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

A method to evaluation rock brittleness based on statistical damage constitutive parameters

Brittleness is an important parameter to evaluate the performance of rock engineering. A scientific and reasonable brittleness evaluation method is of great significance to theoretical research and the engineering practice of rock mechanics. In view of the existing statistical constitutive models of rock based on the Weibull distribution being able to express various constitutive behaviors well, such as brittleness, plasticity, strain softening, this paper wants to determine rock brittleness from constitutive models. First, the parameter m, which can effectively reflect the overall characteristics of the rock stress-strain curve, and the parameter δ, which can reflect the post-peak characteristics, are selected. Then, a brittle evaluation method based on rock statistical damage constitutive parameters is proposed, and the brittleness index Bm (Bm = m·δ) is established. The feasibility is verified by the testing data of granite, sandstone and marble under different conditions. The results show that the brittleness of those hard rocks decrease with the increasing of confining pressure. For confining pressures of 5 MPa and 15 MPa, the brittleness of granite under triaxial unloading test is greater than that under triaxial compression test. The calculation results quantitatively reflect the brittle characteristics of sandstone, marble and granite in the test. Compared with the existing representative brittleness indexes, it is found that the brittleness index Bm can effectively reflect the characteristics of rock brittleness decreasing with increasing confining pressure and enhancement under unloading stress path. This paper provides a way to evaluate rock brittleness from the perspective of a constitutive model, which is helpful to enrich our understanding of rock brittleness

Dynamic and probabilistic multi-class prediction of tunnel squeezing intensity

Tunnel squeezing is a time-dependent process that typically occurs in weak or over-stressed rock masses, significantly influencing the budget and time of tunnel construction. This paper presents a new framework to probabilistically predict the potential squeezing intensity and to dynamically update the prediction during construction based on the sequentially revealed ground information. An extensively well-documented database, which contains quantitative data from 154 squeezing sections with 95 unpublished inventories is established. A Decision Tree method is employed to train a probabilistic multi-classification model to predict the tunnel squeezing intensity. The trained classifier is then integrated with a Markovian geologic model, which features embedded Bayesian updating procedures, to achieve a dynamic prediction on the state probabilities of the geologic parameter within the model and the resulting squeezing intensity during excavation. An under-construction tunnel case—Miyaluo #3 tunnel—is used to illustrate the proposed framework. Results show that the Decision Tree classifier, as opposed to other black-box models, is easy to be interpreted. It provides reliable predictive accuracy while leading to insights into the understanding of the squeezing problem. The strength-stress ratio (SSR) is suggested to be the most important factor. Moreover, the implementation of the updating procedures is efficient since only a simple field test (e.g. Point Load index or Schmidt rebound index) is required. Multiple rounds of predictions within the updating process allow different levels of prediction, for example long-range, short-term, or immediate, to be extracted as useful information towards the decision-making of construction operations. Therefore, this framework can serve as a pragmatic tool to assist the selection of optimal primary-support and other construction strategies based on the potential squeezing risk

A-Eval: A Benchmark for Cross-Dataset Evaluation of Abdominal Multi-Organ Segmentation

Although deep learning have revolutionized abdominal multi-organ segmentation, models often struggle with generalization due to training on small, specific datasets. With the recent emergence of large-scale datasets, some important questions arise: \textbf{Can models trained on these datasets generalize well on different ones? If yes/no, how to further improve their generalizability?} To address these questions, we introduce A-Eval, a benchmark for the cross-dataset Evaluation ('Eval') of Abdominal ('A') multi-organ segmentation. We employ training sets from four large-scale public datasets: FLARE22, AMOS, WORD, and TotalSegmentator, each providing extensive labels for abdominal multi-organ segmentation. For evaluation, we incorporate the validation sets from these datasets along with the training set from the BTCV dataset, forming a robust benchmark comprising five distinct datasets. We evaluate the generalizability of various models using the A-Eval benchmark, with a focus on diverse data usage scenarios: training on individual datasets independently, utilizing unlabeled data via pseudo-labeling, mixing different modalities, and joint training across all available datasets. Additionally, we explore the impact of model sizes on cross-dataset generalizability. Through these analyses, we underline the importance of effective data usage in enhancing models' generalization capabilities, offering valuable insights for assembling large-scale datasets and improving training strategies. The code and pre-trained models are available at \href{https://github.com/uni-medical/A-Eval}{https://github.com/uni-medical/A-Eval}

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

SAM-Med3D

Although the Segment Anything Model (SAM) has demonstrated impressive performance in 2D natural image segmentation, its application to 3D volumetric medical images reveals significant shortcomings, namely suboptimal performance and unstable prediction, necessitating an excessive number of prompt points to attain the desired outcomes. These issues can hardly be addressed by fine-tuning SAM on medical data because the original 2D structure of SAM neglects 3D spatial information. In this paper, we introduce SAM-Med3D, the most comprehensive study to modify SAM for 3D medical images. Our approach is characterized by its comprehensiveness in two primary aspects: firstly, by comprehensively reformulating SAM to a thorough 3D architecture trained on a comprehensively processed large-scale volumetric medical dataset; and secondly, by providing a comprehensive evaluation of its performance. Specifically, we train SAM-Med3D with over 131K 3D masks and 247 categories. Our SAM-Med3D excels at capturing 3D spatial information, exhibiting competitive performance with significantly fewer prompt points than the top-performing fine-tuned SAM in the medical domain. We then evaluate its capabilities across 15 datasets and analyze it from multiple perspectives, including anatomical structures, modalities, targets, and generalization abilities. Our approach, compared with SAM, showcases pronouncedly enhanced efficiency and broad segmentation capabilities for 3D volumetric medical images. Our code is released at https://github.com/uni-medical/SAM-Med3D

Altered Brain Fraction Amplitude of Low Frequency Fluctuation at Resting State in Patients With Early Left and Right Bell’s Palsy: Do They Have Differences?

Purpose: Bell’s palsy refers to acute idiopathic unilateral facial nerve palsy. It is a common disorder of the main motor pathway to the facial muscles. This study aimed to investigate the abnormal fraction amplitude of low frequency fluctuation (fALFF) of the brain in patients with early left and right Bell’s palsy.Materials and Methods: Sixty-seven patients (left 33, right 34) and 37 age- and sex-matched healthy controls underwent resting-state functional magnetic resonance imaging (R-fMRI) examination. The fALFF values were measured from all subjects and were compared among the left palsy, right palsy, and control groups. Then, correlations between the Toronto Facial Grading System (TFGS) scores of the patients and the fALFF values of abnormal brain regions were analyzed.Results: Significant group differences in fALFF values among the three groups were observed mainly in the cerebral cortical, subcortical, and deep gray matter regions. Compared with the right Bell’s palsy group, the left Bell’s palsy group showed significantly decreased fALFF values in the left temporal pole of the superior temporal gyrus (TPOsup), right supramarginal, left and right middle cingulate cortex (MCC), left superior frontal gyrus (SFG), and left precentral gyrus (PreCG), and increased fALFF values were observed in the right SFG and PreCG. Furthermore, altered fALFF values correlated positively with the TFGS scores in the left superior TPO, bilateral MCC, and right PreCG, and correlated negatively with the TFGS scores in the right SFG of the left Bell’s palsy group. Altered fALFF values correlated positively with the TFGS scores in the bilateral MCC and right PreCG and correlated negatively with the TFGS scores in the left superior TPO and SFG of the right Bell’s palsy group.Conclusion: Regulatory mechanisms seem to differ between patients with left and right early Bell’s palsy. The severity of the disease is associated with these functional alterations

Research on the relationship between the formation of local construction culture and geographical environment based on adaptability analysis

Objectives: Since the natural ecology and geographical environment are the basis for the formation of local architecture, based on the adaptability analysis, the article analyzes the natural ecology and geographical environment that affect the creation of regional local architecture, and analyzes the adaptability of traditional architecture to natural ecology and the impact of traditional culture. Methods: The summary of the response methods is to try to find the substantive connotation of vernacular architecture in order to provide basic theoretical support for contemporary vernacular architecture creation. At the same time, combined with the characteristics of the times of the contemporary area, it proposes a typical site design adaptability analysis, a suitability climate adaptability analysis and a construction adaptability analysis returning to the local culture. And for the coastal cultural and historical background of the region, the development of regional ancient towns is discussed, and the location of regional coastal ancient towns is analyzed for the coastal environment. Results: The final selected model was a weighted average based on Akaike weights of 71 logistic candidate models that included all the variables in these 71 candidate models. The importance weights of variables are the criteria for assessing the impact and contribution rate of environmental factors on survival and dispersal, and are the sum of the Akaike weights of all candidate models containing a given predictor variable. The connection between households basically uses the scattered water of the building, does not occupy the foundation, and basically does not damage the landform. The entrance to the building is determined by the terrain on the one hand, and the road on the other, and is generally set on one side of the road. Finally, the coastal environmental background and historical and cultural background of regional ancient towns are summarized, and the research roughly explores the region. Conclusions: At the end of the article, through the interpretation of actual cases, it provides certain evidence and explanations for adaptability analysis, and expresses the design ideas of comprehensive trade-offs in the process of adaptability analysis, in order to provide contemporary local architectural design for the extensive urban and rural construction in the region with theories and adaptability analysis