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XWITM Revolutionary Earth Model Building on the Cloud

What is FWI?

The FWI optimisation feedback loop makes simulations match reality by: 

  • modelling the field experiment with a partial differential equation (PDE) which honours the true physics of wave propagation
  • scoring the alignment between predictions and real world sensor measurements
  • solving a constrained PDE optimisation to find the direction which adjusts the existing model to improve the score
  • using correctly chosen cost functions for global minimum convergence to gain an unprecedented increase in velocity resolution and accuracy

XWITM optimisation framework

XWITM is the industry's leading Full Waveform Inversion (FWI) velocity optimisation algorithm due to:

  • Combining proprietary Adaptive Waveform Inversion (AWITM) and generic Full Waveform Inversion (FWI) optimisation cost functions for robustness against cycle skipping without low frequencies in the field data
  • Using least-squares Reverse Time Migration (RTM) to generate deep reflectors with which to update the macro-velocity model to deeper depths below the diving wave zone with Reflection Waveform Inversion (RWI)
  • Applying total variation (TV) and asymmetric total variation (ATV) regularisation for Constrained Waveform Inversion (CWI)
S-Cube Dashboard showing global convergence using XWI with AWI.

S-Cube Dashboard showing global convergence using XWI with AWI.

S-Cube Dashboard showing local minimum convergence using XWI without AWI

S-Cube Dashboard showing local minimum convergence using XWI without AWI

Why use XWITM on the Cloud?

The S-Cube Cloud hosted on Amazon Web Services (AWS) enables you to access the industry's leading Full Waveform Inversion algorithm on demand in a managed compute environment configured for data parallel wave equation velocity optimisation. It consists of the following components:                                                                                                   

  • XWITM: S-Cube’s cloud-native fork of FULLWAVE3D FWI + additional methodologies such as AWI, CWI, RWI ...
  • S-Cube Cloud: Cloud-based HPC scheduler/worker distributed memory architecture for large-scale parameter-learning PDE-constrained optimisation          
  • S-Cube Cloud Dashboard: web-based dashboard for real-time result visualisation ( and hyper-parameter tuning

It enables you to:

  • Upload your raw field shot gathers to a dedicated AWS S3 bucket with a few commands
  • Parameterise a new job and view the computational workload per shot
  • Select the number of shots per iteration and number of iterations to run
  • Monitor and QC the live job on the S-Cube Cloud dashboard accessed via a URL secured utilising Auth0

Some Concrete Numbers

In this NW Australia deployment, the runtime per iteration is below 30 mins by limiting the number of shots per worker instance down to two.

  • 5186 total shots (9 saillines)
  • Grid size = 25m, nx = 113, ny =1041, nz=161  
  • 20% shots per iterations
  • 550 x 16 core EC2 instances
  • 2 shots per worker instance
  • Average runtime per shot = 14 mins
  • Runtime per iteration = 28 mins

This gives a 5x runtime saving compared to a typical on premises cluster of 100 nodes.

S-Cube Cloud Architecture

We're experts in making FWI work in the cloud. We make heavy use of commodity cloud hardware fleets to minimise cost and maximise burst availability, while adapting a variety of robust cloud-native AWS services to fill some capabilities that traditional HPC systems served.

The stability and performance of cloud-native data storage systems allows acceptable replacement of broadcasting operations for the FWI orchestrator with minimal code change through smartly architected adaptor layers. This means we keep our scientific code doing what it does best, but also raise the bar on all operational aspects around it as well, massively increasing the stability, capability, observability, and burst capacity without needing heavy capital investment.

S-Cube Cloud

Run FWI on the Cloud

Discover an unprecedented increase in the resolution of your velocity model.