Massively-parallel electrical-conductivity imaging of hydrocarbonsusing the Blue Gene/L supercomputer

Large-scale controlled source electromagnetic (CSEM)three-dimensional (3D) geophysical imaging is now receiving considerableattention for electrical conductivity mapping of potential offshore oiland gas reservoirs. To cope with the typically large computationalrequirements of the 3D CSEM imaging problem, our strategies exploitcomputational parallelism and optimized finite-difference meshing. Wereport on an imaging experiment, utilizing 32,768 tasks/processors on theIBM Watson Research Blue Gene/L (BG/L) supercomputer. Over a 24-hourperiod, we were able to image a large scale marine CSEM field data setthat previously required over four months of computing time ondistributed clusters utilizing 1024 tasks on an Infiniband fabric. Thetotal initial data misfit could be decreased by 67 percent within 72completed inversion iterations, indicating an electrically resistiveregion in the southern survey area below a depth of 1500 m below theseafloor. The major part of the residual misfit stems from transmitterparallel receiver components that have an offset from the transmittersail line (broadside configuration). Modeling confirms that improvedbroadside data fits can be achieved by considering anisotropic electricalconductivities. While delivering a satisfactory gross scale image for thedepths of interest, the experiment provides important evidence for thenecessity of discriminating between horizontal and verticalconductivities for maximally consistent 3D CSEM inversions

Velocity Inversion by Coherency Optimization

We introduce an approach to velocity and reflectivity estimation based on optimizing the coherence of multiple shot-gather inversions of reflection seismograms. The resulting algorithm appears to avoid the severe convergence difficulties reported for output (nonlinear) least-squares inversion. We describe in detail an algorithm appropriate for the layered acoustic model, using the convolutional model of the plane-wave (p-tau) seismogram. We give theoretical and numerical evidence with both synthetic and field data sets that coherency optimization, as defined here, yields stable and reasonably accurate estimates of both velocity trend and reflectivity, by exploiting reflection phase moveout and amplitudes in a computationally efficient way. The approach may be modified to apply to determination of elastic models and source parameters as well as to determination of laterally heterogeneous acoustic models

Algorithmic Learning for Auto-deconvolution of GC-MS Data to Enable Molecular Networking within GNPS

Gas chromatography-mass spectrometry (GC-MS) represents an analytical technique with significant practical societal impact. Spectral deconvolution is an essential step for interpreting GC-MS data. No public GC-MS repositories that also enable repository-scale analysis exist, in part because deconvolution requires significant user input. We therefore engineered a scalable machine learning workflow for the Global Natural Product Social Molecular Networking (GNPS) analysis platform to enable the mass spectrometry community to store, process, share, annotate, compare, and perform molecular networking of GC-MS data. The workflow performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization, using a Fast Fourier Transform-based strategy to overcome scalability limitations. We introduce a “balance score” that quantifies the reproducibility of fragmentation patterns across all samples. We demonstrate the utility of the platform with breathomics analysis applied to the early detection of oesophago-gastric cancer, and by creating the first molecular spatial map of the human volatilome