Abstract
Rockfall has become one of the deadliest geohazards in Southwest China and how to comprehensively and effectively assess rockfall hazards is an urgent challenge to overcome. Additionally, comprehensive characterization of fractures on rock mass outcrops is a prerequisite for detecting potential rockfall. In this paper, an image and point cloud-based data fusion technique is applied to characterize regional rock mass fractures. Firstly, the performances of three classical computer vision algorithms are compared and SegFormer is selected as the appropriate base model for fracture detection. After that, according to the coordinate projection transformation criterion, the detected fractures are mapped to the point cloud. The parameter information obtained through fracture characterization is used to develop a representative three-dimensional discrete fracture network (3D-DFN) and then according to the results of the volume distribution of rock blocks, the three frequencies (high-frequency, medium-frequency, and low-frequency) of rockfall events are numerically simulated to obtain the characteristic information of rockfall trajectories. Finally, based on the characteristic information of rockfall trajectories and the GIS platform, the risk of rockfall hazards with three frequencies is evaluated and analyzed. This paper provides a new way for geologists to assess the risk of rockfall hazards and propose reasonable rockfall hazard prevention schemes.
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The datasets generated and analysed during the current study are not publicly available due to the confidentiality of the data.
References
Akin M, Dinçer I, Ok AO, Orhan A, Akin MK, Topal T (2021) Assessment of the effectiveness of a rockfall ditch through 3-D probabilistic rockfall simulations and automated image processing. Eng Geol 283:106001. https://doi.org/10.1016/j.enggeo.2021.106001
Albarelli DSNA, Mavrouli OC, Nyktas P (2021) Identification of potential rockfall sources using UAV-derived point cloud. Bull Eng Geol Environ 80:6539–6561. https://doi.org/10.1007/s10064-021-02306-2
Basirat R, Goshtasbi K, Ahmadi M (2019) Determination of the fractal dimension of the crack network system using image processing technique. Fractal Fract 3(2):17. https://doi.org/10.3390/fractalfract3020017
Bihani A, Daigle H, Santos JE, Landry C, Prodanović M, Milliken K (2022) MudrockNet: semantic segmentation of mudrock SEM images through deep learning. Comput Geosci 158:104952. https://doi.org/10.1016/j.cageo.2021.104952
Bounab A, Kharim YE, Hamdouni RE (2022) The suitability of UAV-derived DSMs and the impact of DEM resolutions on rockfall numerical simulations: a case study of the bouanane active scarp, Tétouan. Northern Morocco Remote Sens 14(24):6205. https://doi.org/10.3390/rs14246205
Bourrier F, Lambert S, Baroth J (2015) A reliability-based approach for the design of rockfall protection fences. Rock Mech Rock Eng 48:247–259. https://doi.org/10.1007/s00603-013-0540-2
Camanni G, Vinci F, Tavani S, Ferrandino V, Mazzoli S, Corradetti A, Parente M, Iannace A (2021) Fracture density variations within a reservoir-scale normal fault zone: a case study from shallow-water carbonates of southern Italy. J Struct Geol 151:104432. https://doi.org/10.1016/j.jsg.2021.104432
Chen N, Kemeny J, Jiang Q, Pan Z (2017) Automatic extraction of blocks from 3D point clouds of fractured rock. Comput Geosci 109:149–161. https://doi.org/10.1016/j.cageo.2017.08.013
Chen LC, Zhu YK, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. Comput vis Pattern Recogn. https://doi.org/10.48550/arXiv.1802.02611
Chen JY, Zhou ML, Huang HW, Zhang DM, Peng ZC (2021) Automated extraction and evaluation of fracture trace maps from rock tunnel face images via deep learning. Int J Rock Mech Min Sci 142:104745. https://doi.org/10.1016/j.ijrmms.2021.104745
CloudCompare (2020) 3D point cloud and mesh processing software open–source project. CloudCompare http://cloudcompare.org
Cundall PA (1971) A computer model for simulating progressive, largescale movement in blocky rock systems. In: Symp. ISRM, Nancy, France, Proc., pp 129−136
Dershowitz WS, Einstein HH (1988) Characterizing rock joint geometry with joint system models. Rock Mech Rock Eng 21:21–51. https://doi.org/10.1007/bf01019674
Dong S, Yi XY, Feng WK (2019) Quantitative evaluation and classification method of the cataclastic texture rock mass based on the structural plane network simulation. Rock Mech Rock Eng 52:1767–1780. https://doi.org/10.1007/s00603-018-1635-6
Du BW, Zhao ZR, Hu X (2021) Landslide susceptibility prediction based on image semantic segmentation. Comput Geosci 155:104860. https://doi.org/10.1016/j.cageo.2021.104860
Fanos AM, Pradhan B (2019) A novel rockfall hazard assessment using laser scanning data and 3D modelling in GIS. CATENA 172:435–450. https://doi.org/10.1016/j.catena.2018.09.012
Fanos AM, Pradhan B, Alamri A, Lee CW (2020) Machine learning-based and 3D kinematic models for rockfall hazard assessment using LiDAR data and GIS. Remote Sens 12:1755. https://doi.org/10.3390/rs12111755
Farkas MP, Hofmann H, Zimmermann G, Zang A, Bethmann F, Meier P, Cottrell M, Josephson N (2021) Hydromechanical analysis of the second hydraulic stimulation in well PX-1 at the Pohang fractured geothermal reservoir. South Korea Geotherm 89:101990. https://doi.org/10.1016/j.geothermics.2020.101990
Feng Y, Wang J, Zhou Q, Bai MY, Peng PH, Zhao D, Guan ZY, Liu XA (2022) Quantitative analysis of vegetation restoration and potential driving factors in a typical subalpine region of the Eastern Tibet Plateau. PeerJ 10:e13358. https://doi.org/10.7717/peerj.13358
Fisher R (1953) Dispersion on a sphere. Proc Royal Soc A 217:295–305. https://doi.org/10.1098/rspa.1953.0064
Francioni M, Salvini R, Stead D, Coggan J (2018) Improvements in the integration of remote sensing and rock slope modelling. Nat Hazards 90:975–1004. https://doi.org/10.1007/s11069-017-3116-8
Francioni M, Antonaci F, Sciarra N, Robiati C, Coggan J, Stead D, Calamita F (2020) Application of unmanned aerial vehicle data and discrete fracture network models for improved rockfall simulations. Remote Sens 12:2053. https://doi.org/10.3390/rs12122053
Ghorbanzadeh O, Xu YH, Zhao HW (2022) The Outcome of the 2022 landslide4sense competition: advanced landslide detection from multisource satellite imagery. IEEE J Selected Topics Appl Earth Observ Remote Sens 15:9927–9942. https://doi.org/10.1109/JSTARS.2022.3220845
Giacomini A, Thoeni K, Santise M, Diotri F, Booth S, Fityus S, Roncella R (2020) Temporal-spatial frequency rockfall data from open-pit highwalls using a low-cost monitoring system. Remote Sens 12(15):2459. https://doi.org/10.3390/rs12152459
Giordan D, Adams MS, Aicardi I (2020) The use of unmanned aerial vehicles (UAVs) for engineering geology applications. Bull Eng Geol Environ 79:3437–3481. https://doi.org/10.1007/s10064-020-01766-2
Giuffrida A, La Bruna V, Castelluccio P, Panza E, Rustichelli A, Tondi E, Giorgioni M, Agosta F (2019) Fracture simulation parameters of fractured reservoirs: analogy with outcropping carbonates of the inner Apulian platform, southern Italy. J Struct Geol 123:18–41. https://doi.org/10.1016/j.jsg.2019.02.007
Gutenberg B, Richter CF (1956) Earthquake magnitude, intensity, energy and acceleration: (Second paper). Bull Seismol Soc Am 46(2):105–145
He K, Zhang X, Ren S, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37:1904–1916. https://doi.org/10.1109/TPAMI.2015.2389824
He K, Li YJ, Ma GT, Hu XW, Liu B, Xu Z (2021) Failure mode analysis of post-seismic rockfall in shattered mountains exemplified by detailed investigation and numerical modelling. Landslides 18:425–446. https://doi.org/10.1007/s10346-020-01532-1
Healy D, Rizzo RE, Cornwell DG, Farrell NJC, Watkins H, Timms NE, Gomez-Rivas E, Smith M (2017) FracPaQ: A MATLAB™ toolbox for the quantification of fracture patterns. J Struct Geol 95:1–16. https://doi.org/10.1016/j.jsg.2016.12.003
Hibert C, Provost F, Malet JP, Maggi A (2017) Automatic identification of rockfalls and volcano-tectonic earthquakes at the Piton de la Fournaise volcano using a random forest algorithm. J Volcanol Geotherm Res 340(15):130–142. https://doi.org/10.1016/j.jvolgeores.2017.04.015
Hungr O, Leroueil S, Picarelli L (2014) The Varnes classification of landslide types, an update. Landslides 11:167–194. https://doi.org/10.1007/s10346-013-0436-y
Hungr O, Evans S (1988) Engineering evaluation of fragmental rockfall hazards. Proceedings of the 5th international symposium on landslides, Lausanne.
Hyman JD, Karra S, Makedonska N, Gable CW, Painter SL, Viswanathan HS (2015) dfnWorks: a discrete fracture network framework for modeling subsurface flow and transport. Comput Geosci 84:10–19. https://doi.org/10.1016/j.cageo.2015.08.001
Islam N, Sarkar B, Basak A, Das P, Paul I, Debnath M, Roy R (2022) A novel GIS-based MCDM approach to identify the potential eco-tourism sites in the Eastern Dooars region (Himalayan foothill) of West Bengal. India Geocarto International 37(26):13145–13175. https://doi.org/10.1080/10106049.2022.2076917
Ivanova VM, Sousa R, Murrihy B, Einstein HH (2014) Mathematical algorithm development and parametric studies with the GEOFRAC three-dimensional stochastic model of natural rock fracture systems. Comput Geosci 67:100–109. https://doi.org/10.1016/j.cageo.2013.12.004
Kromer RA, Abellán A, Hutchinson DJ, Lato M, Chanut MA, Dubois L, Jaboyedoff M (2017) Automated terrestrial laser scanning with near–real–time change detection–monitoring of the Séchilienne landslide. Earth Surf Dyn 5:293–310. https://doi.org/10.5194/esurf-5-293-2017
Kromer RA, Walton G, Gray B, Lato M, Group R (2019) Development and optimization of an automated fixed-location time lapse photogrammetric rock slope monitoring system. Remote Sens 11(16):1890. https://doi.org/10.3390/rs11161890
Lee YK, Kim J, Choi CS, Song JJ (2022) Semi-automatic calculation of joint trace length from digital images based on deep learning and data structuring techniques. Int J Rock Mech Min Sci 149:104981. https://doi.org/10.1016/j.ijrmms.2021.104981
Leng B, Yang H, Hou GP, Lyamin A (2021) Rock mass trace line identification incorporated with grouping algorithm at tunnel faces. Tunn Undergr Space Technol 110:103810. https://doi.org/10.1016/j.tust.2021.103810
Li LC, Wu WB, El Naggar MH, Mei GX, Liang RZ (2019) Characterization of a jointed rock mass based on fractal geometry theory. Bull Eng Geol Env 78:6101–6110. https://doi.org/10.1007/s10064-019-01526-x1
Liu GY, Li JJ (2018) A three-dimensional discontinuous deformation analysis method for investigating the effect of slope geometrical characteristics on rockfall behaviors. Int J Comput Methods 15(6):1850122. https://doi.org/10.1142/S0219876218501220
Liu B, He K, Han M, Hu XW, Wu TW, Wu MY, Ma GT (2021a) Dynamic process simulation of the Xiaogangjian rockslide occurred in shattered mountain based on 3DEC and DFN. Comput Geotech 134:104122. https://doi.org/10.1016/j.compgeo.2021.104122
Liu GY, Li JJ, Wang ZZ (2021b) Experimental verifications and applications of 3D-DDA in movement characteristics and disaster processes of rockfalls. Rock Mech Rock Eng 54:2491–2512. https://doi.org/10.1007/s00603-021-02394-2
Luca S, Carlo R, Luca S, Francesco V, Alessandro I, Mariano P, Stefano T (2022) An integrated approach for the reconstruction of rockfall scenarios from UAV and satellite-based data in the Sorrento Peninsula (southern Italy). Eng Geol 308:106795. https://doi.org/10.1016/j.enggeo.2022.106795
Ma K, Liu GY (2022) Three-dimensional discontinuous deformation analysis of failure mechanisms and movement characteristics of slope rockfalls. Rock Mech Rock Eng 55:275–296. https://doi.org/10.1007/s00603-021-02656-z
Michal K, Andrea V, Paolo F (2019) Key issues in 3d rockfall modeling, natural hazard and risk assessment for rockfall protection in hrensko (Czechia). Acta Geodynamica Et Geomaterialia 16(4):196. https://doi.org/10.13168/AGG.2019.0033
Move (2019) Petroleum Experts. https://www.petex.com
Netti T, Castelli M, Biagi VD (2016) Effect of the number of simulations on the accuracy of a rockfall analysis. Proc Eng 158:464–469. https://doi.org/10.1016/j.proeng.2016.08.473
Rafique MU, Zhu JF, Jacobs N (2022) Automatic segmentation of sinkholes using a convolutional neural network. Earth Space Sci 9:2. https://doi.org/10.1029/2021EA002195
Riquelme AJ, Abellán A, Tomás R, Jaboyedoff M (2014) A new approach for semi-automatic rock mass joints recognition from 3D point clouds. Comput Geosci 68:38–52. https://doi.org/10.1016/j.cageo.2014.03.014
RocPro3D (2014) RocPro3D software. http://www.rocpro3d.com/rocpro3d_en.php
Ruiz-Carulla R, Corominas J, Mavrouli O (2017) A fractal fragmentation model for rockfalls. Landslides 14:875–889. https://doi.org/10.1007/s10346-016-0773-8
Salvini R, Mastrorocco G, Seddaiu M, Rossi D, Vanneschi C (2016) The use of an unmanned aerial vehicle for fracture mapping within a marble quarry (Carrara, Italy): photogrammetry and discrete fracture network modelling. Geomat Nat Haz Risk 8(1):34–52. https://doi.org/10.1080/19475705.2016.1199053
Sarro R, Mateos RM, García-Moreno I, Herrera G, Reichenbach P, Laín L, Paredes C (2014) The Son Poc rockfall (Mallorca, Spain) on the 6th of march 2013: 3D simulation. Landslides 11:493–503. https://doi.org/10.1007/s10346-014-0487-8
Sarro R, Riquelme A, García-Davalillo JC, Mateos RM, Tomás R, Pastor JL, Cano M, Herrera G (2018) Rockfall simulation based on UAV photogrammetry data obtained during an emergency declaration: application at a cultural heritage site. Remote Sens 10:1923. https://doi.org/10.3390/rs10121923
Schiliro L, Robiati C, Smeraglia L, Vinci F, Iannace A, Parente M, Tavani S (2022) An integrated approach for the reconstruction of rockfall scenarios from UAV and satellite-based data in the Sorrento Peninsula (southern Italy). Eng Geol 308:106795. https://doi.org/10.1016/j.enggeo.2022.106795
Shi GH (1992) Discontinuous deformation analysis: a new numerical model for the statics and dynamics of deformable block structures. Eng Comput 9:157–168. https://doi.org/10.1108/eb023855
Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15:1929–1958. https://doi.org/10.1109/CISP.2015.7407967
Stead D, Donati D, Wolter A, Sturzenegger M (2019) Application of remote sensing to the investigation of rock slopes: experience gained and lessons learned. ISPRS Int J Geo Inf 8(7):296. https://doi.org/10.3390/ijgi8070296
Storti F, Billi A, Salvini F (2003) Particle size distributions in natural carbonate fault rocks: insights for non-self-similar cataclasis. Earth Planet Sci Lett 206:173–186. https://doi.org/10.1016/S0012-821X(02)01077-4
Sturzenegger M, Stead D (2009) Quantifying discontinuity orientation and persistence on high mountain rock slopes and large landslides using terrestrial remote sensing techniques. Nat Hazard 9(2):267–287. https://doi.org/10.5194/nhess-9-267-2009
Sujatha ER, Kumaravel P, Rajamanickam GV (2014) Assessing landslide susceptibility using Bayesian probability-based weight of evidence model. Bull Eng Geol Environ 73:147–161. https://doi.org/10.1007/s10064-013-0537-9
Tang XC, Tu ZH, Wang Y (2022) Automatic detection of coseismic landslides using a new transformer method. Remote Sens 14(12):2884. https://doi.org/10.3390/rs14122884
Verma AK, Sardana S, Sharma P, Dinpuia L, Singh TN (2019) Investigation of rockfall-prone road cut slope near Lengpui Airport, Mizoram, India. J Rock Mech Geotech Eng 11:146–158. https://doi.org/10.1016/j.jrmge.2018.07.007
Wang LQ, Xiao T, Liu SL, Zhang WG, Yang BB, Chen LC (2023) Quantification of model uncertainty and variability for landslide displacement prediction based on Monte Carlo simulation. Gondwana Res. https://doi.org/10.1016/j.gr.2023.03.006
Wang X (2005) Stereological interpretation of rock fracture traces on borehole walls and other cylindrical surfaces. Ph.D. thesis. Faculty of the Virginia polytechnic institute and state university
Weir FM, Fowler M (2014) Application of DFN modelling to large open pit slope design in Australia. Paper presented at: DFNE 2014, 1st international conference on discrete fracture network engineering; Vancouver, British Columbia, Canada
Wong LNY, Lai VSK, Tam TPY (2018) Joint spacing distribution of granites in Hong Kong. Eng Geol 245:120–129. https://doi.org/10.1016/j.enggeo.2018.08.009
Xie E, Wang W, Yu Z, Anandkumar A, Alvarez JM, Luo P (2021) SegFormer: simple and efficient design for semantic segmentation with transformers. Comput vis Pattern Recogn. https://doi.org/10.48550/arXiv.2105.15203
Xu JJ, Zhang H, Tang CS, Cheng Q, Tian BG, Liu B, Shi B (2022) Automatic soil crack recognition under uneven illumination condition with the application of artificial intelligence. Eng Geol 296:106495. https://doi.org/10.1016/j.enggeo.2021.106495
Xue YG, Li ZQ, Qiu DH, Zhang LW, Zhao Y, Zhang XL, Zhou BH (2019) Classification model for surrounding rock based on the PCA-ideal point method: an engineering application. Bull Eng Geol Environ 78:3627–3635. https://doi.org/10.1007/s10064-018-1368-5
Ye P, Yu B, Chen WH, Liu K, Ye LZ (2022) Rainfall-induced landslide susceptibility mapping using machine learning algorithms and comparison of their performance in Hilly area of Fujian province, China. Nat Hazards 113:965–995. https://doi.org/10.1007/s11069-022-05332-9
Yilmaz I, Yildirim M, Keskin I (2008) A method for mapping the spatial distribution of RockFall computer program analyses results using ArcGIS software. Bull Eng Geol Environ 67:547–554. https://doi.org/10.1007/s10064-008-0174-x1
Yin TC, Chen QF (2020) Simulation-based investigation on the accuracy of discrete fracture network (DFN) representation. Comput Geotech 121:103487. https://doi.org/10.1016/j.compgeo.2020.103487
Zambrano M, Tondi E, Korneva I, Panza E, Agosta F, Janiseck JM, Giorgioni M (2016) Fracture properties analysis and discrete fracture network modelling of faulted tight limestones, Murge Plateau, Italy. Italian J Geosci 135:55–67. https://doi.org/10.3301/IJG.2014.42
Zhan JW, Yu ZY, Lv Y, Peng JB, Song SY, Yao ZW (2022) Rockfall hazard assessment in the Taihang Grand Canyon scenic area integrating regional-scale identification of potential rockfall sources. Remote Sens 14:3021. https://doi.org/10.3390/rs14133021
Zhang P, Zhao QY, Tannant DD, Ji TT, Zhu HH (2019) 3D mapping of discontinuity traces using fusion of point cloud and image data. Bull Eng Geol Environ 78:2789–2801. https://doi.org/10.1007/s10064-018-1280-z
Zhang D, Wei K, Yao Y, Yang JC, Zheng GL, Li Q (2022) Capture and prediction of rainfall-induced landslide warning signals using an attention-based temporal convolutional neural network and entropy weight methods. Sensors 22(16):6240. https://doi.org/10.3390/s22166240
Zhou C, Yin KL, Cao Y, Ahmed B, Li YY, Catani F, Pourghasemi HR (2018a) Landslide susceptibility modeling applying machine learning methods: a case study from Longju in the Three Gorges Reservoir area, China. Comput Geosci 112:23–37. https://doi.org/10.1016/j.cageo.2017.11.019
Zhou ZW, Siddiquee MMR, Tajbakhsh N, Liang JM (2018b) UNet++: a nested U-net architecture for medical image segmentation deep learning in medical image analysis and multimodal learning for clinical decision support. Lecture Notes Comput Sci. https://doi.org/10.1007/978-3-030-00889-5_1
Zhou ZW, Siddiquee MMR, Tajbakhsh N (2019) UNet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans Med Imaging 39(6):1856–1867. https://doi.org/10.1109/TMI.2019.2959609
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This work is supported by the National Natural Science Foundation of China (NSFC, contract number: U21A2032).
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Peng Ye: conceptualization, methodology, software, investigation, data curation, and writing–original draft. Bin Yu: review and editing, supervision, project administration, funding acquisition. Wenhong Chen: investigation, data collection. Yu Feng: resources, software. Hao Zhou, Xiaolong Luo and Fujin Zhang: data collection, investigation, resources.
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Ye, P., Yu, B., Chen, W. et al. Fracture characterization based on data fusion technology and its application in rockfall hazard assessment. Environ Earth Sci 83, 208 (2024). https://doi.org/10.1007/s12665-024-11517-1
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DOI: https://doi.org/10.1007/s12665-024-11517-1