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

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.

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

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.