Near-surface Velocity Research Articles

P/SVAmplitude Ratios of Shallow Isotropic Explosions and Earthquakes Could Be Indistinguishable at Local Distances: Insights from Single-Station Waveform Simulations

AbstractShear waves play a key role in seismic discrimination between explosions and earthquakes due to their different source mechanisms. However, shear waves are often observed in field explosions with unexpectedly large amplitude, and their generation mechanism is still a significant unresolved question in seismology. Many explanations have been proposed, including the asymmetry of explosive sources, and heterogeneity and/or anisotropy of the Earth’s subsurface. However, it has not been well understood whether source or velocity structure can independently and sufficiently explain the shear waves generated by explosions. Theoretically, tangential SH waves can be converted and scattered from vertical and radial SV waves due to anisotropy and heterogeneity. Thus, it is essential to understand the generation of SV waves by explosions. In this study, we utilize the frequency–wavenumber algorithm and 1D layered velocity models to simulate waveforms of isotropic explosions and double-couple earthquakes at local distances (<20 km). Our results suggest that explosions and earthquakes may generate comparable SV waves if both occurred within a near-surface velocity gradient zone. The earliest SV waves by explosions appear to originate from the near-source region. It implies that P/SV amplitude ratios of explosions and earthquakes could be indistinguishable under certain circumstances.

Joint passive seismic imaging based on surface wave inversion and reflection wavefield retrieval: A case study in the sedimentary basin areas adjacent to Well Songke-2

Owing to their cost-effectiveness, environmental friendliness, and high-efficiency, passive seismic methods are widely used in geophysical exploration. However, field passive seismic data suffer from low signal-to-noise ratio, as well as the fact that different types of seismic waves are naturally mixed together, which can affect the accuracy of subsurface imaging results and the subsequent geological interpretation. In this paper, we demonstrate that these problems can be addressed and present a case study on passive seismic detection in sedimentary basin areas adjacent to Well Songke-2 in Songliao Basin. To obtain accurate and reliable imaging results of the sedimentary strata, a detailed practical scheme is proposed, where fundamental and higher mode surface wave dispersion curves are inverted for obtaining the near-surface S-wave velocity profile, and the body wave component of ambient noise is utilized to retrieve the reflection wavefield information. The obtained profiles from surface wave inversion and reflection wavefield retrieval illustrate similar underground structures. The marker boundaries T2 (1.5 s) and T4 (2.1 s) are well demonstrated, and a low velocity stratum (0.4 s) is detected at a shallow depth of around 400–600 m. Further, the results are highly consistent with the data obtained from borehole logging of Well Songke-2 and the deep reflection seismic profile adjacent to this area, which indicates that the surface wave and body wave in passive seismic data can be utilized together to contribute to a detailed and accurate subsurface imaging and interpretation. Overall, this study investigated and validated the reliability and accuracy of the combination use of passive seismic methods for geological structure exploration, which can further boost their applications for geological interpretation in sedimentary basin areas.

Near-surface velocity estimation using shear-waves and deep-learning with a U-net trained on synthetic data

Estimation of good velocity models under complex near-surface conditions remains a topic of ongoing research. We propose to predict near-surface velocity profiles from surface-waves transformed to phase velocity-frequency panels in a data-driven manner using deep neural networks. This is a different approach from many recent works that attempt to estimate velocity from directly reflected body waves or guided waves. A secondary objective is to analyze the influence on the prediction accuracy of various commonly employed deep learning practices, such as transfer learning and data augmentations. Through numerical experiments on synthetic data as well as a real geophysical example, we demonstrate that transfer learning as well as data augmentations are helpful when using deep learning for velocity estimation. A third and final objective is to study lack of generalization of deep learning models for out-of-distribution (OOD) data in the context of our problem, and present a novel approach to tackle it. We propose a domain adaptation network for training deep learning models that uses a priori knowledge on the range of velocity values in order to constrain mapping of the output. The final comparison on field data, which was not part of the training data, show the deep neural network predictions compare favorably with a conventional velocity model estimation obtained with a dispersion curve inversion workflow.

Mechanistic study on modification of convective internal boundary layer by spanwise surface motion

Modification of a convective internal boundary layer (IBL) by spanwise motion of a warm surface is investigated by imposing different surface moving speeds in the present study. Our analysis shows that the spanwise surface motion reduces the Reynolds shear stress right after the increase in the surface temperature in the convective IBL. The maximum decreasing rate of the Reynolds shear stress is found to be approximately 75% at the largest moving speed of the warm surface considered in the manuscript. Due to the reduction of the Reynolds shear stress, the vertical momentum transport is fundamentally altered, and the mean flow accelerates immediately after the increase in the surface temperature. By scrutinizing the instantaneous and conditional averaged flow fields as well as the pre-multiplied energy spectra, we have attributed the reduction of the Reynolds shear stress to the suppression of the near-surface velocity streaks and quasi-streamwise vortices, and the delayed growth of the convective structures, such as thermal plumes. Our investigation suggests that the developments of the convective IBL can be influenced by a strong spanwise motion of the warm surface, which should be taken into consideration in the prediction model for practical applications.

Early arrival waveform inversion using data uncertainties and matching filters with application to near-surface seismic refraction data

We develop an early arrival waveform inversion (EAWI) technique for high-resolution near-surface velocity estimation by iteratively updating the P-wave velocity model to minimize the difference between the observed and calculated seismic refraction data. Traditional EAWI uses a least-squares penalty function and an acoustic forward-modeling engine. Conventional least-squares error is sensitive to data with low signal-to-noise ratio (S/N) and iterations of EAWI stop at a local-minimum data misfit or at the preassigned maximum number of iterations. These stopping criteria can result in overfitting the data. In addition, fitting the elastic field data with an acoustic modeling engine can introduce artifacts in velocity estimation, especially in land data with significant elastic effects. To overcome these challenges, we develop a robust EAWI (REAWI) method by (1) incorporating the data uncertainties into the penalty function and (2) mitigating the elastic effects using a matching filter workflow. The data uncertainties are estimated from waveform reciprocal errors. When full-waveform reciprocity is not available, trace interpolation is applied. The proposed method prevents closely fitting data with low S/N, avoids overall overfitting by stopping the iterations when a normalized chi-square ([Formula: see text]) waveform misfit of one is achieved, and is less affected by elastic effects. Numerical examples and application to near-surface refraction data at a groundwater contamination site suggest that the final REAWI models are more accurate than the corresponding EAWI models, at the same level of misfit. This is the first known application of a matching filter workflow to real land data. The final REAWI models satisfy an appropriate misfit between the real data and predicted elastic P-wave data, making this approach in this respect equivalent to elastic waveform inversion. We also develop a method to analyze model constraint by examining the energy of the wavefield Fréchet derivative thereby avoiding the influence of the data residuals in traditional Fréchet kernels.

An Envelope Travel-Time Objective Function for Reducing Source–Velocity Trade-Offs in Wave-Equation Tomography

In conventional cross-correlation (CC)-based wave-equation travel-time tomography, wrong source wavelets can result in inaccurate velocity inversion results, which is known as the source–velocity trade-off. In this study, an envelope travel-time objective function is developed for wave-equation tomography to alleviate the non-uniqueness and uncertainty due to wrong source wavelets. The envelope of a seismic signal helps reduce the waveform fluctuations/distortions caused by variations of the source time function. We show that for two seismic signals generated with different source wavelets, the travel-time shift calculated by cross-correlation of their envelopes is more accurate compared to that obtained by directly cross-correlating their waveforms. Then, the CC-based envelope travel-time (ET) objective function is introduced for wave-equation tomography. A new adjoint source has also been derived to calculate the sensitivity kernels. In the numerical inversion experiments, a synthetic example with cross-well survey is first given to show that compared to the traditional CC travel-time objective function, the ET objective function is relatively insensitive to source wavelet variations and can reconstruct the elastic velocity structures more reliably. Finally, the effectiveness and advantages of our method are verified by inversion of early arrivals in a practical seismic survey for recovering near-surface velocity structures.

Sub-surface plasma flows and the flare productivity of solar active regions

The extreme space weather conditions resulting from high energetic events likes solar flares and Coronal Mass Ejections demand for reliable space weather forecasting. The magnetic flux tubes while rising through the convection zone gets twisted by the turbulent plasma flows, energizing the system and resulting in flares. We investigate the relationship between the subsurface plasma flows associated with flaring active regions and their surface magnetic flux and current helicity. The near-surface horizontal velocities derived from the ring-diagram analysis of active region patches using Global Oscillation Network Group Doppler velocity measurements are used to compute the fluid dynamics descriptors like vertical divergence, vorticity and kinetic helicity used in this work. The flaring active regions are observed to have large value of vertical vorticity and kinetic helicity. Also, the horizontal flow divergence, vorticity, flux, kinetic and current helicities are observed to be significantly correlated and evolve in phase with each other. We observe that the integrated values of the above flow and magnetic parameters observed 1 day prior to the flare are significantly correlated with the integrated flare intensity of the active region. Hence, we show that strong vorticity/kinetic helicities lead to larger active region twisting, presumably generating high-intensity flares.

Evaporate salt exploration by two dimensional (2D) seismic reflection method: Ankara-Polatlı region, Central Turkey

The presence of the Evaporate salt zone in Ankara-Polatlı region has been determined by the drillings and is thought to be the largest reserve in Turkey. The seismic reflection method was used to determine the top-bottom levels of the zone; its depth; its thickness and extent boundaries; the horst-graben structures; base depth and tectonic movements affecting the study area. Data were collected on three seismic lines. The near-surface tomographic velocity sections were compatible with the top of the zone depth observed in the drillings. As a result of the study, the depth and thickness of the top-bottom of the zone were determined along the lines. Within the scope of the study, a combined interpretation was made on the lines by using gravity and seismic data. The extent of the ore zone was determined only in the E-W direction section, but not in the north-south direction lines since they are outside the license area and so, the seismic lines. The closest point of the evaporate zone to the surface is approximately 150 m, deepest point is approximately 310 m, average thickness is approximately 100 m and maximum thickness is 185 m.

Adaptive thermal image velocimetry of spatial wind movement on landscapes using near-target infrared cameras

Abstract. Thermal image velocimetry (TIV) is a near-target remote sensing technique for estimating two-dimensional (2D) near-surface wind velocity based on spatio-temporal displacement of fluctuations in surface brightness temperature captured by an infrared camera. The addition of an automated parameterization and the combination of ensemble TIV results into one output made the method more suitable to changing meteorological conditions and less sensitive to noise stemming from the airborne sensor platform. Three field campaigns were carried out to evaluate the algorithm over turf, dry grass, and wheat stubble. The derived velocities were validated with independently acquired observations from fine-wire thermocouples and sonic anemometers. It was found that the TIV technique correctly derives atmospheric flow patterns close to the ground. Moreover, the modified method resolves wind speed statistics close to the surface at a higher resolution than the traditional measurement methods. Adaptive thermal image velocimetry (A-TIV) is capable of providing contactless spatial information about near-surface atmospheric motion and can help to be a useful tool in researching turbulent transport processes close to the ground.

Estimate near-surface velocity with reversals using deep learning and full-waveform inversion: A field-data-driven method

Estimating shallow velocity from land seismic data is challenging due to complexities in the near surface. One such complexity arises from velocity reversals, which can be caused by anomalous high/low-velocity layer intrusions into the otherwise increasing velocity-depth trend. Velocity reversal imposes significant challenges for conventional velocity model building methods to accurately estimate near surface models. Turning ray tomography or tomostatics relies on accurate picking of first breaks, which becomes infeasible when velocity reversals generate shingling events. In this paper, we proposed a field-data-driven deep learning framework, which combines field-specific training, deep learning and full-waveform inversion, to estimate the near surface velocity model from land seismic data. Instead of training over purely random synthetic data, we propose to incorporate a-priori knowledge about the field under study to generate field-specific data set for training the proposed deep neural network. In addition, we combine the deep learning model with full-waveform inversion to further improve the near-surface velocity model. Synthetic and field data examples show that the proposed framework can successfully handle the velocity reversal complexity in land seismic data and achieve promising performance on estimating near surface velocity models.

Sensitivity analysis to near-surface velocity errors of the Kirchhoff single-stack redatuming of multi-coverage seismic data

We present a study of a Kirchhoff-type redatuming approach for irregular multi-coverage land seismic data to remove the disturbing influence of rugged topography and a considerable near-surface velocity inhomogeneity, like a thick weathering layer, on the subsurface reflections. The redatuming method is physically more justified than the commonly used field-static correction methods that provide - in the indicated situation - non-physical temporal adjustments (field statics) by shifting traces in time, which lead to sub-optimal imaging and inversion of the reflections. The redatuming procedure studied here is different: It transforms the multi-coverage seismic reflection data on the irregular measurement surface to a regular measurement configuration on a pre-specified datum, below the inhomogeneous velocity overburden. This is done by stacking/summing the input traces with certain operators or traveltime curves which are calculated by ray tracing. In this way, incoherent subsurface reflections in the input data become coherent and thus recognizable in the output data. To have this happen, the redatuming operation requires a good approximation of the overburden velocity model and a reasonable one of the sub-burden homogeneous velocity model (below the datum). Then the multi-coverage output data on the new flat datum can be imaged and inverted much better than with standard data processing, i.e., with field-static correction. This work is dedicated to explaining the single-stack Kirchhoff-type redatuming approach kinematically using a synthetic model and data based on a real geological situation. However, our main aim is to study the sensitivity of this redatuming method to errors of the overburden velocity model, which is determined from multi-coverage seismic data using first arrivals traveltime tomography commonly used in the oil industry. We apply the redatuming process to a multi-coverage synthetic seismic dataset generated from the models that represent the real geological situation with irregular measurement surfaces and inhomogeneous overburden or near-surface velocity. To assess accuracy, the redatumed data was migrated in depth-domain and the results showed the correct positioning of the reflectors. The results revealed that this redatuming method produces reliable and accurate redatumed data even using approximate velocity models, which can be useful for prestack time and depth imaging.

Evaluation of the 3D Near-Surface Velocity Structure in an Urban Environment from Ambient Noise Array Tomography: The Case of the City of Thessaloniki (Northern Greece)

Abstract We examine the implementation of ambient noise array tomography in an urban environment to assess the 3D near-surface shear wave velocity (VS) structure at an intermediate spatial scale (∼1 km2, depth range 200–300 m). The application employs cross correlation traces of vertical component ambient noise recordings from a local network installed in Thessaloniki city (Northern Greece), allowing the determination of Rayleigh wave travel times for the frequency range of 1.5–14 Hz. The results confirm the presence of a complex subsurface with strong lateral variations in the geology, with travel times varying up to almost one order of magnitude. A surface wave travel time tomography approach was applied for each frequency to determine the spatial variability of the group velocity, involving the use of approximate Fresnel volumes, as well as damping and spatial smoothing constraints to stabilize the results. We also employed an interfrequency smoothing scheme to obtain smooth but data-compatible dispersion curves at the cost of inverting all travel time data simultaneously. Following the application of several quality cutoff criteria, we reconstructed local group slowness dispersion curves for a predefined tomographic grid in the study area. The final 3D velocity model was determined by a modified Monte Carlo inversion of these dispersion curves and the spatial integration of the obtained 1D VS profiles. Different model parameterizations were tested for the inversion to determine the optimal datafit. The final 3D velocity model is in a very good agreement with the local geology, previous larger scale studies, and other geophysical surveys, providing additional structural constraints (such as hidden fault identification) for the complex sedimentary deposits and bedrock formation in Thessaloniki, up to the depth of ∼250–300 m. The introduction of the aforementioned modifications to the ambient noise array tomography suggests that it can be efficiently adjusted and employed as a reliable tool for imaging the 3D seismic structure in urban environments with complex geology.

Characterization of the Aeration and Hydrodynamics in Vertical-Wheel™ Bioreactors.

In this work, the oxygen transport and hydrodynamic flow of the PBS Vertical-Wheel MINI™ 0.1 bioreactor were characterized using experimental data and computational fluid dynamics simulations. Data acquired from spectroscopy-based oxygenation measurements was compared with data obtained from 3D simulations with a rigid-lid approximation and LES-WALE turbulence modeling, using the open-source software OpenFOAM-8. The mass transfer coefficients were determined for a range of stirring speeds between 10 and 100 rpm and for working volumes between 60 and 100 mL. Additionally, boundary condition, mesh refinement, and temperature variation studies were performed. Lastly, cell size, energy dissipation rate, and shear stress fields were calculated to determine optimal hydrodynamic conditions for culture. The experimental results demonstrate that the can be predicted using , with a mean absolute error of 2.08%. Using the simulations and a correction factor of 0.473, the expression can be correlated to provide equally valid results. To directly obtain them from simulations, a partial slip boundary condition can be tuned, ensuring better near-surface velocity profiles or, alternatively, by deeply refining the mesh. Temperature variation studies support the use of this correlation for temperatures up to 37 °C by using a Schmidt exponent of 1/3. Finally, the flow was characterized as transitional with diverse mixing mechanisms that ensure homogeneity and suspension quality, and the results obtained are in agreement with previous studies that employed RANS models. Overall, this work provides new data regarding oxygen mass transfer and hydrodynamics in the Vertical-Wheel bioreactor, as well as new insights for air-water mass transfer modeling in systems with low interface deformation, and a computational model that can be used for further studies.

Curvelet-Based Joint Waveform and Envelope Inversion of Early-Arrival Imaging Shallow Geological Structure

Abstract Near-surface imaging structures often plays a significant role in the field of environmental and engineering geophysics. Early-arrival waveform inversion (EWI) is state-of-the-art method to imaging near-surface structures due to its high resolution. However, the method faces with cycle-skipping issue which might lead to an unexpected local minimum. Envelope inversion (EI) could deal with this issue which contributes to the ultralow-frequency information extracted from the envelope but has a low resolution. We have developed a curvelet-based joint waveform and envelope inversion (CJWEI) method for inverting imaging near-surface velocity structures. By inverting two types of data, we are able to recover the low- and high-wavenumber structures and mitigate the cycle-skipping problem. Curvelet transform was used to decompose seismic data into different scales and provide a multiscale inversion strategy to further reduce non-uniqueness of waveform inversion efficiently. With synthetic test and real data application, we demonstrate that our method can constrain the anomalies and hidden layers in the shallow structure more efficiently as well as is robust in terms of noise. The proposed multiscale joint inversion offers a computational efficiency and high precision to imaging fine-scale shallow underground structures.

Near-surface characterization using urban traffic noise recorded by fiber-optic distributed acoustic sensing

The recently developed fiber-optic distributed acoustic sensing (DAS) technology has attracted widespread attention in engineering applications, oil exploration, and seismological research. Compared with the conventional geophones, DAS can acquire high-resolution data due to a dense sampling and can be deployed conveniently in the complex acquisition environment. These advantages of DAS make it promising for near-surface characterization in the urban city. In this study, a DAS line was utilized to record traffic noise seismic data in the urban city and to investigate the near-surface characterization. Seismic surface waves were reconstructed from the acquired traffic noises using seismic interferometry. Thereafter, we obtain the near-surface shear wave velocity profile below the DAS line by surface wave dispersion curve inversion using a Bayesian Markov Chain Monte Carlo method. The results demonstrate the effectiveness of DAS-based urban traffic noise in near-surface characterization.

Time-lapse data matching using a recurrent neural network approach

Time-lapse seismic data acquisition is an essential tool to monitor changes in a reservoir due to fluid injection, such as CO2 injection. By acquiring multiple seismic surveys in the exact same location, the authors can identify the reservoir changes by analyzing the difference in the data. However, such analysis can be skewed by the near-surface seasonal velocity variations, inaccuracy, and repeatability in the acquisition parameters, and other inevitable noise. The common practice (cross equalization) to address this problem uses the part of the data in which changes are not expected to design a matching filter and then apply it to the whole data, including the reservoir area. Like cross equalization, the authors train a recurrent neural network (RNN) on parts of the data excluding the reservoir area and then infer the reservoir-related data. The RNN can learn the time dependency of the data, unlike the matching filter that processes the data based on the local information obtained in the filter window. The authors determine the method of matching the data in various examples and compare it with the conventional matching filter. Specifically, we start by demonstrating the ability of the approach in matching two traces and then test the method on a prestack 2D synthetic data. Then, the authors verify the enhancements of the 4D signal by providing reverse time migration images. The authors measure the repeatability using normalized root-mean-square and predictability metrics and find that, in some cases, our proposed method performed better than the matching filter approach.

Three-dimensional effects associated with the low-frequency breathing motion of a turbulent separation bubble

In this article, we address the low-frequency dynamics of a turbulent separation bubble (TSB) with a mean length of Lb ≈ 0.18 m that occurs in a one-sided diffuser at an inflow velocity of U∞ = 20 m/s. Distinct low-frequency content at Strouhal numbers of O(f Lb/U∞) = 0.01 is detected through time-resolved wall pressure measurements. By applying Spectral Proper Orthogonal Decomposition to velocity fluctuations in the diffuser symmetry plane, these pressure fluctuations can be linked to a large-scale longitudinal contraction/expansion motion falling into the same frequency range. Further velocity field measurements in cross-sections of one diffuser half-plane indicate that the mean flow field of the TSB is characterized by a near-surface outward-directed velocity component and a large-scale streamwise side wall vortex. During the expanded TSB state, however, these flow features are suppressed as the streamwise velocity is temporarily increased near the side wall but reduced near the symmetry plane. The results presented in this article suggest that the low-frequency breathing motion is a highly three-dimensional phenomenon. Future studies should address the associated complex dynamics that are shown to be governed by aperiodic events of temporary TSB expansion.

Using common-reflection-surface stack for enhanced near-surface seismic reflection imaging: Examples from consolidated and unconsolidated environments

The small geophone spacing and spread lengths commonly used to investigate ultrashallow layers in conjunction with the limited bandwidth of surface impact sources do not allow for clear separation of different seismic arrivals. The interference of events and the high noise levels due to near-source offsets make shallow seismic data processing a challenging task. The wavefront attributes of seismic waves commonly used in conventional seismic processing are suggested to improve near-surface seismic reflection imaging. These wavefront attributes are often used as the stacking parameters in multidimensional time imaging methods such as common-reflection surface (CRS). It is shown in this paper that the CRS method can improve near-surface seismic data processing by enhancing: (1) unstacked gathers via a CRS-based local stacking scheme, (2) semblance picking for velocity model building, and (3) zero-offset stacked data via the CRS global stacking. The CRS-based local stacking can mitigate the loss of data associated with optimum-windowing-based muting of coherent noise. The CRS local stacking infills the muted zones via its robust data interpolation and regularization features and enhances the image quality. Furthermore, the CRS-based enhanced data allow for improved near-surface velocity model building by producing higher coherence and more focused semblance peaks. The CRS global stacking is shown to further smooth and provide more continuity of events in the zero-offset data. Applications to a synthetic and two field data sets collected from high- and low-velocity environments indicate the efficiency and feasibility of the proposed approach.

Geometrical-Feature-Preserving Adjoint Tomography of Near-Surface Structure with Seismic Early Arrival

Early arrival waveform inversion (EWI) is an essential approach to obtaining velocity structures in near-surface. Due to suffering from a cycle‐skipping issue, it is difficult to reach the global minima for conventional EWI with the misfit function of least-squares norm (L2‐norm). Following the optimal transportation theory, we developed an EWI solution with a new objective function based on quadratic‐Wasserstein‐metric (W2-norm) to maintain the geometric characteristics of the distribution and improve the stability and convexity of the inverse problem. First, we gave the continuous form of the adjoint source and the Frechet gradient of the Wasserstein metric for seismic early arrival, which leads to an easy and efficient way to implement in the adjoint-state method. Then, we conducted two synthetic experiments on the target model containing some velocity anomalies and hidden layers to test its effectiveness in mapping accurate and high-resolution near-surface velocity structure. The results show that the W2-normed EWI can mitigate cycle-skipping issues compared with the L2-normed EWI. In addition, it can deal with hidden layers and is robust in terms of noise. The application to a real dataset indicates that this new solution can recover more details in the shallow structure, especially in the aspect of dealing with hidden layers.

Seismic imaging of crystalline structures: improving energy focusing and signal alignment with azimuthal binning and 2.5-D full-waveform inversion

SUMMARY Severely crooked 2-D reflection profiles generate a swath distribution area of common midpoint (CMP) traces. These traces are scattered in all directions and no longer correspond to CMP gathers directly below the acquisition line, effectively posing a pseudo-3-D problem. Standard seismic processing techniques neglect the 3-D character of crooked seismic transects and often yield suboptimal images. The out-of-plane energy ignored in 2-D stacking techniques causes amplitude smearing effects due to anomalous reflection traveltimes. The imaging of dipping structures in geologically complex areas, such as in Larder Lake, are particularly affected because of complex energy scattering. These out-of-plane reflected wavefield components degrade energy focusing and signal alignment, worsening as the obliqueness of the profile with respect to local geology increases. To mitigate the contribution of scattered out-of-plane energy, we implement azimuthal binning. Pseudo-processing lines, parallel to the maximum geological dip, are generated by using locally optimum bin orientations. We use a priori geological information to devise optimum bin orientations in accordance to the local strike. We find that the optimized binning method significantly outperforms the standard approach in terms of in-phase amplitude stacking and reflection continuity. This is because the standard approach is based on a bin geometry always perpendicular to the seismic line traverse, ignoring 3-D effects, whereas in the optimum binning approach each individual bin may have a different orientation along the line. Moreover, we enhance the final reflectivity image by performing seismic migration with a velocity model that incorporates near-surface velocity estimations from full-waveform inversion. We demonstrate that the combination of these two innovations results in a significant impact on the imaging quality of structures at depth. In addition to imaging structures at depth, we also illustrate the significance of their continuation towards the near surface. This is especially important in the Larder Lake area where tectonic activity may have emplaced ore deposits in the first few kilometres of the subsurface.