ST-LSTM-SA:A New Ocean Sound Velocity Field Prediction Model Based on Deep Learning

The scarcity of in-situ ocean observations poses a challenge for real-time information acquisition in the ocean.Among the crucial hydroacoustic environmental parameters,ocean sound velocity exhibits significant spatial and temporal variability and it is highly relevant to oceanic research.In this study,we propose a new data-driven approach,leveraging deep learning techniques,for the prediction of sound velocity fields(SVFs).Our novel spatiotemporal prediction model,STLSTM-SA,combines Spatiotemporal Long Short-Term Memory(ST-LSTM) with a self-attention mechanism to enable accurate and real-time prediction of SVFs.To circumvent the limited amount of observational data,we employ transfer learning by first training the model using reanalysis datasets,followed by fine-tuning it using in-situ analysis data to obtain the final prediction model.By utilizing the historical 12-month SVFs as input,our model predicts the SVFs for the subsequent three months.We compare the performance of five models:Artificial Neural Networks(ANN),Long ShortTerm Memory(LSTM),Convolutional LSTM(ConvLSTM),ST-LSTM,and our proposed ST-LSTM-SA model in a test experiment spanning 2019 to 2022.Our results demonstrate that the ST-LSTM-SA model significantly improves the prediction accuracy and stability of sound velocity in both temporal and spatial dimensions.The ST-LSTM-SA model not only accurately predicts the ocean sound velocity field(SVF),but also provides valuable insights for spatiotemporal prediction of other oceanic environmental variables.

The Formation of Oscillation Patterns Based on the Planetary Gravitational Field and Their Suitability for Earthquake Prediction

The fluctuating planetary gravitational field influences not only activities on the Sun but also on the Earth. A special correlation function describes the harmonics of these fluctuations. Groups of earthquakes form oscillation patterns that differ significantly from randomly chosen control groups. These patterns are suitable as an element of an AI for the probability of earthquakes.

Simulation of Paleotectonic Stress Fields and Distribution Prediction of Tectonic Fractures at the Hudi Coal Mine, Qinshui Basin

Study on tectonic fractures based on the inversion of tectonic stress fields is an effective method. In this study, a geological model was set up based on geological data from the Hudi Coal Mine, Qinshui Basin, a mechanical model was established under the condition of rock mechanics and geostress, and the finite element method was used to simulate the paleotectonic stress field. Based on the Griffith and Mohr-Coulomb criterion, the distribution of tectonic fractures in the Shanxi Formation during the Indosinian, Yanshanian, and Himalayan period can be predicted with the index of comprehensive rupture rate. The results show that the acting force of the Pacific Plate and the India Plate to the North China Plate formed the direction of principal stress is N-S, NW - SE, and NE - SW, respectively, in different periods in the study area. Changes in the direction and strength of the acting force led to the regional gradients of tectonic stress magnitude, which resulted in an asymmetrical distribution state of the stress conditions in different periods. It is suggested that the low-stress areas are mainly located in the fault zones and extend along the direction of the fault zones. Furthermore, the high-stress areas are located in the junction of fold belts and the binding site of multiple folds. The development of tectonic fractures was affected by the distribution of stress intensity and the tectonic position of folds and faults, which resulted in some developed areas with level I and II. There are obvious differences in the development of tectonic fractures in the fold and fault zones and the anticline and syncline structure at the same fold zones. The tectonic fractures of the Shanxi Formation during the Himalayan period are more developed than those during the Indosinian and Yanshanian period due to the superposition of the late tectonic movement to the early tectonic movement and the differences in the magnitude and direction of stress intensity.

CFD prediction of physical field for multi-air channel pulverized coal burner in rotary kiln

A 3-D numerical simulation with CFX software on physical field of multi-air channel coal burner in rotary kiln was carried out. The effects of various operational and structural parameters on flame feature and temperature distribution were investigated. A thermal measurement was conducted on a rotary kiln (4.5m in diameter, 90m in length) with four-air channel coal burner to determine the boundary conditions and to verify the simulation results. The calculation result shows that the distribution of velocity near burner exit is saddle-like; recirculation zones near nozzle and wall are useful for mixture primary air with coal and high temperature fume. A little central airflow can avoid coal backing up and cool nozzle. Adjusting the ratio of internal airflow to outer airflow is an effective and major means to regulate flame and temperature distribution in sintering region. Large whirlcone angle can intensify disturbution range at flame root to accelerate ignition and mixture. Large coal size can reduce high temperature region and result in coal combusting insufficiently. Too much combustion air will lengthen flame and increase heat loss.

The idea and project of the “Medium－Scale Experiment Field for Earthquake Prediction”──Research on observations and applications of mining earthquake in Mentougou Coal Mine

A brief account of the development of the research on mining earthquakes and the general situation of the Mentougou Coal Mine medium scale experiment field for earthquake prediction and the project of monitor and prediction is given. The differences of waveforms between mining earthquakes and natural earthquakes is discussed. The magnitude frequency distribution of the 79 000 mining earthquakes of over M L1.0 from 1984 to 1995 is summarized . Finally, taking PH and PV, the principal compressive stress components of the focal mechanism of the mining earthquakes, as the criteria, analyses the stress background of the 12 large mining earthquakes.

Evaluation and prediction of earth pressure balance shield performance in complex rock strata:A case study in Dalian,China

This research explores the potential for the evaluation and prediction of earth pressure balance shield performance based on a gray system model.The research focuses on a shield tunnel excavated for Metro Line 2 in Dalian,China.Due to the large error between the initial geological exploration data and real strata,the project construction is extremely difficult.In view of the current situation regarding the project,a quantitative method for evaluating the tunneling efficiency was proposed using cutterhead rotation(R),advance speed(S),total thrust(F)and torque(T).A total of 80 datasets with three input parameters and one output variable(F or T)were collected from this project,and a prediction framework based gray system model was established.Based on the prediction model,five prediction schemes were set up.Through error analysis,the optimal prediction scheme was obtained from the five schemes.The parametric investigation performed indicates that the relationships between F and the three input variables in the gray system model harmonize with the theoretical explanation.The case shows that the shield tunneling performance and efficiency are improved by the tunneling parameter prediction model based on the gray system model.

The Statistical Prediction of the Vitrinite Reflectance and Study of the Ancient Geothermal Field in Songliao Basin,China

The resource of the gas from coal and coal measures deep in Songliao Basin has been drawing more and more attention to . It is necessary to find out the evolution regularity of the geothermal field of the basin in addition to a series of geological studies in order to predict its resources because the ancient geothermal field of the basin is one of the main factors controlling the generation , evolution and disappearance of oil and gas . In the recent twenty years , it is generally believed that vitrinite reflectance is the best quantitative marker for the ancient geothermal field . In the present paper , a systematic study of the vitrinite reflectance value of Songliao Basin and its influence factors is made by multiple statistical analysis so as to reconstruct the evolutional process of the Moho and the corresponding geothermal field . Then , an overall prediction is made of the vitrinite reflectance and the distribution of J3-K1 fault basin group at the bottom of Songliao Basin , which provides the evidence for the further prediction of the gas potentiality from coal and coal measures deep in the basin .

Interval finite difference method for steady-state temperature field prediction with interval parameters

A new numerical technique named interval finite difference method is proposed for the steady-state temperature field prediction with uncertainties in both physical parameters and boundary conditions. Interval variables are used to quantitatively describe the uncertain parameters with limited information. Based on different Taylor and Neumann series, two kinds of parameter perturbation methods are presented to approximately yield the ranges of the uncertain temperature field. By comparing the results with traditional Monte Carlo simulation, a numerical example is given to demonstrate the feasibility and effectiveness of the proposed method for solving steady-state heat conduction problem with uncertain-but-bounded parameters.

Study on partition of spontaneous combustion danger zone and prediction of self-ignition in coalmine based on numeric simulation

By solving steady model of air flow diffusion and chemical reaction in loose coal, distribution of oxygen concentration and flow velocity magnitude were obtained. Compared the simulating results with critic value as well as duration of spontaneous combustion from large-scale spontaneous combustion experiment, 'three zones' of spontaneous combustion were partitioned and mining conditions to avoid spontaneous combustion were obtained. The above method was employed to partition 'three zones' in gob of fully mechanized top-coal caving long wall face and got fairly good result. Calculation of the above method is much smaller than simulating the whole process of coal spontaneous combustion, but the prediction precision can satisfy the demand of predicting and extinguishing spontaneous combustion in mining.

PREDICTION OF LOCATION OF HIDDEN ORE DEPOSITS IN THE AGED ORE FIELDS: AN EXAMPLE FROM FENGHUANGSHAN ORE FIELD, TONGLING, CHINA

This paper discusses the strategy for successfully predicting the location of potential hidden ore bodies in aged ore field, and presents the result of location prediction of hidden ore bodies in Fenghuangshan ore field, Tongling. Innovative conceptual targeting procedures based on a genetic understanding of mineralization systems, carefully geological investigation and correct deduction, together with new geochemical and geophysical technology and integrating of comprehensive information are all very important for the successful prediction. In the aged Fenghuangshan ore field, through researching by application of the metallogenic theory of polygenetic compound ore deposits and triple frequency induced polarization method and exploration tectono geochemical method, we predicted location and quality of hidden ore bodies. According to the prediction, hidden high quality Cu Au ore bodies of skarn type and porphyry type have been discovered.

Prediction of oxygen concentration and temperature distribution in loose coal based on BP neural network

An effective method for preventing spontaneous combustion of coal stockpiles on the ground is to control the air-flow in loose coal.In order to determine and predict accurately oxygen concentrations and temperatures within coal stockpiles,it is vital to obtain information of self-heating conditions and tendencies of spontaneous coal combustion.For laboratory conditions,we designed our own experimental equipment composed of a control-heating system,a coal column and an oxygen concentration and temperature monitoring system,for simulation of spontaneous combustion of block coal(13-25 mm) covered with fine coal(0-3 mm).A BP artificial neural network(ANN) with 150 training samples was gradually established over the course of our experiment.Heating time,relative position of measuring points,the ratio of fine coal thickness,artificial density,voidage and activation energy were selected as input variables and oxygen concentration and temperature of coal column as output variables.Then our trained network was applied to predict the trend on the untried experimental data.The results show that the oxygen concentration in the coal column could be reduced below the minimum still able to induce spontaneous combustion of coal — 6% by covering the coal pile with fine coal,which would meet the requirement to prevent spontaneous combustion of coal stockpiles.Based on the prediction of this ANN,the average errors of oxygen concentration and temperature were respectively 0.5% and 7 °C,which meet actual tolerances.The implementation of the method would provide a practical guide in understanding the course of self-heating and spontaneous combustion of coal stockpiles.

Bayesian discriminant analysis for prediction of coal and gas outbursts and application

Based on the principle of Bayesian discriminant analysis, we established a model of Bayesian discriminant analysis for predicting coal and gas outbursts. We selected five major indices which affect outbursts, i.e., initial speed of methane diffusion, a consistent coal coefficient, gas pressure, destructive style of coal and mining depth, as discriminating factors of the model. In our model, we divided the type of coal and gas outbursts into four grades regarded as four normal populations. We then obtained the corresponding discriminant functions through training a set of data from engineering examples as learning samples and evaluated their criteria by a back substitution method to verify the optimal properties of the model. Finally, we applied the model to the prediction of coal and gas outbursts in the Yunnan Enhong Mine. Our results coincided completely with the actual situation. These results show that a model of Bayesian discriminant analysis has excellent recognition performance, high prediction accuracy and a low error rate and is an effective method to predict coal and gas outbursts.

Study on Generation Cost Prediction Based on Coal Quality and Generating Unit Performance

燃料变化条件下发电成本的预测是火电厂普遍关心的重要课题。分析了影响机组性能及发电电量成本的各种煤质指标，重点研究了基于结构参数及燃烧传热机理的锅炉稳态热力性能分析计算方法，讨论了制粉系统、风机、脱硫和脱硝装置性能随燃料等运行条件的变化关系，最终提出了基于煤质和机组性能分析计算的发电电量成本预测模型并研发了“煤炭选配与机组发电成本预测系统”。该系统在我国某600MW火电厂的实际应用经验表明：提出的电量成本分析预测模型合理，研发的软件系统有助于辅助火电厂的选煤和购煤决策，优化动力配煤方案，从而提高火电厂的运行经济性。

Experiment study of optimization on prediction index gases of coal spontaneous combustion

The coal of Anyuan Mine has the characteristic of easy spontaneous combustion. Conventional method is difficult to predict it. Coal samples from this mine were tested in laboratory. The data obtained from laboratory determination were initialized for the value which was defined as "K". The ratio of each index gas and value of "K", and the ratio of combination index gases and value of "K", were analyzed simultaneously. The research results show that for this coal mine, if there is carbon monoxide in the gas sample, the phenomenon of oxidation and temperature rising for coal exists in this mine; if there is C_2H_4 in the gas sample, the temperature of coal perhaps exceeds 130 °C. If the coal temperature is between 35 °C and 130 °C, prediction and forecast for coal spontaneous combustion depend on the value of Φ(CO)/K mainly; if the temperature of coal is between 130 °C and 300 °C, prediction and forecast for coal spontaneous combustion depend on the value of Φ(C_2H_6)/Φ(C_2H_2) and Φ(C_2H_6)/K. The research results provide experimental basis for the prediction of coal spontaneous combustion in Anyuan coal mine, and have better guidance on safe production of this coal mine.

An influence function method based subsidence prediction program for longwall mining operations in inclined coal seams

The distribution of the final surface subsidence basin induced by longwall operations in inclined coal seam could be significantly different from that in flat coal seam and demands special prediction methods. Though many empirical prediction methods have been developed, these methods are inflexible for varying geological and mining conditions. An influence function method has been developed to take the advantage of its fundamentally sound nature and flexibility. In developing this method, significant modifications have been made to the original Knothe function to produce an asymmetrical influence function. The empirical equations for final subsidence parameters derived from US subsidence data and Chinese empirical values have been incorpo- rated into the mathematical models to improve the prediction accuracy. A corresponding computer program is developed. A number of subsidence cases for longwall mining operations in coal seams with varying inclination angles have been used to demonstrate the applicability of the developed subsidence prediction model.

△P index with different gas compositions for instantaneous outburst prediction in coal mines

In this study we measured the △P(initial speed of gas emission) index with different gas concentrations of carbon dioxide(pure CO2,90% CO2+10% CH4,67% CO2+33% CH4,50% CO2+50% CH4,30% CO2+10% CH4 and pure CH4) of coal samples from the No.2 coal seam in the Yaojie Coal Mine,Gansu province,China.The effect of carbon dioxide concentration,gas composition,coal strength and particle size of coal samples on the △P index was investigated.The experimental results show that with gas of various compositions,the △P value of three samples were clearly different.The △P index of coal samples A,B and C(0.2～0.25 mm) were 4,6 and 7 with pure CH4 and 22,30 and 21 when pure CH4 was used.Carbon dioxide concentration affects the △P index markedly.The △P index increases with an increase in carbon dioxide concentration,especially for coal B.Hence,the △P index and K(another outburst index) values tested only with pure CH4 for prediction of the danger of outburst is not accurate.It is important to determine the initial speed of gas emission given the gas composition of the coal seam to be tested for exact outburst prediction.

Theoretical analysis and engineering application of controllable shock wave technology for enhancing coalbed methane in soft and low‑permeability coal seams

Coalbed methane(CBM)is a significant factor in triggering coal and gas outburst disaster,while also serving as a clean fuel.With the increasing depth of mining operations,coal seams that exhibit high levels of gas content and low permeability have become increasingly prevalent.While controllable shockwave(CSW)technology has proven effective in enhancing CBM in laboratory settings,there is a lack of reports on its field applications in soft and low-permeability coal seams.This study establishes the governing equations for stress waves induced by CSW.Laplace numerical inversion was employed to analyse the dynamic response of the coal seam during CSW antireflection.Additionally,quantitative calculations were performed for the crushed zone,fracture zone,and effective CSW influence range,which guided the selection of field test parameters.The results of the field test unveiled a substantial improvement in the gas permeability coefficient,the average rate of pure methane flowrate,and the mean gas flowrate within a 10 m radius of the antireflection borehole.These enhancements were notable,showing increases of 3 times,13.72 times,and 11.48 times,respectively.Furthermore,the field test performed on the CSW antireflection gas extraction hole cluster demonstrated a noticeable improvement in CBM extraction.After antireflection,the maximum peak gas concentration and maximum peak pure methane flow reached 71.2%and 2.59 m^(3)/min,respectively.These findings will offer valuable guidance for the application of CSW antireflection technology in soft and low-permeability coal seams.

Development of regional prediction information system of coal and gas outburst

Coal and gas outburst information system is based on-Geographic Information System(GIS), with which the relation among mine geological structure, coal features, stress field and coal and gas outburst were researched, and also the relation between gas distributed condition and dangerous degrees. Various prediction method, index and technique were applied to realize the data visualization; the accuracy of region prediction was increased. The system has successfully applied in Huainan minging area and Pingdingshan minging area.

Application of dynamic set-pair analysis in coal and gas outburst prediction

Analyzed the factors which affected the coal and gas outburst,then established the corresponding indicator system.Built a dynamic set-pair analysis prediction model which combined of Markov model and set-pair analysis model,and then it applied to coal and gas outburst prediction.Finally,compared the prediction results with the actual results As provided a reference to the coalmine in safety decision-making.The research results indicate that there are four districts in high dangerous level,two districts in middle level and one district in low level,which consistent with the actual situation;the dynamic set-pair analysis model has a good effect in predicting coal and gas outburst.Especially in the continuous time intervals,according to the data of mined exploration and the connec- tion degree analysis,we can deduce the dangerous levels of unexplored districts from the historical data.In different districts,the relevant indicators can be adjusted accordingly,so as to enhance the accuracy of the prediction.

Disaster prediction of coal mine gas based on data mining

The technique of data mining was provided to predict gas disaster in view of the characteristics of coal mine gas disaster and feature knowledge based on gas disaster. The rough set theory was used to establish data mining model of gas disaster prediction, and rough set attributes relations was discussed in prediction model of gas disaster to supplement the shortages of rough intensive reduction method by using information en- tropy criteria.The effectiveness and practicality of data mining technology in the prediction of gas disaster is confirmed through practical application.