advertisement

Multiscale Seismology: the future of inversion W. MENKE Lamont-Doherty Earth Observatory Columbia University E. CHESNOKOV and R.L. BROWN Institute for Theoretical Geophysics University of Oklahoma Thesis The past 15 years has seen a tremendous improvement in the fidelity of many types of seismic images. This improvement was driven by, more and higher quality seismometers, faster computers, better data archiving and processing methodologies. But our ability to integrate knowledge from multiple data types hasn’t kept up. Often different data types are telling us seemingly-contradictory things. Example: Surface Wave Tomography e.g. of North America Woodward and Snieder, 1993 275 seismograms Zhang & Tanimoto, 1993 18,000 seismograms globally, about 324 prorated for area 685 seismograms 400,000 seismograms globally, about 7,200 prorated for area note inversion includes transverse anisotropy “Exponential” growth of data ! Images better & better in evolutionary way But how do these impressive images connect with other things we know about the earth? Connection 1 Continental Scale Body Wave Traveltimes Surface wave models have big asthenospheric LVZ’s that imply very large shadow zones Are such shadows actually observed in continental-scale P or S waves? Connection 2 SKS Shear Wave Splitting North America has large amount of transverse anisotropy From Gaherty Correctly predicts large LoveRayleigh discrepancy along paths parallel to MOMA Array Predicts But inconsistent with SKS splitting results along MOMA array Fouch’s splitting data as plotted by Gaherty No plausible anisotropic material can have fast-axis parallel to array and have large Love-Rayleigh discrepancy parallel to the array, too More overlap in parameters than length scale ! Hypothesis: different length scales strongly influence interpretation Length scale of Length scale of EARTH OBSERVATION (seismic waves) Strong Spooky Interactions Length scale of INVERSION Length scale of Length scale of EARTH OBSERVATION strong spooky interactions (seismic waves) we understand this interaction pretty well (but only in very idealized media) WHAT IS THE STRUCURE OF THIS MEDIUM ? bulk modulus 1 μ, ρ, shear modulus 1, density 1 thickness 1 μ, ρ, bulk modulus 2 shear modulus 2 density 2 thickness 2 WHAT IS THE STRUCTURE OF THIS MEDIUM ? Inhomogenous with various properties of isotropic layers? YES ! when l<<thickness WHAT IS THE STRUCTURE OF THIS MEDIUM ? Strongly Scattering? YES ! when l~thickness WHAT IS THE STRUCURE OF THIS MEDIUM ? Effectively homogeneous and anisotropic? YES ! when l>>thickness Theory for understanding this effect in 3-D media with random heterogeneities is well developed … Elasticity and density written in terms of average and deviation from average 1. elasticity and density are frequency-dependent weird! effective density is a tensor .. • Chesnokov et al. 2000 2. integrals embody interaction of wavefield with scale length of heterogeneities … 3. … through correlation functions “UPSCALING” Example Reconciling Sonic Log with VSP Collect Sonic Logs (500 Hz) of Vp, Vs1, Vs2, density Infer all components of Cijkl(f=500 Hz) Compute Correlation Functions Predict Cijkl(f=50 Hz) Compare with VSP (50 Hz) experiment Is this Inversion? Not quite … Result for C55 Sonic Log VSP Predicted VSP theory can be extended to include more complicated microphysics e.g. fluid/rock interactions There’s been some interesting efforts on this side of the triangle, too Length scale of INVERSION Length scale of OBSERVATION Results of Slip Inversions Highly Dependent on Scale of Model Representation True Slip on Hypothetical Fault Three Inversions That Fit the Data Equally Well Courtesy of Morgan Page A Challenge of the Future Create Earth knowledge that practitioners using different techniques AGREE UPON ! Length scale of Length scale of EARTH OBSERVATION (seismic waves) Joint Inversions that handle multiple scales in a Physics-Based way Length scale of INVERSION Business as Usual Scale1 Data Ad-hoc parameterization 2 Speculative attempts to integrate and reconcile results Scale1 Data Scale2 Data physics-based parameterization 1 feedback Ad-hoc parameterization 1 Scale2 Data The Future ? Assessment of underlying physics Improved knowledge of earth confusion ?!