# Intelligent Identification and Prediction Mineral Resources Deposit Based on Deep Learning

^{1}

^{2}

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## Abstract

**:**

## 1. Introduction

## 2. Literature Review

#### 2.1. Deep Learning for Metallogenic Prediction

#### 2.2. Multi-Scale Feature Fusion Technology for Mineral Prospecting

#### 2.3. The Mechanism of Attention Used in Geodata Analysis

## 3. Materials and Methods

#### 3.1. Materials

#### 3.2. Methods

#### 3.2.1. Data Pre-Processing

_{AUC}value was calculated using Formulas (1)–(3):

_{AUC}meets the standard normal distribution, and the critical value is obtained by comparing the standard normal distribution table, which is used to detect whether there is a significant difference between AUC and 0.5. The results are shown in Table 2. When the Z

_{AUC}value is greater than 0.01, the critical value of 2.58 is selected; that is, Ag, Au, Sn, Cu, Ba, Sb, Hg, and Mo are selected as favorable prospecting factors.

_{i}, y

_{i}) to the grid point (x

_{0}, y

_{0}) is the valuation of the position, i is the observed value at the discrete point, and N is the number of discrete points involved in the calculation. In this study, the inverse distance weight method was applied to Ag, Au, Sn, Cu, Ba, Sb, Hg, Mo, and other elements to generate eight isoline maps of element concentration with a size of 1560 × 1560. Finally, the isoline map of element concentration, the geological layer, and the fault structure layer were superimposed with the known ore deposit layer, respectively, to generate 10 new images of geological prospecting factors, as shown in Figure 2.

#### 3.2.2. Multiscale Feature Attention Framework (MFAF)

#### 3.2.3. Multi-Scale Feature Fusion

_{1}, α

_{2}, α

_{3}, …, α

_{n}} generates convolution of different sizes to check the convolution operation and generates an image of multi-scale features. Specifically, we use the convolution operation of the convolution kernel $M=\{{M}_{1},{M}_{2},\dots ,{M}_{n}\}$ and the set α of expansion coefficients to obtain the multi-scale feature graph F = {F

_{1}, F

_{2}, …, F

_{n}}, where the ith feature is ${F}_{i}$. The specific generation Formula (6) is shown as follows: Where x

_{i}represents the ith feature element, M

_{i}represents the convolution weight corresponding to the generation of the ith feature graph, α

_{i}represents the expansion convolution coefficient corresponding to the generation of the ith feature graph, and r represents the convolution channel.

#### 3.2.4. Channel Attention

#### 3.2.5. Spatial Attention

#### 3.2.6. Fully Connected Layer, Softmax, and Voting

_{i}is the label value of the i geological prospecting factor feature, and loss(*) is the calculation of cross entropy loss after Softmax is activated.

## 4. Results and Discussion

#### 4.1. Experiment Settings

^{−4}, cycle iteration of 220 times, initial learning rate set at 0.02, decay of 40% every 30 times, and batch size of 32. In this paper, MFAF uses α = {α

_{1}, α

_{2}, α

_{3}, …, α

_{n}} MFI Framework [26] expansion coefficient is set to α = {1, 6, 12, 18, 24}.

#### 4.2. Experiment Results and Analysis

#### 4.3. Correlation Analysis Experiment

#### 4.3.1. Ablation Experiments

- Different geological prospecting factors have different degrees of influence on ore deposits. This study adopted channel attention module in the process of training data can reduce the influence of human factors. According to the value of loss in the experiment, the weight values on different channels are adjusted reversely and dynamically, the weight values of important features are increased, the importance of features with little influence is suppressed. the accuracy of the deposit prospecting prediction is improved.
- Spatial attention module is adopted in the MFAF model can consider the difference of geological prospecting factors in different spatial locations on mineralization of ore deposits. The spatial attention module can use spatial attention as a supplement to the convolution operation, which enhances image features at different spatial locations.
- The contributions of these methods to MFAF are different. According to the contribution from large to small, they are ranked as follows: channel attention, spatial attention.

#### 4.3.2. Parameter Analysis Experiments

#### 4.4. Visualization

#### 4.5. Significant Criticism and Research Limitations

## 5. Conclusions

- The deep learning model of MFAF can effectively solve the problems of fine features of geological images and few mineral points in the region. In this model, the expansion coefficient and multi-scale features are used to extract more and more detailed geological image feature information, and expansion convolution with different convolution kernel sizes is used to generate more labeled sample data.
- The network architecture of channel attention and spatial attention mechanism was used to assign different weight coefficients to the geological image feature data of different channels and different spatial locations. It can avoid the influence of human subjective factors and improve the accuracy of intelligent identification and prediction of ore deposits based on geoimage data.
- The smote method was used to enhance the labeled geological image samples. This can effectively expand the number of samples in geoscience image data set, ensure the data sent to the neural network to achieve balance, and complete the effective training of deep learning network model.
- In this study, MFAF was adopted to identify and predict the deposit in Jinshan research area. Experimental results showed that the predicted prospecting target area covered 100% of the known deposits in the study area. The other prediction areas have good metallogenic conditions and can be used as ore deposit prediction areas for further study. The research of this paper can provide resource guarantee and technical support for the sustainable exploitation of mineral resources and the sustainable growth of society and economy.
- Based on the limitations of our research conditions, the accuracy, AUC, recall, and F1-Score are all relatively low. The geological conditions are uncertain and data are difficult to obtain. We can try to adopt transfer learning in the geographic image research and enrich our geoscience and artificial intelligence knowledge in future work.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**Geological Map of the Study Area. (

**a**) Geological map; (

**b**) location of the study area; (

**c**) legend; (

**d**) sampling quantity.

**Figure 2.**Geographic prospecting factors images. (

**a**–

**h**) are Ag, Au, Sn, Cu, Ba, Sb, Hg, Mo element contour maps;(

**i**) Mineral geological map; (

**j**) Fault map.

**Figure 8.**Prediction diagram of prospecting target in the study area. (

**a**) Geological diagram; (

**b**) location of the study area; (

**c**) legend.

**Table 1.**Geochemical element data set (The units of Au and Ag are ng/g, other element units are μg/g).

X | Y | Ag | Au | B | Sn | Cu | Ba | Mn | Pb | Zn | As | Sb | Hg | Mo | W | Bi | F |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

421.63 | 2416.85 | 0.078 | 0.54 | 4 | 2.56 | 7 | 88 | 209 | 12 | 23 | 0.9 | 0.29 | 0.04 | 0.82 | 1.16 | 0.42 | 204 |

420.93 | 2416.80 | 0.06 | 0.81 | 3 | 3.74 | 5 | 885 | 305 | 33 | 22 | 0.58 | 0.36 | 0.04 | 0.82 | 1.11 | 1.41 | 222 |

420.95 | 2416.35 | 0.086 | 0.94 | 4 | 2.41 | 5 | 797 | 267 | 53 | 35 | 1.15 | 0.34 | 0.09 | 0.51 | 1.16 | 0.42 | 212 |

421.21 | 2415.85 | 0.043 | 0.81 | 3 | 1.52 | 5 | 1111 | 423 | 42 | 14 | 0.51 | 0.35 | 0.07 | 0.59 | 0.38 | 0.23 | 222 |

420.30 | 2416.35 | 0.046 | 0.37 | 2 | 1.65 | 6 | 941 | 498 | 38 | 17 | 0.53 | 0.31 | 0.02 | 0.57 | 0.33 | 0.61 | 222 |

419.86 | 2416.15 | 0.033 | 1.09 | 4 | 1.53 | 8 | 427 | 338 | 37 | 29 | 0.74 | 0.28 | 0.07 | 1.68 | 0.73 | 0.47 | 204 |

Element | AUC | Z_{AUC} | Element | AUC | Z_{AUC} |
---|---|---|---|---|---|

Au | 0.6024 | 2.8395 | B | 0.5901 | 2.4839 |

Sn | 0.6065 | 2.9595 | Cu | 0.6311 | 3.6977 |

Ag | 0.6762 | 5.1563 | Ba | 0.6147 | 3.2020 |

Mn | 0.5573 | 1.5617 | Pb | 0.5778 | 2.1341 |

Zn | 0.5450 | 1.2232 | As | 0.5655 | 1.7893 |

Sb | 0.5942 | 2.6017 | Bi | 0.5901 | 2.4839 |

Hg | 0.6393 | 3.9516 | Mo | 0.5983 | 2.7203 |

W | 0.5778 | 2.1341 | F | 0.5696 | 1.9037 |

Methods | Accuracy | AUC | Recall | F1-Score |
---|---|---|---|---|

ResNet18 [48] | 64.84 | 63.13 | 32.05 | 59.41 |

ResNet18* | 72.66 | 73.46 | 42.66 | 63.76 |

ShuffleNetV2 [49] | 62.37 | 61.43 | 18.43 | 53.98 |

ShuffleNetV2* | 67.23 | 65.42 | 37.32 | 63.72 |

GoogLeNet [50] | 62.38 | 61.45 | 20.14 | 56.33 |

MobileNetV2 [51] | 64.23 | 64.13 | 16.23 | 58.36 |

MnasNet [52] | 68.79 | 67.23 | 17.69 | 60.86 |

Methods | Accuracy | AUC | Recall | F1-Score |
---|---|---|---|---|

ResNet18* | 72.66 | 73.46 | 42.66 | 63.71 |

R-CA-ResNet18* | 69.54 | 64.89 | 33.98 | 58.64 |

ShuffleNetV2* | 67.23 | 65.42 | 37.32 | 63.72 |

R-CA-ShuffleNetV2* | 63.42 | 63.24 | 35.51 | 60.99 |

ResNet18* | 72.66 | 73.46 | 42.66 | 63.71 |

R-SA-ResNet18* | 71.13 | 71.68 | 39.46 | 61.34 |

ShuffleNetV2* | 67.23 | 65.42 | 37.32 | 63.72 |

R-SA-ShuffleNetV2* | 64.48 | 63.96 | 35.12 | 62.04 |

Loss Function | Accuracy | AUC | Recall | F1-Score |
---|---|---|---|---|

0 | 64.84 | 63.13 | 32.05 | 59.41 |

0.1 | 71.21 | 70.22 | 39.12 | 60.34 |

0.4 | 72.66 | 73.46 | 42.66 | 63.71 |

0.6 | 71.45 | 70.32 | 38.49 | 60.19 |

0.7 | 68.44 | 65.54 | 37.55 | 58.84 |

0.8 | 64.96 | 63.27 | 32.64 | 60.34 |

Dilation Rate | Accuracy | AUC | Recall | F1-Score |
---|---|---|---|---|

rate 1 | 63.45 | 62.02 | 32.79 | 50.43 |

rate 2 | 67.34 | 66.22 | 38.28 | 61.14 |

rate 3 | 72.66 | 73.46 | 42.66 | 63.71 |

rate 4 | 70.22 | 68.86 | 31.46 | 55.65 |

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## Share and Cite

**MDPI and ACS Style**

Gao, L.; Wang, K.; Zhang, X.; Wang, C.
Intelligent Identification and Prediction Mineral Resources Deposit Based on Deep Learning. *Sustainability* **2023**, *15*, 10269.
https://doi.org/10.3390/su151310269

**AMA Style**

Gao L, Wang K, Zhang X, Wang C.
Intelligent Identification and Prediction Mineral Resources Deposit Based on Deep Learning. *Sustainability*. 2023; 15(13):10269.
https://doi.org/10.3390/su151310269

**Chicago/Turabian Style**

Gao, Le, Kun Wang, Xin Zhang, and Chen Wang.
2023. "Intelligent Identification and Prediction Mineral Resources Deposit Based on Deep Learning" *Sustainability* 15, no. 13: 10269.
https://doi.org/10.3390/su151310269