The effect of the number, dimensions and location of anomalies on the crosswell Traveltime tomography results

نوع مقاله : مقاله پژوهشی


1 Sahand university of technology, Mining Faculty

2 Mining Faculty, Sahand university of technology


Seismic tomography is one of the seismic imaging techniques which are interesting due to the possibility of recording high-frequency seismic waves and providing the possibility of obtaining high-quality images from inside the earth. The ability to record high-frequency waves enables the identification of faults, fractures, and joints with sufficient accuracy, which are very important in reservoir geomechanics studies. In this paper 2D crosswell seismic tomography is simulated by PyGIMLi (python geophysical inversion and modeling library). Crosswell seismic tomography is a routine part of seismic explorations, particularly hydrocarbon exploration. Fast marching and Gauss-Newton methods are the default algorithms for Traveltime forward modeling and inversion, respectively. The fast marching method is a very efficient method for calculating the travel time and path of seismic waves in homogeneous and especially non-homogeneous environments. This method uses the finite difference algorithm to solve the eikonal equation in gridded velocity environments. The Gauss-Newton method is a powerful classic method that can work with all types of geophysical data, which by properly weighting the data and creating a constraint, makes the inversion process faster and more accurate So that the inversion error is below 5% in all cases. The results obtained from the inversion show that the Gauss-Newton method has performed well and the anomalies designed in the models have been correctly detected so that the results can be interpreted without difficulty. Also, changing survey parameters influenced the results of inversion and it was necessary to determine these parameters correctly so that the results of inversion are more accurate and precise. Simulation crosswell seismic tomography is an important step before a successful practical crosswell seismic tomography. In general, simulation before any geophysical survey can be very helpful.


[1] Zhou, B., Greenhalgh, S., Green, A., 2008, nonlinear Traveltime inversion scheme for cross hole seismic tomography in tilted transversely isotropic media: Geophysics, 73(4), 17–33.
[2] Ajo-Franklin, J. B., Peter, J., Doetsch, J., Daley, T. M., 2013, High-resolution characterization of a CO2 plume using crosswell seismic tomography: Cranfield, MS, USA: International Journal of Greenhouse Gas Control, 18, 497–509.
[3] Onishi, K., Ueyama, T., Matsuoka, T., Dai Nobuoka, D., Saito, H., Azuma, H., Xue, Z., 2009, Application of crosswell seismic tomography using difference analysis with data normalization to monitor CO2 flooding in an aquifer: international journal of greenhouse gas control, 3, 31 1 – 32 1. doi:10.1016/j.ijggc.2008.08.003
[4] Bregman, N. D., Bailey, R. C., Chapman, C. H., 1988, Cross hole seismic tomography: Geophysics, 54(2), 200-215.
[5] Mao, S., Lecointre, A., Hilst, R. D., Campillo, M., 2022, Space-time monitoring of groundwater fluctuations with passive seismic interferometry: nature communications.
[6] Barone1, I.,  Cassiani1, G.,  Ourabah, A., Boaga, J., Pavoni1, M., Deiana, R., 2022, Surface wave tomography using dense 3D data around the Scrovegni Chapel in Padua, Italy: Scientific Reports.
[7] Hanafy, S. H., Hoteit, H., Li, J., Schuster, G. T., 2021, Near‑surface real‑time seismic imaging using parsimonious interferometry: Scientific Reports.
[8] Bianco, M. J., Gerstoft, P., Olsen, K. B., Lin, F. C., 2019, High-resolution seismic tomography of Long Beach, CA using machine learning: Scientific Reports.
[9] Lee, D. S., Stevenson, V. M., Johnston, P. F., Mullen, C. F., 1995, time‐lapse crosswell seismic tomography to characterize flow structure in the reservoir during the thermal stimulation: Geophysics, 60(3), 660-665.
[10] Zelt, C. Z., Azaria, A., Levander. A., 2006, 3D seismic refraction Traveltime tomography at a groundwater contamination site: Geophysics, 71(5), 67–78.
[11] Malehmir, A., Tryggvason, A., Wijns, C., Koivisto, E., Lindqvist, T., 2018, why 3D seismic data are an asset for exploration and mine planning? Velocity tomography of weakness zones in the Kevitsa Ni-Cu-PGE mine, northern Finland: Geophysics, 83(2), 33–46.
[12] Bauer, K., Schulze, A., Ryberg, T., Sobolev, S. V., Weber, M. H., 2003, Classification of lithology from seismic tomography: A case study from the Messum igneous complex, Namibia: Journal of Geophysical Research, 108(9), 1-15.
[13] Parra, J. O., Hackert, C. L., 2006, Permeability and porosity images based on P-wave surface seismic data: Application to a south Florida aquifer: Journal of Geophysical Research, 42(2415), 1-14.
[14] Leisi, A.,  Falahat, R., 2021, Investigation of Some Porosity Estimation Methods Using Seismic Data in One of the South Iranian Oil Fields: Journal of Petroleum Research, 31(119), 22–25. (in persian).
[15] Leisi, A., Kheirollahi, H., Shadmanaman, N., 2022, Investigation and comparison of conventional methods for estimating shear wave velocity from well logging data in one of the sandstone reservoirs in southern Iran: Iran. J. Geophys. (in persian).
[16] Kheirollahi, H., Shad Manaman, N., Leisi, A., 2023, Robust estimation of shear wave velocity in a carbonate oil reservoir from conventional well logging data using machine learning algorithms: Journal of Applied Geophysics, 211, 104971.
[17] Leisi, A., Saberi, M. R.,  2022, Petrophysical parameters estimation of a reservoir using integration of wells and seismic data: a sandstone case study: Earth Science Informatics.
[18] Rawlinson, N., Sambridge, M., 2005, The Fast Marching Method: An Effective Tool for Tomographic Imaging and Tracking Multiple Phases in Complex Layered Media: Exploration Geophysics, 36(4), 341-350.
[19] Ronczka, M., Hellman, K., Günther, T., Wisén, R., Dahlin, D., 2017, Electric resistivity and seismic refraction tomography: a challenging joint underwater survey at Äspö Hard Rock Laboratory: Solid Earth, 13, 671-682.
[20] Heincke, H., Günther, T., Dalsegg, E., Rønning, J. S., Ganerød, G. V., Elvebakk. H., 2010, combined three-dimensional electric and seismic tomography study on the Åknes rockslide in western Norway: Journal of Applied Geophysics, 70(4), 292-306.
[21] Sethian, J. A., 1996, a fast marching level set method for monotonically advancing fronts: Proceedings of the National Academy of Science, 93(4), 1591-1595.
[22] Sethian, J. A., 1999, Level Set Methods and Fast Marching Methods, New York, Cambridge University of press.
[23] Rückera, C., Güntherb, T., Wagner, F. M., 2017, PyGIMLi : An open-source library for modelling and inversion in geophysics: Computers and Geosciences, 109, 106-123.
[24] Schaa, R., Gross, L., du Plessis, J., 2016, PDE-based geophysical modelling using finite elements: examples from 3D resistivity and 2D magnetotellurics: Journal of Geophysics and Engineering, 13(2), S59–S73.
[25] Cockett, B., Kang, S., Heagy, L. J., Pidlisecky, A., Oldenburga. D. W., 2015, SimPEG: An open source framework for simulation and gradient based parameter estimation in geophysical applications: Computers and Geosciences, 85, 142-154.
[26] Weigand, M., Kemna, A., 2016, Debye decomposition of time-lapse spectral induced polarisation data: Computers and Geosciences, 86, 34-45.
[27] Guyer, J. E., Wheeler, D., Warren, J. A., 2009, FiPy: Partial Differential Equations with Python: Computing in Science and Engineering, 11(3), 6-15.
[28] Wellmann, J. F., Croucher, A., Regenauer-Lieb, K., 2012, Python scripting libraries for subsurface fluid and heat flow simulations with TOUGH2 and SHEMAT: Computers and Geosciences, 43, 197-206.
[29] Pérez, F., Granger, B. E., Hunter, J. D., 2011, Python: An Ecosystem for Scientific Computing: Computing in Science and Engineering, 13(2), 13-21.
[30] Hector, H., Hinderer, J., 2016, pyGrav, a Python-based program for handling and processing relative gravity data: Computers and Geosciences, 91, 90-97.