An Expert Artificial Intelligence Model for Discriminating Microseismic Events and Mine Blasts

To reduce the workload and misjudgment of manually discriminating microseismic events and blasts in mines, an artificial intelligence model called PSO-ELM, based on the extreme learning machine (ELM) optimized by the particle swarm optimization (PSO) algorithm, was applied in this study. Firstly, based on the difference between microseismic events and mine blasts and previous research results, 22 seismic parameters were selected as the discrimination feature parameters and their correlation was analyzed. Secondly, 1600 events were randomly selected from the database of the microseismic monitoring system in Fankou Lead-Zinc Mine to form a sample dataset. Then, the optimal discrimination model was established by investigating the model parameters. Finally, the performance of the model was tested using the sample dataset, and it was compared with the performance of the original ELM model and other commonly used intelligent discrimination models. The results indicate that the discrimination performance of PSO-ELM is the best. The values of the six evaluation indicators are close to the optimal value, which shows that PSO-ELM has great potential for discriminating microseismic events and blasts. The research results obtained can provide a new method for discriminating microseismic events and blasts, and it is of great significance to ensure the safe and smooth operation of mines.

Seismic radiation patterns of mine blasts

<p>Seismic vibrations induced by mine blasting are often a nuisance to residents and may even threaten the integrity of sensitive structure in the vicinity of mines. In this study we investigate the potential to reduce such vibrations through the interference with a second blast sequence. Assuming perfectly repeatable source wavelets and an acoustic, homogeneous model, we predict the radiation patterns of blast sequences with the Fourier shift theorem as a function of azimuth and incidence, and we benchmark those predictions with observations from a seismic array deployed at the iron ore mine Mt Erzberg, Austria. We then use our model to optimize the delay times of blast sequences with an inverse algorithm geared towards minimizing the predicted vibrations in certain target zones. Due to its symmetry, a single row of blasts has no azimuthal reduction potential. A second, quasi-simultaneous mine blast can, however, reduce blast-induced vibrations by up to 20% according to our model. In this study, we discuss the principles and the potential of this approach to vibration reduction. In a second study, we will present applied results obtained with a fully elastic model.</p>

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

The 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.

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

ABSTRACT This 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.

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

A 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.