研究等業績 - その他 - 鳥屋 剛毅
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Deep learning-based rock type identification using drill vibration frequency spectrum images
Senjoba L.
International Journal of Mining, Reclamation and Environment ( International Journal of Mining, Reclamation and Environment ) 39 ( 1 ) 40 - 55 2025年
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Senjoba L.
Journal of Sustainable Mining ( Journal of Sustainable Mining ) 24 ( 2 ) 282 - 298 2025年
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Advanced UAV photogrammetry for precision 3D modeling in GPS denied inaccessible tunnels
Ikeda H.
Safety in Extreme Environments ( Safety in Extreme Environments ) 6 ( 4 ) 269 - 287 2024年12月
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Fissha Y.
Scientific Reports ( Scientific Reports ) 14 ( 1 ) 2024年12月
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Khatti J.
Multiscale and Multidisciplinary Modeling, Experiments and Design ( Multiscale and Multidisciplinary Modeling, Experiments and Design ) 7 ( 4 ) 3841 - 3864 2024年09月
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Senjoba L.
Applied Sciences (Switzerland) ( Applied Sciences (Switzerland) ) 14 ( 9 ) 2024年05月
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Okada N.
Journal of Material Cycles and Waste Management ( Journal of Material Cycles and Waste Management ) 27 ( 2 ) 685 - 698 2024年
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Enkhbold B.
Mining ( Mining ) 3 ( 4 ) 755 - 772 2023年12月
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Fissha Y.
Mining ( Mining ) 3 ( 4 ) 659 - 682 2023年12月
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Digital Twin Technology in Data Center Simulations: Evaluating the Feasibility of a Former Mine Site
Ikeda H.
Sustainability (Switzerland) ( Sustainability (Switzerland) ) 15 ( 23 ) 2023年12月
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Urban Quarry Ground Vibration Forecasting: A Matrix Factorization Approach
Ikeda H.
Applied Sciences (Switzerland) ( Applied Sciences (Switzerland) ) 13 ( 23 ) 12674 2023年12月
<jats:p>Blasting is routinely carried out in urban quarry sites. Residents or houses around quarry sites are affected by the ground vibrations induced by blasting. Peak Particle Velocity (PPV) is used as a metric to measure ground vibration intensity. Therefore, many prediction models of PPV using experimental methods, statistical methods, and Artificial Neural Networks (ANNs) have been proposed to mitigate this effect. However, prediction models using experimental and statistical methods have a tendency of poor prediction accuracy. In addition, while prediction models using ANNs can produce a highly accurate prediction results, a large amount of measured data is necessarily collected. In an urban quarry site where the number of blastings is limited, it is difficult to collect a lot of measured data. In this study, a new PPV prediction method using Weighted Non-negative Matrix Factorization (WNMF) is proposed. WNMF is a method that approximates a non-negative matrix (including missing data) to the product of two low-dimensional matrices and predicts the missing data. In addition, WNMF is one of the unsupervised learning methods, so it can predict PPV regardless of the amount of data. In this study, PPV was predicted using measured data from 100 sites at the Mikurahana quarry site in Japan. As a result, the proposed method showed higher accuracy when using measured data at 60 sites rather than 100 sites, and the root mean square error for PPV prediction decreased from 0.1759 (100 points) to 0.1378 (60 points).</jats:p>
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Deep Learning-Based Estimation of Muckpile Fragmentation Using Simulated 3D Point Cloud Data
Ikeda H.
Applied Sciences (Switzerland) ( Applied Sciences (Switzerland) ) 13 ( 19 ) 10985 2023年10月
<jats:p>This research introduces an innovative technique for estimating the particle size distribution of muckpiles, a determinant significantly affecting the efficiency of mining operations. By employing deep learning and simulation methodologies, this study enhances the precision and efficiency of these vital estimations. Utilizing photogrammetry from multi-view images, the 3D point cloud of a muckpile is meticulously reconstructed. Following this, the particle size distribution is estimated through deep learning methods. The point cloud is partitioned into various segments, and each segment’s distinguishing features are carefully extracted. A shared multilayer perceptron processes these features, outputting scores that, when consolidated, provide a comprehensive estimation of the particle size distribution. Addressing the prevalent issue of limited training data, this study utilizes simulation to generate muckpiles and consequently fabricates an expansive dataset. This dataset comprises 3D point clouds and corresponding particle size distributions. The combination of simulation and deep learning not only improves the accuracy of particle size distribution estimation but also significantly enhances the efficiency, thereby contributing substantially to mining operations.</jats:p>
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Zhang H.
Remote Sensing ( Remote Sensing ) 15 ( 15 ) 3871 2023年08月
<jats:p>Simultaneous Localization and Mapping (SLAM) forms the foundation of vehicle localization in autonomous driving. Utilizing high-precision 3D scene maps as prior information in vehicle localization greatly assists in the navigation of autonomous vehicles within large-scale 3D scene models. However, generating high-precision maps is complex and costly, posing challenges to commercialization. As a result, a global localization system that employs low-precision, city-scale 3D scene maps reconstructed by unmanned aerial vehicles (UAVs) is proposed to optimize visual positioning for vehicles. To address the discrepancies in image information caused by differing aerial and ground perspectives, this paper introduces a wall complementarity algorithm based on the geometric structure of buildings to refine the city-scale 3D scene. A 3D-to-3D feature registration algorithm is developed to determine vehicle location by integrating the optimized city-scale 3D scene with the local scene generated by an onboard stereo camera. Through simulation experiments conducted in a computer graphics (CG) simulator, the results indicate that utilizing a completed low-precision scene model enables achieving a vehicle localization accuracy with an average error of 3.91 m, which is close to the 3.27 m error obtained using the high-precision map. This validates the effectiveness of the proposed algorithm. The system demonstrates the feasibility of utilizing low-precision city-scale 3D scene maps generated by unmanned aerial vehicles (UAVs) for vehicle localization in large-scale scenes.</jats:p>
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Application of Bayesian Neural Network (BNN) for the Prediction of Blast-Induced Ground Vibration
Fissha Y.
Applied Sciences (Switzerland) ( Applied Sciences (Switzerland) ) 13 ( 5 ) 3128 2023年03月
<jats:p>Rock blasting is one of the most common and cost-effective excavation techniques. However, rock blasting has various negative environmental effects, such as air overpressure, fly rock, and ground vibration. Ground vibration is the most hazardous of these inevitable impacts since it has a negative impact not only on the environment of the surrounding area but also on the human population and the rock itself. The PPV is the most critical base parameter practice for understanding, evaluating, and predicting ground vibration in terms of vibration velocity. This study aims to predict the blast-induced ground vibration of the Mikurahana quarry, using Bayesian neural network (BNN) and four machine learning techniques, namely, gradient boosting, k-neighbors, decision tree, and random forest. The proposed models were developed using eight input parameters, one output, and one hundred blasting datasets. The assessment of the suitability of one model in comparison to the others was conducted by using different performance evaluation metrics, such as R, RMSE, and MSE. Hence, this study compared the performances of the BNN model with four machine learning regression analyses, and found that the result from the BNN was superior, with a lower error: R = 0.94, RMSE = 0.17, and MSE = 0.03. Finally, after the evaluation of the models, SHAP was performed to describe the importance of the models’ features and to avoid the black box issue.</jats:p>
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Research on vibration-based early diagnostic system for excavator motor bearing using 1-D CNN
Yandagsuren D.
Journal of Sustainable Mining ( Journal of Sustainable Mining ) 22 ( 1 ) 65 - 80 2023年
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Kiyama T.
International Geoscience and Remote Sensing Symposium (IGARSS) ( International Geoscience and Remote Sensing Symposium (IGARSS) ) 2023-July 927 - 930 2023年
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An image-capturing system to generate 3D borehole models using multiple fiberscope cameras
Qin J.
Proceedings of SPIE - The International Society for Optical Engineering ( Proceedings of SPIE - The International Society for Optical Engineering ) 12592 2023年
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A Comprehensive Numerical Modeling Study for Parameter Optimization and Slope Stability Analysis in the Baganuur Lignite Coal Mine
鳥屋 剛毅
Mining 3 755 - 772 2023年 [査読有り]
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A Method for Completing Missing 3D Point Cloud Reconstructed from Aerial Multi-View Images Using Self-Attention Mechanism
宍戸 英彦, 鳥屋 剛毅, 北原 格
Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS2023) 1 927 - 930 2023年 [査読有り]
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Development of hyperspectral database and web based classifying system for rock type identification
鳥屋 剛毅, 川村 洋平
Rock Mechanics and Engineering Geology in Volcanic Fields 1 442 - 446 2023年 [査読有り]
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Evaluation and Prediction of Blast-Induced Ground Vibrations: A Gaussian Process Regression (GPR) Approach
鳥屋 剛毅
Mining 3 4 659 - 682 2023年 [査読有り]
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Toriya H.
Applied Sciences (Switzerland) ( Applied Sciences (Switzerland) ) 12 ( 9 ) 2022年05月
国内共著
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One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction
Kilic K.
Applied Sciences (Switzerland) ( Applied Sciences (Switzerland) ) 12 ( 5 ) 2022年03月
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Sinaice B.B.
Minerals ( Minerals ) 12 ( 2 ) 2022年02月
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Yoshino K.
International Journal of the Society of Material Engineering for Resources ( International Journal of the Society of Material Engineering for Resources ) 25 ( 1 ) 78 - 84 2022年
<p>This paper proposes a method to estimate the particle (rock) size distribution of a muckpile using Deep Learning based on 3D shape information given by 3D photogrammetry. Optimization of blasting is crucial to increase the productivity of mining operations. However, since the internal structure of the ground (bedrock) is usually unknown, it is difficult to set the appropriate parameters, amounts of explosives, blasting location and timing. In order to solve the problem, research has been carried out to design and analyze the blasting procedures by measuring the particle size distribution after the blast. Ordinary works focus on developing accurate measurement methods of particle size distribution for the analysis. We aim to increase the measurement accuracy by combining 3D photogrammetry and Deep Learning for 3D shape data. The 3D muckpile model is generated using Structure from Motion (SfM), which is a reconstruction method that can generate 3D point clouds of the target object from multi-view images. The particle size distribution of muckpiles is estimated by using Deep Learning. The proposed network consists of “Local Module” that learns the local shape of rock and “Global Module” that learns the shape of the entire muckpile. From the experiments, it can be said that the fragmentation of the actual muckpiles can be effectively estimated by using the proposed method.</p>
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Deep Learning as an Early Detection System for Rotary Percussion Drilling Malfunctions
Kosugi Y.
International Journal of the Society of Material Engineering for Resources ( International Journal of the Society of Material Engineering for Resources ) 25 ( 2 ) 205 - 211 2022年
<p>To mine in the underground, the method of blasting and blast hole drilling methods are mainly, and widely accepted. The hole drilling methods are done with rotary percussion drill. However, there are problems in terms of difficulty of operating and mining cost resulting from its failure occurs, and thus it is hard for mining companies to find a way of mining underground efficiently, profitability, and safely. From this background, it is necessary to build the early detection system for drill bit failure. This system needs the technology of CNN (Convolutional Neural Network Smart Mining, which is the process of using information, autonomy, and technology to improve safety, reduce operating costs, and improve mine site productivity. In this research, drilling vibration from rotary percussion drill is transmitted as acceleration waveform and used as input data for building the system. The data is collected replacing the kinds of diameter of bit or drilling condition. This data is for developing the model introduced CNN to detect the difference between Normal drilling and the other kinds of drilling with something error. For Firstly, batch of waveform data is input model as training data to make the model recognize the data pattern. Secondly, validation process confirms the correct answer rate against the training data, and then, the test for the model is practiced. Finally, by comparing each accuracy in phase of test from 4 types of models built with different kinds of data and the ideal way of the input waveform data is found.</p>
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Development of Asbestos Containing Serpentinite Identifi cation Method Using Hyperspectral Imaging
Owada N.
International Journal of the Society of Material Engineering for Resources ( International Journal of the Society of Material Engineering for Resources ) 25 ( 2 ) 189 - 194 2022年
<p>Chrysotile is one of the asbestos types minerals and it is the fibrous form in nature. Also, chrysotile may cause health problems. Accordingly, it is better to be known whether chrysotile exists in a construction site in advance so that constructor can take a counter plan for worker health. However, identifying a small amount of chrysotile is very difficult. In a conventional way, experts quantify the amount of chrysotile by using a microscope and X-ray diffraction analysis. It is time-consuming and depends on individual skills. Speaking of identification techniques, it has been reported hyperspectral imaging and machine learning applications show good performance for mineral identification tasks. In this paper, a prediction model to identify chrysotile is trained with hyperspectral data of fibrous chrysotile and serpentine which is very similar to chrysotile. Finally, the model achieved 99.95% accuracy for test data. Then, the model has tested its identification capability by predicting hyperspectral data of the mixture of both serpentine and chrysotile that was unused in the training procedure and performed potential.</p>
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Sinaice B.B.
International Journal of the Society of Material Engineering for Resources ( International Journal of the Society of Material Engineering for Resources ) 25 ( 1 ) 102 - 108 2022年
<p>The adoption of hyperspectral imaging has had positive feedback in multiple industries, especially those heavily reliant on the visual analysis of subjects. Reasons for such are primarily due to the high accuracies achievable from processing high dimensional data. Nevertheless, hyperspectral data is said to possess a ‘dimensionality curse’. This phenomenon, deems it computationally demanding and difficult to employ in rapid field investigations such as the use of drone-mounted spectral cameras to distinguish rocks. To counter this, this study proposes the employment of a method of reducing the number of dimensions used to highlight the most characteristic feature bands referred to as Neighbourhood Component Analysis(NCA). NCA aided in disregarding redundant bands from 204 dimensionalities, to a still highly capable 5 bands dimensionality, which coincides with the current production of 5-band detection drones. To process this data, several machine learning(ML) algorithms were run in order to perform spectral classification of rocks based on the 5 NCA defined bands. This study’s novel findings show that one is able to acquire with NCA and ML, 5 bands, with a post-optimization average global accuracy of 95.4%. Such capabilities are highly sufficient considering the magnitude of the dimensionality reduction combined with the potential field drone applicability.</p>
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Fragmentation Size Distribution Measurement by GNSS-Aided Photogrammetry at Real Mine Site
鳥屋 剛毅, 北原 格, 川村 洋平
Mining ( Mining ) 2(3) ( 3 ) 438 - 448 2022年 [査読有り]
<jats:p>In mining operations that employ explosives and mineral processing, one of the important factors for efficient and low-cost operation is the fragmentation size distribution of rock after it has been blasted. Automatic scaling is a critical component of fragmentation size distribution measurement as it will directly determine the accuracy of the size estimation. In this study, we propose a method to create a system for creating a scaled 3D CG model, without the use of ground truth data such as GCPs (Ground Control Points), for the purpose of improving fragmentation size distribution measurement using positional data such as GNSS (Global Navigation Satellite System)-aided photogrammetry. We confirmed the validation of the method through an experimental evaluation of actual muckpiles. The results showed evidence of improving the scaling aspect of 3D fragmentation measurement systems without using GCPs or manual scales, specifically in surface mines where GNSS data are available.</jats:p>
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Jalali B.A.
International Journal of the Society of Material Engineering for Resources ( International Journal of the Society of Material Engineering for Resources ) 25 ( 2 ) 199 - 204 2022年
<p>Landslide awareness, especially in regions where natural disasters are always happening, is of the most extreme importance. To anticipate the disaster, several statistical methods have been proposed, but it is still unclear which one is more accurate. However, few studies have proposed a dependable method. These strategies are considered rather indiscriminate in this digital world where every study is based on the new technology. Thus, this study endeavors to identify landslides causative factors effectiveness and landslide susceptibility area by the geographic information system (GIS) and frequency ratio (FR) in central parts of Badakhshan province, Afghanistan, which is usually suffering from landslide hazards. The dataset which we have obtained from the Ministry of mines and petroleum of Afghanistan's will be used to track down the expected relationship of the area of the past landslides with landslides' causative variables within the study area for developing the landslide susceptibility map. To determine the major factors' affection to the landslides, spatial databases were constructed from landslides trigger factors related to the landslides those occurred from the data sets. The weight of each factor was estimated by the FR model to analyze their effectiveness in landslides hazard identification and construction of landslides susceptibility map. To verify the results, the constructed susceptibility map was compared with landslides area. The result showed susceptibility mapping of landslides using the GIS and FR model.</p>
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Senjoba L.
International Journal of the Society of Material Engineering for Resources ( International Journal of the Society of Material Engineering for Resources ) 25 ( 2 ) 224 - 228 2022年
<p>In recent years deep learning has gained a lot of popularity because of its ability to work on complex tasks. It has been used in many industries to optimize operations and to help in decision-making. Deep neural networks have often been referred to as ‘Black boxes’, that is they take inputs and give outputs with high accuracies without giving an insight into how they work. It is important to demystify deep neural networks to verify that they are looking at the correct patterns. This paper proposes the use of Gradient-Weighted Class Activation Mapping (Grad-CAM) to visualize the behavior of lithology identification models that use drill vibrations as input to a one- dimensional convolutional neural network (1D CNN). The lithology identification models, time acceleration, and frequency model had 99.8% and 99.0% classification accuracy. The models could distinguish between granite and marble rock based on vibration signatures. With the use of Grad-CAM, it was possible to make the 1D CNN models transparent by visualizing the regions of input that were important for predictions. The Grad-CAM results indicated that the lithology identification models successfully learned the significant frequencies contained in each rock's vibration signal.</p>
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Wi-Fi Direct based WSN Node Deployment in Underground Mine Tunnels
Malgazhdar D.
International Journal of the Society of Material Engineering for Resources ( International Journal of the Society of Material Engineering for Resources ) 25 ( 1 ) 63 - 69 2022年
<p>Fast data transmission is becoming a key parameter in mine planning, operation, and safety. Therefore WSNs (wireless sensor networks) is taking a leading role in underground mine environment communication.WSNs can measure mine environmental parameters by sensors to provide quick and detailed communication for comprehensive assessment of the situation, during both regular operations and emergency situations.Nowadays, WSNs are developing very fast, getting more compact, energy and cost-efficient. On the other hand, underground mines have very specific working conditions characterized by narrow spaces, dynamic environments, and high humidity. This demands WSN nodes to be specifi cally arranged to be functioning efficiently considering limited throughput and energy resources. Wi-Fi Direct is a wireless connection type used in WSN and supported by many manufacturers around the world. This research will consider using Wi-Fi Direct and ad hoc networks for WSN and will analyze the deployment of WSN nodes in underground mine environments. The physical experiment measuring the performance of deterministic Wi-Fi Direct mode node deployment in Osarizawa experimental underground mine was conducted. The experiment indicated that the data packets can be sent without loss to a distance up to a 140 m in a straight tunnel with 4 m<sup>2</sup> crosssection area. The obtained results were applied to planned WSN node deployment at Tishinskiy mine site in East Kazakhstan.</p>
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空撮映像に基づく建物群三次元復元と階層化点群補完
鳥屋 剛毅, 宍戸 英彦, 北原 格
画像電子学会年次大会予稿集 ( 一般社団法人 画像電子学会 ) 50 ( 0 ) 40 2022年
ドローンを使用して大規模なシーンを三次元復元する方法は、三次元都市モデリングで広く使われている方法である。具体的なプロセスは、UAV (Unmanned Aerial Vehicle) を用いて対象都市で空撮の多視点画像を取得し、特徴点のマッチングにより三次元都市点群モデルを復元するものである。このようにして取得した空撮多視点画像は、隣り合う画像間の十分なオーバラップが必要である。オクルージョンなどの影響を減らすために、できるだけ多くのシーン情報を取り込むようにUAVを飛行させる必要があるため、大規模な都市モデリングには非常に時間と労力を要する。本論文では、少数の空撮オルソモザイクビューのみを用いて、都市建物の形状を抽出し、壁などのオクルージョンが存在するシーンを補完できる階層的点群補完手法を提案する。補完結果は、仮想現実空間で取得した都市モデルによって精度を検証した。
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Model scaling in smartphone GNSS‐aided photogrammetry for fragmentation size distribution estimation
Tungol Z.P.L.
Minerals ( Minerals ) 11 ( 12 ) 1301 2021年12月
<jats:p>Fragmentation size distribution estimation is a critical process in mining operations that employ blasting. In this study, we aim to create a low-cost, efficient system for producing a scaled 3D model without the use of ground truth data, such as GCPs (Ground Control Points), for the purpose of improving fragmentation size distribution measurement using GNSS (Global Navigation Satellite System)-aided photogrammetry. However, the inherent error of GNSS data inhibits a straight-forward application in Structure-from-Motion (SfM). To overcome this, the study proposes that, by increasing the number of photos used in the SfM process, the scale error brought about by the GNSS error will proportionally decrease. Experiments indicated that constraining camera positions to locations, relative or otherwise, improved the accuracy of the generated 3D model. In further experiments, the results showed that the scale error decreased when more images from the same dataset were used. The proposed method is practical and easy to transport as it only requires a smartphone and, optionally, a separate camera. In conclusion, with some modifications to the workflow, technique, and equipment, a muckpile can be accurately recreated in scale in the digital world with the use of positional data.</jats:p>
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川村 洋平, 池田 啓, 鳥屋 剛毅
情報地質 ( 日本情報地質学会 ) 32 ( 4 ) 107 - 112 2021年12月
<p>モノのインターネット（IoT）の拡大と拡張現実（AR）と仮想現実（VR）の発展により，デジタル・ツインの概念が注目され始めている．鉱業のためのデジタル・ツインにおいても他産業のデジタル・ツインと同様に物理世界の仮想表現でありクラウドデータプラットフォーム上の代表的な構造に格納される．デジタル・ツインは一般的なモデルやシミュレーションとは異なる．実世界のデータを使用してデジタル空間で再現およびシミュレートするという概念は目新しいものではない．ただし，デジタル・ツインと一般的なシミュレーションの違いは現実世界の変化をデジタル空間で再現でき，リアルタイムで相互にリンクできるところにある．IoTの拡大に伴い，実世界のデータがリアルタイムで自動収集されネットワークを介してデジタル空間に即座に反映されるようになった．これにより，実世界のオブジェクトとデジタル空間モデルの類似性を維持することが可能となった．この観点から，デジタル・ツインは実世界では「ダイナミック」な仮想モデルと見なされる．鉱業におけるデジタル・ツイン技術により、さまざまな目的でのマイニングモデリングが可能になる．</p>
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Senjoba L.
Mining ( Mining ) 1 ( 3 ) 297 - 314 2021年12月
<jats:p>Drill bit failure is a prominent concern in the drilling process of any mine, as it can lead to increased mining costs. Over the years, the detection of drill bit failure has been based on the operator’s skills and experience, which are subjective and susceptible to errors. To enhance the efficiency of mining operations, it is necessary to implement applications of artificial intelligence to produce a superior method for drill bit monitoring. This research proposes a new and reliable method to detect drill bit failure in rotary percussion drills using deep learning: a one-dimensional convolutional neural network (1D CNN) with time-acceleration as input data. 18 m3 of granite rock were drilled horizontally using a rock drill and intact tungsten carbide drill bits. The time acceleration of drill vibrations was measured using acceleration sensors mounted on the guide cell of the rock drill. The drill bit failure detection model was evaluated on five drilling conditions: normal, defective, abrasion, high pressure, and misdirection. The model achieved a classification accuracy of 88.7%. The proposed model was compared to three state-of-the-art (SOTA) deep learning neural networks. The model outperformed SOTA methods in terms of classification accuracy. Our method provides an automatic and reliable way to detect drill bit failure in rotary percussion drills.</jats:p>
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Sinaice B.B.
Minerals ( Minerals ) 11 ( 8 ) 846 2021年08月
<jats:p>Though multitudes of industries depend on the mining industry for resources, this industry has taken hits in terms of declining mineral ore grades and its current use of traditional, time-consuming and computationally costly rock and mineral identification methods. Therefore, this paper proposes integrating Hyperspectral Imaging, Neighbourhood Component Analysis (NCA) and Machine Learning (ML) as a combined system that can identify rocks and minerals. Modestly put, hyperspectral imaging gathers electromagnetic signatures of the rocks in hundreds of spectral bands. However, this data suffers from what is termed the ‘dimensionality curse’, which led to our employment of NCA as a dimensionality reduction technique. NCA, in turn, highlights the most discriminant feature bands, number of which being dependent on the intended application(s) of this system. Our envisioned application is rock and mineral classification via unmanned aerial vehicle (UAV) drone technology. In this study, we performed a 204-hyperspectral to 5-band multispectral reduction, because current production drones are limited to five multispectral bands sensors. Based on these bands, we applied ML to identify and classify rocks, thereby proving our hypothesis, reducing computational costs, attaining an ML classification accuracy of 71%, and demonstrating the potential mining industry optimisations attainable through this integrated system.</jats:p>
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Manjate E.P.A.
Journal of Sustainable Mining ( Journal of Sustainable Mining ) 20 ( 4 ) 296 - 308 2021年