Synthetic Shot Gathers Research Articles

A method for denoising active source seismic data via Fourier transform and spectrum reconstruction

Denoising is a critical step in signal processing. We develop a method for random noise reduction in active source seismic data using spectrum reconstruction. Two methods are developed for modifying the observed data’s amplitude spectrum: one substitutes it with the source wavelet’s amplitude spectrum, whereas the other involves multiplying the source wavelet’s amplitude spectrum with the observed data’s amplitude spectrum. By reconstructing the modified amplitude spectrum while preserving the observed data’s phase spectrum, noise suppression is achieved. Extensive testing with theoretical models, synthetic shot gathers, and field data indicate a notable improvement in the signal-to-noise ratio (S/N) compared with the traditional band-pass filtering method. This method proves particularly effective for enhancing the S/N in the context of active source wide-angle seismic data used in offshore structural studies, eliminating the need for data segmentation based on offset, and thereby improving processing efficiency. Our method relies solely on a single complete cycle of the source wavelet, making it a purely data-driven solution. It has broad applications in processing active source or controlled source data with consistent source wavelets, including but not limited to seismic exploration, acoustic detection, and signal denoising in various ground-penetrating radars used on Mars, the moon, and earth.

Deep learning strategy for salt model building

Velocity models are crucial intermediate products generated in seismic data processing, and the model’s accuracy is essential for constructing quality seismic images. Conventional approaches to velocity-model building (VMB) use a family of inversion methods, among which are ray-based tomography and full-waveform inversion. These methods have been highly optimized throughout the years but are still heavily dependent on continuous human curation of the results, which leads to an overall high time cost, especially in areas with high structural complexity, such as those containing salt tectonics. We have investigated a deep learning (DL) approach that accurately defines salt geometries for VMB. We train our convolutional neural network on synthetic shot gather data, explore a manner of leveraging information through summation of shot data, and determine the influence that the choice of loss function has on the quality and aspect of predicted velocity models. Our residual U-Net model trained on data containing only randomly shaped salt bodies can estimate geologically complex salt geometries such as those in 2D SEG/EAGE salt model slices. Our results find that deeper encoder-decoder models with shortcut connections resolve velocity model structures better than shallower models. Moreover, network models trained with a composite loss function — combining mean absolute error and the multiscale structural similarity index — better delineate the contours of areas with high-velocity contrast and better recover regions with a uniform velocity trend than network models trained with conventional loss functions like the mean squared error. The residual U-Net and loss functions that we use are not task-specific and can be extended to other DL approaches to VMB.

INFLUENCES OF DATA ACQUISITION AND LAYER PARAMETERS ON FUNDAMENTAL DISPERSION CURVE IN ACTIVE MULTICHANNEL ANALYSIS OF SURFACE WAVE (A-MASW) METHOD

Aktif ÇKYDA yönteminde, sığ yeraltının güvenilir bir S-dalgası hız-derinlik profilini elde etmek için en önemli aşamalarından biri, geniş frekans aralığında sürekli ve yüksek ayrımlı bir dispersiyon eğrisinin elde edilmesidir. Bu makalede, tabaka (S- ve P-dalga hızları, yoğunluk ve kalınlık) ve veri toplama (kaynak ofseti-X0, alıcı aralığı-dx ve sayısı-N, serim boyu-L=(N-1)*dx) parametrelerinin, yüzey dalga alanının temel mod dispersiyon eğrisi üzerindeki etkileri, yapay atış verilerinin analizi ile incelenmiştir. Yapay veriler, tabaka yansıma/iletim katsayısı tekniği ile hesaplanan dispersiyon eğrisi kullanılarak, her bir alıcıda harmonik mod toplama tekniği ile oluşturulmuştur. Buna göre, düşük S-dalga hızlı ara tabakalar dispersiyon eğrisinin düşük frekans bölgesinde düşük hızlara kaymasına, yüksek hızlı ara tabakalar ise, dispersiyon eğrisinin yüksek frekanslarda sürekliliğinin bozulmasına (zig-zag etkisi) ve yüksek mod etkisi yanılgısına neden olabileceği gözlenmiştir. Kaynak ofseti yüzey dalgalarının tam dalga formlarının oluşmasında etkin iken, ilk ve son alıcı arası uzaklık olan serim uzunluğunun ise, dispersiyon eğrisinin frekans bandını ve çözünürlüğünü etkilediği görülmüştür. Ayrıca, geniş alıcı aralıklarının, dispersiyon eğrisinin düşük frekans bölgesindeki güvenirliğinin arttırmasının yanında, yüksek frekans bölgesinde dalga sayısı katlanması nedeniyle dispersiyon eğrisinin sürekliliğini bozduğu görülmüştür. Sonuç olarak, farklı dalga hızlarına sahip yer modelleri için üretilen yapay veriler üzerinde yapılan analizler N

Inferring CO2 saturation from synthetic surface seismic and downhole monitoring data using machine learning for leakage detection at CO2 sequestration sites

Inferring CO2 saturation from seismic data is important when seismic methods are applied at CO2 sequestration sites for verification and accounting purposes, such as verifying the total injected CO2 volume, comparing with model predictions for concordance evaluation, tracking the migration of CO2 plume, and detecting possible leakage from the storage reservoir. In this work, we infer CO2 saturation levels at three depths from simulated surface seismic, downhole pressure and total dissolved solids (TDS) data using machine learning (ML) methods. The simulated monitoring data are based on 6000 numerical multi-phase flow simulations of hypothetical wellbore CO2 and brine leakage from a legacy well into shallow aquifers at a model CO2 storage site. We conduct rock physics modeling to estimate changes in seismic velocity due to the simulated CO2 and brine leakage at each time step in the flow simulation outputs, resulting in 120,000 forward seismic velocity models. 2D finite-difference acoustic wave modeling is performed for each velocity model to generate synthetic shot gathers, along a sparse 2D seismic line with only 5 shots and 40 receivers. We extract 6 time-lapse seismic attribute anomalies from each trace in the time window relevant to each geologic layer, and use the seismic features, together with downhole pore pressure, TDS features to train the machine learning algorithms. The impact of seismic noise on the performance of the trained machine learning models has also been investigated. Inferred CO2 saturations from the trained classifiers are in good agreement with observations. Direct pressure and TDS measurements from downhole monitoring can increase the accuracy of the inferred CO2 saturation class from the forward modeled 2D surface seismic data. Our ML workflow represents a promising way to combine measurements from multiple monitoring techniques, together with seismic monitoring to achieve more accurate seismic quantitative interpretation.

Transdimensional and Hamiltonian Monte Carlo inversions of Rayleigh‐wave dispersion curves: a comparison on synthetic datasets

ABSTRACTWe compare two Monte Carlo inversions that aim to solve some of the main problems of dispersion curve inversion: deriving reliable uncertainty appraisals, determining the optimal model parameterization and avoiding entrapment in local minima of the misfit function. The first method is a transdimensional Markov chain Monte Carlo that considers as unknowns the number of model parameters, that is the locations of layer boundaries together with the Vs and the Vp/Vs ratio of each layer. A reversible‐jump Markov chain Monte Carlo algorithm is used to sample the variable‐dimension model space, while the adoption of a parallel tempering strategy and of a delayed rejection updating scheme improves the efficiency of the probabilistic sampling. The second approach is a Hamiltonian Monte Carlo inversion that considers the Vs, the Vp/Vs ratio and the thickness of each layer as unknowns, whereas the best model parameterization (number of layer) is determined by applying standard statistical tools to the outcomes of different inversions running with different model dimensionalities. This work has a mainly didactic perspective and, for this reason, we focus on synthetic examples in which only the fundamental mode is inverted. We perform what we call semi‐analytical and seismic inversion tests on 1D subsurface models. In the first case, the dispersion curves are directly computed from the considered model making use of the Haskell–Thomson method, while in the second case they are extracted from synthetic shot gathers. To validate the inversion outcomes, we analyse the estimated posterior models and we also perform a sensitivity analysis in which we compute the model resolution matrices, posterior covariance matrices and correlation matrices from the ensembles of sampled models. Our tests demonstrate that major benefit of the transdimensional inversion is its capability of providing a parsimonious solution that automatically adjusts the model dimensionality. The downside of this approach is that many models must be sampled to guarantee accurate posterior uncertainty. Differently, less sampled models are required by the Hamiltonian Monte Carlo algorithm, but its limits are the computational effort related to the Jacobian computation, and the multiple inversion runs needed to determine the optimal model parameterization.

SOLUTION OF HIGH VELOCITY ANOMALIES IMPERCEPTIBLE TO SEISMIC RESOLUTION, BY MEANS OF SYNTHETIC MODELS, PENOBSCOT FIELD, CANADA

Penobscot Field is located in the Sable Sub-basin in Nova Scotia, Canada, where a 3D seismic acquisition campaign was carried out in 1991 and also two oil wells were previously drilled (L-30 and B-41). In the interpreted seismic data, a discrepancy was found in the travel times of the seismic waves near well B-41, causing a false structural height in the Naskapi Member, Mississauga Formation (Early Cretaceous or Lower Cretaceous) and deeper formations (10 to 15 ms, i.e. approximately 25 m). It was decided to find a solution of this problem using synthetic models. First, making a delimitation of the study area by means of a seismic subcube, which were later interpreted surfaces of interest, through the well data. The average velocity and density properties were found for each interpreted strata. Subsequently, a 2D seismic acquisition was simulated, choosing a line that crossed both wells and through the use of ray traces, synthetic shot gathers were obtained and processed through the use of different seismic migration tests, and the final solution was found to be Kirchhoff pre-stacking migration in depth.

A novel measurement of the surface wave dispersion with high and adjustable resolution: Multi-channel nonlinear signal comparison

Dispersion measurement of surface-waves is critical for utilizing surface-waves. However, the traditional dispersion measurements of multi-channel seismic data are usually based on linear signal comparison and achieve a low resolution at low frequencies. To improve the accuracy of surface-wave dispersion measurement for multi-channel seismic data, we propose a novel measurement that can produce a high and adjustable resolution among a wide detected frequency range. This measurement is based on a nonlinear signal comparison using multi-channel seismic data. We evaluated the performance of this proposed multi-channel nonlinear signal comparison (MNLSC) on several datasets: one synthetic shotgather containing only surface waves, one synthetic shotgather with full elastic waves, and one field shotgather. These examples show that the MNLSC is promising to be a reliable and effective tool for measuring the surface dispersion from the multi-channel seismic data.

Horizontal resolution of multichannel analysis of surface waves

The multichannel analysis of surface wave (MASW) method has been effectively and widely used to determine near-surface shear-wave velocity. Horizontal resolution of the MASW method represents the minimum horizontal length of recognizable geologic anomalous bodies on a pseudo-2D S-wave velocity [Formula: see text] section. Accurately assessing the achievable lateral resolution is one of the main issues in lateral variation reconstruction using the MASW method. It is difficult to quantitatively estimate the horizontal resolution of the MASW method because of the many influencing factors, such as parameters of the observation system, the depth of an anomalous body, and the velocity contrast between the anomalous body and the surrounding rocks. We first analyzed the horizontal resolution of the MASW method based on numerical simulation experiments. According to different influencing factors of the horizontal resolution, we established different laterally heterogeneous models and observation systems and then simulated several synthetic multichannel records with a finite-difference method along a linear survey line using the roll-along acquisition mode. After the extraction of dispersion curves of Rayleigh waves and inversion for S-wave velocity profiles for each synthetic shot gather, a pseudo-2D S-wave velocity section can be generated by aligning the 1D S-wave velocity models. Ultimately, we evaluated the horizontal resolution capability of the MASW method on pseudo-2D [Formula: see text] maps. Our numerical investigation results and field data analysis indicate that [Formula: see text] values on the maps are not the same as the true [Formula: see text] values for structures whose lateral dimension is shorter than a receiver spread length and that anomalous bodies, which are larger and have high velocity contrast, are easier to distinguish on [Formula: see text] maps with a shorter receiver spread length. The horizontal resolution decreases with the increasing depth and is approximately one-half of the shortest Rayleigh wavelength that can penetrate to the depth.

Seismic denoising using the redundant lifting scheme

Separating linear coherent noise, such as ground roll from reflections, remains a key challenge in seismic processing. By adapting the redundant lifting scheme (RLS), a wavelet transform method, to seismic data, we have determined how the wavelet domain can be used to suppress coherent and random noise. The RLS operates on a trace-by-trace basis decomposing each time series into wavelet-coefficient (WC) time series and consequently a single gather (in a shot, receiver, or common depth point domain) into a series of WC subgathers (SGs). The decomposition changes the relative magnitude of WCs of various events (reflection, head wave, ground roll, etc.) from one SG to another without affecting their moveout. In SG(s) in which the WCs of undesired events were significantly stronger than the desired events, the WCs can be surgically muted. Selective muting in carefully chosen SGs attenuates undesired events while having minimal effects on frequency spectra of the desired events. In addition, random noise can be suppressed in the individual SGs by designing a local thresholding mechanism (we have used modified Otsu thresholding) in combination with adaptive Wiener filtering. We have developed this approach of suppressing coherent and random noise in a step-by-step manner first using a synthetic shot gather, followed by demonstration on two real gathers. Our RLS-based denoising method has minimal effects on the lower end of signal frequency spectra, and it could be a valuable tool in a processor’s toolbox when data preconditioning for advanced processing such as waveform inversion, which benefits from low frequencies, is desired.

Seismic random noise attenuation using artificial neural network and wavelet packet analysis

In this paper, we present a method for attenuating background random noise and enhancing resolution of seismic data, which takes advantage of semi-automatic training of feed forward back propagation (FFBP) artificial neural network (ANN) in a multiscale domain obtained from wavelet packet analysis (WPA). The images of approximations and details of the input seismic sections are calculated and utilized to train neural network to model coherent events by an automatic algorithm. After the modeling of coherent events, the remainder data are assumed to be related to background random noise. The proposed method is applied on both synthetic and real seismic data. The results are compared with that of the adaptive Wiener filter (AWF) in synthetic shot gather and real common midpoint gather and also with that of band-pass filtering on real common offset gather. The comparison indicates substantially higher efficiency of the proposed method in attenuating random noise and enhancing seismic signals.

High angle prestack depth migration with absorption compensation

The absorption effect of actual subsurface media can weaken wavefield energy, decrease the dominating frequency, and further lead to reduced resolution. In migration, some actions can be taken to compensate for the absorption effect and enhance the resolution. In this paper, we derive a one-way wave equation with an attenuation term based on the timespace domain high angle one-way wave equation. A complicated geological model is then designed and synthetic shot gathers are simulated with acoustic wave equations without and with an absorbing term. The derived one-way wave equation is applied to the migration of the synthetic gathers without and with attenuation compensation for the simulated shot gathers. Three migration profiles are obtained. The first and second profiles are from the shot gathers without and with attenuation using the migration method without compensation, the third one is from the shot gathers with attenuation using the migration method with compensation. The first and third profiles are almost the same, and the second profile is different from the others below the absorptive layers. The amplitudes of the interfaces below the absorptive layers are weak because of their absorption. This method is also applied to field data. It is concluded from the migration examples that the migration method discussed in this paper is feasible.

Multiple subtraction method based on a non-causal matching filter

The filter operator used in normal multichannel matching filter is physically realizable. This filter operator only delays seismic data in the filtering process. A noncausal multichannel matching filter based on a least squares criterion is proposed to resolve the problem in which predicted multiple model data is later than real data. The differences between causal and non-causal multichannel matching filters are compared using a synthetic shot gather, which demonstrates the validity of the non-causal matching filter. In addition, a variable length sliding window which changes with offset and layer velocity is proposed to solve the count of events increasing with increasing offset in a fixed length sliding window. This variable length sliding window is also introduced into the modified and expanded multichannel matching filter. This method is applied to the Pluto1.5 synthetic data set. The benefits of the non-causal filter operator and variable length sliding window are demonstrated by the good multiple attenuation result.

Modelling the geoelectric and seismic reservoir response caused by carbon dioxide injection based on multiphase flow simulation: Results from the CO 2SINK project

Results from crosshole geoelectric and surface seismic monitoring for geological storage of carbon dioxide (CO 2) were investigated by forward modelling within the framework of the CO 2SINK project. Selected geological and petrophysical parameters reflect the conditions of the CO 2SINK site. CO 2 saturation distributions were derived from multiphase flow modelling, whereas the alteration of geophysical rock properties by CO 2 was obtained from laboratory experiments. Crosshole geoelectric modelling was performed for three electrode combinations and three time-dependent CO 2 migration scenarios with different reservoir permeabilities. The magnitude and alteration of modelled resistances were analyzed in the pre-inversion domain. Time-lapse alterations were observable on the synthetic data, with diverse characteristics dependent on applied electrode configuration. Analysis of the alterations showed the opportunity to differentiate migration scenarios within the constraints of the ambient noise level. The synthetic time-lapse seismic reflection experiment was performed for the anticline used for CO 2 storage. The geological model incorporates the structural framework, as determined from the seismic interpretation, and velocities derived from seismic processing and velocity logs. Common depth point (CDP) processing of synthetic shot gathers of a baseline and repeat experiment provided the data for a difference stack section exhibiting a CO 2 induced time-lapse signature. Interpretation of the signature in conjunction with the underlying CO 2 distribution has shown that lateral extent of the plume may be accurately detected. The vertical plume extent is concealed in the waveform coda and is unlikely to be retrievable from standard seismic processing.

Sequentially ordered single-frequency 2-D acoustic waveform inversion in the Laplace-Fourier domain

SUMMARY In the conventional frequency-domain waveform inversion, either multifrequency simultaneous inversion or sequential single-frequency inversion has been implemented. However, most conventional frequency-domain waveform inversion methods fail to recover background velocity when low-frequency information is missing. Recently, new waveform inversion techniques in the Laplace and Laplace–Fourier domain have been proposed to recover background velocity structure from data with insufficient low-frequency information. In such techniques, however, all frequencies are inverted simultaneously, and this requires large computational resources and long computation times. In this paper, we propose a sequentially ordered single-frequency 2-D acoustic waveform inversion using the logarithmic objective function in the Laplace–Fourier domain. Our algorithm sequentially inverts single-frequency data in the Laplace–Fourier domain, thus reducing computational resources. Unlike most conventional waveform inversion methods requiring an initial velocity model close to the true model, we propose a one-step waveform inversion method in seeking to find a final velocity structure from the simple initial model through a hybrid combination of the Laplace domain inversion and the Fourier domain inversion. We adopt and evaluate the multiloop algorithm by modifying the double-loop algorithm commonly used in the conventional frequency-domain waveform inversion. Using the multiloop algorithm repeating loop over frequencies, the quality of the inversion results can be improved and the decision problem of the number of iterations for each frequency can be overcome effectively. Because the sequential order of the Laplace–Fourier frequencies in a 2-D plane should be assigned for inverting Laplace–Fourier frequency data consecutively, we present three different sequential orders of Laplace–Fourier frequencies while considering the multiscale and layer-stripping approach, and we compare the inversion results from the numerical experiments. We applied the sequentially ordered single-frequency 2-D acoustic waveform inversion in the full Laplace–Fourier domain to the synthetic seismic data produced from complex structure model and field data. A realistic model could be recovered in an efficient and robust manner, even using the two-layer homogeneous velocity model as an initial model. The inverted velocity model from the field data was validated by examining the migrated image from the pre-stack depth migration and the flattening of the common-image gathers or by comparing the synthetic shot gather with the real shot gather. The proposed one-step waveform inversion algorithm can be easily extended to the sequential inversion of 3-D acoustic or elastic data in the full Laplace–Fourier domain.

Shallow seismic surveying of an Alpine rock glacier

To map the internal structure and lower boundary of an alpine rock glacier, we recorded three shallow seismic profiles and drilled four ∼70‐m‐deep holes through to the underlying bedrock. Although analysis of the seismic data using standard reflection processing schemes did not yield conclusive results because of the dominantly low‐frequency returned signals and the presence of strong source‐generated noise, tomographic inversions of first‐arrival times were successful in mapping several critical subsurface features. A thin, low‐velocity layer of loose boulders, air voids, and snow was found to extend across the entire surveyed area. Below this layer, two distinct velocity regimes superimposed on a general increase in velocity with depth were identified. A broad regime of high velocities was interpreted to contain boulders with numerous ice‐filled voids, whereas an adjacent regime of relatively low velocities was explained in terms of boulders with air‐ and water‐filled voids. This latter region of degraded permafrost, which was unexpected, may be an early result of global warming. The transition from rock glacier to gneissic bedrock was delineated approximately by the 4300‐m/s isovelocity line. Although poorly resolved, its depth varied from ∼35 to ∼70 m beneath the surface of the rock glacier. The tomographic inversion results also provided an explanation for the occurrence of strong source‐generated noise in our data. Using the derived velocity distributions as input, synthetic shot gathers were calculated using a finite‐difference viscoelastic scheme that accounted for surface topography. The resultant synthetic data were dominated by the overwhelming effects of back‐ and side‐scattered waves from shallow heterogeneities and high‐amplitude guided phases that originated from the thin, surficial low‐velocity zone. Had the guided phases not been correctly identified and eliminated during processing, they would have appeared as reflections in the stacked sections. Since low‐velocity layers overlying heterogeneous media are common features of the shallow subsurface, our findings may have implications for a broad range of engineering‐scale seismic investigations (e.g., studies of landfills, rockslides, moraines, talus slopes, alluvial fans).

Sub-basalt imaging problems and the application of Artificial Neural Networks

In many areas of the world, the presence of shallow high velocity, highly heterogeneous layers complicate seismic imaging of deeper reflectors. Of particular economic interest are areas where potentially hydrocarbon-bearing strata are obscured by layers of basalt. Basalt layers are highly reflective and heterogeneous. Using reflection seismic, top basalt is typified by a high-amplitude, coherent reflector with poor resolution of reflectors below the basalt, and even bottom basalt. Here, we present a new approach to the imaging problem using the pattern recognition abilities of a back-propagation Artificial Neural Network (ANN). ANNs are computational systems that attempt to mimic natural biological neural networks. They have the ability to recognize patterns and develop their own generalizations about a given data set. Back-propagation neural networks are trained on data sets for which the solution is known and tested on the data that are not previously presented to the ANN in order to validate the network result. We show that Artificial Neural Networks, due to their pattern recognition capabilities, can invert the medium statistics based on the seismic character. We produce statistically defined models involving a basalt analogous layer, and calculate full wavefield finite difference synthetic seismograms. We vary basalt layer thickness and source frequency to generate a synthetic model that produces seismic that is similar to real sub-basalt seismic, i.e. high amplitude top basalt reflector and the absence of base basalt and sub-basalt events. Using synthetic shot gathers, generated in a synthetic representation of the sub-basalt case, we can invert the velocity medium standard deviation by using an ANN. By inverting the velocity medium standard deviation, we successfully identified the transition from basalt to sub-basalt on the synthetic shot gathers. We also show that ANNs are capable of identifying the basalt to sub-basalt transition in the presence of incoherent noise. This is important for any future applications of this technique to the real-world seismic data, as this data is never completely noise-free. There is always a certain level of residual (noise remaining after initial noise filtering) environmental/ambient noise present on the recorded seismics, hence, neural network training with noise-free synthetic seismic is less than optimal.

Reducing source‐generated noise in shallow seismic data using linear and hyperbolic τ‐ptransformations

High‐resolution seismic reflection data recorded at many locations on the earth are plagued by the overwhelming effects of direct, refracted, guided, and surface waves. These different components of source‐generated noise may completely mask reflections at traveltimes <∼50–100 ms. Conventional processing methods that include the time‐consuming application of mute functions may lead to the misprocessing of source‐generated noise (especially guided waves) as reflected events and/or the unintentional removal of important shallow reflections. We introduce a combined linear and hyperbolic τ‐p processing scheme that results in the effective separation of reflections from source‐generated noise. After applying linear moveout terms that adjust the direct, refracted, and guided arrivals to appear horizontal to subhorizontal, the reduced traveltime shot gathers are transformed into the linear τ‐p domain. It is then straightforward to design a single τ‐p filter that eliminates most of the source‐generated noise throughout the entire data set. Following inverse linear τ‐p transformation and removal of the linear moveout terms, the filtered shot gathers contain reflections and residual elements of the source‐generated noise. Because summing along hyperbolas favors reflections, transforming the filtered shot gathers into the hyperbolic τ‐p domain leads to significant enhancements in the S/N ratio. A simple rescaling of data values in the hyperbolic τ‐p domain, which results in the loss of true amplitude information, increases further the relative strength of the reflected signals. Finally, inverse hyperbolic transformation yields shot gathers dominated by reflections. In tests of the combined τ‐p processing scheme on a synthetic shot gather and on a complete shallow seismic reflection data set recorded in northern Switzerland, significant improvements in the quality of reflections in the prestacked data and on a fully processed section are readily apparent. According to the results of these tests, the new scheme works well for reflections originating from flat and dipping horizons.

Sub-basalt imaging using converted waves: numerical modelling

Abstract Both ray-tracing and frequency-wavenumber integration modelling have been used to investigate the behaviour of P waves and P to S converted waves in simple geological models, which involve a basaltic layer above a lower-velocity layer of sediments. Model parameters were adjusted to study the behaviour of these waves, under conditions of changing water depth, basalt layer thickness and a number of permutations with respect to basalt-sediment stratification. Synthetic shot gathers were generated and then analysed and processed as real data. The general approach was to document changes in reflection curves with offset, changes in the stacking velocity, variations in the imaging of deeper reflectors under different model scenarios and to understand which offsets are useful for converted wave processing. Although this work is preliminary, some general principles have been identified, which may be of use in an operational context. For example, the modelling indicates that S waves have larger amplitudes than P waves at long offsets and that P to S converted waves arrive before the sea bottom arrival at the far offset. Consequently, the recording and identification of converted waves is favoured by long offset ( c. 10 km) acquisition arrays. However, the modelling also indicates that the presence of sediment-basalt interlayering makes the identification of converted waves more difficult.