M. Warner’s research while affiliated with Imperial College London and other places

Conventional seismic velocity model building in complicated salt-affected areas requires the explicit identification of salt boundaries in migrated images and typically involves testing of possible subsurface scenarios through multiple generations. The resulting velocity models are slow to generate and may contain interpreter-driven features that are difficult to verify. We show that it is possible to build a full final velocity model using advanced forms of full-waveform inversion applied directly to raw field data, starting from a model that contains only a simple 1D compaction trend. This approach rapidly generates the final velocity model and migrates processed reflection data at least as accurately as conventionally generated models. We demonstrate this methodology using an ocean-bottom-node data set acquired in deep water over Walker Ridge in the Gulf of Mexico. Our approach does not require exceptionally long offsets or the deployment of special low-frequency sources. We restrict the inversion so it does not use significant energy below 3 Hz or offsets longer than 14 km. We use three advanced forms of waveform inversion to recover the final model. The first is adaptive waveform inversion to proceed from models that begin far from the true model. The second is nonlinear reflection waveform inversion to recover subsalt velocity structure from reflections and their long-period multiples. The third is constrained waveform inversion to produce salt- and sediment-like velocity floods without explicitly identifying salt boundaries or velocities. In combination, these three algorithms successively improve the velocity model so it fully predicts the raw field data and accurately migrates primary reflections, though explicit migration forms no part of the workflow. Thus, model building via waveform inversion is able to proceed from field data to the final model in just a few weeks. It entirely avoids the many cycles of model rebuilding that may otherwise be required.

Plain Language Summary Arc volcanoes, which mark the curved boundaries between converging tectonic plates, host the most explosive events on Earth. The associated hazard depends on how much mobile magma is currently present shallow beneath a volcano. Standard tomographic methods used so far have relatively low resolution and give a blurred picture of only the largest molten‐rock bodies. In particular, they struggle to distinguish between mobile magma and melt spread between tightly packed mineral grains. This study, a first in volcanology, combines a next‐generation tomographic method with extraordinarily dense seafloor recordings of controlled marine sound sources. This state‐of‐the‐art experiment at Kolumbo volcano, offshore of Santorini allowed us to detect a body of mobile magma which has been growing at an average rate of 4 × 10⁶ m³ per year since the last eruption in 1650 CE. This rate is large enough to counteract the effect of cooling and crystallization. Our results show that Kolumbo poses a serious threat and deserves a real‐time monitoring facility. Despite the excellent data coverage, the small magma body was missed by standard tomography. This suggests that applying next‐generation imaging methods to already‐well‐studied volcanoes may lead to similar discoveries. We envision that small‐volume, high‐melt‐fraction reservoirs may be more widespread than previously thought.

The main techniques used to image the brain and obtain structural data are magnetic resonance imaging and X-ray computed tomography. These techniques produce images with high spatial resolution, but with the disadvantage of requiring very large equipment with special installation needs. In addition, X-ray tomography uses ionizing radiation, which limits their use. Ultrasound imaging is a safe technology that is delivered using compact and mobile devices. However, conventional ultrasound reconstruction techniques have failed to obtain images of the brain because of, fundamentally, the presence of the skull and the distortion that it produces on ultrasound. Recent studies have indicated that full-waveform inversion, a computational technique originally from Earth science, has the potential to generate accurate 3-D images of the brain. This technology can overcome the limitations of conventional ultrasound imaging, but a prototype for transcranial applications does not yet exist. Here, we investigate different designs of an annular array of ultrasound transducers to optimize the number of elements and rotations needed to conduct transcranial imaging with full-waveform inversion. This device uses small-diameter, low-frequency transducers that readily propagate ultrasound through the skull with good signal-to-noise ratios. It also incorporates the use of rotations to produce a high-density coverage of the target and acquire redundant traces that are beneficial for full-waveform inversion. We have built a ring of 40 transducers to illustrate that this design is capable of reconstructing images of the brain, retrieving its anatomy and acoustic properties with millimeter resolution. Laboratory results reveal the ability of this device to successfully image a 2.5-D brain- and skull-mimicking phantom using full-waveform inversion. To our knowledge, this is the first prototype ever used for transcranial-like imaging. The importance of these findings and their implications for the design of a 3-D reconstruction system with possible clinical applications are discussed.

We demonstrate that purely acoustic FWI, run at true amplitude, can be used to extract accurate AVO parameters using offset-restricted subsets of raw unprocessed seismic data. Rather than producing conventional AVO parameters directly, acoustic FWI instead produces AVO anomalies showing the departure of the input data from the AVO displayed by a purely acoustic model. It is trivial to transform this into conventional AVO. AVO extraction via acoustic FWI applied to elastic synthetic data show that the AVO recovery using acoustic FWI is near perfect. When combined with full-bandwidth FWI, this approach removes any requirement for conventional data processing, model building, explicit migration or Kirchhoff-based AVO extraction. A final depth-migrated reflectivity volume, and accurate AVO parameters, can both be generated purely by acoustic FWI.