High-resolution Near-surface Geophysical Imaging Methods And Applications

In the onshore and shallow marine seismic oil and gas exploration,long wavelength statics often plays a crucial role for obtaining high quality stacked and migrated images.It influences the subsequent geological interpretation,reservoir prediction,and description.Accurate near-surface long wavelength statics requires a high resolution near-surface velocity model.First-arrival traveltime tomography is a robust tool for near-surface velocity estimation.A common approach to stabilizing the ill-posed inverse problem is to apply Tikhonov regularization to the inversion.However,the Tikhonov regularization method recovers smooth local structures while blurring the sharp features in the model solution.For geophysical inverse problems,total variation regularization imposes sparsity on the gradient of model parameters.Thus,it yields a blocky model with sharp interfaces.However,TV regularization requires substantial computation effort and may be numerically unstable for the non-differentiability of the TV functional at the origin.We present a first-arrival traveltime tomography method with modified total-variation regularization to preserve sharp velocity contrasts and improve the accuracy of velocity inversion.Our synthetic examples show that the new method produces higher resolution models than the conventional traveltime tomography with Tikhonov regularization,and creates fewer artifacts than the total variation regularization method for the models with sharp interfaces.For the field data,pre-stack time migration sections show that the modified total-variation traveltime tomography produces a near-surface velocity model,which makes statics corrections more accurate.We further extend the method to 3D and apply the approach to 3D traveltime tomography.We apply the technique to field data.Stacking section shows significant improvements with statics corrections from the MTV traveltime tomography.The first-arrival traveltime tomography is a common approach for near-surface velocity estimation.However,it cannot resolve complex near-surface structures especially when the subsurface structures contain hidden low-velocity layers and small scatters.Early arrival waveform inversion is a robust tool for imaging the near surface structures,but it requires a good initial model to avoid cycle skipping between the predicted and observed data.Furthermore,waveform inversion requires substantial computation efforts.Therefore,we present joint seismic traveltime and waveform inversion method,and we expect the joint inversion method retains the advantages of both traveltime inversion and full waveform inversion and overcomes their respective drawbacks at the same time.The objective function includes both the traveltime and waveform misfit.At each iteration,the traveltimes are calculated by shortest path raytracing,and the waveforms are computed using a finite-difference method.The nonlinear optimization problem is solved by the conjugate gradient method.We apply the joint inversion method to study complex near-surface area where shallow overthrust and rugged topography present a significant challenge for applying traveltime inversion and waveform inversion alone.We apply the joint inversion method to image a buried tunnel with concrete walls and a void space inside.The joint inversion images the top part of the tunnel as a high-velocity anomaly and interprets the void space as a low-velocity anomaly.As a comparison,conventional full waveform inversion is also applied to the data.The location of the velocity anomalies predicted by our method agrees with the prior knowledge of the tunnel.The numerical test and field example show that the joint inversion provides a better recovery of the tunnel and void space regarding the magnitude and location of the velocity anomalies.Another application of the joint inversion method is image complex overthrust structure in Yumen oil field.The inversion results suggest that the joint traveltime and waveform inversion helps constrain the very shallow velocity structures.Seismic full waveform inversion is a robust velocity model building technique for hydrocarbon exploration.However,only P-wave velocity is reconstructed in most applications.The density reconstruction within the framework of multiparameter full waveform inversion leads to a significant trade-off between the velocity and density,thereby increasing the nonlinearity of the inverse problem.Gravity gradiometry data inversion is an effective method for resolving density distribution.Combining gravity gradiometry data in full waveform inversion could alleviate the nonlinearity of the inversion by contributing low wavenumber information for the velocity model.We develop a 3D joint seismic full waveform and gravity gradiometry inversion method for estimating the velocity and density distribution in the subsurface.The cross-gradient constraint is applied to enhance the structural similarity between the density and velocity models.We demonstrate the effectiveness of the joint inversion algorithm by a 3D checkerboard model and 3D SEAM Phase 1(SEG Advanced Modeling)model.We present a case study from the offshore Sarawak area showing that the joint inversion solution improves the migration image significantly.In addition,the density model details from the joint inversion highlight the faults.