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Abstract Raman spectroscopy is widely used to identify mineral and fluid inclusions in host crystals, as well as to calculate pressure-temperature (P-T) conditions with mineral inclusion elastic thermobarometry, for example quartz-in-garnet barometry (QuiG) and zircon-in-garnet thermometry (ZiG). For thermobarometric applications, P-T precision and accuracy depend crucially on the reproducibility of Raman peak position measurements. In this study, we monitored long-term instrument stability and varied analytical parameters to quantify peak position reproducibility for Raman spectra from quartz and zircon inclusions and reference crystals. Our ultimate goal was to determine the reproducibility of calculated inclusion pressures (“Pinc”) and entrapment pressures (“Ptrap”) or temperatures (“Ttrap”) by quantifying diverse analytical errors, as well as to identify optimal measurement conditions and provide a baseline for interlaboratory comparisons. Most tests emphasized 442 nm (blue) and 532 nm (green) laser sources, although repeated analysis of a quartz inclusion in garnet additionally used a 632.8 nm (red) laser. Power density was varied from <1 to >100 mW and acquisition time from 3 to 270s. A correction is proposed to suppress interference on the ~206 cm–1 peak in quartz spectra by a broad nearby (~220 cm–1) peak in garnet spectra. Rapid peak drift up to 1 cm–1/h occurred after powering the laser source, followed by minimal drift (<0.2 cm–1/h) for several hours thereafter. However, abrupt shifts in peak positions as large as 2–3 cm–1 sometimes occurred within periods of minutes, commonly either positively or negatively correlated to changes in room temperature. An external Hg-emission line (fluorescent light) can be observed in spectra collected with the green laser and shows highly correlated but attenuated directional shifts compared to quartz and zircon peaks. Varying power density and acquisition time did not affect Raman peak positions of either quartz or zircon grains, possibly because power densities at the levels of inclusions were low. However, some zircon inclusions were damaged at higher power levels of the blue laser source, likely because of laser-induced heating. Using a combination of 1, 2, or 3 peak positions for the ~128, ~206, and ~464 cm–1 peaks in quartz to calculate Pinc and Ptrap showed that use of the blue laser source results in the most reproducible Ptrap values for all methods (0.59 to 0.68 GPa at an assumed temperature of 450 °C), with precisions for a single method as small as ±0.03 GPa (2σ). Using the green and red lasers, some methods of calculating Ptrap produce nearly identical estimates as the blue laser with similarly good precision (±0.02 GPa for green laser, ±0.03 GPa for red laser). However, using 1- and 2-peak methods to calculate Ptrap can yield values that range from 0.52 ± 0.06 to 0.93 ± 0.16 GPa for the green laser, and 0.53 ± 0.08 GPa to 1.00 ± 0.45 GPa for the red laser. Semiquantitative calculations for zircon, assuming a typical error of ±0.25 cm–1 in the position of the ~1008 cm–1 peak, imply reproducibility in temperature (at an assumed pressure) of approximately ±65 °C. For optimal applications to elastic thermobarometry, analysts should: (1) delay data collection approximately one hour after laser startup, or leave lasers on; (2) collect a Hg-emission line simultaneously with Raman spectra when using a green laser to correct for externally induced shifts in peak positions; (3) correct for garnet interference on the quartz 206 cm–1 peak; and either (4a) use a short wavelength (blue) laser for quartz and zircon crystals for P-T calculations, but use very low-laser power (<12 mW) to avoid overheating and damage or (4b) use either the intermediate wavelength (green; quartz and zircon) or long wavelength (red; zircon) laser for P-T calculations, but restrict calculations to specific methods. Implementation of our recommendations should optimize reproducibility for elastic geothermobarometry, especially QuiG barometry and ZiG thermometry. 

Scientific applications, especially legacy applications, contain a wealth of scientific knowledge. As hardware changes, applications need to be ported to new architectures and extended to include scientific advances. As a result, it is common to encounter problems like performance bottlenecks and dead code. A visual representation of the dataflow can help performance experts identify and debug such problems. The Computation API of the sparse polyhedral framework (SPF) provides a single entry point for tools to generate and manipulate polyhedral dataflow graphs, and transform applications. However, when viewing graphs generated for scientific applications there are several barriers. The graphs are large, and manipulating their layout to respect execution order is difficult. This paper presents a case study that uses the Computation API to represent a scientific application, GeoAc, in the SPF. Generated polyhedral dataflow graphs were explored for optimization opportunities and limitations were addressed using several graph simplifications to improve their usability. 

SUMMARY Estimation of ambient seismic source distributions (e.g. location and strength) can aid studies of seismic source mechanisms and subsurface structure investigations. One can invert for the ambient seismic (noise) source distribution by applying full-waveform inversion (FWI) theory to seismic (noise) crosscorrelations. This estimation method is especially applicable for seismic recordings without obvious body-wave arrivals. Data pre-processing procedures are needed before the inversion, but some pre-processing procedures commonly used in ambient noise tomography can bias the ambient (noise) source distribution estimation and should not be used in FWI. Taking this into account, we propose a complete workflow from the raw seismic noise recording through pre-processing procedures to the inversion. We present the workflow with a field data example in Hartoušov, Czech Republic, where the seismic sources are CO2 degassing areas at Earth’s surface (i.e. a fumarole or mofette). We discuss factors in the processing and inversion that can bias the estimations, such as inaccurate velocity model, anelasticity and array sensitivity. The proposed workflow can work for multicomponent data across different scales of field data.