Mine Blasts Research Articles

High-Accuracy Discrimination of Blasts and Earthquakes Using Neural Networks With Multiwindow Spectral Data

AbstractThe manual separation of natural earthquakes from mine blasts in data sets recorded by local or regional seismic networks can be a labor-intensive process. An artificial neural network (ANN) applied to automate discriminating earthquakes from quarry and mining blasts in eastern Kentucky suggests that the analyst effort in this task can be significantly reduced. Based on a dataset of 152 local and regional earthquake and 4192 blast recordings over a three-year period in and around eastern Kentucky, ANNs of different configurations were trained and tested on amplitude spectra parameters. The parameters were extracted from different time windows of three-component broadband seismograms to learn the general characteristics of analyst-classified regional earthquake and blast signals.There was little variation in the accuracies and precisions of various models and ANN configurations. The best result used a network with two hidden layers of 256 neurons, trained on an input set of 132 spectral amplitudes and extracted from the P-wave time window and three overlapping time windows from the global maximum amplitude on all three components through the coda. For this configuration and input feature set, 97% of all recordings were accurately classified by our trained model. Furthermore, 96.7% of earthquakes in our data set were correctly classified with mean-event probabilities greater than 0.7. Almost all blasts (98.2%) were correctly classified by mean-event probabilities of at least 0.7. Our technique should greatly reduce the time required for manual inspection of blast recordings. Additionally, our technique circumvents the need for an analyst, or automatic locator, to locate the event ahead of time, a task that is difficult due to the emergent nature of P-wave arrivals induced by delay-fire mine blasts.

Open Pit Mining with Blasting: GeoEcological Aftermath

The article analyzes statistics on micro-solid emissions in mineral mining and discusses features of micro-emission in atmosphere in large-scale blasting in open pit mines. The gas-dynamic calculations of dust and gas cloud elevation after blasting for localization of solid micro particles and determination of their concentration in the troposphere are presented. The influence of large-scale blasting on regional seismicity is illustrated in terms of the Kuznetsk Coal Basin.

Testing a Local-Distance Rg/Sg Discriminant Using Observations from the Bighorn Region, Wyoming

ABSTRACTThis study explores the effectiveness of local-distance (<200 km) seismic discriminant to distinguish between surface mine blasts, single-shot borehole explosions, and earthquakes in the Bighorn Mountains region, Wyoming. We focus on the ratio between local-distance fundamental-mode surface waves (Rg) and the crustal shear-wave (Sg) signals. The observed spectral amplitude measurements are fit to propagation models that account for distance-dependent geometrical spreading and attenuation, and site amplification factors. The results support previous observations that Rg attenuates rapidly, is amplified in sedimentary basins, and has suppressed amplitudes in isolated mountainous terrain. Sg attenuates less rapidly than Rg but exhibits a similar spatial site amplification pattern. We compute an Rg/Sg source discriminant by taking the ratio between site- and distance-corrected Rg and Sg amplitude measurements. The results suggest that the site- and distance-corrected Rg/Sg ratios can distinguish events larger than ML∼1.5 (in the Bighorn region). The discriminant may also be sensitive to explosion emplacement conditions, where the ratios are higher for borehole shots in sedimentary strata and lower for explosions within the basement. The analysis shows that the Rg/Sg discriminant is effective for events in the Bighorn region for events larger than ML∼1.5 if proper considerations are made to account for event size and near-source material.

Attenuation assessment of blast-induced vibrations derived from an underground mine

Attenuation assessment of vibration induced by underground explosions is a source of concern due to its associated with damage analyses of underground facilities and ground structures. Based on the blast events at different levels of ore have different impacts on ground structures, to understand the attenuation rule of vertical propagation is essential for the safe design of mining blasts. In a new-built underground mine, eight seismographs were installed with different depths to monitor the blast vibrations, and 236 events included 24 events on the ground were recorded during mining production over a period of eight months. Certainly, the blasting design, propagating medium and its mechanical parameters were also carefully investigated. In the light of the collected data, various prediction equations are employed to establish a suitable relationship for describing the attenuations of peak particle velocity (PPV) and frequency. The charge weight effect on the frequency attenuation of the induced ground motion is also discussed. Moreover, the effect of surface soil overburden has also been analyzed based on a supplementary monitoring work. The results indicate that scaled distance (SD) with cube root scaling for the explosive quantity can properly perform the far-field vibration. Irrespective of how much the item of charge weight represents, its simplified form still makes the oldest equation still be widely used. The average frequency (AF) and its modified frequency equation perform well in the rock mass. The soil overburden has a low-impact on the ground motion, while the influence of charge weight per delay on ground wave frequency has diminished.

On the issue of defining safe distances and overpressure under impact of shock air blast wave (Magnesitovaya mine)

As the depth of mining increases, attention to the preservation of mine workings and mining blasting is steadily growing. Technological methods of effective mineral development are often limited by dynamic blasting impact causing short-period fluctuations in the rock mass. Therefore, correct explosion impact identification, to a great extent, determines effective mining trends. Some results of researches for minimum blasting impact on mining workings are given in the article. Calculation of safe distances and excess pressure at shock air impact of technological explosions on the Magnezitovaya mine personnel was made. The calculation was made for the 15.5 m mine working section, with two conditions of the total section of the mine workings adjacent to the face of 15.5 m and 31 m with distances of 50 m and 100 m. Overpressure was also calculated for a total cross-section of 15.5 m and distances of 150 and 200 m. It was determined that the safe distance for people under overpressure conditions at the shock air wave front, depending on breaking schemes, is in the range of 150-200 m. The urgency of this research is to ensure industrial safety of underground mining operations.

Modeling Seismic Vibrations under Massive Blasting in Underground Mines

The theoretical model is proposed, which allows tracing continuation of seismic vibrations after cessation of blast load by the analysis of velocigrams recording on ground surface during underground explosions. The comparison of the model and experimental velocigrams of massive blasts shows validity of elliptical filters for second-order low frequencies in modeling waveforms of the velocigrams. The model efficiency in the detection of time-delay errors is proved. The conditions of predicting peak particle velocities based on explosive weight per blast are determined. The pre-conditions for the resonance excitation in the rock mass-guarded object system are discussed, and the blast-induced load is predicted.

Modeling Seismic Vibrations under Massive Blasting in Underground Mines

Modeling Seismic Vibrations under Massive Blasting in Underground Mines

A New Catalog of Explosion Source Parameters in the Utah Region with Application to ML–MC‐Based Depth Discrimination at Local Distances

AbstractA catalog of explosion source parameters is valuable for testing methods of source classification in seismically active regions. We develop a manually reviewed catalog of explosions in the Utah region for 1 October 2012 to 30 June 2018 and use it to assess a newly proposed, magnitude‐based depth discriminant. Within the Utah region we define 26 event clusters that are primarily associated with mine blasts but also include explosions from weapons testing and disposal. The catalog refinement process consists of confirming the explosion source labels, revising the local (ML) and coda duration (MC) magnitudes, and relocating the hypocenters. The primary features used to determine source labels are waveform characteristics such as frequency content, the proximity of the preliminary epicenter to a permitted blast region, the time of day, and prior notification from mine operators. We reviewed 2199 seismic events of which 1545 are explosions, 459 are local earthquakes, and 195 are other event types. Of the reviewed events, 127 (5.8%) were reclassified with new labels. Over 74% of the reviewed explosions have both ML and MC, a sizable improvement over the unreviewed catalog (65%). The mean ML–MC value for the new explosion catalog is −0.196±0.017 (95% confidence interval) compared with a previously determined value of 0.048±0.008 for naturally occurring earthquakes in the Utah region. The shallow depths of the explosions lead to enhanced coda production, which in turn leads to anomalously large MC values. This finding confirms that ML–MC is a useful metric for discriminating explosions from deeper tectonic earthquakes in Utah. However, there is significant variation in ML–MC among the 26 explosion source regions, suggesting that ML–MC observations should be combined with other classification metrics to achieve the best performance in distinguishing explosions from earthquakes.

Classification of Local Seismic Events in the Utah Region: A Comparison of Amplitude Ratio Methods with a Spectrogram‐Based Machine Learning Approach

AbstractThe capability to discriminate low‐magnitude earthquakes from low‐yield anthropogenic sources, both detectable only at local distances, is of increasing interest to the event monitoring community. We used a dataset of seismic events in Utah recorded during a 14‐day period (1–14 January 2011) by the University of Utah Seismic Stations network to perform a comparative study of event classification at local scale using amplitude ratio (AR) methods and a machine learning (ML) approach. The event catalog consists of 7377 events with magnitudes MC ranging from −2 and lower up to 5.8. Events were subdivided into six populations based on location and source type: tectonic earthquakes (TEs), mining‐induced events (MIEs), and mining blasts from four known mines (WMB, SMB, LMB, and CQB). The AR approach jointly exploits Pg‐to‐Sg phase ARs and Rg‐to‐Sg spectral ARs in multivariate quadratic discriminant functions and was able to classify 370 events with high signal quality from the three groups with sufficient size (TE, MIE, and SMB). For that subset of the events, the method achieved success rates between about 80% and 90%. The ML approach used trained convolutional neural network (CNN) models to classify the populations. The CNN approach was able to classify the subset of events with accuracies between about 91% and 98%. Because the neural network approach does not have a minimum signal quality requirement, we applied it to the entire event catalog, including the abundant extremely low-magnitude events, and achieved accuracies of about 94%–100%. We compare the AR and ML methodologies using a broad set of criteria and conclude that a major advantage to ML methods is their robustness to low signal‐to‐noise ratio data, allowing them to classify significantly smaller events.

On the Portability of ML–Mc as a Depth Discriminant for Small Seismic Events Recorded at Local Distances

AbstractWe show that ML–Mc is a viable and regionally portable depth discriminant, and therefore may contribute to nuclear test ban treaty verification. A recent study found that the difference between local magnitude (ML) and coda duration magnitude (Mc) discriminates shallow seismic events (mining blasts, mining‐induced earthquakes, and shallow tectonic earthquakes) from deeper tectonic earthquakes in the Utah region. The shallow seismic events had anomalously high Mc values, with increasingly negative ML–Mc values as depth decreased. Here, we evaluate the performance of ML–Mc as a depth discriminant in three new regions and find that ML–Mc increases between 0 and 9 km depth in all cases. Initially, we investigated ML–Mc as a function of depth for naturally occurring earthquakes in the region around Yellowstone National Park, as recorded by the University of Utah Seismograph Stations. For 3358 Yellowstone earthquakes with well‐constrained depths, we found ML–Mc increased 0.030±0.007 magnitude units (m.u.) for each 1 km increase in depth up to 10 km depth. Next, we examined ML–Mc values for anthropogenic seismicity in northern Oklahoma and southern Kansas, as recorded by the National Earthquake Information Center. For 1628 events with well‐constrained depths, we computed a slope for ML–Mc of 0.022±0.010 m.u./km. Finally, we analyzed ML–Mc for 28,722 well‐located earthquakes in Italy, as recorded by the National Institute of Geophysics and Volcanology, and found an ML–Mc slope of 0.018±0.001 m.u./km. In each case, the quoted error bounds represent 95% confidence regions that exclude zero, implying that the depth dependence of ML–Mc is statistically significant. We performed several robustness tests in which we varied the criterion used to define a well‐constrained depth and the depth range used in the linear fit. In nearly all cases, we found a positive slope for ML–Mc versus depth at a confidence level above 95%.

Predicting blast-induced peak particle velocity using BGAMs, ANN and SVM: a case study at the Nui Beo open-pit coal mine in Vietnam

One of the most adverse effects encountered during blasting in open-pit mines is ground vibration. The peak particle velocity (PPV) is a measure used for ground vibrations; however, accurate prediction of PPV is challenging for blasters as well as managers. Herein, boosted generalised additive models (BGAMs) were applied for estimating the effects of blast-induced PPV. An empirical equation, support vector machine (SVM) and artificial neural network (ANN) were also adapted and used to approximate the blast-induced PPV for comparison. Herein, a database covering 79 blasting cases at Nui Beo’s open-pit coal mine, Vietnam, were used as a case example. Several performance indicators such as the coefficient of determination (R2), root-mean-square error (RMSE) and mean absolute error (MAE) were used to evaluate the quality of each predictive model. According to the results, the proposed BGAM performed better than the other models, yielding the highest accuracy with an R2 of 0.990, RMSE of 0.582 and MAE of 0.430. ANN and SVM models exhibited only slightly lower performance, while the empirical technique had the worst performance. Two testing blasts were performed to validate the accuracy of the developed BGAMs in practical engineering and the results showed that the BGAMs provided high accuracy than other models. Results also revealed that the elevation difference between the blasting site and monitoring point is one of the predominant parameters governing the PPV predictive models.

Prediction of blast induced ground vibration (BIGV) of quarry mining using hybrid genetic algorithm optimized artificial neural network

Efficient prediction of Open Pit mining blast induced ground vibration, has an important role in reduction of the environmental complaints. This paper proposed a new hybrid evolutionary artificial neural network (ANN) optimized by genetic algorithm (GA) to predict peak particle velocity (PPV). The proposed GA-ANN suggests a systematic and automated way to find out a proper ANN architecture namely; number of neurons, activation functions, training algorithm and number of epochs. A data set consisting of maximum charge weight per delay, horizontal distance (HD), radial distance (RD) and a new modified radial distance (MRD) between monitoring and blasting station provided at Sungun Copper Mine site in Iran were used to validate the proposed approach. Comparing the performance of the proposed GA-ANN model by statistical indices indicate the superiority of the GA-ANN model against the empirical predictors and neuro-fuzzy inference system. As an important finding, incorporating MRD instead of the conventional distance measures of HD and RD improves the accuracy of the prediction. Finally results signify the efficiency of the proposed GA-ANN approach in finding optimum architecture of ANN while trying to predict PPV.

The adaptive particle swarm optimization technique for solving microseismic source location parameters

Abstract. An intelligent method is presented for locating a microseismic source based on the particle swarm optimization (PSO) concept. It eliminates microseismic source locating errors caused by the inaccurate velocity model of the earth medium. The method uses, as the target of PSO, a global minimum of the sum of squared discrepancies between differences of modeled arrival times and differences of measured arrival times. The discrepancies are calculated for all pairs of detectors of a seismic monitoring system. Then, the adaptive PSO algorithm is applied to locate the microseismic source and obtain optimal value of the P-wave velocity. The PSO algorithm adjusts inertia weight, accelerating constants, the maximum flight velocity of particles, and other parameters to avoid the PSO algorithm trapping by local optima during the solution process. The origin time of the microseismic event is estimated by minimizing the sum of squared discrepancies between the modeled arrival times and the measured arrival times. This sum is calculated using the obtained estimates of the microseismic source coordinates and P-wave velocity. The effectiveness of the PSO algorithm was verified through inversion of a theoretical model and two analyses of actual data from mine blasts in different locations. Compared with the classic least squares method (LSM), the PSO algorithm displays faster convergence and higher accuracy of microseismic source location. Moreover, there is no need to measure the microseismic wave velocity in advance: the PSO algorithm eliminates the adverse effects caused by error in the P-wave velocity when locating a microseismic source using traditional methods.

Direct Observations of Surface‐Wave Eigenfunctions at the Homestake 3D Array

AbstractDespite the theory for both Rayleigh and Love waves being well accepted and the theoretical predictions accurately matching observations, the direct observation of their quantifiable decay with depth has never been measured in the Earth’s crust. In this work, we present observations of the quantifiable decay with depth of surface‐wave eigenfunctions. This is done by making direct observations of both Rayleigh‐wave and Love‐wave eigenfunction amplitudes over a range of depths using data collected at the 3D Homestake array for a suite of nearby mine blasts. Observations of amplitudes over a range of frequencies from 0.4 to 1.2 Hz are consistent with theoretical eigenfunction predictions. They show a clear exponential decay of amplitudes with increasing depth and a reversal in sign of the radial‐component Rayleigh‐wave eigenfunction at large depths, as predicted for fundamental‐mode Rayleigh waves. Minor discrepancies between the observed eigenfunctions and those predicted using estimates of the local velocity structure suggest that the observed eigenfunctions could be used to improve the velocity model. Our results confirm that both Rayleigh and Love waves have the depth dependence that they have long been assumed to have. This is an important direct validation of a classic theoretical result in geophysics and provides new observational evidence that classical seismological surface‐wave theory can be used to accurately infer properties of Earth structure and earthquake sources.

Analysis of artifacts and systematic errors of the Iranian Seismological Center’s earthquake catalog

This study reviews the history of seismometry in Iran and specifically the evolution of Iranian Seismological Center’s (IRSC) seismic network and investigates the artifacts and systematic errors of this center’s earthquake catalog. Our analysis indicates instability of magnitude estimation in the IRSC catalog to the extent that in the western and eastern parts of Iran, events with the same USGS reported mb magnitude could result in MN values 0.3 units apart. This catalog is contaminated with quarry and mine blasts, in which about 19% of the reported events are most likely not tectonic earthquakes. In addition, heterogeneity of magnitude of completeness as a function of space and time is observed. While Mc has gradually improved since 2006, its reduction in different parts of the country varies between 0 to 0.5 units of magnitude. In addition to artifacts, more than 54% of the reported events were located using a simplified velocity model and need to be relocated using more accurate velocity models. Moreover, in the hypocenter determination of about 7% of earthquakes, at least data from one station with wrong coordinates was employed. For these two groups of events, a clear dependence exists between the azimuthal gap and the value of the observed shift in the location parameters; with increasing the azimuthal gap, the shift increases. Investigations on the manually picked phases in the IRSC catalog show a large number of phase misidentification cases which are the other source of systematic errors. Examining the depth reported for events associated with mine blasts in a region in southern Iran implies systematic errors and high overestimation of depth for these events. The reported depth for most of these blasts is more than 10 km.

Automatic 3D illumination-diagnosis method for large-N arrays: Robust data scanner and machine-learning feature provider

The main issues related to passive-source reflection imaging with seismic interferometry (SI) are inadequate acquisition parameters for sufficient spatial wavefield sampling and vulnerability of surface arrays to the dominant influence of the omnipresent surface-wave sources. Additionally, long recordings provide large data volumes that require robust and efficient processing methods. We address these problems by developing a two-step wavefield evaluation and event detection (TWEED) method of body waves in recorded ambient noise. TWEED evaluates the spatiotemporal characteristics of noise recordings by simultaneous analysis of adjacent receiver lines. We test our method on synthetic data representing transient ambient-noise sources at the surface and in the deeper subsurface. We discriminate between basic types of seismic events by using three adjacent receiver lines. Subsequently, we apply TWEED to 600 h of ambient noise acquired with an approximately 1000-receiver array deployed over an active underground mine in Eastern Finland. We develop the detection of body-wave events related to mine blasts and other routine mining activities using a representative 1 h noise panel. Using TWEED, we successfully detect 1093 body-wave events in the full data set. To increase the computational efficiency, we use slowness parameters derived from the first step of TWEED as input to a support vector machine (SVM) algorithm. Using this approach, we detect 94% of the TWEED-evaluated body-wave events indicating the possibility to limit the illumination analysis to only one step, and therefore increase the time efficiency at the price of lower detection rate. However, TWEED on a small volume of the recorded data followed by SVM on the rest of the data could be efficiently used for a quick and robust (real-time) scanning for body-wave energy in large data volumes for subsequent application of SI for retrieval of reflections.

Traceable calibration of microphones at low frequencies

The accurate measurement of infrasound from wind turbines, mining blasts, earthquakes and other natural phenomena is essential to the correct assessment of the health and safety implications of low-frequency sound. Currently many low-frequency measurements are often made using microphones calibrated at higher frequencies (250 Hz or 1000 Hz) attached to sound level meters (SLMs), and assumptions are made based of manufacturers’ specifications of the response of the microphones at lower frequencies and of the software in the SLM. Presented here is a simple technique for the calibration of low-frequency microphones by comparison with differential pressure gauges, combining two different traceability paths to determine a microphone’s frequency response and sensitivity in the range 0.1 Hz to 20 Hz.

Ionospheric Detection of Explosive Events

AbstractThe ionospheric response to explosions which occur at or below the Earth's surface has been noted since the first detonations of nuclear devices during the early period of aboveground testing. Acoustic gravity waves and traveling ionospheric disturbances were detected in association with test explosions carried out by the Union of Soviet Socialist Republics in Novaya Zemlya in 1961. While research in this area has continued, the standards accepted by the Comprehensive Nuclear Test Ban Treaty for detection and confirmation of nuclear explosions have been based on (1) seismic, (2) hydroacoustic, (3) infrasound, and (4) radionuclide monitoring from ground detectors. We suggest that ionospheric sensing offers a complementary methodology that may allow for robust confirmation of explosive events. One method of ionospheric monitoring of explosive events is analysis of total electron content (TEC), available by processing data from Global Navigation Satellite System (GNSS) receivers distributed globally on land masses. Traveling ionospheric disturbances observed by their signature in TEC have been used to detect and confirm mine collapses, mine blasts, earthquakes, volcanic eruptions, and meteorite strikes as well as underground nuclear tests. While an integrated measurement like TEC is not as sensitive to small‐amplitude density perturbations as other methods, the existence of large networks of continuously operating GNSS stations makes this an intriguing new monitoring asset. We report on the current capabilities for detection of explosions via the ionosphere, the outstanding challenges, and prospects for future developments that have potential to augment the current standards for detection and confirmation of nuclear detonations. By leveraging the worldwide network of GNSS receivers, robust confirmation of impulsive events is possible. This methodology complements existing technologies approved by the Comprehensive Test Ban Treaty. The article outlines background theory, new approaches to monitoring of explosions, and challenges that remain to be addressed.

Numerical simulation on complete process of three-dimensional bench blasting in an open-pit mine based on CDEM

Blasting mining is the most important part of the total cost control in an open pit mine, and numerical simulation is an effective method to optimize the design of blasting mining and to analyse blasting effect. By using the continuum-discontinuum element method (CDEM), the three-dimensional bench blasting process of the open pit mine is simulated. The Landau explosive model is adopted to precisely calculate the blasting effect, and the elastic-damage-fracture constitutive law is used to describe the damage and fracture process of rock. By adopting the semi-spring target face and semi-edge target edge combined contact algorithm, the collision, flying and accumulation process of large number of fragments is simulated efficiently. The numerical simulation of the small scale blasting process with the single free surface is carried out. The block distributing curve and volume of the crater obtained by numerical simulation are more or less the same as those obtained by experiment, which demonstrates that CDEM and corresponding models described in this paper are good at simulating the rock blasting process. Based on the blasting technology in the south region in Anqian open-pit mine, a generalized three-dimensional bench blasting model with 3 rows and 21 bore holes is set up, and the complete process from explosive detonation to muckpile formation is carried out. Numerical results show that, except the tensile crack behind the blasting area, the muckpile shape and heaving height obtained by numerical simulation are accordant with the ones obtained by field test to some extent, which demonstrates the feasibility to simulate the three-dimensional bench blasting by CDEM.

Prediction and Controlling of Flyrock due to Blasting for Kyaukpahto Gold Mine

This paper presents the prediction of maximum flyrock distances due to blasting for Kyaukpahto Gold Mine. Flyrock is an unwanted throw of rock fragments during bench blasting in mines. Flyrock is one of the major undesirable effects of blasting for the surface mines because it is the main cause of fatal accidents and serious injuries. The aim of this study is to control the flyrock impacts due to blasting and the estimation of safe distances. In this paper, important and widely used empirical formulae have been used to predict the maximum flyrock distance for the Kyaukpahto Gold Mine. To select the optimum flyrock distance formula, maximum throw distance empirical formulae analysis were carried out by using the data of Kyaukpahto Gold Mine. Moreover, an excel program is used for the analysis. The paper concludes with the guidelines for applying the maximum flyrock distances based on the various rock types (sandstone, dolomite, and granite), amount of charges per delay and burden distances in Myanmar surface mines.