Journal of Geology & Geophysics
Open Access

Geochemical Anomaly Detection using a Random Forest Model

Mohamed Hassan Bahnassy^{* *}Correspondence: Mohamed Hassan Bahnassy, Department of Soil and Water Sciences, Alexandria University, Alexandria, Egypt, Email:

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Description

Geochemical anomalies are important indicators of mineral deposits and geological structures. The detection of these anomalies is critical for mineral exploration and resource evaluation. Machine learning algorithms, such as Random Forest (RF), have been widely used for the detection of geochemical anomalies. However, the accuracy of the RF model can be further improved by incorporating optimization techniques such as competitive mechanism and beetle antennae search. This article will explore how these optimization techniques can enhance the detection of geochemical anomalies using the RF model.

Random forest model for geochemical anomaly detection

The RF model is a powerful machine learning algorithm that is well-suited for geochemical anomaly detection. The RF model uses multiple decision trees to make predictions, with each tree built using a subset of the input data. The final prediction is made by aggregating the predictions of all the individual trees. The RF model is known for its ability to handle highdimensional data, avoid overfitting, and produce stable and accurate predictions.

Competitive mechanism optimization for geochemical anomaly detection

The competitive mechanism optimization technique is a bioinspired approach that has been used to optimize the performance of machine learning algorithms. In the context of geochemical anomaly detection, the competitive mechanism is used to select the best subset of features from the input data.

The competitive mechanism works by dividing the feature space into sub-regions and selecting the best subset of features for each sub-region. The selection process is based on the competitive mechanism, where the features that provide the best prediction accuracy are selected for each sub-region. This process is repeated for each sub-region, resulting in a set of optimized subsets of features. The RF model is then trained using these optimized subsets of features to improve the accuracy of the predictions.

Beetle antennae search optimization for geochemical anomaly detection

The beetle antennae search optimization technique is another bio-inspired approach that has been used to optimize the performance of machine learning algorithms. The beetle antennae search algorithm is based on the behavior of beetles, where they use their antennae to sense their environment and locate food sources.

In the context of geochemical anomaly detection, the beetle antennae search algorithm is used to select the best subset of features from the input data. The algorithm works by simulating the behavior of beetles, where each beetle represents a potential subset of features. The beetles move through the feature space, and the best subsets of features are selected based on the fitness function, which is a measure of the prediction accuracy of the RF model.

The beetle antennae search algorithm is an efficient optimization technique that can quickly converge to a nearoptimal solution. This makes it well-suited for high-dimensional data, such as geochemical data, where the number of features can be large.

The incorporation of competitive mechanism and beetle antennae search optimization techniques can significantly improve the accuracy of the RF model for geochemical anomaly detection. These optimization techniques can help to select the most relevant features and reduce the noise in the data, resulting in more accurate predictions.

The competitive mechanism optimization technique can help to identify the most important features for each sub-region, resulting in optimized subsets of features that are tailored to the specific characteristics of each sub-region. This can improve the accuracy of the predictions, especially in areas with complex geological structures.

The beetle antennae search optimization technique can quickly converge to a near-optimal solution, resulting in optimized subsets of features that are well-suited for high-dimensional geochemical data. This can improve the efficiency of the optimization process and reduce the computational time required for geochemical anomaly detection.