# Mineral Prospecting Prediction via Transfer Learning Based on Geological Big Data: A Case Study of Huayuan, Hunan, China

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Study Area

#### 2.1. Geological Background

#### 2.1.1. Stratigraphy

#### 2.1.2. Structure

#### Fold Structure

#### Fault Structure

#### 2.1.3. Lithologic Paleogeography

#### 2.2. Mineralization

_{4}+ SO

_{4}

^{2−}

`→`HCO

^{3−}+ HS

^{−}+ H

_{2}O; 2HCO

^{3−}+ Mn

^{2+}

`→`MnCO

_{3}+ CO

_{2}+ H

_{2}O).

_{2}S gas. Meanwhile, H

_{2}S reacts with CO

_{2}in the water body to form HCO

^{3−}, which reacts with manganese ions brought by seawater reflux to form rhombmanganese ore (Figure 3b).

## 3. Research Methods

#### 3.1. Construction of Large Spatial Database

#### 3.2. 3D Modeling and Prediction Layer Construction

#### 3.2.1. Modeling of the Pre-Training Area

#### 3.2.2. Construction of 3D Pre-Training Layer

_{max}= 635,975, X

_{min}= 630,025; Y

_{max}= 3,147,062, Y

_{min}= 3,137,937; Z

_{max}= 450, Z

_{min}= −350).

#### 3.3. 3D CNN Modeling

#### 3.3.1. 3D CNN Algorithm

#### 3.3.2. Localization and Probability Determination

#### 3.4. Transfer Learning Model

#### 3.4.1. Sample Data Expansion

#### 3.4.2. Operating Characteristic Curve

## 4. Results and Discussion

#### 4.1. Comparison of Different Factor Combinations

#### 4.2. Comparative Test on 3DCMM and TL

#### 4.3. Comprehensive Analysis of Prediction Results

## 5. Conclusions

- (1)
- Twenty-two proposed ore-controlling variables were divided into six groups for comparative experiments with different combinations, and each group was further divided into the 3D CNN prediction method and the transfer learning prediction method. After proving the similarity of the regional metallogenic background, the convolution kernel of the Minle area was transferred to that of the Huayuan area with poor data. Then, both were used to train a 3D prediction model to realize the training and transfer of the spatial correlation between the spatial distribution of ore-controlling factors and the manganese ore. The results indicated that the accuracy of the transfer learning model in test 6 could reach 100%, showing the strong stability of the transfer learning prediction model and a fast convergence speed.
- (2)
- By comparing the 3D predicted targets before and after the transfer learning of tests 5 and 6, the 3D CNN model and the prediction models after transfer learning were compared and analyzed in terms of the ROC curve and ore-controlling ratio. It was found that if the 7–12 fracture and each fracture buffer layer are added, the 3D CNN model will perform well, and if they are not added, the transfer learning model will be superior, with an accuracy of 100%. The blocks with high probability values in the prediction results are few and concentrated.
- (3)
- The final prediction results were superimposed and verified with the solid model of the rift basin and the growth fault model in the unpredicted area. The analysis results demonstrated that the delineated metallogenic prospect area had a high degree of overlap with the rift basin, and it is located in the hanging wall of the growth fault, with excellent metallogenic conditions. To sum up, the 3D CNN prediction method has great potential and is advantageous in mineral prediction when big data are available, and transfer learning based on the 3D CNN algorithm greatly improves 3D deep mineral prediction in the case of incomplete data.

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**The regional geological and mineral map and district plan at a scale of 1:200,000. (

**A**) represents the Songtao–Huayuan area; (

**B**) represents the Huayuan Mining area; (

**C**) represents the transfer learning pre-training area (Minle); and (

**D**) represents the area with detailed data (according to the Hunan Bureau of Geology and Mineral Exploration and Development).

**Figure 2.**(

**a**) The lithofacies paleogeography map of Songtao-Huayuan in the Nanhua period and Datangpo period. (

**b**) The sequence stratigraphic division map of the Nanhua period (provided by the Hunan Geological Survey Institute).

**Figure 3.**(

**a**) The metallogenic model of the regional manganese ore. (

**b**) The evolutionary model of the regional sedimentary manganese basin [28].

**Figure 6.**The structure of the 3D CNN prediction model. The first layer is the input layer with 22 voxels, and the size of the voxels is 100 m × 100 m × 100 m. Conv3D1 and Conv3D2 are convolutional layers, and the filter size of both layers is 5 × 5 × 5. MaxPooling3d1 and MaxPooling3d2 are 3D max-pooling layers with a pooling sizes of 2 × 2 × 2. Dropout is applied between the flatten layer and the dense layer, and the fraction of the input units to drop is set to 0.5. Note that the regularization method is applied on the dense layer.

**Figure 8.**Classification of transfer learning (

**a**) based on characteristic space and label space and (

**b**) based on the transfer learning method.

**Figure 9.**The principle of sample expansion [36].

**Figure 10.**The spatial relationship and the confusion matrix: (

**a**) the spatial relationship between prediction results and known mineral deposits (points) and (

**b**) the confusion matric for four types of relationships [41].

**Figure 12.**The output of the convolution kernel under the 3D CNN transfer learning in the Minle mining area.

**Figure 13.**Comparison of the confusion matrix and ROC curves of the six groups of transfer learning models.

**Figure 14.**The accuracy and loss curves of the six groups of transfer learning models. (

**a**–

**f**) respectively shows the training loss, training accuracy, validation accuracy and validation loss of the transfer learning model of the experimental 1–6.

**Figure 15.**The performance evaluation curves of mineral prediction by the 3D CNN model and the 3D CNN-TL model in six tests. (

**a**,

**d**) show the success rate curve and ore-controlling rate curve of twelve tests, respectively. (

**b**) shows the success rate curve of the 3D CNN model in six tests. (

**c**) shows the success rate curve of the 3D CNN-TL model in six tests. (

**e**,

**f**) were plotted by extracting the ore-controlling rate between 0.95 and 1 of the curve in (

**d**). (

**e**) shows the ore-controlling rate curve of the 3D CNN model in six tests. (

**f**) shows the ore-controlling rate curve of the 3D CNN-TL model in six tests.

**Figure 16.**The results comparison of 3D metallogenic prospect delineation in test 5. (

**a**,

**c**,

**e**) show the prediction results of the 3DCNN-TL model; (

**b**,

**d**,

**f**) show the prediction results of the 3D CNN. (

**g**) shows the target section delineated by the 3D CNN-TL prediction model when the threshold value of the metallogenic prospect is 0.65, and (

**h**) shows the target section delineated by the 3D CNN model.

**Figure 17.**The result comparison of 3D metallogenic prospect delineation in test 6. (

**a**,

**c**,

**e**) show the prediction results of the 3D CNN-TL model; (

**b**,

**d**,

**f**) show the prediction results of the 3D CNN. (

**g**) is the target section delineated by the 3D CNN-TL prediction model when the threshold value of the metallogenic prospect is 0.65, and (

**h**) is the target section delineated by the 3D CNN model.

**Figure 18.**The overlayed diagrams of 3D predicted results and basin and growth faults: (

**a**) the 3D model of inferred extensional basins, (

**b**) the 3D overlayed diagram of inferred extensional basins and growth faults, (

**c**) the overlayed diagram of prediction results and basins and faults under the 3D CNN model in test 5, and (

**d**) the overlayed diagram of transfer learning in test 6 with basins and faults (the threshold value is 0.65).

Ore Deposit Type | Factor Type | Ore-Controlling Factors |
---|---|---|

“Datangpo type” sedimentary manganese ore | Rock | Rock strata: interglacial period, thick moraine conglomerate |

Morphology of ore body | Lenticular and interlaminar | |

Stratigraphic marks | Metallogenic age: Datangpo formation of the Nanhua period | |

Manganese-bearing rock series outcropped | ||

Speculated distribution of manganese-bearing rock series | ||

Structural marks | Manganese-forming sedimentary basin | |

Syndepositional fault | ||

Lithofacies paleogeography | ||

Geophysics | Gravity anomalies | |

Gravity anomaly transition zone | ||

Geochemistry | Mn geochemical anomaly | |

P geochemical anomaly | ||

Y geochemical anomaly |

Data Name | Scale | Number | Investigation Depth (m) |
---|---|---|---|

Statistical table of geochemical element content | 1:200,000 | 39 | Earth’s surface |

Geochemistry mapping | 1:200,000 | 20 | Earth’s surface |

Geological and mineral map of the Songtao-Huayuan area | 1:200,000 | 1 | Earth’s surface |

Lithofacies paleogeography map of the Datangpo period in the Songtao-Huayuan area | 1:100,000 | 1 | Earth’s surface |

Distribution map of the Datangpo Formation in the Songtao-Huayuan area | 1:100,000 | 1 | Earth’s surface |

Sedimentary tectonic map of the Songtao-Huayuan area | 1:100,000 | 1 | Earth surface |

Residual gravity anomaly map in the Songtao-Huayuan area | 1:100,000 | 1 | - |

MT profile inversion map | 1:100,000 | 2 | 4000 |

Data Name | Scale | Number | Investigation Depth (m) |
---|---|---|---|

Prospecting line profile map of Minle | 1:2000 | 46 | 700 |

Geological map of Heku and Malichang in the Huayuan area | 1:50,000 | 1 | Earth’s surface |

Borehole data of the Minle Mn mine | - | 212 | 660 |

Lithofacies paleogeography of the Datangpo period in the Songtao-Huayuan area | 1:100,000 | 1 | Earth’s surface |

Schematic diagram of the cut section design | 1:50,000 | 1 | Earth’s surface |

Map cut profiles of the Minle Mn mine | 1:50,000 | 23 | 1500 |

DEM data | Aster30m | 1 | Earth’s surface |

Data Name | Scale | Number | Investigation Depth (m) |
---|---|---|---|

Borehole data of the Minle Mn mine | - | 212 | 660 |

Prospecting line profile map of Minle | 1:2000 | 46 | 700 |

Lithofacies paleogeography of the Datangpo period in the Songtao-Huayuan area | 1:100,000 | 1 | Earth’s surface |

DEM data | Aster30m | 1 | Earth’s surface |

Topographic geological map of the Minle Mn mining area | 1:10,000 | 1 | Earth’s surface |

Schematic diagram of the cut section design | 1:10,000 | 1 | Earth’s surface |

Map cut profiles of the Minle Mn mine | 1:10,000 | 90 | 700 |

Ore-Controlling Factors | Metallogenic Geological Anomaly | Ore Prospecting Digital Model |
---|---|---|

Stratum | The stratum of the Datangpo Formation | Stratum digital model of the Datangpo Formation |

Rock stratum indicator | The thick moraine conglomerate, as the top plate of the ore bed, can indicate the mineralization | Digital model of the Nantuo moraine layer |

Structure | Deep fracture and tectonic convergence (submarine volcanic eruption, sedimentary manganese deposit, and ancient natural gas leakage) | Fracture digital model |

Lithofacies paleogeography | The secondary rift basin | Digital model of the basin control fracture and basin margin fracture |

Ore body | The manganese ore body | 3D digital model of the manganese ore body |

Ore-Controlling Factors | Metallogenic Geological Anomaly | 3D Spatial Reconstruction of Metallogenic Anomaly |
---|---|---|

Stratum | Sub-members under the Datangpo Formation | 3D spatial reconstruction of the lower sub-members under the Datangpo Formation |

3D spatial reconstruction of the medium sub-members under the Datangpo Formation | ||

3D spatial reconstruction of the upper sub-members under the Datangpo Formation | ||

Sequence stratigraphy | Favorable third-sequence stratigraphy | 3D spatial reconstruction of the condensation layer |

3D spatial reconstruction of the high-stand domain | ||

Rock stratum indicator | Favorable rock stratum indicator | 3D spatial reconstruction of manganese-bearing rock series |

Lithofacies paleogeography | Favorable sedimentary facies | Secondary rift basin |

Ancient landform | Ancient landform of the geological evolution period related to mineralization | 3D spatial reconstruction of the ancient landform |

Predictor Numbers | Evidence Item | W+ | W- | C |
---|---|---|---|---|

Predictor 1 | The third sequence | 4.337955 | 1.802756 | 2.5352 |

Predictor 2 | Datangpo Formation | 3.620267 | 1.877229 | 1.743037 |

Predictor 3 | Favorable metallogenic buffer zone of the Nantuo moraine layer | 4.367813 | −0.36435 | 4.732164 |

Predictor 4 | Interlaminar anomalous body of ancient landform of Datangpo Formation | 3.546471 | −7.98981 | 11.53628 |

Predictor 5 | Interlaminar anomalous body of ancient landform of Nantuo Formation | 2.821807 | 2.024608 | 0.797199 |

Predictor 6 | Interlaminar anomalous body of ancient landform of Nanhua Period | 2.866911 | 1.833689 | 1.033222 |

Predictor 7 | Fault | 2.861902 | 1.564909 | 1.296994 |

Predictor 8 | 50 m Fault buffer | 2.776001 | 1.329269 | 1.446732 |

Predictor 9 | 100 m Fault buffer | 2.673975 | 1.17519 | 1.498785 |

Predictor 10 | 150 m Fault buffer | 2.535793 | 0.878388 | 1.657405 |

Predictor 11 | 200 m Fault buffer | 0.687016 | 2.197602 | −1.51059 |

Predictor 12 | 250 m Fault buffer | 2.535793 | 0.878388 | 1.657405 |

Predictor 13 | Interlaminar anomalous body of ancient landform of Fulu Formation | 2.535793 | 0.878388 | 1.657405 |

Predictor 14 | 300 m Fault buffer | 2.264051 | 2.09229 | 0.171761 |

Predictor 15 | 350 m Fault buffer | 0 | 0 | 0 |

Predictor 16 | Equidensity | 2.540403 | 2.085783 | 0.45462 |

Predictor 17 | Azimuth anomalous degree | 2.624474 | 2.092335 | 0.532139 |

Predictor 18 | Anomalous azimuth | 2.2477 | 2.091067 | 0.156634 |

Predictor 19 | Centrosymmetry degree | 0 | 0 | 0 |

Predictor 20 | Normalization frequency | 0 | 0 | 0 |

Predictor 21 | Number of normalization intersections | 4.337955 | 1.802756 | 2.5352 |

Predictor 22 | Nantuo moraine layer | 3.620267 | 1.877229 | 1.743037 |

No | Prediction Factors/pcs | 3D Prediction Layers Combination | Network Size/m | Iteration Frequency/Times | Training Accuracy | Training Loss | Validation Accuracy /% | Validation Loss |
---|---|---|---|---|---|---|---|---|

1 | 22 | 1–22 | 100 × 100 × 100 | 215 | 100% | 0.000021 | 100% | 0.000116 |

2 | 21 | 1–21 (remove 22) | 100 × 100 × 100 | 215 | 100% | 0.000203 | 100% | 0.000299 |

3 | 15 | 1–15 (remove 16–22) | 100 × 100 × 100 | 215 | 100% | 0.000576 | 100% | 0.000741 |

4 | 13 | 1–13 (remove 14–22) | 100 × 100 × 100 | 215 | 100% | 0.000004 | 100% | 0.000099 |

5 | 12 | 1–12 (remove 13–22) | 100 × 100 × 100 | 215 | 100% | 0.000105 | 100% | 0.000079 |

6 | 6 | 1–6 (remove 7–22) | 100 × 100 × 100 | 215 | 100% | 0.000022 | 100% | 0.000022 |

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## Share and Cite

**MDPI and ACS Style**

Li, S.; Liu, C.; Chen, J. Mineral Prospecting Prediction via Transfer Learning Based on Geological Big Data: A Case Study of Huayuan, Hunan, China. *Minerals* **2023**, *13*, 504.
https://doi.org/10.3390/min13040504

**AMA Style**

Li S, Liu C, Chen J. Mineral Prospecting Prediction via Transfer Learning Based on Geological Big Data: A Case Study of Huayuan, Hunan, China. *Minerals*. 2023; 13(4):504.
https://doi.org/10.3390/min13040504

**Chicago/Turabian Style**

Li, Shi, Chang Liu, and Jianping Chen. 2023. "Mineral Prospecting Prediction via Transfer Learning Based on Geological Big Data: A Case Study of Huayuan, Hunan, China" *Minerals* 13, no. 4: 504.
https://doi.org/10.3390/min13040504