Source and receiver deghosting by demigration-based supervised learning

Deghosting of marine seismic data is an important and challenging step in the seismic processing flow. We describe a novel approach to train a supervised convolutional neural network to perform joint source and receiver deghosting of single-component (hydrophone) data. The training dataset is generated by demigration of stacked depth migrated images into shot gathers with and without ghosts using the actual source and receiver locations from a real survey. To create demigrated data with ghosts, we need an estimate of the depth of the sources and receivers and the reflectivity of the sea surface. In the training process, we systematically perturbed these parameters to create variability in the ghost timing and amplitude and show that this makes the convolutional neural network more robust to variability in source/receiver depth, swells and sea surface reflectivity. We tested the new method on the Marmousi synthetic data and real North Sea field data and show that, in some respects, it performs better than a standard deterministic deghosting method based on least-squares inversion in the τ-p domain. On the synthetic data, we also demonstrate the robustness of the new method to variations in swells and sea-surface reflectivity.publishedVersio

Uncertainty analysis of depth predictions from seismic reflection data using Bayesian statistics

Velocity model building is a critical step in seismic reflection data processing. An optimum velocity field can lead to well focused images in time or depth domains. Taking into account the noisy and band limited nature of the seismic data, the computed velocity field can be considered as our best estimate of a set of possible velocity fields. Hence, all the calculated depths and the images produced are just our best approximation of the true subsurface. This study examines the quantification of uncertainty of the depths to drilling targets from two dimensional (2D) seismic reflection data using Bayesian statistics. The approach was tested in Mentelle Basin (south west of Australia), aiming to make depths predictions for stratigraphic targets of interest related with the International Ocean Discovery Program (IODP), leg 369. For the purposes of the project, Geoscience Australia 2D seismic profiles were reprocessed. In order to achieve robust predictions, the seismic reflection processing sequence was focused on improving the temporal resolution of the data by using deterministic deghosting filters in pre-stack and post-stack domains. The filters, combined with isotropic/anisotropic pre-stack time and depth migration algorithms, produced very good results in terms of seismic resolution and focusing of subsurface features. The application of the deghosting filters was the critical step for the subsequent probabilistic depth estimation of drilling targets. The best estimate of the velocity field along with the migrated seismic data were used as input to the Bayesian algorithm. The analysis, performed in one seismic profile intersecting the site location MBAS-4A, produced robust depth predictions for lithological boundaries of interest compared to the observed depths as reported in the IODP expedition. The significance of the result is more pronounced taking into account the complete lack of independent velocity information. Petrophysical information collected from the expedition was used to perform well-seismic tie, mapping the lithological boundaries with the reflectivity in the seismic profile. A very good match between observed and modelled traces was achieved and a new interpretation of the Mentelle Basin lithological boundaries in seismic image was provided. Velocity information from sonic logs was also implemented to perform anisotropic pre-stack depth migration. The migrated image successfully mapped the subsurface targets to their correct depth location while preserving the focus of the image. The pre-drilling depth estimation of subsurface targets using Bayesian statistics can be considered as a great example of successfully quantifying the uncertainty in depths and effectively merging seismic reflection data processing with statistical analysis. The derived well-seismic tie in MBAS-4A will be a valuable tool towards a more complete regional interpretation of the Mentelle Basin

Machine learning and artificial neural networks for seismic processing

Nylig har tilgjengeligheten av kraftige GPUer og "open source"-programvare gjort det mulig for kunstige nevrale nettverk å løse flere praktiske og industrielle problemer. Vi kan bruke nevrale nettverk til viktige seismiske prosesseringstrinn som dønning-støydemping, seismisk interferensdemping, debobling, deghosting og deblending. I dag krever mange av disse prosesseringstrinnene betydelig testing og beregningstid som nevrale nettverk har potensialet til å redusere. I tillegg kan nevrale nettverk gi gode resultater og takle endinger i innsamlingsdataene. I løpet av det siste tiåret har det blitt gjort betydelige fremskritt i å konstruere nevrale nettverk, og denne utviklingen vil mest sannsynlig øke i fremtiden. Luftkanoner er en av de mest brukte kildene i en marin seismikkinnsamling. Luftkanoner har mange fordeler framfor andre kilder, som er grunnen til at de er så ofte brukt. Det er imidlertid noen utfordringer ved bruk av luftkanoner. Luftkanonen slipper ut en luftboble som svinger mellom å utvide seg eller å trekke seg sammen. Dette skaper boblestøy etter den første utvidelsen. Boblestøyen er ganske sterk, men ved å kombinere flere luftkanoner kan vi dempe mye av boblestøyen. Det er imidlertid fortsatt en betydelig mengde boblestøy igjen i fjernfeltkildesignaturen, noe som forlenger signaturen og forstyrrer det seismiske bildet. Modellering og estimering av boblestøyen og fjerning av støyen er mulig, men utfordrende på grunn av det komplekse samspillet mellom hver luftboble. For å gjøre det mer komplekst, vil enhver endring i en luftboble også endre de andre luftboblene og følgelig endre boblestøyen observert i fjernfeltsignaturen. Som et resultat kan været, relative posisjoner til luftkanoner, luftkanondybde, dønninger, luftkanontrykk og luftkanoner som ikke skyter, endre kildesignaturen. Derfor kan signaturen endre seg fra skudd til skudd i løpet av en seismisk innsamling. Noen metoder prøver å estimere signaturen til et hvert skudd. Dette krever imidlertid betydelig kvalitetskontroll og testtid. De seismiske bølgene som reflekteres fra havoverflaten kalles seismiske "ghosts". Havoverflatens refleksjonskoeffisient er nær -1, noe som betyr at en ghost ankommer en hydrofon med et polaritetsskift og en liten tidsforsinkelse. Totalt observerer vi tre ghosts, kilde-ghost, mottaker-ghost og kilde-mottaker-ghost. Ghosts er problematiske fordi de forlenger og forvrenger det seismiske signalet og forårsaker hakk i frekvensspekteret, noe som reduserer oppløsningen. Derfor er det viktig å fjerne ghosts for å forbedre oppløsningen og signal-til-støy-forholdet, noe som er viktig før geologisk tolkning eller seismisk inversjon. Vi kan bruke forskjellige innsamlingsgeometrier for å dempe ghosts. Likevel kan ikke innsamlingsgeometrien alene løse ghosts-problemet. Derfor er det utviklet prosesseringsmetoder for å dempe ghosts. En ulempe med mange konvensjonelle metoder er at de krever kunnskap om kildeposisjonen, mottakerposisjoner og havoverflatens refleksjonskoeffisient. I en marin seismisk undersøkelse vil vi oppleve dønningsbølger, vær og feil kilde- og mottakerposisjoner. Mange konvensjonelle metoder er følsomme for noen av disse faktorene. I denne oppgaven beskriver jeg en alternativ tilnærming ved bruk av et konvolusjonelt nevralt nettverk for debobling og deghosting. For debobling trente jeg et nettverk på ekte data som inneholdt et omfattende utvalg av kildesignaturer for å gjøre nettverket robust for signaturvariasjoner. Hvis signaturen i prediksjonsdataene er lik en av signaturene i treningsdataene, fungerer nettverket bra. I tillegg kan nettverket tilpasse seg en endring i signaturen midt i en seillinje. Dessuten, hvis testdataene har lignende geologi som treningsdataene, yter nettverket bedre. Jeg brukte ekte data fra to steder på norsk sokkel for å teste denne metoden. For dehosting laget jeg treningsdata ved å bruke demigrering av et dybdemigrert bilde til skuddsamlinger med og uten ghosts. Jeg trenger kilde- og mottakerposisjonene og havoverflatens refleksjonskoeffisient for å lage demigrerte data med ghosts. Jeg forandrer disse parameterne for å skape variasjon i ghosts-modellen, noe som gjør nettverket mer robust. På syntetiske data demonstrerer jeg robustheten til den nye metoden overfor variasjoner i dønninger og hav-overflate-refleksjonskoeffisienter. Jeg har utviklet en metode for kun trykk-deghosting og to-komponent deghosting. Begge fungerer godt på reelle data sammenlignet med konvensjonell deterministisk deghosting basert på minste kvadraters inversjon i tau-p domenet og P-Vz summering.The recent availability of powerful GPUs and open-source software have enabled artificial neural networks to solve several practical and industrial problems. We can apply neural networks to critical seismic processing steps such as swell noise attenuation, seismic interference attenuation, debubbling, deghosting, and deblending. Today, many of these processing steps involve significant testing and computational time. A neural network has the potential of reducing testing and computational time. In addition, neural networks can produce good results and be robust to changes in the input data. During the last decade, significant advancements in neural networks structures have been made and this development will most likely increase in the future. An array of air guns is one of the most used sources in a marine seismic acquisition. Air guns have many advantages, which is why they are so commonly used. However, there are a few challenges when using air guns. The air-gun release an air bubble that oscillates and creates bubble noise after the first expansion. The bubble is quite strong compared to the peak, but combining multiple air guns can attenuate much of the bubble noise. However, a significant amount of bubble noise is still left in the far-field source signature, which elongates the signature and disturbs the seismic image. Modeling or estimating the bubble noise and removing it is possible but challenging because of the complex interaction between each air bubble. To add more complexity, any change in one air bubble will also change the other air bubbles and consequently change the bubble noise observed in the far-field signature. As a result, weather, relative positions of air guns, air gun depth, swell waves, air gun pressure, and air guns not firing can change the source signature. Therefore, the signature could change from shot to shot over a full survey. Some methods try to estimate the signature on each shot. However, this requires significant quality control and testing time. The seismic waves reflected from the sea surface are called seismic ghosts. The sea surface reflection coefficient is close to -1, meaning that the ghost arrives with a polarity shift and a small time delay. In total, we observe three ghosts, the source ghost, the receiver ghost, and the source-receiver ghost. Ghosts are problematic because they elongate and distort the seismic signal and cause notches in the frequency spectrum, reducing the temporal resolution. Therefore, it is important to remove the ghosts to improve the resolution and signal-to-noise ratio, which is important before geological interpretation or seismic inversion. We can use different acquisition geometries to attenuate the ghosts. However, the acquisition geometry alone cannot solve the deghosting problem. Therefore, processing methods have been developed to attenuate the ghost. One disadvantage of many conventional methods is that they require knowledge of the source, receiver positions, and sea surface reflection coefficient. However, marine seismic surveys include swell waves, weather, and incorrect source and receiver positions. Many conventional methods are, therefore, sensitive to any of these factors. In this thesis, I describe an alternative approach using a convolutional neural network for debubbling and deghosting. For debubbling, I trained a network on real data containing an extensive range of source signatures to make the network robust to signature variations. If the signature in the prediction data is equal to one of the signatures in the training data, the network performs well. In addition, the network can adapt to a change in the signature in the middle of a sail line. Moreover, if the test data have similar geology to the training data, the network performs better than if not. I used real data from two locations on the Norwegian Continental Shelf to test this method. However, for deghosting, I created training data using demigration of stacked depth migrated images into shot gathers with and without ghosts. I need the source and receiver positions and the sea surface reflection coefficient to create demigrated data with ghosts. I perturb these parameters to generate variability in the ghost model, which makes the network more robust. On synthetic data, I demonstrate the robustness of the new method to variations in swells and sea-surface reflection coefficients. I have developed a method for pressure-only deghosting and dual-component deghosting. Both work well on real data when compared to conventional deterministic deghosting based on least-squares inversion in the tau-p domain and P-Vz sum.Doktorgradsavhandlin

Comparison / Sensitivity Analysis of Various Deghosting Methods

Conventional marine data is acquired by towed streamers and air-gun sources deployed at small distances below the sea level. A towed hydrophone records both upward travelling waves as well as downward travelling waves including the receiver ghost which reflects from the sea-surface and therefore changes its polarity. Every subsurface reflection is disturbed by the ghost. Ghost reflections interfere with the primary reflections and distort the frequency spectrum of the recorded seismic data. Spectral notches are introduced at different frequencies depending upon the towed streamer depth, affecting the bandwidth of the data. To minimize the ghost effect least-squares filtering can be applied. However these methods do not introduce any new information and thus do not fundamentally change the poor signal to noise ratio at these notch frequencies. So in order to suppress the effect of the receiver ghost additional data is needed. This includes acquisition using two streamers placed at different depths or alternatively using a dual-sensor streamer including particle velocity sensors. The main objective of this thesis is to investigate the performance of different deghosting techniques applied to real data acquired using conventional streamer, over/under towed streamer and dual-sensor streamer

Finite-difference modelling of wavefield constituents

The finite-difference method is among the most popular methods for modelling seismic wave propagation. Although the method has enjoyed huge success for its ability to produce full wavefield seismograms in complex models, it has one major limitation which is of critical importance for many modelling applications; to naturally output up- and downgoing and P- and S-wave constituents of synthesized seismograms. In this paper, we show how such wavefield constituents can be isolated in finite-difference-computed synthetics in complex models with high numerical precision by means of a simple algorithm. The description focuses on up- and downgoing and P- and S-wave separation of data generated using an isotropic elastic finite-difference modelling method. However, the same principles can also be applied to acoustic, electromagnetic and other wave equation