Adaptive waveform inversion (AWI) is one of a new breed of full-waveform inversion (FWI) algorithms that seek to mitigate the effects of cycle skipping (Warner & Guasch, 2016). The phenomenon of cycle skipping is inherent to the classical formulation of FWI, owing to the manner in which it tries to minimize the difference between oscillatory signals. AWI avoids this by instead seeking to drive the ratio of the Fourier transform of the same signals to unity. One of the strategies most widely employed by FWI practitioners when trying to overcome cycle skipping, is to introduce progressively the more nonlinear components of the data, referred to as multiscale inversion. Since AWI is insensitive to cycle skipping, we assess here whether this multiscale approach still provides an appropriate strategy for AWI. Presentation Date: Tuesday, September 26, 2017 Start Time: 3:05 PM Location: 361F Presentation Type: ORAL
Summary Several recent studies have established that seismic full-waveform inversion (FWI) can be used to generate interpretable models of acoustic reflectivity from practically raw seismic data. Owing to their use of the full wavefield and an iterative least-squares approach to optimisation, these models, referred to as FWI images, offer an improvement in image quality over conventional approaches to depth migration, such as Kirchhoff pre-stack depth migration. Furthermore, the ability of FWI – when combined with an appropriate objective function – to begin from a basic initial model and unprocessed data means that these images can begin to be built shortly after acquisition. The effectively limitless scale of public cloud compute allows for these workloads to then be turned around quickly, while reasonable costs can be maintained by leveraging spare capacity markets. In an exploration setting, the availability of high-quality FWI images soon after acquisition can aid in improved and faster decision-making. In this abstract, we demonstrate our proposed workflow using a large subset of a modern surface-streamer dataset that was recently acquired for exploration purposes. 45 and 60 Hz FWI images were generated within weeks of the survey concluding and prior to a conventional fast-track image being delivered.
Seismic full-waveform inversion (FWI) is a computationally intensive but embarrassingly parallel procedure, where production workloads are typically distributed across large volumes of high-performance computing (HPC) resources. In the absence of available local HPC resources, public cloud HPC is a popular alternative due to the almost limitless scale and pay-as-you-go models offered by most providers, although these benefits often entail increased costs. In this work, we introduce and discuss several strategies for making cloud-based FWI more affordable without sacrificing stability or efficiency.
Summary FWI has become a standard in velocity model building, however standalone FWI has not. To address this, FWI is brought into the model building sequence earlier by alternating RWI and AWI to recover the long-wavelength acoustic velocity model that is usually built by ray-based tomography. The corresponding long-wavelength anisotropy model is extracted using semi-global FWI. Least-squares FWI then has an adequate starting point to commence introducing the full range of length scales into the final model. The outcome is a high-resolution velocity model bypassing tomography, which penetrates over a kilometre deeper than the turning point of the deepest diving waves.
One of reverse time migration’s main limitations is that an unscaled adjoint operator is prone to produce images with low resolution, inaccurate amplitudes, and even artifacts. Least-squares reverse time migration (LSRTM) has been introduced to mitigate this inadequacy via the use of an approximation to an inverse operator. LSRTM suffers from its own limitations, most importantly from poor condition, which often manifests itself as image artifacts. One approach to ameliorate this issue is to constrain the optimization problem by introducing a penalty term to the cost function. Penalizing estimated parameters for sparsity is one such constraint that has been shown to be effective. A drawback of this technique is that it introduces a trade-off between data fitting and image sparsity. Furthermore, if using the Cauchy constraint, an additional trade-off is introduced due to the requirement to estimate a hyperparameter. We introduce an alternative approach that mitigates these trade-offs by combining a multiplicative cost function with an effective means for determining the Cauchy hyperparameter. We also introduce a new formulation of the multiplicative cost function that avoids over-penalization by the constraint via the introduction of a relaxation term. Finally, we seek to improve the computational efficiency by introducing a new approach for computing the step length. As such, our method introduces three novel aspects to constrained LSRTM: (1) a relaxed multiplicative cost function, (2) semiautomatic estimation of the Cauchy hyperparameter, and (3) efficient computation of the step length. We discuss the theory and implementation, followed by application to three synthetic data sets and a real ultrasonic data set. Given the presence of large salt bodies, elasticity, and noise, along with the directivity of piezoelectric ultrasonic transducers, these data sets provide a challenging test of the approach outlined. Results demonstrate that our method is robust in handling the challenges imposed by these scenarios.
Finite difference is the most widely used method for seismic wavefield modeling. However, most finite-difference implementations discretize the Earth model over a fixed grid interval. This can lead to irregular model geometries being represented by 'staircase' discretization, and potentially causes mispositioning of interfaces within the media. This misrepresentation is a major disadvantage to finite difference methods, especially if there exist strong and sharp contrasts in the physical properties along an interface. The discretization of undulated seabed bathymetry is a common example of such misrepresentation of the physical properties in finite-difference grids, as the seabed is often a particularly sharp interface owing to the rapid and considerable change in material properties between fluid seawater and solid rock. There are two issues typically involved with seabed modeling using finite difference methods: firstly, the travel times of reflections from the seabed are inaccurate as a consequence of its spatial mispositioning; secondly, artificial diffractions are generated by the staircase representation of dipping seabed bathymetry. In this paper, we propose a new method that provides a solution to these two issues by positioning sharp interfaces at fractional grid locations. To achieve this, the velocity model is first sampled in a model grid that allows the center of the seabed to be positioned at grid points, before being interpolated vertically onto a regular modeling grid using the windowed sinc function. This procedure allows undulated seabed bathymetry to be represented with improved accuracy during modeling. Numerical tests demonstrate that this method generates reflections with accurate travel times and effectively suppresses artificial diffractions.
Geoscience Visual Presentation G11 Full-waveform inversion (FWI) has evolved in recent years from a technique for recovering long- to intermediate-wavelength updates of acoustic overburden velocities as part of a broader model building workflow to a standalone tool for high-fidelity seismic imaging using raw seismic data – with ever greater resolution and ever more sophisticated physics being sought. In doing so, it has moved notably closer to fulfilling the vision of its original inventors in the late 70s and early 80s. In this work, two novel approaches to FWI along with enabling technologies for their effective deployment are considered. The first of these allows for robust long-wavelength updates to be generated from raw seismic data in the absence of diving wave energy, providing reliable estimates of acoustic velocity at depths equivalent to or greater than the longest offsets available in the data – thereby enabling more effective imaging with data of limited quality. The second allows for the estimation of elastic amplitude-versus-angle behaviour while still using an acoustic wave equation, thereby mitigating the significant computational burden typically associated with elastic FWI. Finally, it is demonstrated that these approaches can be deployed in a reliable and cost-effective manner via the considered use of public cloud resources, with learnings relevant for analogous massively parallel scientific applications. Case studies from two marine environments are provided for discussion, one being from deep-water US Gulf of Mexico and the other from intermediate water depths offshore Western Australia. To access the Visual Presentation click the link on the right. To read the full paper click here
Presented on Wednesday 22 May: Session 17 A monitoring trial of subsea distributed acoustic sensing (DAS) conducted in the marine waters of Australia is presented. This trial explores the concept of repurposing existing submarine telecommunications cables for remote monitoring of the environment and geophysical phenomena. The data were collected from a pre-existing fibre-optic cable, 50 km in length, that links two offshore hydrocarbon production platforms off the northwest coast of Australia. Initial data analyses confirmed the ability to detect underwater sounds from various sources, including marine animals (such as baleen whales), anthropogenic activities (such as vessels), and natural geophysical phenomena (such as earthquakes). The study underscores the efficacy of DAS for capturing and locating marine mammal vocalisations, specifically highlighting signals from pygmy blue whales – a species granted the highest protection status in Australia – and Omura’s whales, both of which migrate biannually through the offshore waters of Western Australia. These findings indicate the potential of subsea DAS for detecting and tracking marine fauna regionally. Moreover, they suggest its applicability for future monitoring in support of environmental impact assessments and the development of adaptive management strategies to prevent or minimise impacts on migratory whale species from offshore industries. To access the Oral Presentation click the link on the right. To read the full paper click here
Summary The case study presented in this work represents a subsea distributed acoustic sensing (DAS) monitoring field trial in Australian waters. The trial demonstrates how existing subsea telecommunications cables can be utilised for remote environmental and geophysical monitoring. The data were acquired in marine waters off the north-west coast of Australia, utilising an existing fibre-optic cable that is approximately 50 km in length and connects two offshore hydrocarbon production facilities (platforms). Preliminary analyses of the data acquired have verified the detection of underwater sounds produced by marine fauna (baleen whales), anthropogenic sources (vessels), and natural earthquakes. The study highlights the effectiveness of DAS for recording marine mammal sounds, showcasing pygmy blue whale and Omura's whale signals. The findings demonstrate (i) the potential for real-time detection and regional tracking of marine fauna via subsea DAS and (ii) the potential for future data acquisition to support offshore industry environmental impact assessments and adaptive management strategies to avoid or mitigate impacts to migratory whale species.
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