in mining area from HyMap airborne hyperspectral imagery based on ensemble learning](https://scholariq.org/papers/estimating-the-distribution-trend-of-soil-heavy-metals-in-mining-area-from-hymap/)
- [Machine Learning Algorithms for Automatic Lithological Mapping Using Remote Sensing Data: A Case Study from Souk Arbaa Sahel, Sidi Ifni Inlier, Western Anti-Atlas, Morocco](https://scholariq.org/papers/machine-learning-algorithms-for-automatic-lithological-mapping-using-remote/)
- [Remote sensing data in lithium (Li) exploration: A new approach for the detection of Li-bearing pegmatites](https://scholariq.org/papers/remote-sensing-data-in-lithium-li-exploration-a-new-approach-for-the-detection/)
- [Semi-Automatization of Support Vector Machines to Map Lithium (Li) Bearing Pegmatites](https://scholariq.org/papers/semi-automatization-of-support-vector-machines-to-map-lithium-li-bearing/)
- [Hyperspectral imagery reveals large spatial variations of heavy metal content in agricultural soil - A case study of remote-sensing inversion based on Orbita Hyperspectral Satellites (OHS) imagery](https://scholariq.org/papers/hyperspectral-imagery-reveals-large-spatial-variations-of-heavy-metal-content-in/)
- [Bagging-based Positive–Unlabeled Data Learning Algorithm with Base Learners Random Forest and XGBoost for 3D Exploration Targeting in the Kalatongke District, Xinjiang, China](https://scholariq.org/papers/bagging-based-positive-unlabeled-data-learning-algorithm-with-base-learners/)
- [Coniform stromatolites and the Vindhyan Supergroup, Central India: implication for basinal correlation and age](https://scholariq.org/papers/coniform-stromatolites-and-the-vindhyan-supergroup-central-india-implication-for/)
- [Using Nix Color Sensor and Munsell Soil Color Variables to Classify Contrasting Soil Types and Predict Soil Organic Carbon in Eastern India](https://scholariq.org/papers/using-nix-color-sensor-and-munsell-soil-color-variables-to-classify-contrasting/)
- [Multi-Element Dataset Across Diverse Climatic Zones and Soil Profiles in China’s Mountains](https://scholariq.org/papers/multi-element-dataset-across-diverse-climatic-zones-and-soil-profiles-in-china-s/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.