A review of machine learning in geochemistry and cosmochemistry: Method improvements and applications

He, Yuyang; Zhou, You; Wen, Tao; Zhang, Shuang; Huang, Fang; Zou, Xinyu; Ma, Xiaogang; Zhu, Yueqin

doi:10.1016/j.apgeochem.2022.105273

Cited by 40 publications

(13 citation statements)

References 215 publications

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“…A machine learning model is data-driven, learning from data and improving accuracy without explicit programming. Over time, machine learning has evolved into various learning technologies, including connectionism, symbolism, and statistical learning [62]. In the context of groundwater, machine learning has several important tasks.…”

Section: Machine Learning History and Groundwatermentioning

confidence: 99%

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Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review

Zaresefat

Derakhshani

2023

Water

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Developing precise soft computing methods for groundwater management, which includes quality and quantity, is crucial for improving water resources planning and management. In the past 20 years, significant progress has been made in groundwater management using hybrid machine learning (ML) models as artificial intelligence (AI). Although various review articles have reported advances in this field, existing literature must cover groundwater management using hybrid ML. This review article aims to understand the current state-of-the-art hybrid ML models used for groundwater management and the achievements made in this domain. It includes the most cited hybrid ML models employed for groundwater management from 2009 to 2022. It summarises the reviewed papers, highlighting their strengths and weaknesses, the performance criteria employed, and the most highly cited models identified. It is worth noting that the accuracy was significantly enhanced, resulting in a substantial improvement and demonstrating a robust outcome. Additionally, this article outlines recommendations for future research directions to enhance the accuracy of groundwater management, including prediction models and enhance related knowledge.

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Section: Machine Learning History and Groundwatermentioning

confidence: 99%

“…One of machine learning's key learning technologies is symbolism. Logic involves manipulating symbols, rules, and operations [62]. Symbolism has been used to create groundwater expert systems, which are rule-based systems that mimic human decisionmaking [63].…”

Section: Symbolismmentioning

confidence: 99%

Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review

Zaresefat

Derakhshani

2023

Water

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“…In addition to images (e.g., geochemical maps, electron microscope images), a significant amount of important geochemical information is stored in tabular data, such as the concentrations and speciation of chemical compounds, elemental concentrations, and isotopic ratios. When applied to these data sets, machine learning (ML) can reveal deep structural patterns with the data, thereby bringing new geochemical insights (Chicchi et al., 2023; He et al., 2022; Morrison et al., 2017; Petrelli & Perugini, 2016; Prabhu et al., 2021; Qin et al., 2022; Stracke et al., 2022; Tao et al., 2021; Wen et al., 2021). Although flourishing, ML implementation is laborious and time‐consuming for most geochemists because they must, for example, locate codes from scikit‐learn (Pedregosa et al., 2011), modify codes to fit their unique data set(s), and tune the model's hyperparameters.…”

Section: Introductionmentioning

confidence: 99%

Geochemistry π: Automated Machine Learning Python Framework for Tabular Data

ZhangZhou,

He,

Sun

et al. 2024

Geochem Geophys Geosyst

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Although machine learning (ML) has brought new insights into geochemistry research, its implementation is laborious and time‐consuming. Here, we announce Geochemistry π, an open‐source automated ML Python framework. Geochemists only need to provide tabulated data and select the desired options to clean data and run ML algorithms. The process operates in a question‐and‐answer format, and thus does not require that users have coding experience. After either automatic or manual parameter tuning, the automated Python framework provides users with performance and prediction results for the trained ML model. Based on the scikit‐learn library, Geochemistry π has established a customized automated process for implementing classification, regression, dimensionality reduction, and clustering algorithms. The Python framework enables extensibility and portability by constructing a hierarchical pipeline architecture that separates data transmission from the algorithm application. The AutoML module is constructed using the Cost‐Frugal Optimization and Blended Search Strategy hyperparameter search methods from the A Fast and Lightweight AutoML Library, and the model parameter optimization process is accelerated by the Ray distributed computing framework. The MLflow library is integrated into ML lifecycle management, which allows users to compare multiple trained models at different scales and manage the data and diagrams generated. In addition, the front‐end and back‐end frameworks are separated to build the web portal, which demonstrates the ML model and data science workflow through a user‐friendly web interface. In summary, Geochemistry π provides a Python framework for users and developers to accelerate their data mining efficiency with both online and offline operation options.

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“…For identifying weak anomalies in continuous-field stream sediment geochemical data, local singularity analysis (LSA) was proposed by Cheng (2007) and demonstrated by Wang et al (2018) and Zuo et al (2009). Recently, a variety of machine learning and deep learning algorithms have been widely developed and applied to map geochemical anomalies (He et al, 2022;Wu et al, 2022;Xiong and Zuo, 2022;Zhang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Single‐element Anomaly Mapping from Stream Sediment Geochemical Landscapes Aided by Digital Terrain Analysis

Xiang

Xia

Xiao

et al. 2023

Acta Geologica Sinica (Eng)

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The identification of anomalies within stream sediment geochemical data is one of the fastest developing areas in mineral exploration. The various means used to achieve this objective make use of either continuous or discrete field models of stream sediment geochemical data. To map anomalies in a discrete field model of such data, two corrections are required: background correction and downstream dilution correction. Topography and geomorphology are important factors in variations of element content in stream sediments. However, few studies have considered, through the use of digital terrain analysis, the influence of geomorphic features in downstream dilution correction of stream sediment geochemical data. This study proposes and demonstrates an improvement to the traditional downstream dilution correction equation, based on the use of digital terrain analysis to map single‐element anomalies in stream sediment geochemical landscapes. Moreover, this study compares the results of analyses using discrete and continuous field models of stream sediment geochemical data from the Xincang area, Tibet. The efficiency of the proposed methodology was validated against known mineral occurrences. The results indicate that catchment‐based analysis outperforms interpolation‐based analysis of stream sediment geochemical data for anomaly mapping. Meanwhile, the proposed modified downstream dilution correction equation proved more effective than the original equation. However, further testing of this modified downstream dilution correction is needed in other areas, in order to investigate its efficiency further.

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A review of machine learning in geochemistry and cosmochemistry: Method improvements and applications

Cited by 40 publications

References 215 publications

Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review

Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review

Geochemistry π: Automated Machine Learning Python Framework for Tabular Data

Single‐element Anomaly Mapping from Stream Sediment Geochemical Landscapes Aided by Digital Terrain Analysis

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