arXiv subjects

Yaolin Shi

Publications and source records attributed to Yaolin Shi.

4 recordsLinked to original sources

arXiv2026

Juxtaposition of Shallow Reservoir-Triggered Seismicity and Deep Tectonic Locking in the Qiaojia-Dongchuan Seismic Gap

Identifying the critical state of mature seismic gaps is challenging, especially when anthropogenic stress perturbations, such as reservoir impoundment, superimpose on tectonic loading. Here, utilizing a high-resolution dense array catalog from the Qiaojia-Dongchuan seismic gap (hosting the second-largest hydropower station in the world), we reveal a distinct vertical decoupling mechanism. The shallow activities exhibit high b-values (1.0), indicative of fluid-driven reservoir-triggered seismicity. Conversely, deep seismicity (20 km) outlines a 'locked asperity' characterized by low b-values (less than 0.8) and high Coulomb stress accumulation rate. We further identify a complex dipping structure, suggesting compound fault kinematics. Additionally, the calculated stress accumulation suggests this seismic gap is in a critical state with elevated rupture potential. Our findings indicate that shallow induced seismicity can mask the silent accumulation of deep tectonic strain. This decoupling model provides a new framework for assessing seismic risks in reservoir-fault systems globally.

physics.geo-ph↗

arXiv2021

Machine Learning Percolation Model

Recent advances in machine learning have become increasingly popular in the applications of phase transitions and critical phenomena. By machine learning approaches, we try to identify the physical characteristics in the two-dimensional percolation model. To achieve this, we adopt Monte Carlo simulation to generate dataset at first, and then we employ several approaches to analyze the dataset. Four kinds of convolutional neural networks (CNNs), one variational autoencoder (VAE), one convolutional VAE (cVAE), one principal component analysis (PCA), and one $k$-means are used for identifying order parameter, the permeability, and the critical transition point. The former three kinds of CNNs can simulate the two order parameters and the permeability with high accuracy, and good extrapolating performance. The former two kinds of CNNs have high anti-noise ability. To validate the robustness of the former three kinds of CNNs, we also use the VAE and the cVAE to generate new percolating configurations to add perturbations into the raw configurations. We find that there is no difference by using the raw or the perturbed configurations to identify the physical characteristics, under the prerequisite of corresponding labels. In the case of lacking labels, we use unsupervised learning to detect the physical characteristics. The PCA, a classical unsupervised learning, performs well when identifying the permeability but fails to deduce order parameter. Hence, we apply the fourth kinds of CNNs with different preset thresholds, and identify a new order parameter and the critical transition point. Our findings indicate that the effectiveness of machine learning still needs to be evaluated in the applications of phase transitions and critical phenomena.

cond-mat.dis-nn↗

arXiv2020

On the correlation of earthquake occurrence among major fault zones in the eastern margin of the Tibetan Plateau by Big Data Analysis

The subsequent series of responses to big events may exhibit a synchronicity of event number, frequency and energy release in different fault zones. This synchronicity is a reliable source for probing non-intuitive geological structures, assessing regional seismicity hazard map and even predicting the next big events. The synchronicity of main faults in the eastern margin of the Qinghai-Tibetan Plateau is still unknown to us. We propose to examine the correlation of earthquake occurrence among different fault zones to indicate this synchronicity, and to obtain a preliminary understanding of geodynamics processes and the unrecognized characteristics of deep evolution in the eastern margin of the Qinghai-Tibetan Plateau. We estimate temporal changes of completeness level, frequency seismicity, and intensity seismicity, referring respectively to Mc, Z, and E values, of 21 main fault zones, using a seismic catalogue from 1970 to 2015. Our results reveal that six fault zone pairs of fault zones exhibit relative high correlation (>0.6) by all three indicators, while four fault zone pairs are non-adjacent with close internal affinity offsetting the limit of spatial distance, such as the pair of Rongjing-mabian fault and Minjiang-huya fault. Most strikingly, some fault zone pairs showing typical high correlation (>0.8) of seismicity frequency or seismicity intensity, the faults surprisingly belong to neither the same seismic belt nor the same geological block, exhibiting a regional scale remote triggering pattern of earthquakes or structures. An embryonic pattern to predict the next possible events will also be presented. This correlation analysis discovers a previously unrecognized strong coupling relationship among main faults with high earthquake risk in the eastern margin of the Qinghai-Tibetan Plateau.

physics.geo-ph↗

arXiv2020

Comparison of Machine Learning Methods for Predicting Karst Spring Discharge in North China

The quantitative analyses of karst spring discharge typically rely on physical-based models, which are inherently uncertain. To improve the understanding of the mechanism of spring discharge fluctuation and the relationship between precipitation and spring discharge, three machine learning methods were developed to reduce the predictive errors of physical-based groundwater models, simulate the discharge of Longzici Spring's karst area, and predict changes in the spring on the basis of long time series precipitation monitoring and spring water flow data from 1987 to 2018. The three machine learning methods included two artificial neural networks (ANNs), namely, multilayer perceptron (MLP) and long short-term memory-recurrent neural network (LSTM-RNN), and support vector regression (SVR). A normalization method was introduced for data preprocessing to make the three methods robust and computationally efficient. To compare and evaluate the capability of the three machine learning methods, the mean squared error (MSE), mean absolute error (MAE), and root-mean-square error (RMSE) were selected as the performance metrics for these methods. Simulations showed that MLP reduced MSE, MAE, and RMSE to 0.0010, 0.0254, and 0.0318, respectively. Meanwhile, LSTM-RNN reduced MSE to 0.0010, MAE to 0.0272, and RMSE to 0.0329. Moreover, the decrease in MSE, MAE, and RMSE were 0.0910, 0.1852, and 0.3017, respectively, for SVR. Results indicated that MLP performed slightly better than LSTM-RNN, and MLP and LSTM-RNN performed considerably better than SVR. Furthermore, ANNs were demonstrated to be prior machine learning methods for simulating and predicting karst spring discharge.

eess.SP↗