Integrated processing method for microseismic signal based on deep neural network

Denoising and onset time picking of signals are essential before extracting source information from collected seismic/microseismic data. We proposed an advanced deep dual-tasking network (DDTN) that integrates these two procedures sequentially to achieve the optimal performance. Two homo-structured encoder–decoder networks with specially designed structures and parameters are connected in series for handling the denoising and detection of microseismic signals. Based on the similarity of data types, the output of the denoising network will be imported into the detection network to obtain labels for the signal duration. The procedures of denoising and duration detection can be completed in an integrated way, where the denoised signals can improve the accuracy of onset time picking. Results show that the method has a good performance for the denoising of microseismic signals that contain various types and intensities of noise. Compared with existing methods, DDTN removes the noise with a minor waveform distortion. It is ideal for recovering the microseismic signal while maintaining a good capacity for onset time picking when the signal-to-noise ratio is low. Based on that, the second network can detect a more accurate duration of microseismic signals and thus obtain more accurate onset time. The method has great potential to be extended to the study of exploration seismology and earthquakes

Study on Stress-Type Rockburst Mechanism Based on Continuous-Discontinuous Element Method

Rockburst plays a serious threat to personnel and equipment during underground engineering construction. The study of the rockburst mechanism is helpful to its prediction and prevention. Based on the characteristic analysis of a large number of rockburst cases, Li et al. proposed three stress-type and three stress-structural rockbursts and obtained the geological characteristics and occurrence criteria of these rockbursts, but the evolution process of rockbursts is still unclear. Based on the continuous-discontinuous element method, the characteristics of failure process, surrounding rock stress, motion, and energy of three stress-rockburst blocks are analyzed. The results show that rockburst failure generally goes through several stages, such as a few surfaces tensile failure, shallow shear failure, deep extension of tensile failure, shear failure communication, and rockburst occurrence. The total volume of rockburst blocks and the main distribution intervals of block diameters for different types of rockbursts are quite different, which are mainly affected by stress state and geological structure. The ejection velocity of the small block is always higher than that of the large block during the same one rockburst simulation, and the ejection velocity of the small block is from the surface. In the process of rockburst, not only the elastic strain energy is released but also the elastic strain energy is accumulated. The greater the rockburst intensity, the more the elastic strain energy is released, and the steeper the prepeak curve of elastic strain energy. The research results provide a reference for further understanding the mechanism of rockburst and lay a theoretical basis for the prevention and control of rockburst in underground engineering

Failure process and stability analysis of landslides in Southwest China while considering rainfall and supporting conditions

Landslides frequently occur in several mountainous areas because of their unique engineering–geological conditions and other external factors (earthquakes, rainfall, etc.). In this paper, the landslide in Southwest China is used as the research objective to examine the landslide’s stability under different working conditions. The influencing factors and the formation mechanism of the landslide are analyzed based on the geological environment and essential characteristics of the landslide. In addition, the transfer coefficient method and the GeoStudio software were used to assess the landslide stability. The analysis results demonstrate that the joint action of landforms, geological structures, rainfall, and other factors caused the landslide. Furthermore, the slipped tension fracture induced the failure mode. The transfer coefficient method results showed that the landslide was stable under natural conditions and unstable under rainstorm conditions, which is consistent with the numerical simulation result. The shear strength sensitivity analysis results depicted an apparent linear relationship among cohesion c, internal friction angle φ, and stability coefficient. Moreover, the stability of the unstable slope is more sensitive to φ than to c

A dynamic learning method based on the Gaussian process for tunnel boring machine intelligent driving

Introduction: The application of intelligent learning methods to the mining of characteristics and rules of time-series data has gained increasing attention with the rapid development of deep learning. One critical application of such methods is the intelligent assistant driving of tunnel boring machines (TBMs), for which the optimization of driving parameters is essential to improve construction efficiency. However, existing prediction models for TBM parameters are “static” and cannot dynamically capture parameter evolution during real-time driving cycles.Methods: In this study, we propose a novel dynamic learning model for TBM parameters by introducing the Gaussian process to address this problem. The model can learn decision-making experiences from historical driving cycles, dynamically update the model based on small sample data from current driving cycles, and simultaneously achieve driving parameter prediction. We focused on real-time prediction of TBM parameters in a tunnel project in western China.Results: The results show that the average relative errors of predicted total thrust and torque values were 1.9% and 2.7%, respectively, and the prediction accuracy was higher than that of conventional models such as random forest and long short-term memory. The model fully exploited updating of small samples of parameters, reducing the average time cost of the model to 29.7 s, which satisfies the requirements of efficient application.Discussion: The dynamic learning strategy of time-series data adopted in this study provides a reference for other similar engineering applications. The proposed model can improve the prediction accuracy of TBM parameters, thus facilitating the optimization of driving parameters and enhancing the construction efficiency of tunnels.Conclusion: In summary, this study establishes a dynamic learning model of TBM parameters that can dynamically capture parameter evolution and achieve accurate real-time driving parameter prediction. The proposed model can contribute to the development of intelligent assistant driving of TBMs and similar engineering applications

Experimental Investigation on Anisotropy of Rocks Using Digital Drilling Technology

Accurate determination of rock anisotropy is of great significance for analyzing the safety and stability of engineering rock mass. In this paper, uniaxial compression tests are carried out on four kinds of rocks: slate, gneiss, sandstone and shale, to obtain the uniaxial compressive strength of each rock in the different directions. Digital drilling tests are carried out on four kinds of rocks to study the anisotropy of drilling parameters. According to the working principle of the drill bit, its force balance analysis model is established, and the concept of cutting strength ratio is proposed. Using the drilling parameters (drilling depth, drilling time, torque and thrust, etc.) in the different directions for each rock, the interrelationships between them are analyzed. The anisotropy index of rock is defined according to the ratio of cutting strength in different directions of drill parameters, and a new method for judging rock anisotropy is proposed. The results show that the thrust and torque in all directions of the rock increase with the drilling depth. The torque in all directions of the rock has a positive linear relationship with the thrust. The ranking of the anisotropy degree for the four types of rocks is as follows: gneiss > slate > shale > sandstone. The anisotropy results have been validated by an alternative method utilizing uniaxial compressive strength. The determination results are verified by the uniaxial compressive strength of the rocks, and the degree of anisotropy of the four rocks is consistent with the determination results. This method can help engineers analyze the anisotropy of rock, and provide a new idea for studying the integrity and stability of rock mass

Quantitative Evaluation of the “Non-Enclosed” Microseismic Array: A Case Study in a Deeply Buried Twin-Tube Tunnel

The high-stress hazards of underground engineering have stimulated the exploration of microseismic monitoring and early warning methods. To achieve a good monitoring effect, the monitoring object is usually enclosed by a microseismic array (sensor array) (e.g., slope engineering, etc.). However, some characteristics of a buried tunnel, including “linear”, “deep-buried”, and “long”, make it difficult to deploy a reasonable microseismic array, which leads to the microseismic array being non-enclosed for the monitoring object. Application of the non-enclosed microseismic array yields decreases the accuracy of the source location. To solve the problem wisely, this paper deals with the feasibility of non-enclosed microseismic arrays (axial-extended, lateral-extended, and twin-tube arrays) by introducing a quantitative method. To this end, an optimized microseismic array with the best source location accuracy for a twin-tube expressway tunnel is proposed. The obtained results reveal that the non-enclosed microseismic arrays, which are unavoidable in expressway tunnel engineering, do not introduce errors but reduce the ability to resist them. Further, the twin-tube array achieves a better source location accuracy than the axial and lateral-extended arrays. In the application of the source location based on the particle swarm optimization (PSO) algorithm to the twin-tube array, microseismic events, which cluster in the rockburst section, are wholly gathered, and the maximum error is reduced by about 30−50 m, indicating its greater feasibility with respect to the single-tube array

Multi-Classification of Complex Microseismic Waveforms Using Convolutional Neural Network: A Case Study in Tunnel Engineering

Due to the complexity of the various waveforms of microseismic data, there are high requirements on the automatic multi-classification of such data; an accurate classification is conducive for further signal processing and stability analysis of surrounding rock masses. In this study, a microseismic multi-classification (MMC) model is proposed based on the short time Fourier transform (STFT) technology and convolutional neural network (CNN). The real and imaginary parts of the coefficients of microseismic data are inputted to the proposed model to generate three classes of targets. Compared with existing methods, the MMC has an optimal performance in multi-classification of microseismic data in terms of Precision, Recall, and F1-score, even when the waveform of a microseismic signal is similar to that of some special noise. Moreover, semisynthetic data constructed by clean microseismic data and noise are used to prove the low sensitivity of the MMC to noise. Microseismic data recorded under different geological conditions are also tested to prove the generality of the model, and a microseismic signal with Mw ≥ 0.2 can be detected with a high accuracy. The proposed method has great potential to be extended to the study of exploration seismology and earthquakes

Fine Classification Method for Massive Microseismic Signals Based on Short-Time Fourier Transform and Deep Learning

Numerous microseismic signals are produced by rock mass fracture during earthquakes, geological disasters, or underground excavations. Moreover, a large amount of noise signals are captured during microseismic signal monitoring. Specifically, some noise signals closely resemble microseismic signals, which severely impedes the rapid and accurate detection of the latter and the assessment of geological hazards. Therefore, we propose a precise model for identifying and classifying microseismic signals based on deep learning technology and short-time Fourier transform (STFT) technology. First, the STFT time–frequency analysis reveals the unique characteristics of noise, microseismic, and blasting signals, thereby allowing noise signals that are very similar to microseismic signals in the time domain to be finely distinguished. Second, the introduced attention mechanism focuses the classification on essential signal features. Finally, because tens of thousands of actual monitoring data points are considered, the deep neural network for microseismic classification is trained and tested under complex geological engineering conditions. The results demonstrate that the neural network model has good time–frequency feature extraction ability, and the well-trained model can satisfactorily complete daily classifications. Moreover, the model performs well when classifying similar noise and low-SNR microseismic signals. We believe that this type of signal-processing method, which considers multiple perspectives, can be extended to data processing in many other data-driven fields

Measurement of Coupling Coordination Degree and Spatio-Temporal Characteristics of the Social Economy and Ecological Environment in the Chengdu–Chongqing Urban Agglomeration under High-Quality Development

With rapid urbanization and industrialization, ecological disorders and environmental degradation have become serious, and the promotion of the coordinated development of the social economy and ecological environment is not only a pressing problem to be solved, but also an important step towards sustainable development. The coordinated development of the social economy and eco-environment is conducive to sustainable development. Considering the Chengdu–Chongqing urban agglomeration as a case study, this paper adopts panel data and establishes an index system to evaluate the coupling coordination degree (CCD) between the social economy and ecological environment based on the concept of high-quality development. From the perspective of time and space, the changing laws and characteristics of the CCD are analyzed, and the key factors affecting it are determined using regression analysis. The results show the following: (1) the CCD between the social economy and ecological environment of the Chengdu–Chongqing urban agglomeration presents a low level overall; (2) the CCD in more developed regions is significantly higher than that in less developed regions; thus, the characteristics of spatial differences are obvious; (3) the urbanization rate, ratio of actual use of foreign capital and GDP, ratio of total export-import volume and GDP, proportion of days with good air quality, and per capita public green space area are the main factors affecting the coordinated development of the social economy and ecological environment in the Chengdu–Chongqing urban agglomeration; and (4) Chongqing has obvious endogeneity. Finally, corresponding policy recommendations are provided aimed at promoting rapid economic development in the Chengdu–Chongqing urban agglomeration while focusing on environmental protection and promoting high-quality economic development with ecological environmental protection, while putting forward decision-making suggestions for high-quality development of urban agglomerations