Computational technique used in geophysics to create high-resolution models of subsurface structures. #### Market Size of Geophysical Services - $14.4 billion (2021) - $21.4 billion (2031) ### Industry Use Cases - **Oil and Gas Exploration:** Aids in subsurface imaging and reservoir characterization. - **Seismic Imaging:** Improves high-resolution subsurface models for geological mapping and resource identification. - **Geothermal Energy:** Assists in geothermal exploration and reservoir assessment. - **Civil Engineering:** Helps assess subsurface conditions for construction and infrastructure projects. - **Environmental Studies:** Aids in mapping groundwater resources and environmental monitoring. #### Important Challenges 1. **Computational demands:** Requires significant computational resources and time. Current Supercomputers would take roughly 32 years to achieve 3D FWI. 2. **Nonlinearity:** Finding the best-fitting model is challenging due to multiple local minima. 3. **Initialisation:** Obtaining an accurate initial model can be difficult, especially in complex geology or data-limited regions. 4. **Incomplete data coverage:** Gaps or limited sensor deployment can hinder FWI convergence or result in inaccuracies. 5. **Frequency bandwidth:** Balancing frequency range for optimal resolution and accuracy is a challenge. 6. **Uncertain physics and assumptions:** Deviations from assumptions and inaccurate physics models introduce uncertainties. 7. **Sensitivity to noise:** FWI is sensitive to noise in seismic data, requiring robust noise mitigation techniques. #### Potential Quantum Opportunities 1. Hybrid Optimisation: To drive **faster convergence and better inversion results through optimising the parameter estimation** **process.**Potentially could be a Quadratic Unconstrained Binary Optimisation (QUBO) problem formulation, effective at overcoming local minima, obtaining better start points in the complex landscape and getting more optimal solutions. External Sources: 1. Application of Quantum Annealing Computing to Seismic Inversion [here](https://arxiv.org/abs/2005.02846). 2. Application of gate-based quantum computing to traveltime seismic inversions [here](https://www.researchgate.net/publication/361268181_Employing_gate-based_quantum_computing_for_traveltime_seismic_inversion). 2. Physical Hybrid Networks: To help develop **more accurate subsurface property estimation models,** considering their ability to handle noise, and extract relevant information by effectively taking advantage of the properties of Multi-Layer Perceptron (MLPs) and Variational Quantum Circuits (VQCs). Refer to our publication [here](https://arxiv.org/abs/2303.03227). #### Existing Solution Providers 1. [CGG](https://www.cgg.com/geoscience/subsurface-imaging/full-waveform-inversion): Offers FWI software solutions as part of their geoscience services and software portfolio. 2. [Ikon Science](https://www.ikonscience.com/): Offer advanced FWI algorithms and workflows to enhance subsurface imaging. 3. [Paradigm (Aspen tech)](https://www.aspentech.com/en/acquisition/subsurface-science-and-engineering)[:](https://www.aspentech.com/en/acquisition/subsurface-science-and-engineering) offers FWI capabilities within their seismic processing and imaging software suite. 4. [Seismic City](http://www.seismiccity.com/About.html): offer FWI solutions to enhance subsurface model building and reservoir characterization. 5. [Landmark (Halliburton):](https://www.halliburton.com/en/software) FWI software is designed to improve seismic imaging and reservoir characterization in the oil and gas industry. #quantum ![[Pasted image 20231004114333.png]] [[Reverse Time Migration]] [Lecture Notes](https://seiscope2.osug.fr/IMG/pdf/lectures_adobe_vers_9-2.pdf) [Seiscope](https://seiscope2.osug.fr/IMG/pdf/abstract_lecturefwi_operto_2013.pdf) ----