Stochastic differential equation modeling of time-series mining induced ground subsidence
Mining-induced ground subsidence is a commonly observed geo-hazard that leads to loss of life, property damage, and economic disruption. Monitoring subsidence over time is essential for predicting related geo-risks and mitigating future disasters. Machine-learning algorithms have been applied to dev...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2022.1026895/full |
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| author | Wanjia Guo Wanjia Guo Song Ma Song Ma Lianze Teng Xin Liao Xin Liao Nisong Pei Nisong Pei Xingyu Chen |
| author_facet | Wanjia Guo Wanjia Guo Song Ma Song Ma Lianze Teng Xin Liao Xin Liao Nisong Pei Nisong Pei Xingyu Chen |
| author_sort | Wanjia Guo |
| collection | DOAJ |
| description | Mining-induced ground subsidence is a commonly observed geo-hazard that leads to loss of life, property damage, and economic disruption. Monitoring subsidence over time is essential for predicting related geo-risks and mitigating future disasters. Machine-learning algorithms have been applied to develop predictive models to quantify future ground subsidence. However, machine-learning approaches are often difficult to interpret and reproduce, as they are largely used as “black-box” functions. In contrast, stochastic differential equations offer a more reliable and interpretable solution to this problem. In this study, we propose a stochastic differential equation modeling approach to predict short-term subsidence in the temporal domain. Mining-induced time-series data collected from the Global Navigation Satellite System (GNSS) in our case study area were utilized to conduct the analysis. Here, the mining-induced time-series data collected from GNSS system regarding our case study area in Miyi County, Sichuan Province, China between June 2019 and February 2022 has been utilized to conduct the case study. The proposed approach is capable of extracting the time-dependent structure of monitored subsidence data and deriving short-term subsidence forecasts. The predictive outcome and time-path trajectories were obtained by characterizing the parameters within the stochastic differential equations. Comparative analysis against the persistent model, autoregressive model, and other improved autoregressive time-series models is conducted in this study. The computational results validate the effectiveness and accuracy of the proposed approach. |
| first_indexed | 2024-04-10T23:52:24Z |
| format | Article |
| id | doaj.art-642917c0b10946d0ab15762b3a0522d2 |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-04-10T23:52:24Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-642917c0b10946d0ab15762b3a0522d22023-01-10T16:17:57ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632023-01-011010.3389/feart.2022.10268951026895Stochastic differential equation modeling of time-series mining induced ground subsidenceWanjia Guo0Wanjia Guo1Song Ma2Song Ma3Lianze Teng4Xin Liao5Xin Liao6Nisong Pei7Nisong Pei8Xingyu Chen9Sichuan Academy of Safety Science and Technology, Chengdu, Sichuan, ChinaKey Laboratory of Measurement and Control of Major Hazard Sources in Sichuan Province, Chengdu, ChinaSichuan Academy of Safety Science and Technology, Chengdu, Sichuan, ChinaKey Laboratory of Measurement and Control of Major Hazard Sources in Sichuan Province, Chengdu, ChinaArchives of Scientific and Technological Research Achievements of Sichuan Province, Chengdu, Sichuan, ChinaSichuan Academy of Safety Science and Technology, Chengdu, Sichuan, ChinaKey Laboratory of Measurement and Control of Major Hazard Sources in Sichuan Province, Chengdu, ChinaSichuan Academy of Safety Science and Technology, Chengdu, Sichuan, ChinaKey Laboratory of Measurement and Control of Major Hazard Sources in Sichuan Province, Chengdu, ChinaSchool of Architecture and Civil Engineering, Chengdu University, Chengdu, ChinaMining-induced ground subsidence is a commonly observed geo-hazard that leads to loss of life, property damage, and economic disruption. Monitoring subsidence over time is essential for predicting related geo-risks and mitigating future disasters. Machine-learning algorithms have been applied to develop predictive models to quantify future ground subsidence. However, machine-learning approaches are often difficult to interpret and reproduce, as they are largely used as “black-box” functions. In contrast, stochastic differential equations offer a more reliable and interpretable solution to this problem. In this study, we propose a stochastic differential equation modeling approach to predict short-term subsidence in the temporal domain. Mining-induced time-series data collected from the Global Navigation Satellite System (GNSS) in our case study area were utilized to conduct the analysis. Here, the mining-induced time-series data collected from GNSS system regarding our case study area in Miyi County, Sichuan Province, China between June 2019 and February 2022 has been utilized to conduct the case study. The proposed approach is capable of extracting the time-dependent structure of monitored subsidence data and deriving short-term subsidence forecasts. The predictive outcome and time-path trajectories were obtained by characterizing the parameters within the stochastic differential equations. Comparative analysis against the persistent model, autoregressive model, and other improved autoregressive time-series models is conducted in this study. The computational results validate the effectiveness and accuracy of the proposed approach.https://www.frontiersin.org/articles/10.3389/feart.2022.1026895/fullground subsidenceGNSStime-series analysisstochastic differential equationshort-term prediction |
| spellingShingle | Wanjia Guo Wanjia Guo Song Ma Song Ma Lianze Teng Xin Liao Xin Liao Nisong Pei Nisong Pei Xingyu Chen Stochastic differential equation modeling of time-series mining induced ground subsidence Frontiers in Earth Science ground subsidence GNSS time-series analysis stochastic differential equation short-term prediction |
| title | Stochastic differential equation modeling of time-series mining induced ground subsidence |
| title_full | Stochastic differential equation modeling of time-series mining induced ground subsidence |
| title_fullStr | Stochastic differential equation modeling of time-series mining induced ground subsidence |
| title_full_unstemmed | Stochastic differential equation modeling of time-series mining induced ground subsidence |
| title_short | Stochastic differential equation modeling of time-series mining induced ground subsidence |
| title_sort | stochastic differential equation modeling of time series mining induced ground subsidence |
| topic | ground subsidence GNSS time-series analysis stochastic differential equation short-term prediction |
| url | https://www.frontiersin.org/articles/10.3389/feart.2022.1026895/full |
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