Seismic, Super-Virtual Refraction Interferometry

Al-Shuhail, Abdullatif A.

doi:10.1007/978-3-030-10475-7_272-1

Cited by 4 publications

(1 citation statement)

References 19 publications

(23 reference statements)

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“…Data preparation for the SR method involves mainly the first arrival picking. The first arrival picking technique, similar to the method applied by Sherif et al [ 43 ], was used in this study, and the supervirtual seismic refraction interferometry (SVI) technique [ 44 , 45 ] was used for part of the SR data, having low signal-to-noise ratio (SNR). After that, the local and global optimization algorithms were applied.…”

Section: Methodsmentioning

confidence: 99%

Application of Combined Local and Global Optimization Algorithms in Joint Interpretation of Direct Current Resistivity and Seismic Refraction Data: A Case Study of Dammam Dome, Eastern Saudi Arabia

Edigbue

Demirci

Akca

et al. 2022

Sensors

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The main geological structures in the Dammam Dome are defined by integrating geophysical measurements and applying new methodological approaches. Dammam Dome is characterized by a well-developed fracture/joints system; thus, high complexity of the subsurface is expected. Direct Current Resistivity (DCR) and Seismic Refraction (SR) geophysical survey aimed to map the Dammam dome’s near-surface features. The geophysical data were acquired along two profiles in the northern part of Dammam Dome. To maximize the results from conducting DCR and SR measurements over a complex area, a combined local and global optimization algorithm was used to obtain high-resolution near-surface images in resistivity and velocity models. The local optimization technique involves individual and joint inversion of the DCR and SR data incorporating appropriate regularization parameters, while the global optimization uses single and multi-objective genetic algorithms in model parameter estimation. The combined algorithm uses the output from the local optimization method to define a search space for the global optimization algorithm. The results show that the local optimization produces satisfactory inverted models, and that the global optimization algorithm improves the local optimization results. The joint inversion and processing of the acquired data identified two major faults and a deformed zone with an almost N–S direction that corresponds with an outcrop were mapped in profile one, while profile two shows similar anomalies in both the resistivity and velocity models with the main E–W direction. This study not only demonstrates the capability of using the combined local and global optimization multi-objectives techniques to estimate model parameters of large datasets (i.e., 2D DCR and SR data), but also provides high-resolution subsurface images that can be used to study structural features of the Dammam Dome.

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Section: Methodsmentioning

confidence: 99%

Application of Combined Local and Global Optimization Algorithms in Joint Interpretation of Direct Current Resistivity and Seismic Refraction Data: A Case Study of Dammam Dome, Eastern Saudi Arabia

Edigbue

Demirci

Akca

et al. 2022

Sensors

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Extrapolated supervirtual refraction interferometry

Zhang

Juhlin

et al. 2021

Geophysical Journal International

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Summary Accurate picking of head-wave arrival times is an important component of first-arrival traveltime tomography. Far-offset traces in particular have low signal-to-noise ratio (SNR), but picking on these traces is necessary in order to obtain velocity information at depth. Furthermore, there is often an insufficient number of far-offset traces for obtaining reliable models at depth. We present here an extrapolation method for increasing the number of first arrivals beyond the maximum recorded offset, thereby extending the supervirtual refraction interferometry (SVI) method. We refer to the method as extrapolated supervirtual refraction interferometry (ESVI). It is a novel attempt to extrapolate first arrivals using a fully data-driven method. We first test the methodology on synthetic data sets, and we then apply ESVI to two published real data sets over the Pärvie fault system in northern Sweden. These data sets were acquired along the same profile at different times with different acquisition parameters and noise levels. The results show that ESVI enhances the SNR of head waves when the noise level is high. That is the same as the conventional SVI. ESVI also increases the number of pickable first arrivals by extrapolating head waves past the original maximum offset of each shot. We also show that the significant increase in first-arrival traveltime picks is beneficial for improving resolution and penetration depth in the tomographic imaging and, consequently, better revealing the subsurface velocity distribution. The tomographic images show higher velocities in the hanging walls of the main Pärvie fault and another subsidiary fault, as interpreted relative to migrated images from previous seismic reflection processing.

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