|
|
|
| Reserve prediction method based on the dynamic information on the utilization status of mineral resources |
YANG Pei( ) |
| Guangdong Mineral Resource Reserves Evaluation Center, Guangzhou 510080, China |
|
|
|
|
Abstract Due to the dynamic and multidimensional properties of mineral resource data, the information on the utilization status of mineral resources fails to accurately reflect the actual utilization status of mineral resources, leading to inaccurate predictions of mineral resources. Therefore, this study proposed a reserve prediction method based on the dynamic information on the utilization status of mineral resources. The exploration data of mineral resources, subjected to format conversion and encoding, were input into the geographic information system (GIS). They were categorized by the GIS into spatial and attribute data, thereby establishing a resource utilization status management system. A fully relational database was introduced to manage the utilization status of mineral resources. A 3D geological model was employed to calculate the ore-forming favorability and delineate the mineralization target area. Based on this, a grade-tonnage model was constructed. Finally, the statistical sampling theory was applied to predict the resource reserves in the mining area. The experimental results demonstrate that the mineral reserves predicted using the proposed method aligned with the actual reserves, indicating a relatively high prediction accuracy.
|
|
Received: 09 September 2024
Published: 30 December 2025
|
|
|
|
|
|
|
Management system for mineral resource utilization status
|
| 数据类型 | 字段名称 | 字段类型 | 字段长度 | | 空间数据 | 矿产名称 | VARCHAR | 255 | | 地理位置 | VARCHAR | 4 | | 品位 | DECIMAL | 255 | | 开发利用现状 | VARCHAR | 100 | | 勘查时间 | DATE | - | | 坐标系统 | VARCHAR | 50 | | 形态描述 | TEXT | - | | 属性数据 | 矿区范围 | VARCHAR | 50 | | 开采期限 | DATE | 18 | | 已开采量 | DECIMAL | 2 | | 地质情况 | TEXT | 100 | | 矿产资源ID | INT | 200 | | 矿种 | VARCHAR | 100 | | 倾向 | DECIMAL | 10 | | 平均厚度 | DECIMAL | 10 |
|
Managing databases
|
|
Reserve prediction process
|
|
Example of interface for mineral resource information management and prediction software system
|
|
Overview of the study area on the development and utilization of mineral resources in the research area
|
|
Profile of the southern ore layer in the research area
|
| 序号 | 项目 | 描述 | | 1 | 矿产资源类型 | 片麻岩(含少量花岗岩等) | | 2 | 已开采量/t | 1.32 | | 3 | 利用率/% | 85 | | 4 | 年开采量/t | 11.2 | | 5 | 主要矿体特征 | 矿品位高,易于开采 | | 6 | 回采率/% | 70 | | 7 | 开采期限/年 | 45 |
|
Information on the development and utilization of mineral resources in the research area
|
|
Geological 3D model and delineation of mining target area
|
|
Prediction results of mineral resource reserves
|
| 块段品位/% | 稳定裕度 | | 方法1 | 方法2 | 本文方法 | | 0.1 | 0.54 | 0.66 | 0.87 | | 0.2 | 0.57 | 0.75 | 0.92 | | 0.3 | 0.49 | 0.80 | 0.85 | | 0.4 | 0.66 | 0.57 | 0.90 | | 0.5 | 0.70 | 0.63 | 0.88 | | 0.6 | 0.69 | 0.58 | 0.97 | | 0.7 | 0.57 | 0.75 | 0.95 | | 0.8 | 0.40 | 0.81 | 0.92 | | 0.9 | 0.55 | 0.69 | 0.94 | | 1.0 | 0.47 | 0.77 | 0.99 |
|
Comparison of three methods for predicting results
|
| [1] |
叶育鑫, 刘家文, 曾婉馨, 等. 基于本体指导的矿产预测知识图谱构建研究[J]. 地学前缘, 2024, 31(4):16-25.
|
| [1] |
Ye Y X, Liu J W, Zeng W X, et al. Ontology-guided knowledge graph construction for mineral prediction[J]. Earth Science Frontiers, 2024, 31(4):16-25.
|
| [2] |
王成彬, 王明果, 王博, 等. 融合知识图谱的矿产资源定量预测[J]. 地学前缘, 2024, 31(4):26-36.
|
| [2] |
Wang C B, Wang M G, Wang B, et al. Knowledge graph-infused quantitative mineral resource forecasting[J]. Earth Science Frontiers, 2024, 31(4):26-36.
|
| [3] |
Ding K, Xue L F, Ran X J, et al. Siamese network based prospecting prediction method:A case study from the Au deposit in the Chongli mineral concentrate area in Zhangjiakou,Hebei Province,China[J]. Ore Geology Reviews, 2022, 148:105024.
|
| [4] |
郑孝诚, 张明华, 任伟. 卷积神经网络在山东金矿勘查预测中的应用[J]. 物探与化探, 2023, 47(6):1433-1440.
|
| [4] |
Zheng X C, Zhang M H, Ren W. Application of convolution neural networks in gold exploration and prediction in Shandong Province[J]. Geophysical and Geochemical Exploration, 2023, 47(6):1433-1440.
|
| [5] |
Babii K, Kratkovskyi I, Kuantay A. Prediction of the mineral components spatial distribution in tailings ferruginous quartzite enrichments[J]. IOP Publishing Ltd., 2023, 47 (23):147-152.
|
| [6] |
曾小龙, 李谦, 魏宏超, 等. 基于南海巨厚塑性泥岩地层特征的钻速预测模型[J]. 煤田地质与勘探, 2023, 51 (11):159-168.
|
| [6] |
Zeng X L, Li Q, Wei H C, et al. Drilling rate prediction model based on the characteristics of thick plastic mudstone strata in the South China Sea[J]. Coal Geology and Exploration, 2023, 51(11):159-168.
|
| [7] |
李凤廷, 苗虎林, 付佳, 等. 那陵郭勒河下游重磁异常与铁多金属矿找矿预测[J]. 西北地质, 2023, 56 (6):155-165.
|
| [7] |
Li F T, Miao H L, Fu J, et al. Gravity and magnetic anomalies in the lower reaches of the Nalingguo River and prospecting prediction of iron polymetallic deposits[J]. Northwest Geology, 2023, 56(6):155-165.
|
| [8] |
丁培超, 郭勤强, 刘玉刚, 等. 河南嵩县庙岭金矿矿体赋存规律及深部找矿预测[J]. 地质通报, 2023, 42 (6):953-965.
|
| [8] |
Ding P C, Guo Q Q, Liu Y G, et al. Ore body occurrence pattern and deep prospecting prediction of Miaoling gold mine in Song County,Henan Province[J]. Geological Bulletin of China, 2023, 42(6):953-965.
|
| [9] |
王岩, 王登红, 秦锦华, 等. 南岭东段区域成矿规律与成矿预测[J]. 地质学报, 2023, 97(4):1315-1328.
|
| [9] |
Wang Y, Wang D H, Qin J H, et al. Regional metallogenic regularity and metallogenic prediction in the eastern Nanling[J]. Acta Geologica Sinica, 2023, 97(4):1315-1328.
|
| [10] |
柳炳利, 谢淼, 孔韫辉, 等. 基于卷积自编码网络的夏河—合作地区金矿定量预测[J]. 地球学报, 2023, 44 (5):877-886.
|
| [10] |
Liu B L, Xie M, Kong Y H, et al. Quantitative prediction of gold mines in Xiahe-Hezuo area based on convolutional autoencoder network[J]. Acta Geologica Sinica, 2023, 44(5):877-886.
|
| [11] |
姬果, 裴中朝, 王永辉, 等. 河南省石墨矿床地质特征、成矿区带划分及找矿预测[J]. 金属矿山, 2023(4):158-167.
|
| [11] |
Ji G, Pei Z C, Wang Y H, et al. Geological characteristics,metallogenic zone division and prospecting prediction of graphite deposits in Henan Province[J]. Metal Mine, 2023(4):158-167.
|
| [12] |
庞振山, 薛建玲, 程志中, 等. 成矿地质体找矿预测理论与方法在矿产勘查中的应用[J]. 地质通报, 2023, 42 (6):883-894.
|
| [12] |
Pang Z S, Xue J L, Cheng Z Z, et al. Application of prospecting prediction theory and method of ore-forming geological bodies in mineral exploration[J]. Geological Bulletin of China, 2023, 42(6):883-894.
|
| [13] |
Yang N, Zhang Z, Yang J, et al. Applications of data augmentation in mineral prospectivity prediction based on convolutional neural networks[J]. Computers & Geosciences, 2023, 47 (10):154-155.
|
| [14] |
罗杰, 周仲礼, 邹天一, 等. 基于PSO-CNN的深部找矿预测模型构建[J]. 成都理工大学学报:自然科学版, 2022, 49(6):697-708.
|
| [14] |
Luo J, Zhou Z L, Zou T Y, et al. Construction of deep mineral exploration prediction model based on PSO-CNN[J]. Journal of Chengdu University of Technology:Natural Science Edition, 2022, 49(6):697-708.
|
| [15] |
张磊, 王万银, 汪孝博, 等. 基于重磁场特征的洛宁地区构造特征研究及矿产预测[J]. 物探与化探, 2023, 47(3):608-617.
|
| [15] |
Zhang L, Wang W Y, Wang X B, et al. Research on tectonic characteristics and mineral prediction of Luoning area based on gravity and magnetic field characteristics[J]. Geophysical and Geochemical Exploration, 2023, 47(3):608-617.
|
| [1] |
LU Xing-Chen, XING Qian, XU Yong, LYU Guo-Sen, CHEN Xiang-Zhong, WANG Rui-Xing, HUANG Shen-Shuo. Exploring the occurrence characteristics of geothermal resources in the Nangong geothermal field based on the magnetotelluric method[J]. Geophysical and Geochemical Exploration, 2025, 49(3): 559-568. |
| [2] |
SUN Dong-Hua, CHEN Wei, CHENG Sha-Sha, SHI Lian-Cheng, ZHANG Jun-Wei, QI Ping, YANG Yu-Qin. A 3D geological modeling technology using multivariate geoscience information for exploration of sandstone-type uranium deposits[J]. Geophysical and Geochemical Exploration, 2025, 49(3): 631-641. |
|
|
|
|