Articles | Volume 14, issue 6
https://doi.org/10.5194/se-14-603-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/se-14-603-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Jun 2023
Research article |
| 15 Jun 2023
| 15 Jun 2023
Detailed investigation of multi-scale fracture networks in glacially abraded crystalline bedrock at Åland Islands, Finland
Nikolas Ovaskainen
CORRESPONDING AUTHOR
Geological Survey of Finland, P.O. Box 96, Espoo, 02151, Finland
Department of Geography and Geology, University of Turku, Turku, 20014, Finland
Pietari Skyttä
Department of Geography and Geology, University of Turku, Turku, 20014, Finland
Nicklas Nordbäck
Geological Survey of Finland, P.O. Box 96, Espoo, 02151, Finland
Department of Geography and Geology, University of Turku, Turku, 20014, Finland
Jon Engström
Geological Survey of Finland, P.O. Box 96, Espoo, 02151, Finland
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Cited articles
Alstott, J., Bullmore, E., and Plenz, D.: Powerlaw: a Python package for
analysis of heavy-tailed distributions, PLoS ONE, 9, e85777,
https://doi.org/10.1371/journal.pone.0085777, 2014. a, b, c
Andrews, B. J., Roberts, J. J., Shipton, Z. K., Bigi, S., Tartarello, M. C., and Johnson, G.: How do we see fractures? Quantifying subjective bias in fracture data collection, Solid Earth, 10, 487–516, https://doi.org/10.5194/se-10-487-2019, 2019. a, b
Bond, C., Gibbs, A., Shipton, Z., and Jones, S.: What do you think this is?
“Conceptual uncertainty” in geoscience interpretation, GSA Today, 17,
4, https://doi.org/10.1130/GSAT01711A.1, 2007. a
Bossennec, C., Frey, M., Seib, L., Bär, K., and Sass, I.: Multiscale
Characterisation of Fracture Patterns of a Crystalline Reservoir
Analogue, Geosciences, 11, 371, https://doi.org/10.3390/geosciences11090371, 2021. a
Bour, O., Davy, P., Darcel, C., and Odling, N.: A statistical scaling model for
fracture network geometry, with validation on a multiscale mapping of a joint
network (Hornelen Basin, Norway), J. Geophys. Res., 107,
2113, https://doi.org/10.1029/2001JB000176, 2002. a, b
Ceccato, A., Tartaglia, G., Antonellini, M., and Viola, G.: Multiscale lineament analysis and permeability heterogeneity of fractured crystalline basement blocks, Solid Earth, 13, 1431–1453, https://doi.org/10.5194/se-13-1431-2022, 2022. a, b
Chabani, A., Trullenque, G., Ledésert, B. A., and Klee, J.: Multiscale
Characterization of Fracture Patterns: A Case Study of the
Noble Hills Range (Death Valley, CA, USA), Application to
Geothermal Reservoirs, Geosciences, 11, 280,
https://doi.org/10.3390/geosciences11070280, 2021. a, b, c, d
Cottrell, M.: Occurrence of Large Fractures in the Host Rock for a
Spent Nuclear Fuel Repository: Earthquake Study, Working Report
2021-09, Posiva, Olkiluoto, https://www.posiva.fi/en/index/media/reports.html, last access: 10 June 2022. a
Davy, P.: On the frequency-length distribution of the San Andreas Fault
System, J. Geophys. Res.-Sol. Ea., 98, 12141–12151,
https://doi.org/10.1029/93JB00372, 1993. a
Davy, P., Bour, O., De Dreuzy, J.-R., and Darcel, C.: Flow in multiscale
fractal fracture networks, Geol. Soc. Lond. Spec. Publ.,
261, 31–45, https://doi.org/10.1144/GSL.SP.2006.261.01.03, 2006. a, b
Davy, P., Le Goc, R., Darcel, C., Bour, O., de Dreuzy, J. R., and Munier, R.: A
likely universal model of fracture scaling and its consequence for crustal
hydromechanics, J. Geophys. Res., 115, B10411,
https://doi.org/10.1029/2009JB007043, 2010. a, b, c, d
Dichiarante, A. M., McCaffrey, K. J. W., Holdsworth, R. E., Bjørnarå, T. I., and Dempsey, E. D.: Fracture attribute scaling and connectivity in the Devonian Orcadian Basin with implications for geologically equivalent sub-surface fractured reservoirs, Solid Earth, 11, 2221–2244, https://doi.org/10.5194/se-11-2221-2020, 2020. a, b, c
Dühnforth, M., Anderson, R. S., Ward, D., and Stock, G. M.: Bedrock fracture
control of glacial erosion processes and rates, Geology, 38, 423–426,
https://doi.org/10.1130/G30576.1, 2010. a, b
Engström, J., Markovaara-Koivisto, M., Ovaskainen, N., Nordbäck, N., Paananen, M., Aaltonen, I., Martinkauppi, A., Laxström, H., and Wik, H.: Aerogeophysics and light detecting and ranging (LiDAR)-based lineament interpretation of Finland at the scale of 1:500 000, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-448, 2023. a
Frey, M., Bossennec, C., Seib, L., Bär, K., Schill, E., and Sass, I.: Interdisciplinary fracture network characterization in the crystalline basement: a case study from the Southern Odenwald, SW Germany, Solid Earth, 13, 935–955, https://doi.org/10.5194/se-13-935-2022, 2022. a
Geological Survey of Finland: Striations,
https://hakku.gtk.fi/en (last access: 8 May 2023), 2014. a
Gillies, S., van der Wel, C., Van den Bossche, J., Taves, M. W., Arnott, J., Ward, B. C., and others: Shapely (Version
1.8.5.post1), GitHub [code],
https://github.com/shapely/shapely/tree/1.8.5.post1, last access: 4 November 2022 2022. a
Glasser, N. F., Roman, M., Holt, T. O., Žebre, M., Patton, H., and Hubbard,
A. L.: Modification of bedrock surfaces by glacial abrasion and quarrying:
Evidence from North Wales, Geomorphology, 365, 107283,
https://doi.org/10.1016/j.geomorph.2020.107283, 2020. a
Goodchild, M. F.: Metrics of scale in remote sensing and GIS, Int.
J. Applied Earth Obs., 3, 114–120,
https://doi.org/10.1016/S0303-2434(01)85002-9, 2001. a, b
Goodchild, M. F.: Scale in GIS: An overview, Geomorphology, 130, 5–9,
https://doi.org/10.1016/j.geomorph.2010.10.004, 2011. a, b, c
Haapala, I. and Rämö, O. T.: Tectonic setting and origin of the Proterozoic
rapakivi granites of southeastern Fennoscandia, Earth Env.
Sci. T. R. Soc., 83, 165–171,
https://doi.org/10.1017/S0263593300007859, 1992. a
Hardebol, N. J., Maier, C., Nick, H., Geiger, S., Bertotti, G., and Boro, H.:
Multiscale fracture network characterization and impact on flow: A case
study on the Latemar carbonate platform, J. Geophys. Res.-Sol. Ea., 120, 8197–8222, https://doi.org/10.1002/2015JB011879, 2015. a, b
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P.,
Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R.,
Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río,
J. F., Wiebe, M., Peterson, P., Gérard-Marchant, P., Sheppard, K., Reddy,
T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant, T. E.: Array
programming with NumPy, Nature, 585, 357–362,
https://doi.org/10.1038/s41586-020-2649-2, 2020. a, b
Hartley, L., Appleyard, P., Baxter, S., Hoek, J., Joyce, S., Mosley, K.,
Williams, T., Fox, A., Cottrell, M., La Pointe, P., Gehör, S., Darcel, C.,
Le Goc, R., Aaltonen, I., Vanhanarkaus, O., Löfman, J., and Poteri, A.:
Discrete Fracture Network Modelling (Version 3) in Support of
Olkiluoto Site Description 2018, Working Report 2017-32, Posiva,
Olkiluoto, https://www.posiva.fi/en/index/media/reports.html (last access: 10 June 2022), 2018. a, b
Hautaniemi, H., Kurimo, M., Multala, J., Leväniemi, H., and Vironmäki, J.: The “three in one” aerogeophysical concept of GTK in 2004,
Aerogeophysics in Finland 1972–2004 Methods, System Characteristics and Applications, edited by: Airo, M.-L.,
Special Paper 39, Geological Survey of Finland, Espoo, Finland, 21–74, ISBN 951-690-915-9, ISSN 0782-8535, https://tupa.gtk.fi/julkaisu/specialpaper/sp_039.pdf (last access: 10 May 2022),
2005. a
Heffer, K. and Bevan, T.: Scaling Relationships in Natural Fractures:
Data, Theory, and Application, in: All Days,
SPE, The Hague, Netherlands, SPE–20 981–MS, https://doi.org/10.2118/20981-MS, 1990. a, b, c, d
Hunter, J. D.: Matplotlib: A 2D graphics environment, Comput. Sci.
Eng., 9, 90–95, https://doi.org/10.1109/MCSE.2007.55, 2007. a
Jordahl, K., Bossche, J. V. d., Fleischmann, M., McBride, J., Wasserman, J.,
Richards, M., Badaracco, A. G., Snow, A. D., Gerard, J., Tratner, J., Perry,
M., Ward, B., Farmer, C., Hjelle, G. A., Cochran, M., Taves, M., Gillies, S.,
Caria, G., Culbertson, L., Bartos, M., Eubank, N., Bell, R., sangarshanan,
Flavin, J., Rey, S., maxalbert, Bilogur, A., Ren, C., Arribas-Bel, D., and
Mesejo-León, D.: geopandas/geopandas: v0.12.1, Zenodo [code], https://doi.org/10.5281/zenodo.7262879,
2022. a
Karell, F., Ehlers, C., and Airo, M.-L.: Emplacement and magnetic fabrics of
rapakivi granite intrusions within Wiborg and Åland rapakivi granite
batholiths in Finland, Tectonophysics, 614, 31–43,
https://doi.org/10.1016/j.tecto.2013.12.006, 2014. a
Korja, A. and Heikkinen, P. J.: Proterozoic extensional tectonics of the
central Fennoscandian Shield: Results from the Baltic and Bothnian
Echoes from the Lithosphere experiment, Tectonics, 14, 504–517,
https://doi.org/10.1029/94TC02905, 1995. a
Kosunen, P.: The rapakivi granite plutons of Bodom and Obbnäs, southern
Finland: petrography and geochemistry, B. Geol. Soc.
Finland, 71, 275–304, https://doi.org/10.17741/bgsf/71.2.005, 1999. a, b
Kruhl, J. H.: Fractal-geometry techniques in the quantification of complex rock
structures: A special view on scaling regimes, inhomogeneity and
anisotropy, J. Struct. Geol., 46, 2–21,
https://doi.org/10.1016/j.jsg.2012.10.002, 2013. a
Lahiri, S.: Estimating effective permeability using connectivity and branch
length distribution of fracture network, J. Struct. Geol., 146,
104314, https://doi.org/10.1016/j.jsg.2021.104314, 2021. a
Laitakari, I., Rämö, T., Suominen, V., Niin, M., Stepanov, K., and Amantov,
A.: Subjotnian: Rapakivi granites and related rocks in the surroundings of the gulf of Finland, Explanation to the Map of Precambrian basement of the Gulf of Finland and surrounding area 1:1 mill., edited by: Koistinen, T. J., Geological Survey of Finland, 59–98, ISBN 951-690-627-3,
ISSN 0782-8535, https://tupa.gtk.fi/julkaisu/specialpaper/sp_021.pdf (last access: 27 September 2022),
1996. a
Libby, S., Turnbull, R., Cottrell, M., Bym, T., Josephson, N., Munier, R.,
Selroos, J., and Mas Ivars, D.: Grown Discrete Fracture Networks: a new
method for generating fractures according to their deformation history,
ARMA, New York, vol. 19, p. 8, https://www.researchgate.net/profile/Lee-Hartley/publication/334597154 (last access: 9 December 2021), 2019. a, b
Luosto, U., Tiira, T., Korhonen, H., Azbel, I., Burmin, V., Buyanov, A.,
Kosminskaya, I., Ionkis, V., and Sharov, N.: Crust and upper mantle structure
along the DSS Baltic profile in SE Finland, Geophys. J.
Int., 101, 89–110, https://doi.org/10.1111/j.1365-246X.1990.tb00760.x, 1990. a
Maillot, J., Davy, P., Le Goc, R., Darcel, C., and de Dreuzy, J.: Connectivity,
permeability, and channeling in randomly distributed and kinematically
defined discrete fracture network models: PERMEABILITY OF DISCRETE
FRACTURE NETWORK, Water Resour. Res., 52, 8526–8545,
https://doi.org/10.1002/2016WR018973, 2016. a
Manzocchi, T.: The connectivity of two-dimensional networks of spatially
correlated fractures, Water Resour. Res., 38, 1162,
https://doi.org/10.1029/2000WR000180, 2002. a, b, c, d
Marrett, R.: Aggregate properties of fracture populations, J.
Struct. Geol., 18, 169–178, https://doi.org/10.1016/S0191-8141(96)80042-3, 1996. a
Marrett, R., Ortega, O. J., and Kelsey, C. M.: Extent of power-law scaling for
natural fractures in rock, Geology, 27, 799,
https://doi.org/10.1130/0091-7613(1999)027<0799:EOPLSF>2.3.CO;2, 1999. a, b, c
Mauldon, M., Dunne, W., and Rohrbaugh, M.: Circular scanlines and circular
windows: new tools for characterizing the geometry of fracture traces,
J. Struct. Geol., 23, 247–258,
https://doi.org/10.1016/S0191-8141(00)00094-8, 2001. a, b
Mercuri, M., Tavani, S., Aldega, L., Trippetta, F., Bigi, S., and Carminati,
E.: Are open-source aerial images useful for fracture network
characterisation? Insights from a multi-scale approach in the Zagros
Mts, J. Struct. Geol., 171, 104866,
https://doi.org/10.1016/j.jsg.2023.104866, 2023. a
Middleton, M., Schnur, T., Sorjonen-Ward, P., and Hyvönen, E.: Geological lineament interpretation using the object-based image analysis approach: results of semi-automated analyses versus visual interpretation, Novel technologies for greenfield exploration, edited by: Sarala, P., Special
Paper 57, Geological Survey of Finland, Finland, 135–154,
https://tupa.gtk.fi/julkaisu/specialpaper/sp_057.pdf (last access: 8 March 2022), 2015. a, b
Mäkel, G. H.: The modelling of fractured reservoirs: constraints and potential
for fracture network geometry and hydraulics analysis, Geol. Soc.
Lond. Spec. Publ., 292, 375–403, https://doi.org/10.1144/SP292.21, 2007. a, b, c
National Land Survey of Finland: Laser scanning data 0,5 p,
https://tiedostopalvelu.maanmittauslaitos.fi/tp/kartta?lang=en (last access: 11 November 2018),
2010. a
Nironen, M.: The Svecofennian Orogen: a tectonic model, Precambrian
Res., 86, 21–44, https://doi.org/10.1016/S0301-9268(97)00039-9, 1997. a
Nixon, C. W., Sanderson, D. J., and Bull, J. M.: Analysis of a strike-slip
fault network using high resolution multibeam bathymetry, offshore NW
Devon U.K., Tectonophysics, 541–543, 69–80,
https://doi.org/10.1016/j.tecto.2012.03.021, 2012. a, b
Nur, A.: The origin of tensile fracture lineaments, J. Struct.
Geol., 4, 31–40, https://doi.org/10.1016/0191-8141(82)90004-9, 1982. a
Nyberg, B., Nixon, C. W., and Sanderson, D. J.: NetworkGT: A GIS tool for
geometric and topological analysis of two-dimensional fracture networks,
Geosphere, 14, 1618–1634, https://doi.org/10.1130/GES01595.1, 2018. a, b
Odling, N. E.: Scaling and connectivity of joint systems in sandstones from
western Norway, J. Struct. Geol., 19, 1257–1271,
https://doi.org/10.1016/S0191-8141(97)00041-2, 1997. a, b
Odling, N. E., Gillespie, P., Bourgine, B., Castaing, C., Chiles, J. P.,
Christensen, N. P., Fillion, E., Genter, A., Olsen, C., Thrane, L., Trice,
R., Aarseth, E., Walsh, J. J., and Watterson, J.: Variations in fracture
system geometry and their implications for fluid flow in fractures
hydrocarbon reservoirs, Petrol. Geosci., 5, 373–384,
https://doi.org/10.1144/petgeo.5.4.373, 1999. a
Ojala, A. and Sarala, P.: Editorial: LiDAR – rapid developments in remote
sensing of geological features, B. Geol. Soc.
Finland, 89, 61–63, https://doi.org/10.17741/bgsf/89.2.ed, 2017. a
Ovaskainen, N.: nialov/multi-scale-fracture-networks-aland-islands-2022,
Zenodo [code], https://doi.org/10.5281/zenodo.7919843, 2023. a, b
Ovaskainen, N. and Nordbäck, N.: G, Zenodo [data set], https://doi.org/10.5281/zenodo.4719627, 2021. a
Palamakumbura, R., Krabbendam, M., Whitbread, K., and Arnhardt, C.: Data acquisition by digitizing 2-D fracture networks and topographic lineaments in geographic information systems: further development and applications, Solid Earth, 11, 1731–1746, https://doi.org/10.5194/se-11-1731-2020, 2020. a, b
Palmu, J.-P., Ojala, A. E., Ruskeeniemi, T., Sutinen, R., and Mattila, J.:
LiDAR DEM detection and classification of postglacial faults and
seismically-induced landforms in Finland: a paleoseismic database, GFF,
137, 344–352, https://doi.org/10.1080/11035897.2015.1068370, 2015. a
Pickering, G., Bull, J., and Sanderson, D.: Sampling power-law distributions,
Tectonophysics, 248, 1–20, https://doi.org/10.1016/0040-1951(95)00030-Q, 1995. a, b, c
Piipponen, K., Martinkauppi, A., Korhonen, K., Vallin, S., Arola, T., Bischoff,
A., and Leppäharju, N.: The deeper the better? A thermogeological analysis
of medium-deep borehole heat exchangers in low-enthalpy crystalline rocks,
Geothermal Energy, 10, 12, https://doi.org/10.1186/s40517-022-00221-7, 2022. a
Prabhakaran, R., Bruna, P.-O., Bertotti, G., and Smeulders, D.: An automated fracture trace detection technique using the complex shearlet transform, Solid Earth, 10, 2137–2166, https://doi.org/10.5194/se-10-2137-2019, 2019. a
Priest, S. D.: Discontinuity Analysis for Rock Engineering, Springer
Netherlands, Dordrecht, https://doi.org/10.1007/978-94-011-1498-1, 1993. a, b
Rohrbaugh, M. B., Dunne, W., and Mauldon, M.: Estimating fracture trace
intensity, density, and mean length using circular scan lines and windows,
AAPG Bulletin, 86, 2089–2104, https://doi.org/10.1306/61EEDE0E-173E-11D7-8645000102C1865D, 2002. a, b
Rämö, O. and Haapala, I.: Chapter 12 Rapakivi Granites, in: Developments
in Precambrian Geology, Elsevier, vol. 14, 533–562,
https://doi.org/10.1016/S0166-2635(05)80013-1, 2005. a, b, c
Sanderson, D. J. and Nixon, C. W.: Topology, connectivity and percolation in
fracture networks, J. Struct. Geol., 115, 167–177,
https://doi.org/10.1016/j.jsg.2018.07.011, 2018. a, b
Sanderson, D. J. and Peacock, D. C.: Making rose diagrams fit-for-purpose,
Earth-Sci. Rev., 201, 103055, https://doi.org/10.1016/j.earscirev.2019.103055,
2020. a, b
Skyttä, P., Kinnunen, J., Palmu, J.-P., and Korkka-Niemi, K.: Bedrock
structures controlling the spatial occurrence and geometry of 1.8Ga younger
glacifluvial deposits – Example from First Salpausselkä, southern
Finland, Global Planet. Change, 135, 66–82,
https://doi.org/10.1016/j.gloplacha.2015.10.007, 2015. a
Skyttä, P., Ovaskainen, N., Nordbäck, N., Engström, J., and Mattila, J.:
Fault-induced mechanical anisotropy and its effects on fracture patterns in
crystalline rocks, J. Struct. Geol., 146, 104304,
https://doi.org/10.1016/j.jsg.2021.104304, 2021. a
Skyttä, P., Nordbäck, N., Ojala, A., Putkinen, N., Aaltonen, I., Engström,
J., Mattila, J., and Ovaskainen, N.: The interplay of bedrock fractures and
glacial erosion in defining the present‐day land surface topography in
mesoscopically isotropic crystalline rocks, Earth Surface Proc.
Land., 1–13, https://doi.org/10.1002/esp.5596, 2023. a, b, c, d, e
Sornette, A., Davy, P., and Sornette, D.: Growth of fractal fault patterns,
Phys. Rev. Lett., 65, 2266–2269, https://doi.org/10.1103/PhysRevLett.65.2266,
1990. a
Strijker, G., Bertotti, G., and Luthi, S. M.: Multi-scale fracture network
analysis from an outcrop analogue: A case study from the
Cambro-Ordovician clastic succession in Petra, Jordan, Mar.
Petrol. Geol., 38, 104–116, https://doi.org/10.1016/j.marpetgeo.2012.07.003, 2012. a
Torvela, T. and Annersten, H.: PT-conditions of deformation within the
Palaeoproterozoic South Finland shear zone: Some geothermobarometric
results, B. Geol. Soc. Finland, 77, 151–164,
https://doi.org/10.17741/bgsf/77.2.004, 2005. a
Torvela, T., Mänttäri, I., and Hermansson, T.: Timing of deformation phases
within the South Finland shear zone, SW Finland, Precambrian
Res., 160, 277–298, https://doi.org/10.1016/j.precamres.2007.08.002, 2008. a, b, c
Tyrén, S.: Lineament interpretation. Short review and methodology, Technical
Report SSM-2010-33, Swedish Radiation Safety Authority, Stockholm, Sweden,
https://www.osti.gov/etdeweb/biblio/1013181 (last access: 24 November 2021), 2011. a
Verduzco, B., Fairhead, J. D., Green, C. M., and MacKenzie, C.: New insights
into magnetic derivatives for structural mapping, Leading Edge, 23,
116–119, https://doi.org/10.1190/1.1651454, 2004. a
Vigneresse, J.: The specific case of the Mid-Proterozoic rapakivi granites
and associated suite within the context of the Columbia supercontinent,
Precambrian Res., 137, 1–34, https://doi.org/10.1016/j.precamres.2005.01.001,
2005. a
Väisänen, M. and Skyttä, P.: Late Svecofennian shear zones in southwestern
Finland, GFF, 129, 55–64, https://doi.org/10.1080/11035890701291055, 2007. a
Woodard, J. B., Zoet, L. K., Iverson, N. R., and Helanow, C.: Linking bedrock
discontinuities to glacial quarrying, Ann. Glaciol., 60, 66–72,
https://doi.org/10.1017/aog.2019.36, 2019. a
Zeeb, C., Gomez-Rivas, E., Bons, P. D., and Blum, P.: Evaluation of sampling
methods for fracture network characterization using outcrops, AAPG Bulletin,
97, 1545–1566, https://doi.org/10.1306/02131312042, 2013. a, b
Short summary
We studied bedrock fracturing at Åland Islands from bedrock outcrops,
digital elevation models and geophysics using multiple scales of observation.
Using the results we can compare properties of the fractures of different sizes
to find similarities and differences; e.g. we found that glacial erosion has a
probable effect on the study of larger bedrock structures. Furthermore, we
collected data from 100 to 500 m long fractures, which have previously
proved to be difficult to sample.
We studied bedrock fracturing at Åland Islands from bedrock outcrops,
digital elevation models...
