The Zhuhai Formation in Lufeng Depression is mainly deposited during a depression stage. In this paper, we employ cores, wireline logs, and seismic reflections to reveal sequence architecture and depositional evolution in target intervals. The results indicated that one second‐order sequence and two third‐order sequences were identified and sequence stratigraphic framework was established. The second‐order sequence was developed in response to base‐level falling, while third‐order sequences were mainly formed during base‐level rising. In addition, each third‐order sequence did not change too much in thickness. Then the shore deposits (shoreface and foreshore) were delineated, and the depositional characteristics were described within the framework with the help of related data. The shoreface and foreshore deposits within the sequence stratigraphic framework varied significantly in stacking patterns of wireline logs and cores. Based on comprehensive analysis, it can be concluded that base‐level changes played an important role in controlling sequence architecture. And lacustrine fluctuations derived from accommodation space changes exerted an essential effect on depositional evolution. This study provides significant insights into revealing the sequence architecture and depositional evolution and discussing hydrocarbon potential in the transition stage in the Zhuhai Formation.
Earthquakes in the Taiwan region have caused significant economic losses. We develop a model to assess seismic economic losses in the Taiwan region using the records of economic losses caused by historical earthquakes. Unlike existing models, we introduce Gross National Income Per Capita (GNIPC) as a parameter that responds to the influence of socio-economic development. The results show that our model can accurately estimate the earthquake economic losses in Taiwan region. The difference between the results of this model and those of existing models is evident, indicating differences between the Taiwan region and mainland China regarding geological background, seismic tectonics, and social resistance to earthquakes. The prediction results also imply that the society significantly underestimates the seismic economic losses in the Taiwan region. Our model can help the Taiwan region in disaster prevention and preparedness, contingency planning, allocation of relief resources, and post-disaster socio-economic recovery.
Abstract Spontaneous dynamic‐rupture simulation in 3D is a difficult task in seismology, especially for the rupture dynamics of a fault with complex geometry and a free surface. In this study, we model an irregular fault that reaches the free surface and investigate the rupture dynamics of the intersection between the earthquake‐induced fault and the Earth’s surface. We use the recently proposed curved grid finite‐difference method (CG‐FDM) to simulate a spontaneous dynamic rupture. However, this involves solving the inversion of an ill‐conditioned matrix that is required in finite‐difference method modeling at the point of intersection, which must be addressed to achieve stable simulation conditions. We achieve stable conditions at the intersection between the fault plane and the free surface by considering the continuity of the fault’s normal displacement, ensuring that the point of intersection meets the conditions of both the fault and the free surface. To verify our method, we simulate the spontaneous dynamic rupture of a rough fault in half‐space and compare the results with those from another method. The good agreement between two methods validates our mathematical strategy for modeling the intersection between an irregular fault plane and a free surface using CG‐FDM.
Focusing on the problems that the traditional genetic algorithm optimization excitation trajectory cannot meet the constraints in the robot parameter identification, a dynamic parameter identification method based on the improved genetic algorithm is proposed. Firstly, a linearized industrial robot dynamic model is built. Secondly, the excitation trajectory is obtained by optimizing the improved genetic algorithm. Finally, calculate the robot dynamic parameters by the least square method. The experimental results show that the optimal excitation trajectory obtained by this method can meet the constraints, shorten the optimization time and effectively improve the efficiency and effect of dynamic parameter identification.
Abstract The calculation of the spectral blue operator in the traditional spectral blue scaling method has singularity, and when applied to poststack seismic, it can be observed that the scaling effect is not good by observing seismic profiles and seismic spectra. To this end, a frequency spreading technique based on matching pursuit(MP) and spectral bluening is proposed. Through time-frequency analysis processing, it is shown that the seismic signal extracted by matching tracking method has good stability and higher resolution. The specific process of the method in this paper firstly uses the matching tracking method to accurately divide the post-stack seismic data into multiple frequency-division seismic bodies; Then, in the process of calculating the spectral blue ization operators for each frequency band, the weighting idea is used to calculate the weights of the optimized spectral blue ization operators for each frequency band based on the differences in energy in different frequency bands; Finally, the optimized spectral blue operator is convolved with seismic reflection coefficients to obtain high-resolution seismic data. The actual test results of poststack seismic data have proven that the frequency raising method proposed in this paper is superior to the traditional spectral blue ization algorithm, greatly improving the high-frequency component information of poststack seismic data. After frequency extension, there are more seismic events and higher resolution. Finally, the practicability and rationality of the seismic data after frequency extraction are verified by a series of operations such as attribute extraction, well seismic calibration and inversion.
We developed an organophotoredox catalytic system to facilitate the decarboxylative allylation coupling process concerning α-amino acids and related C-terminal carboxylate peptides using Morita-Baylis-Hillman adducts as allylic precursors. This metal-free method operates under mild conditions and is compatible with various amino acids. The versatility of this protocol, particularly in chemical biology research, has been preliminarily demonstrated through the ligation of bioactive peptide chains.
With the help of pre-trained language models, tasks such as sentiment analysis and text classification have achieved good results. With the advent of prompt tuning, especially previous studies have shown that in the case of few data, the prompt tuning method has significant advantages over the general tuning method of additional classifiers. At present, there are relatively few studies on sentiment analysis of Korean Chinese texts.This paper proposes a low resource sentiment classification method based on pre-trained language models (PLMs) combined with prompt tuning. In this work, we chose to use the pre-trained language model KLUE and elaborated a Korean prompt template with an expanded knowledge base and filtering in the verbalizer section. We focus on collecting external knowledge and integrating it into the utterance to form a prompt tuning of knowledge to improve and stabilize the prompt tuning. Specifically, we use the K-means clustering algorithm to construct the label wordspace of the external knowledge base (kb) extended language, and use PLM itself to refine the extended labeled wordspace before using the extended labeled wordspace for prediction. A large number of experiments on the few-shot emotion classification task prove the effectiveness of knowledge prompt tuning.
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