Geostatistical characterisation of internal structure of mass-transport deposits from seismic reflection images and borehole logs

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Jonathan Ford , Angelo Camerlenghi


Seismic reflection images of mass-transport deposits often show apparently chaotic, disorded or low-reflectivity internal seismic facies. The lack of laterally coherent reflections can prevent horizon-based interpretation of internal structure. This study instead inverts for geostatistical parameters which characterise the internal heterogeneity of mass-transport deposits from depth-domain seismic reflection images. A Bayesian Markov Chain Monte Carlo inversion is performed to estimate posterior probability distributions for each geostatistical parameter. If the internal heterogeneity approximates an anisotropic von K\{a}rm\{a}n random medium these parameters can describe the structural fabric of the imaged mass-transport deposit in terms of lateral and vertical dominant scale lengths and the Hurst number (roughness). To improve the discrimination between vertical and lateral dominant scale lengths, an estimate of the vertical dominant scale length from a borehole is used as a prior in the inversion. The method is first demonstrated on a synthetic multi-channel seismic reflection image. The vertical and lateral dominant scale lengths are estimated with lower uncertainty when data from a synthetic borehole data are included. We then apply the method to a real data example from Nankai Trough, offshore Japan, where a large mass-transport deposit is imaged in a seismic profile and penetrated by a borehole. The results of the inversion show a downslope shortening in lateral scale length, consistent with progressive down-slope disaggregation of the mass-flow during transport. The dominant scale lengths can be used as a proxy for strain history, which can improve understanding of post-failure dynamics and emplacement of subacqueous mass-movements, important for constraining the geohazard potential from future slope failure.



Earth Sciences, Geophysics and Seismology, Physical Sciences and Mathematics


geostatistics, submarine landslides, Bayesian inversion, fractals


Published: 2019-07-24 07:46

Last Updated: 2019-12-18 01:05

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CC BY Attribution 4.0 International

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