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The D-bar method for electrical impedance tomography requires the computation of an intermediate function known as the scattering transform from the measured data. An approximation to the scattering transform utilizing the standard Green's function for the Laplacian was introduced for the 2D D-bar method in Mueller and Siltanen (2003 SIAM J. Sci. Comp. 24 1232-66) and tested on simple numerically simulated conductivity distributions. In this work, the approximation is implemented for experimental data for the first time. It is tested on both tank and human chest data, and the results demonstrate decreased blurring toward the boundary in the images than in images computed with the t(exp) approximation to the scattering transform.

Multicomponent seismic data, combining P-wave and converted P-to-SV wave (C-wave) wavefields, provide independent measurements of rock and fluid properties. Unlike P waves, C waves are minimally affected by changes in pore fluids, and in cases of azimuthal anisotropy, will be split into two modes (fast and slow) with differing polarization. The 4C, 3D ocean-bottom cable (OBC) multicomponent seismic data discussed here were acquired in shallow water (<300 ft) offshore Louisiana over approximately 455 miles 2 (Figure 1). Because these data are still being marketed to interested oil and gas operators, only data above targeted oil and gas reservoirs (<5000 ft) were used. The P-wave migrated data extend to only 1 s and the C-wave migrated data volume to only 2 s. The initial objectives of the survey were to improve Pwave reflection quality by combining hydrophone and vertical-geophone data and to improve structural interpretation in the presence of "gas clouds" with C-wave data. Our additional research objectives were to evaluate seismic attributes, such as V P /V S velocity ratios and Poisson's ratio derived from

Conventional P-P seismic images of geothermal reservoirs are often of poor quality because P-P data tend to have a low signal-to-noise ratio across geothermal prospects. Fracture identification, fluid prediction, and imaging inside subsurface areas influenced by superheated fluids are some of the challenges facing the geothermal industry. We showed that multicomponent seismic technology is effective for addressing all of these challenges across geothermal reservoirs, even when P-P data are of low quality. Although multicomponent seismic technology has advantages in geothermal exploration, there are not many published examples of multicomponent seismic data being used to characterize geothermal reservoirs. We evaluated data examples that illustrate advantages of multicomponent seismic technology for imaging within and below zones having superheated fluids, estimating fracture attributes, analyzing reservoir trapping structures, differentiating lithologies, and predicting spatial distributions of pore fluids. All examples we tested are from the Wister geothermal field in Southern California. IntroductionConventional P-wave (P-P) seismic technology often does not provide information that engineers need to optimize geothermal energy production, usually because P-P data have a low signal-to-noise ratio (S/N) in numerous geothermal environments. The reasons why P-P data have low S/N values across geothermal systems vary from prospect to prospect, but common causes are complex faulting, high attenuation of P-waves in zones having concentrations of superheated fluids in rock pores, and dramatic lateral variations in P-wave velocities in geothermal strata.Joint interpretations of P-wave and S-wave data across oil and gas prospects provide more information about subsurface structures, lithology distributions, and pore-fluid saturants than do interpretations of only P-P data (Stewart, 2010). Fundamentally, S-wave seismic data have equal value to P-wave data in geologic interpretations, which leads to the conclusion that seismic stratigraphy analyses in any geologic province should be based on joint interpretations of P and S data rather than restricting interpretation to only single-component P-wave data (Hardage et al., 2011). Our reason for publishing this work is to provide a case history that emphasizes the importance of joint interpretations of P and S data across geothermal areas. Our study uses 3D converted-S (P-SV) data acquired at Wister geothermal