The exploration of urban underground spaces is of great significance to urban planning,geological disaster prevention,resource exploration and environmental monitoring.However,due to the existing of severe interferenc...The exploration of urban underground spaces is of great significance to urban planning,geological disaster prevention,resource exploration and environmental monitoring.However,due to the existing of severe interferences,conventional seismic methods cannot adapt to the complex urban environment well.Since adopting the single-node data acquisition method and taking the seismic ambient noise as the signal,the microtremor horizontal-to-vertical spectral ratio(HVSR)method can effectively avoid the strong interference problems caused by the complex urban environment,which could obtain information such as S-wave velocity and thickness of underground formations by fitting the microtremor HVSR curve.Nevertheless,HVSR curve inversion is a multi-parameter curve fitting process.And conventional inversion methods can easily converge to the local minimum,which will directly affect the reliability of the inversion results.Thus,the authors propose a HVSR inversion method based on the multimodal forest optimization algorithm,which uses the efficient clustering technique and locates the global optimum quickly.Tests on synthetic data show that the inversion results of the proposed method are consistent with the forward model.Both the adaption and stability to the abnormal layer velocity model are demonstrated.The results of the real field data are also verified by the drilling information.展开更多
In the IoT-based users monitor tasks in the network environment by participating in the data collection process by smart devices.Users monitor their data in the form of fog computing(mobile mass monitoring).Service pr...In the IoT-based users monitor tasks in the network environment by participating in the data collection process by smart devices.Users monitor their data in the form of fog computing(mobile mass monitoring).Service providers are required to pay user rewards without increasing platform costs.One of the NP-Hard methods to maximise the coverage rate and reduce the platform costs(reward)is the Cooperative Based Method for Smart Sensing Tasks(CMST).This article uses chaos theory and fuzzy parameter setting in the forest optimisation algorithm.The proposed method is implemented with MATLAB.The average findings show that the network coverage rate is 31%and the monitoring cost is 11%optimised compared to the CMST scheme and the mapping of the mobile mass monitoring problem to meta-heuristic algorithms.And using the improved forest optimisation algorithm can reduce the costs of the mobile crowd monitoring platform and has a better coverage rate.展开更多
基金Supported by projects of National Natural Science Foundation of China(No.42074150)National Key Research and Development Program of China(No.2023YFC3707901)Futian District Integrated Ground Collapse Monitoring and Early Warning System Construction Project(No.FTCG2023000209).
文摘The exploration of urban underground spaces is of great significance to urban planning,geological disaster prevention,resource exploration and environmental monitoring.However,due to the existing of severe interferences,conventional seismic methods cannot adapt to the complex urban environment well.Since adopting the single-node data acquisition method and taking the seismic ambient noise as the signal,the microtremor horizontal-to-vertical spectral ratio(HVSR)method can effectively avoid the strong interference problems caused by the complex urban environment,which could obtain information such as S-wave velocity and thickness of underground formations by fitting the microtremor HVSR curve.Nevertheless,HVSR curve inversion is a multi-parameter curve fitting process.And conventional inversion methods can easily converge to the local minimum,which will directly affect the reliability of the inversion results.Thus,the authors propose a HVSR inversion method based on the multimodal forest optimization algorithm,which uses the efficient clustering technique and locates the global optimum quickly.Tests on synthetic data show that the inversion results of the proposed method are consistent with the forward model.Both the adaption and stability to the abnormal layer velocity model are demonstrated.The results of the real field data are also verified by the drilling information.
文摘In the IoT-based users monitor tasks in the network environment by participating in the data collection process by smart devices.Users monitor their data in the form of fog computing(mobile mass monitoring).Service providers are required to pay user rewards without increasing platform costs.One of the NP-Hard methods to maximise the coverage rate and reduce the platform costs(reward)is the Cooperative Based Method for Smart Sensing Tasks(CMST).This article uses chaos theory and fuzzy parameter setting in the forest optimisation algorithm.The proposed method is implemented with MATLAB.The average findings show that the network coverage rate is 31%and the monitoring cost is 11%optimised compared to the CMST scheme and the mapping of the mobile mass monitoring problem to meta-heuristic algorithms.And using the improved forest optimisation algorithm can reduce the costs of the mobile crowd monitoring platform and has a better coverage rate.
