Assessment of Seismic Hazards in Underground Mine Operations using Machine Learning

The most common causes of coal mining accidents are seismic hazard, fires, explosions, and landslips. These accidents are usually caused by various factors such as mechanical and technical failures, as well as social and economic factors. An analysis of these accidents can help identify the exact causes of these accidents and prevent them from happening in the future. There are also various seismic events that can occur in underground mines. These include rock bumps and tremors. These have been reported in different countries such as Australia, China, France, Germany, India, Russia, and Poland. Through the use of advanced seismological and seismic monitoring systems, we can now better understand the rock mass processes that can cause a seismic hazard. Unfortunately, despite the advancements, the accuracy of these methods is still not perfect. One of the main factors that prevent the development of effective seismic hazard prediction techniques is the complexity of the seismic processes. In order to carry out effective seismic risk assessment in mines, it is important that the discrimination of seismicity in different regions is carried out. The widespread use of machine learning in analyzing seismic data, it provides reliability and feasibility for preventing major mishaps. This paper provides uses various machine learning classifiers to predict seismic hazards

Chapter Machine Learning in Volcanology: A Review

A volcano is a complex system, and the characterization of its state at any given time is not an easy task. Monitoring data can be used to estimate the probability of an unrest and/or an eruption episode. These can include seismic, magnetic, electromagnetic, deformation, infrasonic, thermal, geochemical data or, in an ideal situation, a combination of them. Merging data of different origins is a non-trivial task, and often even extracting few relevant and information-rich parameters from a homogeneous time series is already challenging. The key to the characterization of volcanic regimes is in fact a process of data reduction that should produce a relatively small vector of features. The next step is the interpretation of the resulting features, through the recognition of similar vectors and for example, their association to a given state of the volcano. This can lead in turn to highlight possible precursors of unrests and eruptions. This final step can benefit from the application of machine learning techniques, that are able to process big data in an efficient way. Other applications of machine learning in volcanology include the analysis and classification of geological, geochemical and petrological “static” data to infer for example, the possible source and mechanism of observed deposits, the analysis of satellite imagery to quickly classify vast regions difficult to investigate on the ground or, again, to detect changes that could indicate an unrest. Moreover, the use of machine learning is gaining importance in other areas of volcanology, not only for monitoring purposes but for differentiating particular geochemical patterns, stratigraphic issues, differentiating morphological patterns of volcanic edifices, or to assess spatial distribution of volcanoes. Machine learning is helpful in the discrimination of magmatic complexes, in distinguishing tectonic settings of volcanic rocks, in the evaluation of correlations of volcanic units, being particularly helpful in tephrochronology, etc. In this chapter we will review the relevant methods and results published in the last decades using machine learning in volcanology, both with respect to the choice of the optimal feature vectors and to their subsequent classification, taking into account both the unsupervised and the supervised approaches

Earthquake Early Warning and Preparatory Phase Detection through the use of Machine Learning Techniques

In this thesis I present 3 different works developed during the PhD. These three works are already published. My research has been focused on onsite EEW techniques oriented to the seismic risk reduction for buildings. As matter of fact, in the first work (Iaccarino et al., 2020; Chapter 2), "Onsite earthquake early warning: Predictive models for acceleration response spectra considering site effects" , we presented an EEW method that predict Response Spectra of Acceleration (RSA) at nine different periods from P-wave parameters (i. e., Pd and IV2) on 3s window. RSA is a ground motion parameter of particular interest for structural engineers since it better correlates with structural damage than peak parameters such as PGA and PGV (Elenas and Meskouris, 2001). To account for site-effects, we retrieved a partially non-ergodic model using a mixed-effect regression analysis. This procedure helped us to reduce the prediction uncertainty. Finally, we analyzed the correction terms by station, and we found that the stations with the more positive ones (grater RSA) were the same stations to have amplification effects highlighted by H/V analysis. Furthermore, our models improve the EEW performances both in terms of true negatives and false positives. The second work I present, "Earthquake Early Warning System for Structural Drift Prediction using Machine Learning and Linear Regressors" (Iaccarino et al., 2021; Chapter 3), uses data recorded from in-building sensors from Japanese and Californian structures. Here, we developed a method to predict Structural Drift using P-wave features (i. e., Pd, IV2, and ID2) from 1s, 2s, and 3s windows. We studied the effects of the complexity of the dataset on the predictions subdividing the Japanese dataset in three subsets: data from one building; data from buildings with the same material of construction; entire dataset. From this study, we found that the variability of the dataset plays a key role in the predictions increasing the uncertainties of the predictions for the complete dataset. Moreover, we compared the performances of linear least square models and non-linear machine learning regressors finding that the last ones perform always better. In the end, we tried to export the model retrieved on Japanese buildings to the Californian buildings, finding that the drift predictions are underestimated by a bias. We proposed to correct this bias using magnitude dependent correction terms, finding that the linear models are more able to adapt in these conditions. In the end, I present "Forecasting the Preparatory Phase of Induced Earthquakes by Recurrent Neural Network" (Chapter 4; Picozzi and Iaccarino, 2021). Here, we used catalogue information from a very complete dataset of the Californian geothermal area, The Geysers. From the catalogue, we chose 8 events with M>=3.9, and we selected the first 5 as training set and rest as testing set. Then, we extracted 9 features as time-series: the b-value and completeness magnitude, Mc, of the Gutenberg-Richter law; the fractal dimension of hypocenters, Dc; the generalized distance between pairs of earthquakes, η; the Shannon's information entropy, h; the moment magnitude, Mw, and moment rate, M ̇_0; the total duration of event groups, ΔT, and the inter-event time, Δt. We wanted to assess the possibility to detect changes in time of these features that can be related to deviations from the background seismicity. We built two Recurrent Neural Networks, one to detect preparatory phase the other to detect the aftershocks phase. The method is able to discriminate both the preparatory phase and the aftershock phase on the testing set. In the end, merging the predictions of two methods, we found that all the three events in testing set present a preparatory phase that lasts from 4 hours to 2 days before the main event

EARTHQUAKE FORECASTING USING ARTIFICIAL NEURAL NETWORKS

Earthquake is one of the most devastating natural calamities that takes thousands of lives and leaves millions more homeless and deprives them of the basic necessities. Earthquake forecasting can minimize the death count and economic loss encountered by the affected region to a great extent. This study presents an earthquake forecasting system by using Artificial Neural Networks (ANN). Two different techniques are used with the first focusing on the accuracy evaluation of multilayer perceptron using different inputs and different set of hyper-parameters. The limitation of earthquake data in the first experiment led us to explore another technique, known as nowcasting of earthquakes. The nowcasting technique determines the current progression of earthquake cycle of higher magnitude earthquakes by taking into account the number of smaller earthquake events in the same region. To implement the nowcasting method, a Long Short Term Memory (LSTM) neural network architecture is considered because such networks are one of the most recent and promising developments in the time-series analysis. Results of different experiments are discussed along with their consequences

Earthquake Prediction

Among the countless natural disasters, earthquakes are capable to inflict vast devastation to a large number of buildings and constructions at the blink of an eye. Lack of knowledge and awareness on earthquake as well as its comeback is conspicuous and results in disaster; leading to bitter memories. Therefore, earthquake forecast has been a polemical study theme that has defied even the most intelligent of minds. In this chapter, an attempt was made to do an extensive overview in the area of the earthquake prediction as well as classifying them into the main strategies comprising short‐, immediate‐, and long‐term prediction. An example of each strategy was carried out by mentioning their corresponding approaches/algorithms, such as ΔCFS, CN, MSc, M8, ANN, FFBPANN, KNN, GRNN, RBF, and LMBP; depending on the importance of each strategy. Based on these, it was concluded that, after the Tohoku‐Oki earthquake with M9.0, the current orientation of the Headquarters for earthquake Research Promotion of MEXT in Japan declare that, their mission would be long‐term statistical forecast of seismicity. Even, it is claimed that they do not emphasize on short‐term forecasting. Besides, intermediate‐term estimations are not capable to be used for prevention of all damages and protect all human life, but they may be utilized to undertake certain affordable activities to decrease damage, losses, and modify postdisaster relief. And, despite the long‐term prediction is more concerned by researchers, there is no certain satisfactory level to content them. De facto, the made covenant of 1970 that investigators will be capable to forecast/predict ground excitations within a decade, still remains unmet

Understanding AI Application Dynamics in Oil and Gas Supply Chain Management and Development: A Location Perspective

The purpose of this paper is to gain a better understanding of Artificial Intelligence (AI) application dynamics in the oil and gas supply chain. A location perspective is used to explore the opportunities and challenges of specific AI technologies from upstream to downstream of the oil and gas supply chain. A literature review approach is adopted to capture representative research along these locations. This was followed by descriptive and comparative analysis for the reviewed literature. Results from the conducted analysis revealed important insights about AI implementation dynamics in the oil and gas industry. Furthermore, various recommendations for technology managers, policymakers, practitioners, and industry leaders in the oil and gas industry to ensure successful AI implementation were outlined. Doi: 10.28991/HIJ-SP2022-03-01 Full Text: PD