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24 results on '"Yu, Ziye"'

1. PRIME-DP: Pre-trained Integrated Model for Earthquake Data Processing

Author

Yu, Ziye, Cai, Yuqi, Wang, Weitao, An, Yanru, Li, Lu, Xia, Yueyang, and Zhang, Yunpeng

Subjects
Physics - Geophysics
Abstract

We propose a novel seismic wave representation model, namely PRIME-DP (Pre-trained Integrated Model for Earthquake Data Processing), specifically designed for processing seismic waveforms. Most existing models are designed to solve a singular problem. Unlike these models, PRIME-DP is capable of multi-task single station seismic waveform processing, including Pg/Sg/Pn/Sn phase picking and P polarization classification. Moreover, it can be fine-tunned to various tasks, such as event classification without architecture modifications. PRIME-DP can achieve a recall rate of over 85% for Pg and Sg phases on continuous waveforms and achieves over 80% accuracy in P polarization classification. By fine-tuning classification decoder with NeiMeng dataset, PRIME-DP achieves 95.1% accuracy on event.

Published
2024

5. Evaluation of Medium-Deep Geothermal Resources Based on Seismic Imaging Technology: A Case Study of the Midu Basin in Yunnan Province.

Author

Li, Jie, Zhang, Xuebin, Xu, Chao, Li, Chuan, Tan, Hui, Yu, Ziye, and Zhang, Yunpeng

Subjects
CENOZOIC Era, PALEOZOIC Era, IMAGING systems in seismology, POWER resources, SEISMIC tomography, GEOTHERMAL resources
Abstract

The effective utilization of medium-high temperature geothermal energy is pivotal in reducing carbon emissions and plays a crucial role in developing clean energy technologies. The MiDu geothermal field, situated in the southeastern region of Dali Prefecture, Yunnan Province, lies within the Mediterranean–Himalayan high-temperature geothermal belt and is characterized by abundant geothermal resources. However, due to its considerable depth, exploration poses significant risks, resulting in a total utilization rate of less than 0.5% of the total reserves. This study employs natural seismic data to perform a tomographic analysis of the geothermal system in the Midu basin. By examining the P-wave velocity (Vp) and the velocity ratio of P-waves and S-waves (Vp/Vs) at various depths, the findings reveal that the basin comprises two distinct structural layers: the thrust basement of the Mesozoic and Paleozoic eras and the strike–slip extensional sedimentary layer of the Cenozoic era. A low-velocity anomaly in the central basin corresponds to the loose Cenozoic sedimentary layer. In contrast, high-velocity anomalies at the basin edges correlate with boundary faults and the Mesozoic–Paleozoic strata. Below a depth of 4 km, the Red River Fault and MiDu Fault continue to dominate the basin's structure, whereas the influence of the Malipo Fault diminishes. The MiDu Fault exhibits higher thermal conductivity than the Yinjie Fault. It interfaces with multiple carbonate and basalt formations characterized by well-developed pores and fractures, making it a crucial conduit for water and a control point for geothermal storage. Consequently, the existence of medium-high temperature (>90 °C) geothermal resources for power generation should be concentrated around the Midu fault on the western side of the basin, while the Yinjie fault area is more favorable for advancements in heating and wellness. [ABSTRACT FROM AUTHOR]

Published
2024
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12. Combined CNN and RNN Neural Networks for GPR Detection of Railway Subgrade Diseases

Author

Liu, Huan (author), Wang, Shilei (author), Jing, Guoqing (author), Yu, Ziye (author), Yang, Jin (author), Zhang, Yong (author), Guo, Y. (author), Liu, Huan (author), Wang, Shilei (author), Jing, Guoqing (author), Yu, Ziye (author), Yang, Jin (author), Zhang, Yong (author), and Guo, Y. (author)

Abstract

Vehicle-mounted ground-penetrating radar (GPR) has been used to non-destructively inspect and evaluate railway subgrade conditions. However, existing GPR data processing and interpretation methods mostly rely on time-consuming manual interpretation, and limited studies have applied machine learning methods. GPR data are complex, high-dimensional, and redundant, in particular with non-negligible noises, for which traditional machine learning methods are not effective when applied to GPR data processing and interpretation. To solve this problem, deep learning is more suitable to process large amounts of training data, as well as to perform better data interpretation. In this study, we proposed a novel deep learning method to process GPR data, the CRNN network, which combines convolutional neural networks (CNN) and recurrent neural networks (RNN). The CNN processes raw GPR waveform data from signal channels, and the RNN processes features from multiple channels. The results show that the CRNN network achieves a higher precision at 83.4%, with a recall of 77.3%. Compared to the traditional machine learning method, the CRNN is 5.2 times faster and has a smaller size of 2.6 MB (traditional machine learning method: 104.0 MB). Our research output has demonstrated that the developed deep learning method improves the efficiency and accuracy of railway subgrade condition evaluation., Railway Engineering

Published
2023
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22. Theoretical Study on One-Dimensional Consolidation of Underconsolidated Marine Clay Induced by Ramp Loading

Author

Mengfan Zong, Guojun Zhai, Wenbing Wu, Meijuan Xu, and Yu Ziye

Subjects
Consolidation (soil), Geotechnical engineering, Geology
Abstract

The position of maximum excess pore water pressure is found to be a critical indication in the design of horizontal drainage systems for saturated soil, controlled by the drainage capacity at the boundaries and the self-weight of soil both of which are often ignored. In sea reclamation works, however, the dredged clay used is mostly under consolidated and will undergo the self-weight consolidation. In this paper, an exponentially time-growing drainage boundary condition is adopted, based on which the analytical solution of one-dimensional consolidation of underconsolidated soil under a ramp loading is obtained by using the eigenfunction expansion technique. The obtained solution is verified and a parameter analysis is later carried out. It turns out that when considering the self-weight of the soil subjected to the ramp loading, the variation of the position of maximum excess pore water pressure over time depends heavily on the symmetry of the boundary conditions. The smaller the over consolidation ratio (OCR

Published
2021

24. Semi‐Analytical Solution for One‐Dimensional Nonlinear Consolidation of Multilayered Soil Considering Self‐Weight and Boundary Time Effect.

Author

Zong, Mengfan, Zhang, Jing, Wu, Wenbing, Yu, Ziye, Zhang, Yi, and Mei, Guoxiong

Subjects
*PORE water pressure, *SOIL consolidation, *FINITE differences, *SOIL depth, *ANALYTICAL solutions
Abstract

ABSTRACT The self‐weight stress in multilayered soil varies with depth, and traditional consolidation research seldom takes into account the actual distribution of self‐weight stress, resulting in inaccurate calculations of soil consolidation and settlement. This paper presents a semi‐analytical solution for the one‐dimensional nonlinear consolidation of multilayered soil, considering self‐weight, time‐dependent loading, and boundary time effect. The validity of the proposed solution is confirmed through comparison with existing analytical solutions and finite difference solution. Based on the proposed semi‐analytical solution, this study investigates the influence of self‐weight, interface parameter, soil properties, and nonlinear parameters on the consolidation characteristics of multilayered soil. The results indicate that factoring in the true distribution of self‐weight leads to a faster dissipation rate of excess pore water pressure and larger settlement and settlement rate, compared to not considering self‐weight. Both boundary drainage performance and soil nonlinearity have an impact on consolidation. If the boundary drainage capacity is inadequate, the influence of soil nonlinearity on consolidation diminishes. [ABSTRACT FROM AUTHOR]

Published
2024
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