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New formula to determine flyrock distance on sedimentary rocks with low strength

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Open Geosciences
From the journal Open Geosciences Volume 16 Issue 1

Abstract

There are many methods that discuss the prediction of flyrock distance in blasting operation, but none of them specifically discusses flyrock distance in sedimentary rock with low strength. The empirical method based on a statistical approach also has no specific research on it. This study aims to obtain a formula for predicting flyrock distance due to blasting on a sedimentary rock with low strength using ammonium nitrate fuel oil. A total of 196 samples were obtained from the blasting that has been operated. The variables included for building the new prediction model of flyrock distance are stemming, blast-hole height, powder factor, and average charge per blast hole. The analysis was carried out using a statistical approach with the concept of regression and correlation. Unlike the previous model that applied a dimensional approach, the new model set each of the predictor variables to have their respective regression coefficients in order to see how they play a role in predicting the flyrock distance. The results show that burden, stemming, blast-hole height, powder factor, and average charge per blast hole significantly affect the flyrock distance. The variance in flyrock distance can be explained uniquely 3.50% by burden, 10.74% by stemming, 2.55% by blast-hole height, 2.32% by powder factor, and 2.76% by average charge per blast hole. The new proposed model of flyrock distance is better than the previous model by looking at the mean absolute percentage error. To predict the flyrock distance of sedimentary rock with low strength, the new model can be used.

Keywords: flyrock; throw distance prediction; regression model; semi-partial correlation; sedimentary rock

1 Introduction

Mine blasting environmental management and safety need to be given careful consideration [1], and the risk level needs to be assessed [2] to reduce the dangers already present [3,4]. The mechanism of flyrock is strongly controlled by the geological structure of the location [5]. Flyrock can potentially occur in small to large sizes, depending on the mechanism. Research on flyrock during boulder blasting has been carried out [6] using the classification and regression trees technique. Parameters that affect the flyrock distance are rock constant, blast-hole burden and spacing, explosive charge mass, stemming height, powder factor, rock density, and blast-hole diameter.

Based on research by Richards and Moore [7], there are three basic mechanisms for flyrock occurrence in blasting operations.

1.1 Face burst

If the burden on the front row of blasting in the field is too close to the free face, flyrock can potentially occur. The burden condition usually controls the throw distance of the flyrock in front of the free face. The maximum flyrock distance is [7]

L max = k 2 g m B 2.6 ,

where L max is the maximum flyrock distance (m), g is the gravitational acceleration (9.8 m/s2), m is the explosive charge mass per meter (kg/m), B is the initial burden (m), and k is the rock constant.

1.2 Cratering

Cratering occurs when the stemming height is too short, and there is a zone of weak rock in the blast hole. The weak zone is usually broken materials resulting from the previous blasting. Based on this condition, flyrock can be thrown in all directions from the initial blast hole. The maximum flyrock distance is [7]

L max = k 2 g m SH 2.6 ,

where L max is the maximum flyrock distance (m), g is the gravitational acceleration (9.8 m/s2), m is the explosive charge mass per meter (kg/m), SH is the stemming height (m), and k is the rock constant.

1.3 Rifling

Rifling is a condition where stemming height is adequate to prevent cratering, but the material used for stemming is inadequate, and it is usually accompanied by a high-intensity blasting noise. Flyrock due to rifling is more likely to be analyzed from the angle of the blast hole, because if the blast hole is upright, the flyrock can be assumed to return to its original point. The maximum flyrock distance is [7]

L max = k 2 g m SH 2.6 sin 2 θ ,

where L max is the maximum flyrock distance (m), g is the gravitational acceleration (9.8 m/s2), m is the explosive charge mass per meter (kg/m), SH is the stemming height (m), k is the rock constant, and θ is the blast-hole angle.

Lundborg [8] developed an empirical equation to predict the maximum flyrock distance:

L max = 143 d ( q − 0.2 ) ,

where L max is the maximum flyrock distance (m), d is the blast-hole diameter (in), and q is the specific charge (kg/m3). The Swedish Detonic Research Foundation proposed an empirical theory in 1975 to determine the maximum flyrock distance using the basic formula of ballistic trajectories, where the initial velocity V 0 (m/s) is defined as a function of blast hole diameter (D) in inch, rock fragmentation (T b) in meter, and rock density (P r) in kg/m3:

V 0 = 10 D × 260 T b P r .

The developed model has third-order independent variables. The mathematical form of the model is as follows [9]:

D [ m ] = 331.534 + 240.488 Q − 47.417 B 2 − 6.433 S 3 ,

where D [m] is the measured flyrock distance (m), Q is the specific charge (kg/m3), B is the burden (m), and S is the stemming (m).

Flyrock distance can be determined empirically based on the actual condition in the field or numerically based on a certain algorithm. Theoretical calculations use empirical methods based on Richards and Moore [7] and Lundborg [8]. A deep neural network approach to quarry is more precise than an artificial neural network (ANN) to predict the flyrock distance [10]. However, based on the root mean square error (RMSE), the particle swarm optimization (PSO) method of ANN has higher accuracy in predicting the flyrock distance [11, 12, 13]. Regardless of that, any method used still has weakness, so it is still necessary to develop flyrock models based on the site characteristics [14]. Many other studies have been carried out to predict flyrock throw distance from the blasting site. The previous research on flyrock is shown in Table 1.

Table 1

Previous research studies on flyrock

No Author Research
1. J. Zhou, N. Aghili, E. N. Ghaleini, D. T. Bui, M. M. Tahir, M. Koopialipoor [15] A Monte Carlo simulation approach for effective assessment of flyrock based on an intelligent system of neural network
2. A. K. Raina, V. S. M. R. Murthy, A. K. Soni [16] Prediction of flyrock distance in open pit blasting using surface response analysis
3. D. J. Armaghani, A. Mahdiyar, M. Hasanipanah, R. S. Faradonbeh, M. Khandelwal, H. B. Amnieh [17] Risk assessment and prediction of flyrock distance by combined multiple regression analysis and Monte Carlo simulation of quarry blasting
4. A. K. Raina, V. M. S. R. Murthy [18] Flyrock in bench blasting: a comprehensive review
5. H. N. Rad, I. Bakhshayeshi, W. A. W. Jusoh, M. M. Tahir, L. K. Foong [19] Prediction of flyrock in mine blasting: a new computational intelligence approach
6. P. F. Asl, M. Monjezi, J. K. Hamidi, D. J. Armaghani [20] Optimization of flyrock and rock fragmentation in the Tajareh limestone mine using metaheuristics method of firefly algorithm
7. E. Bakhtavar, H. Nourizadeh, A. A. Sahebi [21] Toward predicting blast-induced flyrock: a hybrid dimensional analysis fuzzy inference system
8. P. T. Kalaivaani, T. Akila, M. M. Tahir, M. Ahmed, A. Surendar [22] A novel intelligent approach simulate the blast-induced flyrock based on recurrent fuzzy neural network (RFNN) combined with PSO
9. M. Khandelwal, M. Monjezi [23] Prediction of flyrock in open pit blasting operation using machine learning method
10. M. Monjezi, H. A. Khoshalan, A. Y. Varjani [24] Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach
11. D. J. Armaghani, E. T. Mohamad, M. Hajihassani, S. V. A. N. K. Abad, A. Marto, M. R. Moghaddam [25] Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods
12. M. Hasanipanah, D. J. Armaghani, H. B. Amnieh, M. Z. A. Majid, M. M. D. Tahir [26] Application of PSO to develop a powerful equation for prediction of flyrock due to blasting
13. F. Faramarzi, H. Mansouri, M. A. E. Farsangi [27] Development of rock engineering system-based models for flyrock risk analysis and prediction of flyrock distance in surface blasting
14. R. S. Faradonbeh, D. J. Armaghani, H. B. Amnieh, E. T. Mohamad [28] Prediction and minimization of blast-induced flyrock using gene expression programming and firefly algorithm
15. M. Koopialipoor, A. Fallah, D. J. Armaghani, A. Azizi, E. T. Mohamad [11] Three hybrid intelligent models in estimating flyrock distance resulting from blasting
16. M. Rezaei, M. Monjezi, A. Y. Varjani [29] Development of a fuzzy model to predict flyrock in surface mining
17. R. Trivedi, T. N. Singh, A. K. Raina [30] Prediction of blast-induced flyrock in Indian limestone mines using neural networks

From the previous research that has been conducted, no one has touched on the discussion of sedimentary rocks with low strength. Blasting on low-strength sedimentary rocks is not intended to create fragmentation but rather to increase excavation productivity. The calculations also have not discussed the weight of each variable, so when there is a change in the geological condition, the results of previous studies need to be calibrated according to the detailed geological condition. In this study, there will be an explanation of the weight of each variable that contributes to building a prediction model of flyrock distance. The analysis uses not only an empirical approach to the blasting operation but also a measurement of the existing flyrock in order to obtain a relatively more accurate result. An empirical approach involving all variables that affect blasting in sedimentary rocks with low strength is a novelty of this study.

2 Materials and methods

The study was conducted in the Warukin Formation which consists of sandstone and claystone units as well as coal insert. The sedimentary rock has low strength and it will experience degradation of the mechanical properties when it is exposed [31]. The sandstone is composed of fine-to-coarse-sized quartz minerals with a rupture angle of 53° [32]. There are very few clay minerals in claystone because the clay-sized material in claystone is in the form of clay-sized quartz minerals [33]. The bedrock has a slope of 14°, but the bedding ratio has not yet been determined. The bedding ratio has an important role in low-wall stability [34], where the bedding ratio is separated from the thin layer that has a high plasticity with a cohesion of 0 and a friction angle of 13° [35]. The clay mineral contains around 15% kaolinite and 8% illite [33] due to the provenance of quartz minerals, which come from the older formation as a result of the recycled orogenic process. The ground water level at the research area is high, with an average of 5 m from the surface [36]. The design parameters of the blasting operation during the study are shown in Table 2.

Table 2

Design parameters

Parameter Unit Claystone Sandstone
Specific gravity — 2.62 2.65
Weight density kg/cm2 0.21 0.21
Strength MPa 1.30 2.90
Modulus elasticity kg/cm2 589 2.408
Poisson ratio — 0.30 0.31
Cohesion kPa 119.63 172.81
Friction angle degree 23.98 26.18

The data used for the analysis were obtained from the monitoring results of blasting operations that have been carried out over the past year with ammonium nitrate fuel oil (ANFO) for explosives. A total of 196 blasting produced flyrock and the maximum throw distances are recorded in Table 3. In Table 3, the actual flyrock distance (Fd actual) is the result of direct measurement, while the theoretical flyrock distance (Fd theory) is the result of theoretical calculation based on Ghasemi et al. [37]. The other data complementary to it are data on burden, spacing, stemming, blast-hole height, blast-hole diameter, powder factor, and average charge per blast-hole. With the available data, a prediction model of flyrock distance will be built whose results will then be compared with the data of theoretical flyrock distance.

Table 3

Actual and theoretical flyrock distance of some data samples

No Fd actual Fd theory No Fd actual Fd theory No Fd actual Fd theory No Fd actual Fd theory
1 112.3 36.20 16 53.6 56.58 31 82.3 61.39 46 49.6 47.83
2 44.7 36.76 17 41.4 32.20 32 133.2 134.48 47 94 55.31
3 67.3 62.98 18 83.7 53.34 33 31.8 28.12 48 32.5 33.83
4 53.1 41.28 19 31.4 36.22 34 26 28.07 49 57.4 52.67
5 76.2 45.48 20 56 49.92 35 13 30.47 50 77.8 72.68
6 37.4 34.20 21 31.8 42.84 36 37 39.34 51 37.3 40.58
7 85.5 48.69 22 93 86.43 37 15.1 30.15 52 78.2 42.43
8 43.2 46.43 23 45 44.33 38 31.5 39.37 53 88 42.72
9 88.4 48.35 24 112 92.09 39 29.3 40.71 54 63 70.65
10 40 44.43 25 27 30.48 40 22.7 37.25 55 37 40.25
11 38.6 30.94 26 49 46.80 41 42.1 37.56 56 43.8 42.01
12 50.9 48.07 27 103 69.86 42 81.3 84.05 57 50.1 47.07
13 39.5 38.93 28 121.8 103.39 43 48.1 46.77 58 26.4 27.74
14 66.2 54.03 29 94.1 54.97 44 91.4 62.54 59 58.1 47.69
15 49.5 37.52 30 127.9 69.42 45 59.4 60.65 60 41 61.14

The theoretical flyrock distance (Fd theory) in Table 3 was obtained from the concept of the flyrock model by Ghasemi et al. [37]:

Fd ( P / Q ) 1 / 3   = b 0 + b 1 B ( P / Q ) 1 / 3   + b 2 S ( P / Q ) 1 / 3   + b 3 St ( P / Q ) 1 / 3   + b 4 H ( P / Q ) 1 / 3   + b 5 D ( P / Q ) 1 / 3 .

Supandi [36] applied a dimensional approach to building a flyrock (Fd) model based on the burden (B), spacing (S), stemming (St), blast-hole height (H), blast-hole diameter (D), powder factor (P), and average charge per blast hole (Q). The powder factor (P) and average charge per blast hole (Q) were selected as repeating variables, so, by taking into account all the variable dimensions, the other variables were multiplied by (P/Q)1/3 in order to obtain dimensionless parameters. Then, the dimensionless parameters were used to build the flyrock prediction model using multiple linear regression analysis. By applying the same steps to the data in this study, the following model was obtained (equation (1)):

(1) Fd = 0.005 ( B − 35.11 S 35.62 St − 1.69 H − 1.29 D − 0.33 ( P / Q ) − 1.27 ) ,

where Fd is the flyrock distance (m), B is the Burden (m), S is the spacing (m), St is the stemming (m), H is the height of blast hole (m), D is the diameter of blast hole (m), P is the powder factor (kg/m3), and Q is the average charge per blast hole (kg).

A new model will be built by using a similar concept with different methods using robust regression. Robust regression is a method used in statistics as an alternative when linear least-squares estimates falter to fit a model to data that may contain non-normal error distribution or the errors exhibit heavy tails [38]. Robust regression aims to provide more stable estimates of the relationship between variables by minimizing the influence of outliers with the use of weighting functions. One commonly used weighting function in robust regression is the bisquare. The objective and weight functions for bisquare are equations (2) and (3), respectively [38]:

(2) ρ B ( e ) = k 2 6 1 − 1 − e k 2 3 , ∣ e ∣ ≤ k k 2 6 , ∣ e ∣ > k ,

(3) ω B ( e ) = 1 − e k 2 2 , ∣ e ∣ ≤ k 0 , ∣ e ∣ > k ,

where k is the tuning constant and e is the residual. Bisquare weighting provides a flexible mechanism for down-weighting outliers without entirely discarding them from the analysis.

To obtain information about the specific effect of each independent variable on the dependent variable, semi-partial correlation was calculated [39,40]. Semi-partial correlation measures the degree of correlation between two variables while controlling for the influence of third variable(s). The mathematical formula for semi-partial correlation between variables X and Y, controlling for the influence of variable T, is denoted as [39]

(4) r Y · X | T = r Y . X − r Y · T r X · T 1 − r X · T 2 ,

where r Y · X represents the correlation between variables X and Y, r Y · T represents the correlation between variables T and Y, and r X · T represents correlation between variables T and X. Semi-partial correlation also expresses the specific portion of variance explained by a given independent variable in a multiple linear regression analysis.

3 Results and discussion

Equation (1) models the flyrock distance based on burden, spacing, stemming, blast-hole height, blast-hole diameter, and ratio of powder factor to average charge per blast hole. Summary statistics of the eight variables is presented in Table 4.

Table 4

Summary statistics of study variables

Mean Variance Minimum Maximum
Flyrock distance (Fd) 48.36 549.20 8.60 133.20
Burden (B) 7.09 0.0796 7 8
Spacing (S) 8.09 0.0796 8 9
Stemming (St) 3.74 0.50 1.90 5.40
Height of blast hole (H) 6.32 0.97 3.10 8.40
Diameter of blast hole (D) 0.20 0.00001 0.17 0.20
Powder factor (P) 0.1532 0.0004 0.0800 0.1900
Charge per blast hole (Q) 56.06 136.55 22.68 89.38

The flyrock distance (Fd) ranges from 8.60 to 133.20 m with an average of 48.36 m. Variations in the flyrock values were obtained based on the burden (B) of 7–8 m, spacing (S) of 8–9 m, stemming (St), which is in the range of 1.90–5.40 m with an average of 3.74 m, the blast-hole height (H) of 3.10–8.40 m with an average of 6.32 m, blast-hole diameter (D) of 0.17–0.20 m, the powder factor (P) ranging from 0.08 to 0.19 kg/m3 with an average of 0.1532 kg/m3, and the charge per blast hole (Q) from 22.68 to 89.38 kg with an average of 56.06 kg.

Equation (1) shows that flyrock distance is a function of burden, spacing, stemming, blast-hole height, blast-hole diameter, powder factor, and average charge per blast hole. The equation is equivalent to linear regression of the log-transformed variables as follows (equation (5)):

(5) ln Fd = b 0 + b 1 ln B + b 2 ln S + b 3 ln St + b 4 ln H + b 5 ln D + b 6 ln P Q ,

where b 0 is the intercept and b 1, b 2, b 3, b 4, b 5, and b 6 are regression coefficients. The powder factor (P) and average charge per blast hole (Q) for the new model will have their respective regression coefficients to see how they play a role in predicting flyrock distance (Fd). These parameters will be estimated through robust regression analysis.

The correlations between burden (B), spacing (S), stemming (St), blast-hole height (H), blast-hole diameter (D), powder factor (P), and average charge per blast hole (Q) as independent variables, as well as flyrock distance (Fd) as dependent variable, were analyzed through correlation analysis. Figure 1 shows plots of the relationship between variables with the correlation magnitudes, and the correlation test results are given in Table 5. Based on the results of correlation tests, the independent variables were significantly correlated with flyrock distance (Fd), except for blast-hole diameter (D). Burden (B) and spacing (S) have a perfect correlation, so one of them is going to be removed since they give the same information.

Figure 1 Plot of the relationship between variables.
Figure 1

Plot of the relationship between variables.

Table 5

Correlation analysis result

Variable Correlation p-Value Variable Correlation p-Value
Fd with B 0.177 0.01287 S with H −0.301 1.82 × 10−5
Fd with S 0.177 0.01287 S with D 0.038 0.5929
Fd with St −0.649 <2.2 × 10−16 S with P −0.023 0.7448
Fd with H −0.564 <2.2 × 10−16 S with Q 0.076 0.2894
Fd with D −0.032 0.6573 St with H 0.840 <2.2 × 10−16
Fd with P −0.138 0.05273 St with D 0.071 0.3223
Fd with Q −0.409 2.5 × 10−9 St with P 0.225 0.00149
B with S 1 <2.2 × 10−16 St with Q 0.625 < 2.2 × 10−16
B with St −0.398 7.45 × 10−9 H with D −0.107 0.135
B with H −0.301 1.82 × 10−5 H with P 0.210 0.00311
B with D 0.038 0.5929 H with Q 0.757 <2.2 × 10−16
B with P −0.023 0.7448 D with P 0.043 0.5519
B with Q 0.076 0.2894 D with Q −0.028 0.6935
S with St −0.398 7.45 × 10−9 P with Q 0.699 <2.2 × 10−16

By applying equations (2) and (3) for robust regression, the flyrock model was built as follows (equation (6)):

(6) ln Fd = 7.709 − 4.105 ln B − 1.733 ln St − 1.407 ln H − 0.387 ln D − 1.403 ln P + 1.409 ln Q Fd = 2229.204   ( B − 4.105 St − 1.733 H − 1.407 D − 0.387 P − 1.403 Q 1.409 ) .

Simultaneous testing on the regression parameters resulted in an F-value of 41.87 with a p-value of <2.2 × 10−16, so with a significance level of 5%, burden (B), stemming (St), blast-hole height (H), blast-hole diameter (D), powder factor (P), and average charge per blast hole (Q) simultaneously affect flyrock distance (Fd).

Partially, the regression parameter test result is given in Table 6. With a significance level of 5%, burden (B), stemming (St), blast-hole height (H), powder factor (P), and average charge per blast hole (Q) significantly affect flyrock distance (Fd), while the effect of blast-hole diameter (D) is insignificant. This is in line with the result of correlation analysis given in Table 5. Blast-hole diameter (D) is not correlated with flyrock distance (Fd).

Table 6

Partial regression parameter test result

Estimate Std. error t p-Value
Burden (B) −4.1052 1.2201 −3.3646 0.0011
Stemming (St) −1.7327 0.2177 −7.9576 7.47 × 10−14
Height of blast hole (H) −1.4067 0.6666 −2.1103 0.0313
Diameter of blast hole (D) −0.387 1.115 −0.3471 0.7112
Powder factor (P) −1.4033 0.5948 −2.3594 0.0161
Charge per blast hole (Q) 1.409 0.6008 2.345 0.01707

In order to find out how each relationship of burden (B), stemming (St), blast-hole height (H), blast-hole diameter (D), powder factor (P), and average charge per blast hole (Q) as independent variables on flyrock distance (Fd), semi-partial correlation was calculated by using equation (4), with the result shown in Table 7. Semi-partial correlation measures the correlation between one of those independent variables and flyrock distance (Fd) by taking into account the other variables. The R 2 in Table 7 shows the percentage of variance in flyrock distance (Fd) that can be explained by each variable of burden (B), stemming (St), blast-hole height (H), blast-hole diameter (D), powder factor (P), and average charge per blast hole (Q) uniquely, with control of the other variables.

Table 7

Semi-partial correlation analysis result

R R 2 p-Value
Fd with B −0.1872 0.0350 9.51 × 10−3
Fd with St −0.3276 0.1074 3.71 × 10−6
Fd with H −0.1598 0.0255 0.0271
Fd with D 0.0013 1.60 × 10−6 0.9861
Fd with P −0.1523 0.0232 0.0354
Fd with Q 0.1663 0.0276 0.0215

The p-value for all correlation test results, except for flyrock distance (Fd) with blast-hole diameter (D), in Table 7 is less than the significance level of 5%, so all those correlations are considered to be significant. The interpretation of R 2 in Table 7 is as follows:

  • In the relationship between burden (B) and flyrock distance (Fd), 3.50% of the variance in Fd can be explained by B uniquely (excluding the influence of B along with St, H, D, P, and Q).

  • In the relationship between stemming (St) and flyrock distance (Fd), 10.74% of the variance in Fd can be explained by St uniquely (excluding the influence of St along with B, H, D, P, and Q).

  • In the relationship between blast-hole height (H) and flyrock distance (Fd), 2.55% of the variance in Fd can be explained by H uniquely (excluding the influence of H along with B, St, D, P, and Q).

  • In the relationship between blast-hole diameter (D) and flyrock distance (Fd), 0% of the variance in Fd can be explained by D uniquely (excluding the influence of D along with B, St, H, P, and Q).

  • In the relationship between powder factor (P) and flyrock distance (Fd), 2.32% of the variance in Fd can be explained by P uniquely (excluding the influence of P along with B, St, H, D, and Q).

  • In the relationship between average charge per blast hole (Q) and flyrock distance (Fd), 2.76% of the variance in Fd can be explained by Q uniquely (excluding the influence of Q along with B, St, H, D, and P).

Figure 2 illustrates the distribution of actual flyrock distance (Fd actual) and the predicted flyrock distance (Fd theory) based on the previous model and the new model. The distribution of flyrock distance based on the new model looks more like the shape of actual flyrock distance distribution. The previous model has a mean absolute percentage error (MAPE) of 24.79% and an RMSE of 17.565, while the new model has an MAPE of 24.24% and an RMSE of 17.645. Based on the MAPE, the new model is better, but based on the RMSE, the previous model is better. If a model is better at a measure and the other model is better at another measure, the models may be very similar in terms of mean error [41,42].

Figure 2 Histogram of actual and theoretical flyrock distance (Fd) based on the previous model (left) and the new model (right).
Figure 2

Histogram of actual and theoretical flyrock distance (Fd) based on the previous model (left) and the new model (right).

4 Conclusion

Based on the analysis results, a new model to determinate flyrock distance with ammonium nitrate fuel oil for explosives was proposed:

Fd = 2229.204 ( B − 4.105 St − 1.733 H − 1.407 D − 0.387 P − 1.403 Q 1.409 ) ,

where Fd is the flyrock distance, B is the burden, St is the stemming, H is the blast-hole height, D is the blast-hole diameter, P is the powder factor, and Q is the average charge per blast hole. The new model has lower MAPE. However, it should be noted that this model was built from data that have almost constant values of burden (B), spacing (S), and blast-hole diameter (D), so this model is likely to be more suitable for data with the same condition.

Acknowledgments

The authors appreciate the PT Borneo Indobara for supporting this research and allowing the publication of the results.

  1. Funding information: No funding was received for conducting this study.

  2. Conflict of interest: The authors have no conflicts of interest to declare that are relevant to the content of this article.

  3. Data availability statement: The authors confirm that the data supporting the findings of this study are available within the article.

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Received: 2023-10-30
Revised: 2024-02-28
Accepted: 2024-03-13
Published Online: 2024-04-04

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  4. Evolutionary game analysis of government, businesses, and consumers in high-standard farmland low-carbon construction
  5. On the use of low-frequency passive seismic as a direct hydrocarbon indicator: A case study at Banyubang oil field, Indonesia
  6. Water transportation planning in connection with extreme weather conditions; case study – Port of Novi Sad, Serbia
  7. Zircon U–Pb ages of the Paleozoic volcaniclastic strata in the Junggar Basin, NW China
  8. Monitoring of mangrove forests vegetation based on optical versus microwave data: A case study western coast of Saudi Arabia
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  10. Multisource remote sensing image fusion processing in plateau seismic region feature information extraction and application analysis – An example of the Menyuan Ms6.9 earthquake on January 8, 2022
  11. Identification of magnetic mineralogy and paleo-flow direction of the Miocene-quaternary volcanic products in the north of Lake Van, Eastern Turkey
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  13. Adolescents’ consumption intentions toward leisure tourism in high-risk leisure environments in riverine areas
  14. Petrogenesis of Jurassic granitic rocks in South China Block: Implications for events related to subduction of Paleo-Pacific plate
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https://doi.org/10.1515/geo-2022-0624
Keywords for this article
flyrock; throw distance prediction; regression model; semi-partial correlation; sedimentary rock
Creative Commons
BY 4.0

Articles in the same Issue

  1. Regular Articles
  2. Theoretical magnetotelluric response of stratiform earth consisting of alternative homogeneous and transitional layers
  3. The research of common drought indexes for the application to the drought monitoring in the region of Jin Sha river
  4. Evolutionary game analysis of government, businesses, and consumers in high-standard farmland low-carbon construction
  5. On the use of low-frequency passive seismic as a direct hydrocarbon indicator: A case study at Banyubang oil field, Indonesia
  6. Water transportation planning in connection with extreme weather conditions; case study – Port of Novi Sad, Serbia
  7. Zircon U–Pb ages of the Paleozoic volcaniclastic strata in the Junggar Basin, NW China
  8. Monitoring of mangrove forests vegetation based on optical versus microwave data: A case study western coast of Saudi Arabia
  9. Microfacies analysis of marine shale: A case study of the shales of the Wufeng–Longmaxi formation in the western Chongqing, Sichuan Basin, China
  10. Multisource remote sensing image fusion processing in plateau seismic region feature information extraction and application analysis – An example of the Menyuan Ms6.9 earthquake on January 8, 2022
  11. Identification of magnetic mineralogy and paleo-flow direction of the Miocene-quaternary volcanic products in the north of Lake Van, Eastern Turkey
  12. Impact of fully rotating steel casing bored pile on adjacent tunnels
  13. Adolescents’ consumption intentions toward leisure tourism in high-risk leisure environments in riverine areas
  14. Petrogenesis of Jurassic granitic rocks in South China Block: Implications for events related to subduction of Paleo-Pacific plate
  15. Differences in urban daytime and night block vitality based on mobile phone signaling data: A case study of Kunming’s urban district
  16. Random forest and artificial neural network-based tsunami forests classification using data fusion of Sentinel-2 and Airbus Vision-1 satellites: A case study of Garhi Chandan, Pakistan
  17. Integrated geophysical approach for detection and size-geometry characterization of a multiscale karst system in carbonate units, semiarid Brazil
  18. Spatial and temporal changes in ecosystem services value and analysis of driving factors in the Yangtze River Delta Region
  19. Deep fault sliding rates for Ka-Ping block of Xinjiang based on repeating earthquakes
  20. Improved deep learning segmentation of outdoor point clouds with different sampling strategies and using intensities
  21. Platform margin belt structure and sedimentation characteristics of Changxing Formation reefs on both sides of the Kaijiang-Liangping trough, eastern Sichuan Basin, China
  22. Enhancing attapulgite and cement-modified loess for effective landfill lining: A study on seepage prevention and Cu/Pb ion adsorption
  23. Flood risk assessment, a case study in an arid environment of Southeast Morocco
  24. Lower limits of physical properties and classification evaluation criteria of the tight reservoir in the Ahe Formation in the Dibei Area of the Kuqa depression
  25. Evaluation of Viaducts’ contribution to road network accessibility in the Yunnan–Guizhou area based on the node deletion method
  26. Permian tectonic switch of the southern Central Asian Orogenic Belt: Constraints from magmatism in the southern Alxa region, NW China
  27. Element geochemical differences in lower Cambrian black shales with hydrothermal sedimentation in the Yangtze block, South China
  28. Three-dimensional finite-memory quasi-Newton inversion of the magnetotelluric based on unstructured grids
  29. Obliquity-paced summer monsoon from the Shilou red clay section on the eastern Chinese Loess Plateau
  30. Classification and logging identification of reservoir space near the upper Ordovician pinch-out line in Tahe Oilfield
  31. Ultra-deep channel sand body target recognition method based on improved deep learning under UAV cluster
  32. New formula to determine flyrock distance on sedimentary rocks with low strength
  33. Assessing the ecological security of tourism in Northeast China
  34. Effective reservoir identification and sweet spot prediction in Chang 8 Member tight oil reservoirs in Huanjiang area, Ordos Basin
  35. Detecting heterogeneity of spatial accessibility to sports facilities for adolescents at fine scale: A case study in Changsha, China
  36. Effects of freeze–thaw cycles on soil nutrients by soft rock and sand remodeling
  37. Vibration prediction with a method based on the absorption property of blast-induced seismic waves: A case study
  38. A new look at the geodynamic development of the Ediacaran–early Cambrian forearc basalts of the Tannuola-Khamsara Island Arc (Central Asia, Russia): Conclusions from geological, geochemical, and Nd-isotope data
  39. Spatio-temporal analysis of the driving factors of urban land use expansion in China: A study of the Yangtze River Delta region
  40. Selection of Euler deconvolution solutions using the enhanced horizontal gradient and stable vertical differentiation
  41. Phase change of the Ordovician hydrocarbon in the Tarim Basin: A case study from the Halahatang–Shunbei area
  42. Using interpretative structure model and analytical network process for optimum site selection of airport locations in Delta Egypt
  43. Geochemistry of magnetite from Fe-skarn deposits along the central Loei Fold Belt, Thailand
  44. Functional typology of settlements in the Srem region, Serbia
  45. Hunger Games Search for the elucidation of gravity anomalies with application to geothermal energy investigations and volcanic activity studies
  46. Addressing incomplete tile phenomena in image tiling: Introducing the grid six-intersection model
  47. Evaluation and control model for resilience of water resource building system based on fuzzy comprehensive evaluation method and its application
  48. MIF and AHP methods for delineation of groundwater potential zones using remote sensing and GIS techniques in Tirunelveli, Tenkasi District, India
  49. New database for the estimation of dynamic coefficient of friction of snow
  50. Measuring urban growth dynamics: A study in Hue city, Vietnam
  51. Comparative models of support-vector machine, multilayer perceptron, and decision tree ‎predication approaches for landslide ‎susceptibility analysis
  52. Experimental study on the influence of clay content on the shear strength of silty soil and mechanism analysis
  53. Geosite assessment as a contribution to the sustainable development of Babušnica, Serbia
  54. Using fuzzy analytical hierarchy process for road transportation services management based on remote sensing and GIS technology
  55. Accumulation mechanism of multi-type unconventional oil and gas reservoirs in Northern China: Taking Hari Sag of the Yin’e Basin as an example
  56. TOC prediction of source rocks based on the convolutional neural network and logging curves – A case study of Pinghu Formation in Xihu Sag
  57. A method for fast detection of wind farms from remote sensing images using deep learning and geospatial analysis
  58. Spatial distribution and driving factors of karst rocky desertification in Southwest China based on GIS and geodetector
  59. Physicochemical and mineralogical composition studies of clays from Share and Tshonga areas, Northern Bida Basin, Nigeria: Implications for Geophagia
  60. Geochemical sedimentary records of eutrophication and environmental change in Chaohu Lake, East China
  61. Research progress of freeze–thaw rock using bibliometric analysis
  62. Mixed irrigation affects the composition and diversity of the soil bacterial community
  63. Examining the swelling potential of cohesive soils with high plasticity according to their index properties using GIS
  64. Geological genesis and identification of high-porosity and low-permeability sandstones in the Cretaceous Bashkirchik Formation, northern Tarim Basin
  65. Usability of PPGIS tools exemplified by geodiscussion – a tool for public participation in shaping public space
  66. Efficient development technology of Upper Paleozoic Lower Shihezi tight sandstone gas reservoir in northeastern Ordos Basin
  67. Assessment of soil resources of agricultural landscapes in Turkestan region of the Republic of Kazakhstan based on agrochemical indexes
  68. Evaluating the impact of DEM interpolation algorithms on relief index for soil resource management
  69. Petrogenetic relationship between plutonic and subvolcanic rocks in the Jurassic Shuikoushan complex, South China
  70. A novel workflow for shale lithology identification – A case study in the Gulong Depression, Songliao Basin, China
  71. Characteristics and main controlling factors of dolomite reservoirs in Fei-3 Member of Feixianguan Formation of Lower Triassic, Puguang area
  72. Impact of high-speed railway network on county-level accessibility and economic linkage in Jiangxi Province, China: A spatio-temporal data analysis
  73. Estimation model of wild fractional vegetation cover based on RGB vegetation index and its application
  74. Lithofacies, petrography, and geochemistry of the Lamphun oceanic plate stratigraphy: As a record of the subduction history of Paleo-Tethys in Chiang Mai-Chiang Rai Suture Zone of Thailand
  75. Structural features and tectonic activity of the Weihe Fault, central China
  76. Application of the wavelet transform and Hilbert–Huang transform in stratigraphic sequence division of Jurassic Shaximiao Formation in Southwest Sichuan Basin
  77. Structural detachment influences the shale gas preservation in the Wufeng-Longmaxi Formation, Northern Guizhou Province
  78. Distribution law of Chang 7 Member tight oil in the western Ordos Basin based on geological, logging and numerical simulation techniques
  79. Evaluation of alteration in the geothermal province west of Cappadocia, Türkiye: Mineralogical, petrographical, geochemical, and remote sensing data
  80. Numerical modeling of site response at large strains with simplified nonlinear models: Application to Lotung seismic array
  81. Quantitative characterization of granite failure intensity under dynamic disturbance from energy standpoint
  82. Characteristics of debris flow dynamics and prediction of the hazardous area in Bangou Village, Yanqing District, Beijing, China
  83. Rockfall mapping and susceptibility evaluation based on UAV high-resolution imagery and support vector machine method
  84. Statistical comparison analysis of different real-time kinematic methods for the development of photogrammetric products: CORS-RTK, CORS-RTK + PPK, RTK-DRTK2, and RTK + DRTK2 + GCP
  85. Hydrogeological mapping of fracture networks using earth observation data to improve rainfall–runoff modeling in arid mountains, Saudi Arabia
  86. Petrography and geochemistry of pegmatite and leucogranite of Ntega-Marangara area, Burundi, in relation to rare metal mineralisation
  87. Prediction of formation fracture pressure based on reinforcement learning and XGBoost
  88. Hazard zonation for potential earthquake-induced landslide in the eastern East Kunlun fault zone
  89. Monitoring water infiltration in multiple layers of sandstone coal mining model with cracks using ERT
  90. Study of the patterns of ice lake variation and the factors influencing these changes in the western Nyingchi area
  91. Productive conservation at the landslide prone area under the threat of rapid land cover changes
  92. Sedimentary processes and patterns in deposits corresponding to freshwater lake-facies of hyperpycnal flow – An experimental study based on flume depositional simulations
  93. Study on time-dependent injectability evaluation of mudstone considering the self-healing effect
  94. Detection of objects with diverse geometric shapes in GPR images using deep-learning methods
  95. Behavior of trace metals in sedimentary cores from marine and lacustrine environments in Algeria
  96. Spatiotemporal variation pattern and spatial coupling relationship between NDVI and LST in Mu Us Sandy Land
  97. Formation mechanism and oil-bearing properties of gravity flow sand body of Chang 63 sub-member of Yanchang Formation in Huaqing area, Ordos Basin
  98. Diagenesis of marine-continental transitional shale from the Upper Permian Longtan Formation in southern Sichuan Basin, China
  99. Vertical high-velocity structures and seismic activity in western Shandong Rise, China: Case study inspired by double-difference seismic tomography
  100. Spatial coupling relationship between metamorphic core complex and gold deposits: Constraints from geophysical electromagnetics
  101. Disparities in the geospatial allocation of public facilities from the perspective of living circles
  102. Research on spatial correlation structure of war heritage based on field theory. A case study of Jinzhai County, China
  103. Formation mechanisms of Qiaoba-Zhongdu Danxia landforms in southwestern Sichuan Province, China
  104. Magnetic data interpretation: Implication for structure and hydrocarbon potentiality at Delta Wadi Diit, Southeastern Egypt
  105. Deeply buried clastic rock diagenesis evolution mechanism of Dongdaohaizi sag in the center of Junggar fault basin, Northwest China
  106. Application of LS-RAPID to simulate the motion of two contrasting landslides triggered by earthquakes
  107. The new insight of tectonic setting in Sunda–Banda transition zone using tomography seismic. Case study: 7.1 M deep earthquake 29 August 2023
  108. The critical role of c and φ in ensuring stability: A study on rockfill dams
  109. Evidence of late quaternary activity of the Weining-Shuicheng Fault in Guizhou, China
  110. Extreme hydroclimatic events and response of vegetation in the eastern QTP since 10 ka
  111. Spatial–temporal effect of sea–land gradient on landscape pattern and ecological risk in the coastal zone: A case study of Dalian City
  112. Study on the influence mechanism of land use on carbon storage under multiple scenarios: A case study of Wenzhou
  113. A new method for identifying reservoir fluid properties based on well logging data: A case study from PL block of Bohai Bay Basin, North China
  114. Comparison between thermal models across the Middle Magdalena Valley, Eastern Cordillera, and Eastern Llanos basins in Colombia
  115. Mineralogical and elemental analysis of Kazakh coals from three mines: Preliminary insights from mode of occurrence to environmental impacts
  116. Chlorite-induced porosity evolution in multi-source tight sandstone reservoirs: A case study of the Shaximiao Formation in western Sichuan Basin
  117. Predicting stability factors for rotational failures in earth slopes and embankments using artificial intelligence techniques
  118. Origin of Late Cretaceous A-type granitoids in South China: Response to the rollback and retreat of the Paleo-Pacific plate
  119. Modification of dolomitization on reservoir spaces in reef–shoal complex: A case study of Permian Changxing Formation, Sichuan Basin, SW China
  120. Geological characteristics of the Daduhe gold belt, western Sichuan, China: Implications for exploration
  121. Rock physics model for deep coal-bed methane reservoir based on equivalent medium theory: A case study of Carboniferous-Permian in Eastern Ordos Basin
  122. Enhancing the total-field magnetic anomaly using the normalized source strength
  123. Shear wave velocity profiling of Riyadh City, Saudi Arabia, utilizing the multi-channel analysis of surface waves method
  124. Effect of coal facies on pore structure heterogeneity of coal measures: Quantitative characterization and comparative study
  125. Inversion method of organic matter content of different types of soils in black soil area based on hyperspectral indices
  126. Detection of seepage zones in artificial levees: A case study at the Körös River, Hungary
  127. Tight sandstone fluid detection technology based on multi-wave seismic data
  128. Characteristics and control techniques of soft rock tunnel lining cracks in high geo-stress environments: Case study of Wushaoling tunnel group
  129. Influence of pore structure characteristics on the Permian Shan-1 reservoir in Longdong, Southwest Ordos Basin, China
  130. Study on sedimentary model of Shanxi Formation – Lower Shihezi Formation in Da 17 well area of Daniudi gas field, Ordos Basin
  131. Multi-scenario territorial spatial simulation and dynamic changes: A case study of Jilin Province in China from 1985 to 2030
  132. Review Articles
  133. Major ascidian species with negative impacts on bivalve aquaculture: Current knowledge and future research aims
  134. Prediction and assessment of meteorological drought in southwest China using long short-term memory model
  135. Communication
  136. Essential questions in earth and geosciences according to large language models
  137. Erratum
  138. Erratum to “Random forest and artificial neural network-based tsunami forests classification using data fusion of Sentinel-2 and Airbus Vision-1 satellites: A case study of Garhi Chandan, Pakistan”
  139. Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part I
  140. Spatial-temporal and trend analysis of traffic accidents in AP Vojvodina (North Serbia)
  141. Exploring environmental awareness, knowledge, and safety: A comparative study among students in Montenegro and North Macedonia
  142. Determinants influencing tourists’ willingness to visit Türkiye – Impact of earthquake hazards on Serbian visitors’ preferences
  143. Application of remote sensing in monitoring land degradation: A case study of Stanari municipality (Bosnia and Herzegovina)
  144. Optimizing agricultural land use: A GIS-based assessment of suitability in the Sana River Basin, Bosnia and Herzegovina
  145. Assessing risk-prone areas in the Kratovska Reka catchment (North Macedonia) by integrating advanced geospatial analytics and flash flood potential index
  146. Analysis of the intensity of erosive processes and state of vegetation cover in the zone of influence of the Kolubara Mining Basin
  147. GIS-based spatial modeling of landslide susceptibility using BWM-LSI: A case study – city of Smederevo (Serbia)
  148. Geospatial modeling of wildfire susceptibility on a national scale in Montenegro: A comparative evaluation of F-AHP and FR methodologies
  149. Geosite assessment as the first step for the development of canyoning activities in North Montenegro
  150. Urban geoheritage and degradation risk assessment of the Sokograd fortress (Sokobanja, Eastern Serbia)
  151. Multi-hazard modeling of erosion and landslide susceptibility at the national scale in the example of North Macedonia
  152. Understanding seismic hazard resilience in Montenegro: A qualitative analysis of community preparedness and response capabilities
  153. Forest soil CO2 emission in Quercus robur level II monitoring site
  154. Characterization of glomalin proteins in soil: A potential indicator of erosion intensity
  155. Power of Terroir: Case study of Grašac at the Fruška Gora wine region (North Serbia)
  156. Special Issue: Geospatial and Environmental Dynamics - Part I
  157. Qualitative insights into cultural heritage protection in Serbia: Addressing legal and institutional gaps for disaster risk resilience
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