Michael E. Glinsky

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We introduce a new open-source toolkit for model-based Bayesian seismic inversion called Delivery. The prior model in Delivery is a trace–local layer stack, with rock physics information taken from log analysis and layer times initialised from picks. We allow for uncertainty in both the fluid type and saturation in reservoir layers: variation in seismic(More)
We introduce a new open-source toolkit for the well-tie or wavelet extraction problem of estimating seismic wavelets from seismic data, time-to-depth information, and well-log suites. The wavelet extraction model is formulated as a Bayesian inverse problem, and the software will simultaneously estimate wavelet coefficients, other parameters associated with(More)
We introduce a new open-source program for transforming inversion data from the open-source Delivery seismic inversion software to industry-standard cornerpoint grid formats suitable for reservoir modelling and flow simulations. The seismic inversion data produced by Delivery is an array of trace-local stochastic samples from a Bayesian posterior(More)
We describe algorithms for automating the process of picking seismic events in prestack migrated common depth image gathers. The approach uses supervised learning and statistical classification algorithms along with advanced signal/image processing algorithms. No model assumption is made such as hyperbolic moveout. We train a probabilistic neural network(More)
Mainstream object-oriented languages such as Java and C#, through the use of object-oriented abstractions and managed runtimes (virtual machines), have significantly improved productivity and portability in multiple application domains. However, despite many attempts in the past, the effect of these improvements on high-performance numeric computations has(More)
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