A prediction model for amplitude-frequency characteristics of blast-induced seismic waves

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. T159-T173 ◽  
Author(s):  
Chenglong Yu ◽  
Zhongqi Wang ◽  
Wengong Han
Keyword(s):  
Prediction Model ◽  
Field Experiment ◽  
Seismic Waves ◽  
Initial Pressure ◽  
Dominant Frequency ◽  
Frequency Characteristics ◽  
Adiabatic Exponent ◽  
The Difference ◽  
Explosive Properties

We have developed a prediction model for dominant frequency and amplitude of blast-induced seismic waves. A blast expansion cavity is used to establish a relationship between the explosive properties and amplitude frequency of blast-induced seismic waves. In this model, the dominant frequency and amplitude of blast-induced seismic waves are mainly influenced by the initial pressure and the adiabatic exponent of explosives in the same medium. The dominant frequency increases with the decreasing initial pressure or the increasing adiabatic exponent. This prediction model is compared with the experiments. The difference in the blast cavity between the prediction model and the field experiment is in the range of 5%–9%, and the difference in the dominant frequency is within 18.8%–46.0%. The comparison indicates that the model can reasonably predict the frequency and amplitude of blast-induced seismic waves.

scholarly journals A Non-Uniform Broadcast Fertilization Method and Its Performance Analysis under Basin Irrigation

Water ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 292
Author(s):  
Kai Zhang ◽  
Meijian Bai ◽  
Yinong Li ◽  
Shaohui Zhang ◽  
Di Xu
Keyword(s):  
Field Experiment ◽  
Field Experiments ◽  
Transport Model ◽  
Inflow Rate ◽  
Application Performance ◽  
Solute Content ◽  
Basin Surface ◽  
Movement Characteristics ◽  
The Difference ◽  
Basin Irrigation

The broadcast fertilization method is widely used under basin irrigation in China. A reasonable broadcast fertilization method can effectively improve application performance of fertilization and reduce pollution from non-point agricultural sources. In this study, firstly, a non-uniform broadcast fertilization method and a non-uniform application coefficient were proposed. The value of non-uniform application coefficient is defined in this paper. It represents the ratio of the difference between the maximum and the average fertilization amount of fertilizer applied on the basin surface to the average fertilization amount of fertilizer applied on the basin surface. Secondly, field experiments were conducted to study the movement characteristics of fertilizer under non-uniform broadcast fertilization for basin irrigation. Field experiment results showed that under the condition of basin irrigation, the non-uniform broadcast fertilization method could weaken the non-uniform distribution of fertilizer due to erosion and transport capacity of solid fertilizer by irrigation water flow, which could significantly improve the uniformity of soil solute content. Thirdly, the solute transport model for broadcast fertilization was corroborated by the field experiment results. The variation rule of fertilization performance with non-uniform application coefficient under different basin length and inflow rate was achieved by simulation. The simulation results showed that fertilization uniformity and fertilization storage efficiency increased first and then decreased with the increase of non-uniform application coefficient. In order to be practically applicable, suitable irrigation programs of non-uniform application coefficient under different basin length and inflow rate conditions were proposed by numerical simulation.

Dominant frequency characteristics analysis of reclosing transient overvoltage in 10 kV cable-overhead hybrid line

2020 ◽  
Vol 178 ◽  
pp. 106052
Author(s):  
Jiaming Li ◽  
Xi Chen ◽  
Long Xu ◽  
Aixuan Zhao ◽  
Junbo Deng ◽  
...  
Keyword(s):  
Dominant Frequency ◽  
Frequency Characteristics ◽  
Hybrid Line ◽  
Characteristics Analysis ◽  
Transient Overvoltage

Factors controlling N mineralization, nitrification, and nitrogen losses in an Oxisol amended with sewage sludge

Soil Research ◽  
2001 ◽  
Vol 39 (3) ◽  
pp. 519 ◽  
Author(s):  
J. Sierra ◽  
S. Fontaine ◽  
L. Desfontaines
Keyword(s):  
Sewage Sludge ◽  
Field Experiment ◽  
Water Content ◽  
N Mineralization ◽  
Initial Period ◽  
Factorial Experiment ◽  
Net N Mineralization ◽  
N Losses ◽  
Mineral N ◽  
The Difference

Laboratory incubations and a field experiment were carried out to determine the factors controlling N mineralization and nitrification, and to estimate the N losses (leaching and volatilization) in a sewage-sludge-amended Oxisol. Aerobically digested sludge was applied at a rate equivalent to 625 kg N/ha. The incubations were conducted as a factorial experiment of temperature (20˚C, 30˚C, and 40˚C) soil water (–30 kPa and –1500 kPa) sludge type [fresh (FS) water content 6230 g/kg; dry (DS) water content 50 g/kg]. The amount of nitrifiers was determined at the beginning and at the end of the experiment. The incubation lasted 24 weeks. The field study was conducted using bare microplots (4 m) and consisted of a factorial experiment of sludge type (FS and DS) sludge placement (subsurface, I+; surface, I–). Ammonia volatilization and the profile (0–0.90 m) of mineral N concentration were measured during 6 and 29 weeks after sludge application, respectively. After 24 weeks of incubation at 40˚C and –30 kPa, net N mineralization represented 52% (FS) and 71% (DS) of the applied N. The difference between sludges was due to an initial period of N immobilization in FS. Nitrification was more sensitive than N mineralization to changes in water potential and it was fully inhibited at –1500 kPa. The introduction of a large amount of nitrifiers with FS did not modify the rate of nitrification, which was principally limited by soil acidity (pH 4.9). Although N mineralization was greatest at 30˚C, nitrification increased continuously with temperature. Nitrogen mineralization from DS was well described by the double-exponential equation. For FS, the equation was modified to take into account an immobilization-remineralization period. Sludge placement significantly affected the soil NO-3/NH+4 ratio in the field: 16 for I+ and 1.5 for I–, after 11 weeks. In the I– treatment, nitrification of the released NH+4 was limited by soil moisture because of the dry soil mulch formed a few hours after rain. At the end of the field experiment, the estimated losses of N by leaching were 432 kg N/ha for I+ and 356 kg N/ha for I–. Volatilization was not detectable in the I+ microplots and it represented only 0.5% of the applied N in the I– microplots. The results showed that placement of sludge may be a valuable tool to decrease NO-3 leaching by placing the sludge under unfavourable conditions for nitrification.

scholarly journals Linear traveltime perturbation interpolation: a novel method to compute 3-D traveltimes

2015 ◽  
Vol 203 (1) ◽  
pp. 548-552 ◽  
Author(s):  
Jianzhong Zhang ◽  
Junjie Shi ◽  
Lin-Ping Song ◽  
Hua-wei Zhou
Keyword(s):  
Seismic Waves ◽  
Homogeneous Medium ◽  
Cell Boundary ◽  
Routine Method ◽  
Bilinear Interpolation ◽  
Linear Distribution ◽  
Heterogeneous Model ◽  
Computational Errors ◽  
Novel Method ◽  
The Difference

Abstract The linear traveltime interpolation has been a routine method to compute first arrivals of seismic waves and trace rays in complex media. The method assumes that traveltimes follow a linear distribution on each boundary of cells. The linearity assumption of traveltimes facilitates the numerical implementation but its violation may result in large computational errors. In this paper, we propose a new way to mitigate the potential shortcoming hidden in the linear traveltime interpolation. We use the vertex traveltimes in a calculated cell to introduce an equivalent homogeneous medium that is specific to the cell boundary from a source. Therefore, we can decompose the traveltime at a point on the cell boundary into two parts: (1) a reference traveltime propagating in the equivalent homogeneous medium and (2) a perturbation traveltime that is defined as the difference between the original and reference traveltimes. We now treat that the traveltime perturbation is linear along each boundary of cells instead of the traveltime. With the new assumption, we carry out the bilinear interpolation over traveltime perturbation to complete traveltime computation in a 3-D heterogeneous model. The numerical experiments show that the new method, the linear traveltime perturbation interpolation, is able to achieve much higher accuracy than that based on the linear traveltime interpolation.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

scholarly journals Numerical Study of Fracture Characteristics of Deep Granite Induced by Blast Stress Wave

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Zheming Zhu ◽  
Weiting Gao ◽  
Duanying Wan ◽  
Meng Wang ◽  
Yun Shu
Keyword(s):  
Shear Failure ◽  
Numerical Study ◽  
Numerical Models ◽  
Rock Fracture ◽  
Initial Pressure ◽  
3D Models ◽  
In Situ Stress ◽  
Vertical Pressure ◽  
The Difference ◽  
Radial Cracks

To study the characteristics of rock fracture in deep underground under blast loads, some numerical models were established in AUTODYN code. Weibull distribution was used to characterize the inhomogeneity of rock, and a linear equation of state was applied to describe the relation of pressure and volume of granite elements. A new stress initialization method based on explicit dynamic calculation was developed to get an accurate stress distribution near the borehole. Two types of in situ stress conditions were considered. The effect of heterogeneous characteristics of material on blast-induced granite fracture was investigated. The difference between 2D models and 3D models was discussed. Based on the numerical results, it can be concluded that the increase of the magnitude of initial pressure can change the mechanism of shear failure near the borehole and suppress radial cracks propagation. When initial lateral pressure is invariable, with initial vertical pressure rising, radial cracks along the acting direction of vertical pressure will be promoted, and radial cracks in other directions will be prevented. Heterogeneous characteristics of material have an obvious influence on the shear failure zones around the borehole.

scholarly journals Experimental Investigation on Time-Frequency Characteristics of Microseismic Signals in the Damage Evolution Process of Coal and Rock

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 809
Author(s):  
Wei Yang ◽  
Chengwu Li ◽  
Rui Xu ◽  
Xunchang Li
Keyword(s):  
Damage Evolution ◽  
Resonance Effect ◽  
Low Frequency ◽  
Dominant Frequency ◽  
Frequency Characteristics ◽  
Evolution Process ◽  
Deformation And Failure ◽  
Time Frequency ◽  
Rock Materials ◽  
Damage Degree

The deformation and failure of coal and rock materials is the primary cause of many engineering disasters. How to accurately and effectively monitor and forecast the damage evolution process of coal and rock mass, and form a set of prediction methods and prediction indicators is an urgent engineering problems to be solved in the field of rock mechanics and engineering. As a form of energy dissipation in the deformation process of coal and rock, microseismic (MS) can indirectly reflect the damage of coal and rock. In order to analyze the relationship between the damage degree of coal and rock and time-frequency characteristics of MS, the deformation and fracture process of coal and rock materials under different loading modes was tested. The time-frequency characteristics and generation mechanism of MS were analyzed under different loading stages. Meanwhile, the influences of properties of coal and rock materials on MS signals were studied. Results show that there is an evident mode cutoff point between high-frequency and low-frequency MS signals. The properties of coal and rock, such as the development degree of the original fracture, particle size and dense degree have a decisive influence on the amplitude, frequency, energy and other characteristic parameters of MS signals. The change of MS parameters is closely related to material damage, but has no strong relation with the loading rate. The richness of MS signals before the main fracture depends on the homogeneity of materials. With the increase of damage, the energy release rate increases, which can lead to the widening of MS signals spectrum. The stiffness and natural frequency of specimens decreases correspondingly. Meanwhile, the main reason that the dominant frequency of MS detected by sensors installed on the surface of coal and rock materials is mainly low-frequency is friction loss and the resonance effect. In addition, the spectrum and energy evolution of MS can be used as a characterization method of the damage degree of coal and rock materials. Furthermore, the results can provide important reference for prediction and early warning of some rock engineering disasters.

scholarly journals Influence of Graphene Oxide on Interfacial Transition Zone of Mortar

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Hongfang Sun ◽  
Zhili Ren ◽  
Li Ling ◽  
Shazim Ali Memon ◽  
Jie Ren ◽  
...  
Keyword(s):  
Compressive Strength ◽  
Image Analysis ◽  
Graphene Oxide ◽  
Prediction Model ◽  
Transition Zone ◽  
Cement Mortar ◽  
Interfacial Transition Zone ◽  
Backscattered Electron ◽  
The Difference

In this paper, the influence of graphene oxide (GO) on the microstructure of interfacial transition zone (ITZ) in cement mortar was investigated through image analysis (IA) of backscattered electron (BSE) micrographs. The results showed that the incorporation of GO significantly reduced the thickness of ITZ. The porosity in ITZ and bulk paste decreased due to the introduction of GO; meanwhile, the compressive strength of the mortar samples was improved. The addition of GO also narrowed the gap between the porosity of ITZ and bulk paste, and therefore, the entire microstructure of mortar became more homogenous. Based on the above results, the model to predict the compressive strength of mortar was modified for better precision. The improved prediction model indicated that the difference between the compressive strength of ITZ and bulk paste was reduced upon the refinement of ITZ by GO.

scholarly journals Separation of diffracted waves via SVD filter

2020 ◽  
Vol 17 (5) ◽  
pp. 1259-1271
Author(s):  
Hong-Yan Shen ◽  
Qin Li ◽  
Yue-Ying Yan ◽  
Xin-Xin Li ◽  
Jing Zhao
Keyword(s):  
Seismic Waves ◽  
Original Data ◽  
Identification Accuracy ◽  
Reflected Waves ◽  
Wave Separation ◽  
Imaging Results ◽  
Diffracted Waves ◽  
Fast Processing ◽  
The Difference ◽  
Value Decomposition

Abstract Diffracted seismic waves may be used to help identify and track geologically heterogeneous bodies or zones. However, the energy of diffracted waves is weaker than that of reflections. Therefore, the extraction of diffracted waves is the basis for the effective utilization of diffracted waves. Based on the difference in travel times between diffracted and reflected waves, we developed a method for separating the diffracted waves via singular value decomposition filters and presented an effective processing flowchart for diffracted wave separation and imaging. The research results show that the horizontally coherent difference between the reflected and diffracted waves can be further improved using normal move-out (NMO) correction. Then, a band-rank or high-rank approximation is used to suppress the reflected waves with better transverse coherence. Following, separation of reflected and diffracted waves is achieved after the filtered data are transformed into the original data domain by inverse NMO. Synthetic and field examples show that our proposed method has the advantages of fewer constraints, fast processing speed and complete extraction of diffracted waves. And the diffracted wave imaging results can effectively improve the identification accuracy of geological heterogeneous bodies or zones.

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