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S-Cube delivering breakthrough X-Waveform-Inversion (XWITM) technology. 

Developing the industry's leading sound waveform algorithm.
Differentiated cost functions to uncover every detail. Turn dark data into smart data.
Laying the foundations for a Net Zero, integrated offshore Energy Transition.

S-Cube Intro

We are a high-growth venture spinout from a world-leading research group at Imperial College London, known for developing revolutionary technology which combines state-of-the-art soundwave segregation techniques with sophisticated wavefield simulation and computational optimisation. 

X-Waveform-Inversion (XWITM) unravels raw soundwaves from the earth to automatically reveal new understanding of the subsurface. It specifically targets sound waves reflected from the deepest interfaces within the earth where economically valuable natural resources lie hidden close to existing infrastructure.


S-Cube Growth and Energy Transition Milestones

Our growth is fully directed to matching the needs of the energy transition and laying the foundations for an integrated, decarbonised offshore energy system for the North Sea and beyond.

Progress in XWITM has centred on:

  • an adaptive optimisation toolbox for inverting reflected soundwaves at greater depths
  • a scalable cloud-native parallelisation to run the algorithm at high-speed 
  • an intelligent user-interface for automated integrated workflows
2015- Theoretical Breakthroughs

2015- XWI Breakthroughs

Differentiated capability in sound segregation through advanced mathematical loss-function formulations which lead to the best possible predictive capability predicting unseen drilling logs. It is the continued innovations from Adaptive Waveform Inversion onwards which have set S-Cube’s soundwave segregation apart from day one.

2018: On Prem to Cloud

2018- Cloud Transition

Taking the leap beyond homogenous constrained on-prem systems. S-Cube were able to uniquely engineer AWS tools and capabilities to run XWITM cloud-natively at scale with a large dynamic pool of EC2 Spot compute. This enabled S-Cube to apply XWITM at scale and apply deep learning alongside classical optimisation to generate the best possible seismic image.

2019: First Cloud Pilots

2019- First Cloud Pilots

A collaboration between S-Cube, AWS and the operator to stress test the XWITM cloud architecture. This saw XWITM applied on a subset of a 2500km2 survey in a prolific activity hub in the central Atlantic. XWITM was used to keep all the data on the cloud, performing a frequency sweep going as high as necessary to get the required detail to unlock new understanding.

2019-: AWI and RWI Pilots

2019- AWI and RWI Pilots

Maintaining a laser focus on optimisation toolbox and uniquely-engineered cost function development through a series of pilots. Technology which proved discoveries but discoveries which proved the technology. Enhanced pre-drill predictive power, better decision-making and ready to transform the way operators do business.

2020: Intelligent UI

2020- Integrated Innovation

Taking XWITM to the next level with a differentiated UI and integration with off-the-shelf E&P platforms. Get access to results faster. Collaborate seamlessly. Start a new job with only an internet connection. Run leading software from your laptop. Start iterating the weights within minutes of plugging in inputs. Combine the best of human and machine intelligence with the best optimisation toolbox.

2020: OGTC North Sea

2020- Net Zero North Sea

S-Cube receive multi-million-pound grant as part of the Net Zero North Sea programme where XWITM will be used to optimally calibrate existing well infrastructure where current technology has failed. Partnering with AWS, S-Cube will deploy the optimisation toolbox to high-frequencies in this infrastructure-led project, paving the way for regional productivity gains, reduced footprint activity and repurposing of existing infrastructure for CCUS or hydrogen production.


Automated Velocity Model Building

XWITM is regarded as the industry's leading Full Waveform Inversion formulation due to:

  • integrating adaptive (AWI) and reflection (RWI) cost functions, for more accurate model updates extending deeper down
  • gradient-conditioning using total-variation based regularisation constraints (CWI), for dealing with highly contrasting geology
  • multi-parameter macro-model global search (QPSO), for deriving anisotropy and other unknowns 

The outcome is more predictive power and more automation starting further away from the true answer with a very wrong initial guess.

XWI Field Trials

Adaptive Waveform Inversion (AWI) was the theoretical breakthrough which led to the formation of S-Cube. Following the patented solution to a long standing problem in numerical optimisation, it was field trialed in 3 separate settings.

It was able to demonstrate automated global minimum convergence (prediction error minimisation) with gradient-based steepest-descent optimisation from multiple starting points without the need for human intervention.

In each case, false local minimum convergence was avoided without the need for human-driven tomography.

Chevron- NW Australia

Chevron- NW Australia

Characterising a gas-filled low velocity producing field.

Equinor- North Sea

Equinor- North Sea

Defining a gas-filled low velocity drilling hazard.

Woodside- Myanmar

Woodside- Myanmar

Identifying a gas-filled low velocity exploration target.

Backed by E&P operators and major software providers for superior Full Waveform Inversion 

XWITM on AWS PoC Success

1000's of EC2 Spot workers

Burst Compute Capacity

Multiple Spot Fleets running in parallel

Four pillars of differentiated capability:



Predictive Power


Cloud-native architecture:

MPI-free parallelisation

Multi-scenario job submission

Parameter hyper-tuning

Woodside FutureLab

Woodside discusses the benefits of Full Waveform Inversion in Myanmar. 


Industry Awards and Measures of Esteem

Academic founders internationally recognised as leaders in the field

Distinguished Awards

The first major application of time-domain waveform inversion incorporating anisotropic wave propagation was conducted by founding members of S-Cube, a study which received the 2013 Best Paper in Geophysics.

Just a few years later, Adaptive Waveform Inversion (AWI) was patented and became the recipient of the Bonarelli Award in 2015 by the EAGE, the leading European professional organisation in petroleum geoscience for the long-standing problem in localised optimisation it was able to provide a solution for. 

AWI was the start of a non-stop development and growth in the story of S-Cube. This has paved the way for S-Cube to gain technical and business recognition for commercial achievement and innovation at the forefront of the energy sector.

2013 Best Paper in Geophysics

2013 Best Paper in Geophysics

S-Cube founders are recognised by The Society of Exploration Geophysics Best Paper Award titled "Anisotropic 3D Full Waveform Inversion" 

2015 Guido Bonarelli Award

2015 Guido Bonarelli Award

S-Cube is recognised by the European Association of Geoscientists and Engineers Guido Bonarelli Award 2015 for The Best Oral Presentation - "Adaptive waveform inversion - FWI without cycle skipping - theory".

2019 - Young Entrepreneur of the Year 2019

2019 - Young Entrepreneur of the Year

Non stop growth. Our CEO, Nikhil Shah named Young Entrepreneur of the Year 2019 at the UK Asian Business Awards.

2020 Artificial Intelligence Award

2020 Artificial Intelligence Award

S-Cube is shortlisted as a finalist for the Lloyds National Business Awards 2020 - The Artificial Intelligence Award.

2020 - Data Excellence Award

2020 - Data Excellence Award

S-Cube is shortlisted as a finalist for the Lloyds National Business Awards 2020 - The Data Excellence Award.

2020 - LCD Growth Through Innovation Award

2020 - LCD Growth Through Innovation Award

S-Cube is shortlisted as a finalist for the Lloyds National Business Awards 2020 - LCD Growth Through Innovation Award.

S-Cube Cloud

Run Full Waveform Inversion on the Cloud

Use XWITM on AWS to discover an unprecedented increase in the resolution of your velocity model.