Research Themes

Work Packages

WP1 – Collection, preparation and analysis of seismic signals and Telraam sensor data. This work will provide ground-truth data for comparison of seismic signals with recorded traffic categories.

WP2 – Signal feature extraction and feature engineering for machine learning approaches.

WP3 – Development of a predictive model using machine learning to forecast temporal changes in road traffic volumes.

Aims

1. To quantitatively compare time series seismic signals (velocity and displacement – data at RaspberryShake public servers) with traffic counts (from traffic cameras – data at https://manchester-i.com/projects) to correlate anthropogenic activity to seismic noise, and to isolate anthropogenic acoustic and seismic noise from natural seismic signals. 

2. Using machine learning and FTT for feature extraction to forecast future trends in traffic volumes along the Oxford Road corridor.

This work forms part of a live NERC project, with University of Aberdeen: http://gotw.nerc.ac.uk/list_full.asp?pcode=NE%2FT007826%2F1

References

1. BEIS (2019) - https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2019

2. Diaz, J., Ruiz, M., Schimmel, M., Carbonell, R. 2020. Using a dense seismic array to track the evolution of the COVID19 lockdown within Barcelona (Spain).  American Geophysical Union, Fall Meeting 2020, abstract #S004-06.

3. Grecu,B.,  Borleanu, F., Tiganescu, A., Poiata, N., Dinescu, R., and Tataru, D. 2021. The effect of 2020 COVID-19 lockdown measures on seismic noise recorded in Romania. Solid Earth. 12, p.p. 2351-2368.

4. IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., et al. (eds.)]. Cambridge University Press. In Press

5. Lecocq,T., et al. Global quieting of high-frequency seismic noise due to COVID-19 pandemic lockdown measures. Science. 369, p.p. 1338-1343.

6. Lüthi, D., Le Floch, M., Bereiter, B., Blunier, T., Barnola, J.-M., Siegenthaler, U., Raynaud, D., Jouzel, J., Fischer, H., Kawamura, K., & Stocker, T. F. (2008). High-resolution carbon dioxide concentration record 65,000-800,000 years before present. Nature, 453. https://doi.org/10.1038/nature06949

7. Singha Roy, K., Sharma, J., Kumar, S. and Ravi Kumar, M. 2021. Effect of coronavirus lockdowns on the ambient seismic noise levels in Gujarat, northwest India. Nature Scientific Reports. https://doi.org/10.1038/s41598-021-86557-9. 

Introduction

A climate emergency was acknowledged in 2021 (IPCC, 2021) as greenhouse gas concentrations in the atmosphere are continually rising and are a catalyst for global warming. A key factor of the acceleration of atmospheric greenhouse gases is fossil fuel combustion; national estimates indicate that 38% of atmospheric CO2 comes from road transportation (BEIS, 2019). To gain accurate local data and a better understanding of the sector’s impact, a network of Raspberry Shake seismometers has been deployed across Greater Manchester, as part of a UKRI NERC-funded project “Listen to Manchester” (Twitter: @listen2mcr). These seismometers possess high sensitivity to high frequency anthropogenic noise, making them suitable for capturing local seismic signatures.

The urgent need to comprehend and predict urban traffic patterns stems from the alarming levels of atmospheric CO2, which have reached their highest point in the last 650,000 years (Lüthi et al. 2008), and the visible air quality improvements seen during the 2020 global pandemic. Understanding factors influencing air quality and traffic volume is essential for sustainable urban planning and the development of effective transportation management strategies.

In recent years, the application of artificial intelligence (AI) techniques in geosciences has gained attention and has had positive impacts on geoscience research. In this study, we employ AI algorithms to detect seismic signals associated with anthropogenic noise. By extracting features from the power spectrum of the frequency domain, we predict urban traffic volumes along Manchester City Centre's Oxford Road corridor.

Our research aims to provide valuable insights into the local dynamics of CO2 emissions and the influence of road travel, particularly when combined with air quality data. These insights will be vital for smart city development and advancing our understanding of climate change. The integration of AI techniques with geoscientific data holds immense potential to facilitate evidence-based decision-making to mitigate its detrimental effects.

Aims

1. Correlate air pollution data with urban traffic volumes. This requires adjustment for wind speed and direction data in time series format and is dependent upon Raspberry Shake seismometers being placed close to traffic cameras and air quality sensors.

This work forms part of a live NERC project, with University of Aberdeen: http://gotw.nerc.ac.uk/list_full.asp?pcode=NE%2FT007826%2F1

References

1. BEIS (2019) - https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2019

2. Diaz, J., Ruiz, M., Schimmel, M., Carbonell, R. 2020. Using a dense seismic array to track the evolution of the COVID19 lockdown within Barcelona (Spain).  American Geophysical Union, Fall Meeting 2020, abstract #S004-06.

3. Grecu,B.,  Borleanu, F., Tiganescu, A., Poiata, N., Dinescu, R., and Tataru, D. 2021. The effect of 2020 COVID-19 lockdown measures on seismic noise recorded in Romania. Solid Earth. 12, p.p. 2351-2368.

4. IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., et al. (eds.)]. Cambridge University Press. In Press

5. Lecocq,T., et al. Global quieting of high-frequency seismic noise due to COVID-19 pandemic lockdown measures. Science. 369, p.p. 1338-1343.

6. Lüthi, D., Le Floch, M., Bereiter, B., Blunier, T., Barnola, J.-M., Siegenthaler, U., Raynaud, D., Jouzel, J., Fischer, H., Kawamura, K., & Stocker, T. F. (2008). High-resolution carbon dioxide concentration record 65,000-800,000 years before present. Nature, 453. https://doi.org/10.1038/nature06949

7. Singha Roy, K., Sharma, J., Kumar, S. and Ravi Kumar, M. 2021. Effect of coronavirus lockdowns on the ambient seismic noise levels in Gujarat, northwest India. Nature Scientific Reports. https://doi.org/10.1038/s41598-021-86557-9. 

Work Packages

WP1 – 3D Model Building

WP2 – Numerical Modelling

WP3 – Feature Engineering and Data Analysis 

WP3A: Feature engineering - extract model complexity, computational time, memory usage, energy consumption

WP3B: Data analysis - statistical analysis and data visualisation

WP3C: Machine Learning - build machine learning models to estimate computational cost and energy use based on extracted features

WP4 – Comparative Analysis

Aims

The primary objective of this research is to comprehensively investigate the computational efficiency and accuracy of 3D modelling in comparison to 2D modelling through the application of data science and AI methodologies. The study aims to address the following specific objectives: 

1. Quantify Computational Costs

2. Estimate Energy Consumption

3. Develop Predictive Models

4. Validate Predictive Models

5. Assess Accuracy

6. Identify Trade-Offs Between Accuracy and Computational Cost

7. Determine Optimal Modelling Approach

8. Contribute to Sustainable Practices

The research aims to bridge the gap between traditional 2D modelling practices and the emerging trend of 3D modelling in geoscience, leveraging data science and AI techniques to offer a holistic and data-driven perspective on the computational efficiency and accuracy of these modelling approaches. The findings of this investigation are expected to advance the understanding of computational methods in geoscience and inform decision-making in favour of sustainable and efficient modelling practices. 

References

1. Taylor, R.L., Hodgetts, D., Rutter, E.H., Seers, T. and Valleti, L. Petrophysical and mechanical modelling of fault rocks as an analogue for fractured basement rocks: a comparison of 2D versus 3D approaches to fracture characterisation. In prep.

2. Seers, T.D. and Hodgetts, D. 2016. Extraction of three-dimensional fracture trace maps from calibrated image sequences. Geosphere, Vol. 12, No. 4, pp. 1323-1340.

3. Taylor, R.L., Rutter, E.H., Nippress, S.E.J. and Brodie, K.H. 2015. Seismic velocity modelling of the Carboneras Fault Zone, SE Spain. Tectonophysics, Vol. 646, pp. 20-35.

Work Packages

WP1 – Observations over multiple scales and tectonic regimes

WP2 – Statistical analysis of earthquake probability and precursory signals

WP3 – Machine learning forecasting

Aims

The theory of tidal triggering of earthquakes is in its infancy but there have been a number of observations that earthquakes have been correlated in some way to tides, however, statistical studies of a large number of earthquakes of magnitude >5.0 are needed to aid earthquake forecasting.

1. Statistically analyse the probability of an earthquake after tidal loading or unloading.

2. Determine the likelihood of tidal stresses triggering an earthquake event under different tectonic regimes and different magnitudes. 

References

1. Beeler, N.M. and Lockner, D.A. Why earthquakes correlate weakly with the solid earth tides: Effects of periodic stress on the rate and probability of earthquake occurrence. Journal of Geophysical Research. 108(B8). (2003)

2. Bowman, D.D., Ouillon, G., Sammis, C.G., Sornette, A. and Sornette, D.An observational test of the critical earthquake concept. Journal of Geophysical Research. 103(NB10), p.p. 24,359-24,373. (1998)

3. Cochran, E.S., Vidale, J.E. and Tanaka, S., Earth tides can trigger shallow thrust fault earthquakes.Science. 306, p.p. 1164-1166. (2004)

4. Grasso, J-R. and Sornette, D. Testing self-organized criticality by induced seismicity. Journal of Geophysical Research. (1998)

5. Hanada, H., Tsubokawa, T. and Tsuruta, S., Possible large systematic error source in absolute gravimetry. Metrologia. 33, p.p. 155-160. (1996)

6. Hardebeck, J.L., Nazareth, J.J. and Hauksson, E. Journal of Geophysical Research. 103, p.p. 24,427. (1998)

7. Hill, D.P., Pollitz, F. and Newhall, C. Earthquake-volcano interactions. Physics Today. p.p. 41-47. (2002)

8. Kasahara, J., Tides, earthquakes and volcanoes. Science. 297, p.p. 348-349. (2002)

9. Keilis-Borok, V. The Lithosphere of the earth as a large non-linear system. Geophysics for the Next Generation, Geophysics Monogr. Series. 60, p.p. 81-84. (1990)

10. Klein, F.W., Eruption forecasting at Kilauea Volcano, Hawaii. Journal of Geophysical Research. 89, p.p. 3059-3073. (1984)

11. Lockner, D. A. and Beeler, N. M. Premonitory slip and tidal triggering of earthquakes. Journal of Geophysical Research. 104, p.p. 20,133-20,152. (1999)

12. McNutt, S.R., and Bevan, R.J.,Volcanic earthquakes at Pavlof Volcano correlated with the solid earth tide. Nature. 294, p.p. 615-618. (1981)

13. McNutt, S.R., and Bevan, R.J., Patterns of earthquakes and the effect of solid earth and ocean load tides at Mount St. Helens prior to the May 18, 1980, eruption. Journal of Geophysical Research. 89, p.p. 3075-3086. (1984)

14. Melchoir, P., Earth tides. Geophysical Surveys. 1, p.p. 275-303. (1974) 

15. Nasu, N. et al. Bulletin. Earthquake Research Institute. 9, p.p. 22. (1931)

16. Bürgmann, R. Reliable earthquake precursors?, Science, 381, 6655, (266-267), (2023). 

17. Sornette, A. and Sornette, D. Earthquake rupture as a critical point: Consequences for telluric precursors. Tectonophysics. 179, p.p. 327-334. (1990)

18. Stein, R.S. The role of stress transfer in earthquake occurrence. Nature. 402, p.p. 605-609. (1999)

19. Tanaka, S., Ohtake, M. and Sato, H. Evidence for tidal triggering as revealed from statistical analysis of global data. Journal of Geophysical Research. 107(B10). (2002)

20. Tanaka, S., Ohtake, M. and Sato, H. Tidal triggering of earthquakes in Japan related to the regional tectonic stress. (200X2001)

21. Tolstoy, M., Vernon, F.L., Orcutt, J.A. and Wyatt, F.K. Breathing on the seafloor: Tidal correlations of seismicity at Axial volcano. Geology. 30, p.p. 503-506 (2002)

22. Tsuruoka, H., Ohtake, M., and Sato, H. et al.Statistical test of the tidal triggering of earthquakes – Contribution of the ocean tide loading effect. Geophysics Journal International. 122, p.p. 183-194 (1995)

23.  Vidale, J.E., Agnew, D.C., Johnston, M.J.S. and Oppenheimer, D.H. Absence of earthquake correlation with Earth tides: An indication of high preseimic fault stress rate. Journal of Geophysical Research. 103, p.p. 24,567-24,572. (1998)

24. Vidale, J., Agnew, D.C., Oppenheimer, D.H., Rodriquez, C. & Houston, H. A weak correlation between earthquakes and extensional normal stress and stress rate from lunar tides. Eos, Trans. AGU, supplement 79, F641 (1998).

25. Wilcox, W.S.D. Tidal triggering of microearthquakes on the Juan de Fuca Ridge. Geophysical Research Letters. 28(20), p.p. 3999-4002. (2001)

26. Yin, X.C., Chen, X.Z., Song, Z.P. and Yin, C. A new approach to earthquake prediction – the load-unload response ratio (LURR) theory. Pure and Applied Geophysics. 145, p.p. 701-715. (1995)

27. Yin, X.C., Mora, P., Peng, K., Wang, Y.C and Weatherley, D. Load-Unload Response Ratio and Accelerating Moment/Energy Release critical region scaling and earthquake prediction. Pure and Applied Geophysics. 159, p.p. 2511-2523. (2002)

Work Packages

WP1 – Fieldwork

WP1A: photographing and logging fracture outcrops   

WP1B: seismic

WP2 – Laboratory measurement of petrophysical properties

WP2A: measurement of matrix and stress-dependent permeability

WP2B: influence of normal and shear stresses on hydraulic conductivity

WP3 – Imaging over multiple scales

WP4 – Modelling

WP4A: digital fracture modelling

WP4B: modelling fracture complexities starting with the ‘cubic law’

WP4C: validating fracture models with observed seismic data

WP4D: modelling hydraulic conductivity

WP5 – Development of algorithms/workflows

WP5A: AI/deep learning

WP5B: Ant colony optimisation (ACO) algorithms

Aims

The main aims of this research outlined below will be delivered via five work packages (WP1-5).

1.  Describe fracture characteristics in a subset of rock types representing different tectonic regimes/burial histories.

a.       Collection of data to form a database of examples – WP1, WP2.

b.       Characterisation of the behaviour (i.e. geometry) of fractures – WP1, WP2, WP3.

2. Improve the method of assessing influence of crack arrays on bulk hydraulic conductivity of fractured rocks.

a.       Using laboratory measurements to constrain bulk material properties – WP2.

b.       Understanding the role of fractures in conducting fluid – WP2, WP4, WP5.

c.   Understanding the influence of small-scale structures on fluid flow – WP1B, WP3.

3. Validation of fracture models with laboratory and field data – WP1, WP2, WP3, WP4.

4. Develop algorithms to use laboratory and field data to predict impact fractures have on fluid flow – WP4, WP5.

References

1. Noroozi, M., Kakaie, R. and Jalali, S.M.E. 2015. 3D stochastic rock fracture modelling related to strike-slip faults. Journal of Mining & Environment, Vol. 6, No. 2, pp. 169-181.

2. Taylor, R.L., Rutter, E.H., Nippress, S.E.J. and Brodie, K.H. 2015. Seismic velocity modelling of the Carboneras Fault Zone, SE Spain. Tectonophysics, Vol. 646, pp. 20-35.

3. Rutter, E.H., Mecklenburgh, J., McKernan, R.E. and Taylor, R.L. 2015. Pressure-dependent permeability of shales. Conference Paper. DOI: 10.399/2214-4609.201414080.

4. Seers, T.D. and Hodgetts, D. 2016. Extraction of three-dimensional fracture trace maps from calibrated image sequences. Geosphere, Vol. 12, No. 4, pp. 1323-1340.

5. Zimmerman, 2012. The history and role of the cubic law for fluid flow in fractured rocks. AGU Fall Meeting, San Francisco, 2012.

6. Louis, C. 1969. A study of groundwater flow in jointed rock and its influence on the stability of rock masses. Rock Mechanics Research Report 10, 90 pp. Imperial College, London.

7. Wang, L., Bayani Cardenas, M., Slottke, D.T., Ketcham, R. and Sharp, J. 2015. Modification of the Local Cubic Law of fracture flow for weak inertia, tortuosity, and roughness. Water Resources Research. Vol. 51. DOI: 10.1002/2014WR015815.

8. Chivers. T.C. 2002. The influence of surface roughness on fluid flow through cracks. Fatigue and Fracture of Engineering Materials and Structure. Vol 25, pp. 1095-1102.

9. Rutter, E.H. and Mecklenburgh, J. 2017. Hydraulic conductivity of bedding-parallel cracks in shale as a function of shear and normal stress. Geological Society Special Publication. DOI: 10.1144/SP454.9.

10. Matthäi, S., Menzentsev, A. and Belayneh, M. 2005. Control-volume finite-element two phase flow experiments with fractured rock represented by unstructured 3D hybrid meshes. SPE 93341. DOI: 10.2118/93341-MS.

11. Sanderson, D.J. and Nixon, C.W. 2015. The use of topology in fracture network characterization. Journal of Structural Geology, Vol 72, pp. 55-66. DOI: 10.1016/j.jsg.2015.01.005.

12. Lüthi, D., Le Floch, M., Bereiter, B., Blunier, T., Barnola, J-M., Siegenthaler, U., Raynaud, D., Jouzel, J., Fischer, H., Kawamura, K. and Stocker, T.F. 2008. High-resolution carbon disoxide concentration record 65,000-800,000 years before present. Nature, Vol. 453. DOI: 10.1038/nature06949.

13. Korjani, M., Popa, A., Grijalva, E., Cassidy, S. and Ershaghi, I. 2016. A new approach to reservoir characterization using deep learning neural networks. Society of Petroleum Engineers. DOI: 10.2118/180359-MS.

14. Crnkovic-Friis, L. and Erlandson, M. 2015. Geology driven EUR prediction using deep learning. Socity of Petroleum Engineers. DOI: 10.2118/174799-MS.

15. Ma, L., Taylor, K.G., Lee, P.D., Dobson, K.J. Dowey, P.J. and Courtois, L. 2016. Novel 3D centimetre-to-nano-scale quantification of an organic-rich mudstone: The Carboniferous Bowland Shale, Northern England. Marine and Petroleum Geology, Vol. 72, pp. 193-205.

16. Taylor, R.L., Hodgetts, D., Rutter, E.H., Seers, T. and Valleti, L. Petrophysical and mechanical modelling of fault rocks as an analogue for fractured basement rocks: a comparison of 2D versus 3D approaches to fracture characterisation. In prep.

17. Pedersen, S., Skov. T., Randen, T. and Sønneland, L. 2005. In: (Eds) Bock, H.G., Hoog, F., Friedman, A., Gupta, A., Neunzert, H., Pulleyblank, W.R., Rusten, T., Santosa, F., Tornberg, A-K., Capasso, V., Mattheii, R. and Scherzer, O. Mathematical methods and modelling in hydrocarbon exploration and production, Springer, Berlin Heidelberg, pp. 107-116.

18. Head, W., Hodgetts, D. and Smith, N. 2013. Digital outcrop characterisation and fracture modelling for a GDF in a crystalline basement. Geophysical Research Abstracts, Vol. 15, EGU2013-3184.

19. Smith, N., Shevelan, J., Hodgetts, D. and Head, W. 2013. Innovative 3D and 4D geological interpretation, modelling and visualisation techniques for subsurface characterisation of complex industrial sites – examples in the UK nuclear industry. Geophysical Research Abstracts, Vol. 15, EGU2013-5080.

Work Packages

WP1 – Rock model construction and outcrop analogue

WP2 – Estimation of petrophysical and mechanical rock properties

WP3 – Adaption of DEM software

WP4 – Estimation of ice sheet load models

WP5 – Simulation of ice sheet loads and contamination scenarios

WP6 – Production of risk maps

Aims

1. 1. Build a structural model of the UK crust and lithosphere based upon current BGS geological mapping and integration with published geophysical data (gravity and magnetics).

   2. Use experimental rock mechanics to constrain bulk material properties for a variety of host rock scenarios to act as a key input for the forward model.

   3. Develop a series of ice sheet load geometries based on existing field observations and results from ice sheet models. 

   4. Use the results of objectives 1,2 and 3 to forward model the effects of ice sheet loading and associated sea level change upon the flexure of the UK lithosphere and identify associated variations in tectonic stress as a result.

Then using the results of objective 4:

1. 5. Produce probability maps of fault reactivation across the UK.

   6. Investigate the probability of halokinesis across the UK (it has been suggested old salt mines may also be a good host for a GDF).

   7. Model the effects of the ice sheet loading on fracture aperture in the subsurface. This will control the main fluid migration pathways and help understand the possible migration pathways of any escaped contamination from a GDF, or for CCS projects may result in leakage from the geological storage site.

References

1. Bornev, B., Hundt, C., Kurth, T., Pathak, J., Baust, M., Kashinath, K., Anandkumar, A., Kossaifi, J., Azizzadenesheli, K. Modeling Earth’s Atmosphere with Spherical Fourier Neural Operators. https://developer.nvidia.com/blog/modeling-earths-atmosphere-with-spherical-fourier-neural-operators/

2. BRITICE-CHRONO 2011-2018 https://britice-chrono.sites.sheffield.ac.uk/

3. Clark, C.D., Hughes, A.L.C., Greenwood, S.L., Jordan, C., Sejrup, H.P., 2012. Pattern and timing of retreat of the last British-Irish Ice Sheet. Quaternary Science Reviews 44, 112-146.

4. Clark, C.D., et al., 2017. BRITICE Glacial Map, version 2: a map and GIS database of glacial landforms of the last British-Irish Ice Sheet. Boreas 47, 1-27.

5. Corlett, H., Hodgetts, D., Hirani, J. Rotevatn, A. Taylor, R.L., Hollis, C., 2021. A Geocellular Modelling Workflow for Partially Dolomitized Remobilized Carbonates: An Example from the Hammam Faraun Fault Block, Gulf of Suez, Egypt. Marine and Petroleum Geology 126.

6. Finch, E. and Gawthorpe, R., 2017. Growth and Interaction of Normal Faults and Fault Network Evolution in Rifts: Insights from Three-Dimensional Discrete Element Modelling. Geological Society London Special Publications 439(1). 

7. Musson, R., Sargeant, S., 2007. Eurocode 8 seismic hazard zoning maps for the UK.

8. Taylor, R.L., Mecklenburgh, J., McKernan, R., Rutter, E.H., Chandler, M.R., 2015. An Investigation of the Stress-Dependence of Shale Permeability. 11th Euro-Conference in Rock Physics and Geomechanics.

9. Tesauro, M., Kaban, M.K., Cloetingh, S.A.P.L., 2009. How rigid is Europe's lithosphere? Geophysical Research Letters 36.

10. Tian, J., Qi, C., Sun, Y., Yaseen, Z.M., Pham, B.T, 2021. Permeability Prediction of Porous Media Using a Combination of Computational Fluid Dynamics and Hybrid Machine Learning Methods. Engineering with Computers 37, 3455-3471.