Mine Monitoring Research Articles

A New Model for Steam Cavity Expansion in Vertical Wells of Heavy Oil Reservoirs

Summary Steamflooding is one of the most effective and mature methods for developing heavy oil. However, existing research has not fully explained the phenomena of steam overburden and heat loss, particularly overlooking the steam velocity loss at the steam cavity front. Therefore, the condensation heat transfer coefficient (CHTC) was introduced to describe the decrease in steam velocity at the steam cavity front. A steam cavity front expansion model, considering shape factor, pseudofluidity ratio, and CHTC ratio (CHTCR), was established and validated by using mine monitoring data. The research results show that the steam cavity of the revised model is in the shape of a “funnel,” and the steam overburden phenomenon is more pronounced at the steam cavity front. Through comparison with the field data of the P6 and P612-1 well groups, it is observed that the average displacement error of the steam cavity front of the two is only 6.58%. As steam injection progresses, the steam cavity is more degraded, and the steam overburden phenomenon is more intensified compared with the initial stage. Taking into account the phenomenon of kinetic energy loss, a higher formation heat loss rate is calculated during the middle and late stages of steam injection in the modified model. The pressure gradient at the steam cavity front is analyzed to determine an optimal displacement radius of 40 m for vertical well steamflooding in heavy oil reservoirs. When parameters such as shape factor, steam injection rate, and CHTC are increased, the development shape of the steam cavity is more ideal, but the convexity of the steam cavity front is increasingly pronounced as the pseudomobility ratio increases. The CHTC was incorporated into the steam cavity expansion model, elucidating the steam overburden phenomenon and advancing the theoretical understanding of steamflooding. By improving the existing model, the analytical formula can be used to rapidly predict the position of the steam cavity front and estimate of the steam-saturated zone, even in the absence of some field data or numerical models. Practical guidance for optimizing steam injection strategies is provided, with the potential to significantly enhance the recovery of heavy oil reservoirs.

Internet of Things Long-Range-Wide-Area-Network-Based Wireless Sensors Network for Underground Mine Monitoring: Planning an Efficient, Safe, and Sustainable Labor Environment

Underground mines are considered one of the riskiest facilities for human activities due to numerous accidents and geotechnical failures recorded worldwide over the last century, which have resulted in unsafe labor conditions, poor health outcomes, injuries, and fatalities. One significant cause of these accidents is the inadequate or nonexistent capacity for the real-time monitoring of safety conditions in underground mines. In this context, new emerging technologies linked to the Industry 4.0 paradigm, such as sensors, the Internet of Things (IoT), and LoRaWAN (Long Range Wide Area Network) wireless connectivity, are being implemented for planning the efficient, safe, and sustainable performance of underground mine labor environments. This paper studies the implementation of an ecosystem composed of IoT sensors and LoRa wireless connectivity in a data-acquisition system, which eliminates the need for expensive cabling and manual monitoring in mining operations. Laying cables in an underground mine necessitates cable support and protection against issues, such as machinery operations, vehicle movements, mine operator activities, and groundwater intrusion. As the underground mine expands, additional sensors typically require costly cable installations unless wireless connectivity is employed. The results of this review indicate that an IoT LoRaWAN-based wireless sensor network (WSN) provides real-time data under complex conditions, effectively transmitting data through physical barriers. This network presents an attractive low-cost solution with reliable, simple, scalable, secure, and competitive characteristics compared to cable installations and manually collected readings, which are more sporadic and prone to human error. Reliable data on the behavior of the underground mine enhances productivity by improving key performance indicators (KPIs), minimizing accident risks, and promoting sustainable environmental conditions for mine operators. Finally, the adoption of IoT sensors and LoRaWAN wireless connectivity technologies provides information of the underground mine in real-time, which supports better decisions by the mining industry managers, by ensuring compliance with safety regulations, improving the productive performance, and fostering a roadmap towards more environmentally friendly labor conditions.

Study on the Characteristics of Combustible Mixed Gas Production during Lignite Oxidation Process

CO, H2, and other combustible gases will be produced during coal oxidation in coal mines, which will increase the risk of explosion when mixed with methane. Therefore, it is very important to understand the production characteristics of combustible gas during coal oxidation. In this paper, a programmed temperature gas test system is built to study the impact of lignite on the production of gases at different particle sizes and temperatures, and the release characteristics of gases are also analyzed. The result shows that the production of combustible gas is influenced by the coal particle size significantly when the temperature is above 200 °C, and it decreases as the particle size increases. CO is the main gas during the early stage of coal spontaneous combustion, and the release of CH4 and H2 increases after 300 °C. The fitted equations of gas generation and temperature are consistent with the experimental results. The research results are helpful in understanding the hazards of coal spontaneous combustion and have a certain guiding significance for coal mine monitoring and prevention of coal spontaneous combustion.

Model and criteria for slope monitoring and early warning in open-pit mines using ground-based real aperture radar

Model and criteria for slope monitoring and early warning in open-pit mines using ground-based real aperture radar

Mapping annual dynamics of surface mining disturbances in the northeastern Tibetan Plateau using Landsat imagery and LandTrendr algorithm.

The exploitation and utilization of coal resources have significantly contributed to global energy security. However, this mining activity has inflicted considerable damage on the ecological environment, particularly on the Tibetan Plateau, where the impact on ecosystems may be even more detrimental. The implementation of high-intensity mining activities leads to rapid changes in land cover/land use. Consequently, it is essential to accurately and effectively monitor mining disturbances. In this study, we propose an approach to capture surface mining disturbances using spatial-temporal rules and time series stacks of Landsat data. First, a time series of annual mining disturbance probability was generated based on Landsat temporal-spectral metrics and random forest. Second, the Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr) algorithm was employed to segment the time series and detect breakpoints. Finally, mining disturbances were captured by further restricting the output of LandTrendr based on spatial-temporal rules of mining disturbances. This approach was applied and evaluated in the Muli mining area of the northeastern Tibetan Plateau, which experienced large-scale and rapid mining disturbances from 2004 to 2014, and identified a disturbed mining area of 43.62 km2. The mining sites have been reclaimed after mining, and all reclamation work was done after 2016, with a total reclaimed area of 22.28 km2. The validation results indicated that the overall accuracy of mining disturbance and reclamation mapping ranges from 0.7333 to 0.8667, and the F1 scores for mining disturbances and reclamation range from 0.7551 to 0.8723. This study provides a reliable framework for monitoring mining disturbances and reclamation in surface mines, promising to be useful in realizing disturbance monitoring in surface mines for a wide range of mineral types.

Analysis of fracture modes and acoustic emission characteristics of low‐frequency disturbed coal rock bodies with different cyclic amplitudes

AbstractFrequent mining operations significantly disturb the security of deep weakly cemented rock roadways in western mining areas, constituting one of the primary causes of deformation, instability, and failure within the coal‐rock body. In this paper, dynamic uniaxial compression tests of soft rock‐coal combinations under low‐frequency disturbance with different cyclic amplitudes were conducted based on acoustic emission to elucidate fracture modes. The findings are as follows: Different cycle amplitudes manifested significant degradation effects on the soft rock‐coal combination. With increasing cycle amplitude, the proportion of tensile cracks initially decreased before subsequently increasing, demonstrating a general upward trajectory. The sudden increase in the acoustic emission RA value, the large decrease in the b‐value, and AE counts reaching the peak mean that failure and destabilization of the specimen begin to occur. The results of this study will furnish theoretical direction for dynamic disaster monitoring and early warning in soft rock mines located in western mining regions.

Development of a Conceptual Model for Environmental Monitoring in Underground Coal Mining

Development of a Conceptual Model for Environmental Monitoring in Underground Coal Mining

Predicting mine water inflow volumes using a decomposition-optimization algorithm-machine learning approach

Disasters caused by mine water inflows significantly threaten the safety of coal mining operations. Deep mining complicates the acquisition of hydrogeological parameters, the mechanics of water inrush, and the prediction of sudden changes in mine water inflow. Traditional models and singular machine learning approaches often fail to accurately forecast abrupt shifts in mine water inflows. This study introduces a novel coupled decomposition-optimization-deep learning model that integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Northern Goshawk Optimization (NGO), and Long Short-Term Memory (LSTM) networks. We evaluate three types of mine water inflow forecasting methods: a singular time series prediction model, a decomposition-prediction coupled model, and a decomposition-optimization-prediction coupled model, assessing their ability to capture sudden changes in data trends and their prediction accuracy. Results show that the singular prediction model is optimal with a sliding input step of 3 and a maximum of 400 epochs. Compared to the CEEMDAN-LSTM model, the CEEMDAN-NGO-LSTM model demonstrates superior performance in predicting local extreme shifts in mine water inflow volumes. Specifically, the CEEMDAN-NGO-LSTM model achieves scores of 96.578 in MAE, 1.471% in MAPE, 122.143 in RMSE, and 0.958 in NSE, representing average performance improvements of 44.950% and 19.400% over the LSTM model and CEEMDAN-LSTM model, respectively. Additionally, this model provides the most accurate predictions of mine water inflow volumes over the next five days. Therefore, the decomposition-optimization-prediction coupled model presents a novel technical solution for the safety monitoring of smart mines, offering significant theoretical and practical value for ensuring safe mining operations.

A dataset of drilling site object detection in underground coal mines

Drilling in underground coal mine is an important measure for dealing with gas, water and hidden geological disasters, which can significantly enhance the effectiveness of disaster prevention and control in coal mining operations. In order to monitor the drilling process in real time and improve drilling efficiency, it is necessary to carry out object detection to identify and locate key targets at the drilling site. Compared with traditional object detection method, deep learning-based object detection method can improve accuracy, timeliness and stability of object detection, but it requires high-quality datasets to perform well. At present, research on object detection in underground coal mine drilling sites mainly relies on small-scale private datasets, which are insufficient for providing necessary or reliable data for deep neural network model training. In this study, we constructed a dataset of drilling site object detection using photos taken by intrinsic safety law enforcement recorders. This dataset is developed through several steps, including data cleaning, data labeling, and expert sampling verification. The mainstream YOLO series object detection model is used for data quality assessment. This dataset comprises 70,948 images from drilling sites under different environmental conditions, covering five categories of objects: gripper, chuck, coal miner, mine safety helmet, and drill pipe. It provides annotated files in PASCAL VOC format. This dataset can provide strong data support for object detection research in underground coal mine drilling sites, and plays an important role in promoting intelligent underground coal mine monitoring and early warning.

A Clustering Approach for Atmospheric Phase Error Correction in Ground-Based SAR Using Spatial Autocorrelation.

When using ground-based synthetic aperture radar (GB-SAR) for monitoring open-pit mines, dynamic atmospheric conditions can interfere with the propagation speed of electromagnetic waves, resulting in atmospheric phase errors. These errors are particularly complex in rapidly changing weather conditions or steep terrain, significantly impacting monitoring accuracy. In such scenarios, traditional regression model-based atmospheric phase correction (APC) methods often become unsuitable. To address this issue, this paper proposes a clustering method based on the spatial autocorrelation function. First, the interferogram is uniformly divided into multiple blocks, and the phase consistency of each block is evaluated using the spatial autocorrelation function. Then, a region growing algorithm is employed to classify each block according to its phase pattern, followed by merging adjacent blocks based on statistical data. To verify the feasibility of the proposed method, both the traditional regression model-based method and the proposed method were applied to deformation monitoring of an open-pit mine in Northwest China. The experimental results show that for complex atmospheric phase scenarios, the proposed method significantly outperformed traditional methods, demonstrating its superiority.

Research on Monitoring and Application of Ecological Restoration Engineering in Open Pit Backfilling Mines Based on Satellite Remote Sensing Data

Abstract. The restoration and management of mining ecological environment is an important component of China's ecological civilization construction. The implementation of restoration projects is to avoid the serious impact caused by the continuous deterioration of mining ecological structure, and is a key way to maintain regional ecological security and energy and mineral security. Therefore, efficient and rapid monitoring and evaluation of the effectiveness of mining ecological restoration is an essential and important link. With the rapid development of remote sensing technology, remote sensing monitoring has become an important means to objectively, quickly, and accurately obtaining changes in the mining environment. It can provide timely and long-term monitoring of mining and backfilling conditions in mining areas. This article uses the multi temporal stereo images of the Resource 3 satellite over four years to monitor the mining and backfilling situation of the Zhungeer open-pit coal mine area in Inner Mongolia. By reconstructing the Digital Surface Model (DSM) of the open-pit mining area, the changes and thresholds of the multi temporal DSM data are statistically analyzed to extract the mining and backfilling areas, and the earthwork volume is calculated. The results showed that from 2013 to 2016, the main large-scale mining faces in the study area of Zhungeer open-pit mining area had a total mining operation of about 563.5 million cubic meters, and a total backfilling operation of about 604.29 million cubic meters; During the period from 2016 to 2018, the main large-scale mining faces in the region had a total of approximately 721.71 million cubic meters of mining operations and a total of approximately 805.42 million cubic meters of backfilling operations. The overall operational intensity increased from 2016 to 2018. The research results show that based on satellite image data, it is convenient and efficient to obtain DSM and corresponding changes of multiple time periods in mining areas. Monitoring results can provide important support for regional environmental governance and protection, as well as safety production in mining areas.

Forecasting the risks of underground roadway stability loss based on mine research data

The article presents results of the research on forecasting the risks of underground roadway stability loss based on the results of mine monitoring. The method is used for safety control in the process of conducting mining operations and re-exploitation of roadways as industrial facilities. The risk assessment was carried out with allowance for spread of physical and mechanical parameters of rocks; this approach is confirmed by procession of data of more than 1000 experiments. Mining-and-technical factors and their negative impact on the rock destruction were identified and systematized. The structure of risk control in a geotechnical system “rock massif - roadway” was substantiated. The method for forecasting the risks of underground roadway destruction based on the results of the mine research was further developed. The method differs from the well-known ones by taking into account the dominant influencing factors, variations of values and standard deviations of the forecasted risks of roadway functionality loss and criteria for violation of its technological air gaps at a certain point in time. This makes it possible to assess the degree of danger of mine roadway stability loss and to undertake response measures more reasonably.

Methane Emission Estimation Tools as a Basis for Sustainable Underground Mining of Gas-Bearing Coal Seams

Underground coal mining of gas-bearing coal seams is accompanied by the emission of large amounts of methane, which increases with depth. Coal seam methane is not only a major cause of major accidents in coal mines, but is also a greenhouse gas that has a significant negative impact on the Earth’s atmosphere. Analysis of the efficiency of underground coal mining suggests that as the depth of mining increases, the productivity of a longwall decreases by a factor of 3–5 or more, while the specific volume of methane emitted increases manifold and the efficiency of methane management decreases. Effective management of coal seam methane can only be achieved by monitoring its content at key points in a system of workings. Monitoring of methane not only eliminates the risk of explosions, but also lets us assess the effectiveness of using methane management techniques and their parameters to improve efficiency and reduce the cost of methane management (including a methane drainage) for ensuring sustainable underground coal mining. The aim of this article is to develop a software and hardware complex for monitoring methane in a coal mine by creating a simulation model for monitoring methane. The Arduino Uno board and the methane sensor MQ-4 were used for this purpose. In this article, the causes of methane emissions in coal mines, gas control systems, the structure of the mine monitoring system, and the causes of risks and occurrence of accidents in coal mines are considered. As a result of the work, the mathematical model of the methane measurement sensor was developed; the Arduino Uno board developed a simulation system for methane monitoring; and the numerical results of the research are presented in the graphs.

3D DAS VSP for Coal Seam Exploration: A Case Study from Queensland, Australia.

Seismic methods are extensively used in coal mining for expanding resource discoveries and definition as well as for mine monitoring. However, the use of borehole seismic methods is relatively uncommon due to the high cost of borehole seismic acquisition using conventional downhole tools. The introduction of distributed acoustic sensing (DAS), which uses optical fibres to record seismic data, has dramatically increased the cost-effectiveness of borehole seismic methods. Fibre-optic cables are inexpensive and, once deployed in a borehole, can be abandoned or used later for further monitoring of the subsurface. The case study presented here concerns the use of DAS to record a 3D VSP (vertical seismic profiling) for coal seam exploration in Queensland, Australia. This study trialled effective strategies for deploying cables into boreholes and demonstrated how this technology could be incorporated into the standard coal exploration process. The final processing results produced a high-resolution 3D seismic cube where the coal seams below the basalt cover are clearly identifiable around the boreholes. Permanent installation of the fibre-optic cables into a set of boreholes provides immediate benefits of 3D seismic imaging and can create additional value in utilising these sensors for further discrete or continuous subsurface measurements, including stability monitoring of underground workings and detection of methane accumulations.

Participatory Geomonitoring for Future Mining—Resilience Management in the Cavern Storage Epe (Germany)

Integrated geo- and environmental monitoring in mining represents a high-dimensional challenge (location, altitude/depth, time and sensors). This is challenging for experts but poses great problems for a multitude of participants and stakeholders in building up a complete process understanding. The Epe research cooperation aims to elucidate the ground movement at the Epe cavern storage facility with a public participation process. The research cooperation was founded by the city of Gronau, the citizens’ initiative cavern field Epe, the company EFTAS, Münster, and the Research Center of Post-Mining at the Technische Hochschule Georg Agricola, Bochum. This research cooperation is the first in Germany to involve direct collaboration between science and the public. In the cavern field, which has been in operation since the 1970s, brine is extracted, and at the same time natural gas, crude oil and helium, as well as hydrogen in the future, are stored in the subsurface. The technical focus of this work was the development of a high-resolution spatiotemporal analysis of ground movements. The area is monitored annually by the mining company’s mine surveyor. The complexity of the monitoring issue lies in the fact that the western part is a bog area and a former bog area. Furthermore, the soils in the eastern part are very humus-rich and show strong fluctuations in the groundwater and therefore complex hydraulic conditions. At the same time, there are few fixed scatterers or prominent points in the area that allow high-resolution spatiotemporal monitoring using simple radar interferometry methods. Therefore, the SBAS method (Small Baseline Subset), which is based on an aerial method, was used to analyze the radar interferometric datasets. Using an SBAS analysis, it was possible to evaluate a time series of 760 scenes over the period from 2015 to 2023. The results were integrated with the mine survey maps on the ground movement and other open geodata on the surface, the soil layers and the overburden. The results show complex forms of ground movement. The main influence is that of mining. Nevertheless, the influence of organic soils with drying out due to drought years and uplift in wet years is great. Thus, in dry years, ground subsidence accelerates, and in wet years, ground subsidence not only slows down but in some cases also causes uplift. This complexity of ground movements and the necessary understanding of the processes involved has been communicated to the interested public at several public information events as part of the research cooperation. In this way, an understanding of the mining process was built up, and transparency was created in the subsurface use, also as a part of the energy transition. In technical terms, the research cooperation also provides a workflow for developing the annual mine survey maps into an integrated geo- and environmental monitoring system with the development of a transparent participatory geomonitoring process to provide resilience management to a mining location.

Application and Research of Microseismic Monitoring System and Hydraulic Fracturing Technology in Coal Mines

In order to improve the effectiveness of coal mine gas control and enhance the level of coal mine safety production, the application of a microseismic monitoring system and hydraulic fracturing technology in coal mines was studied. Applying hydraulic fracturing technology to coal mine gas treatment, firstly, the geological structure and gas concentration in the mining area are detected using the radio tunnel perspective method and infrared differential absorption method. Then, the relevant parameters of hydraulic fracturing are determined, and finally, hydraulic fracturing technology is implemented. Microseismic monitoring technology is used to monitor the cracks formed during hydraulic fracturing construction and evaluate the fracturing effect. The instantaneous energy envelope is obtained from the microseismic data of each detection channel after stacking and Hilbert transform static correction. A microseismic in-phase inversion positioning objective function based on travel time residuals is constructed, and under the constraints of polarization analysis, the optimal solution is obtained through search iteration to complete microseismic in-phase inversion positioning. Experimental results have shown that after applying this method to coal mine gas control, the gas concentration decreases below the execution standard, achieving good control effects. Under microseismic monitoring in coal mines, the hydraulic fracturing effect can be effectively and reasonably evaluated, and the safety production level of coal mines can be improved.

Mapping open-pit mining area in complex mining and mixed land cover zone using Landsat imagery

The monitoring of open-pit mining and reclamation activities is an important part of ecological protection across various countries. Among the many methods of monitoring open-pit mining areas, satellite remote sensing is the most widely used and effective. However, few works have been conducted to map open-pit mines over a larger geographical scale, or have considered complex land cover types, or a wide variety of mine properties. This work proposes a framework for mapping open-pit mines for areas over complex land cover and mining properties, including preprocessing, classification, and postprocessing steps. First, the pixels that did not contain mines were masked, second the mines were divided into two categories according to their spectral characteristics, then the selected samples and classified to obtain the classification results of mines, and third mis-classified pixels were removed. This work used Landsat images and Google Earth Engine to map the open-pit mines over Shanxi in 5-year steps from 2000 to 2020. The average producer and user accuracy of the results was found to be 79.4% and 83.2% respectively. The experimental results showed that open-pit mining areas in Shanxi first increased and subsequently decreased over the past twenty years. Additionally, the new preprocessing, classification, and postprocessing workflow was found to be effective, especially in environments with more vegetative cover.

The Effects of Layer-Induced Elastic Anisotropy on Microseismic Monitoring Records in Underground Coal Mines

Real-time microseismic observations are extensively used in underground hard rock mines. However, microseismic monitoring in coal mines is not common. In this study, the features of microseismic (MS) event waveforms were discussed under conditions of thin-layered sedimentary media. The possibility of source mechanism evaluation was considered for the MS event recorded in coal mine. Moment tensor (MT) inversion of the waveforms can be challenging due to influence of thin-layered medium structure. Splitting of shear SH- and SV-waves is one of the characteristic features that affect MS records in coal mines. The effect of splitting was demonstrated using real microseismic data collected by an underground seismic array installed in the Polosukhinskaya coal mine, Russia. Velocities of shear SH- and SV-waves were estimated from travel times. The results reveal significant variations in shear wave velocities composed of a radial anisotropy of 75%, thus confirming the importance of a thin-layered model for moment tensor inversion. The thin-layered model was built for geological conditions at the Polosukhinskaya coal mine and synthetic seismograms were generated using QSEIS (Pyrocko OS). A full moment tensor of a single typical event was found employing the layered 1D velocity model.

A low-light image enhancement method for personnel safety monitoring in underground coal mines

Intelligent monitoring technology plays an important role in promoting the development of coal mine safety management. Low illumination in the coal mine underground leads to difficult recognition of monitoring images and poor personnel detection accuracy. To alleviate this problem, a low illuminance image enhancement method proposed for personnel safety monitoring in underground coal mines. Specifically, the local enhancement module maps low illumination to normal illumination at pixel level preserving image details as much as possible. The transformer-based global adjustment module is applied to the locally enhanced images to avoid over-enhancement of bright areas and under-illumination of dark areas, and to prevent possible color deviations in the enhancement process. In addition, a feature similarity loss is proposed to constrain the similarity of target features to avoid the possible detrimental effect of enhancement on detection. Experimental results show that the proposed method improves the detection accuracy by 7.1% on the coal mine underground personal dataset, obtaining the highest accuracy compared to several other methods. The proposed method effectively improves the visualization and detection performance of low-light images, which contributes to the personnel safety monitoring in underground coal mines.

A novel identification method of microseismic events based on empirical mode decomposition and artificial neural network features

Microseismic technology has been widely used in geological hazard monitoring. However, the effective identification and classification of microseismic events in mines have always been a challenge in hazard monitoring. In this paper, a new method combining the empirical mode decomposition (EMD) algorithm and artificial neural network is proposed for the classification and identification of microseismic waveforms. This is the first study to explore such a model in the microseismic monitoring of coal mines. The data for this study were collected from a coal mine in northern China. A total of 2768 microseismic events and 2435 non-microseismic events were manually selected from a large amount of data. Firstly, the microseismic waveform reconstruction was performed using the 4th to 8th Intrinsic Mode Function (IMF) obtained by the EMD algorithm. Secondly, the peak factor, clearance factor, impulse factor, kurtosis, skewness, and waveform factor of each reconstructed microseismic waveform were used as features. Subsequently, the convolutional neural network (CNN) was used to classify microseismic events. Among them, data from 3600 microseismic events were used as the training set, and data from 1603 microseismic events were used as the test set. Finally, the original waveform data were used as input and compared with the classification results processed with the EMD algorithm. The classification methods of the BP neural network and Radial Basis Function (RBF) neural network were used for verification. The results of the processed features showed a significant increase in accuracy. The classification accuracy of CNN, BP neural network, and RBF neural network are 97.37%, 91.99%, and 94.23%, respectively. The results show that the utilization of the feature processing technique and CNN algorithm in this study demonstrates superior efficacy in microseismic event classification, which can be used in practice.