助理教授 W

DESCRIPTION (topics, expectations, comments): Job Description The candidate will join the group carrying out the NCN SOATA BIS-14 project – „Optimization of Ocean-Bottom Seismometer Acquisition for Enhanced Crustal-Scale Seismic Imaging”, Agreement nr UMO- 2024/54/E/ST10/00204 The core scientific problem this project seeks to address is the need to evolve the current paradigm of how OBS data are acquired during academic seismic surveys and how they are routinely processed.

A key limitation of these surveys (from the perspective of modern processing techniques such as full-waveform inversion - FWI) is the sparse receiver deployments, which result in insufficient illumination of the target, as well as a lack of multi-azimuthal coverage from typical 2D profiles.

This situation persists despite the availability of well-established imaging algorithms supported by advanced computing power, which are capable of generating high-fidelity regional geological models but cannot be fully exploited without optimized OBS data.

From a seismic survey perspective, "optimal OBS acquisition" refers to a set of key parameters - such as survey geometry, source/receiver configurations, etc. - that will allow for the best possible reconstruction of the target.

Therefore, it must be tailored to the method, target, and available resources.

But is it possible to determine these acquisition parameters based on limited a priori knowledge of the subsurface?

How should the geometry of the acquisition change when considering ray-based traveltime tomography (TT) versus wave-based FWI?

In what scenarios might a sparser 3D OBS deployment be preferable to a densely sampled 2D profile?

Conversely, we can consider optimizing OBS acquisition at the data processing stage.

Here, the objective is to fine-tune the processing approach so that it makes the best use of the existing dataset to reconstruct the target within the available computational resources.

Therefore, is it possible to design a data-selection or data-weighting approach that achieves optimal or uniform subsurface sampling for a given processing method?

Moreover, could the dataset be "sparsified" to optimize subsurface sampling while minimizing the computational load of large-scale regional models?

How much could compressive sensing and advanced regularization techniques help reduce the data volume that needs to be processed?

Answering these questions and finding associated solutions are the primary scientific goals of this project.

The position is for an Assistant Professor (W/M) in the Department of Seismic Research.

Candidates should have a strong background in Earth Sciences, with expertise in seismic data inversion, offshore seismic data, seismic imaging, and velocity model-building.