Browsing by Author "Chen, Jianxiong"
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Item Application of Frequency-dependent Traveltime Tomography and Full Waveform Inversion to Realistic Near-surface Seismic Refraction Data(Society of Exploration Geophysicists, 2016) Chen, Jianxiong; Zelt, Colin A.We present a synthetic test that uses a workflow consisting of a new frequency-dependent traveltime tomography (FDTT) method to provide a starting model for full waveform inversion (FWI) for near-surface seismic velocity estimation from refraction data. Commonly used ray-theory-based traveltime tomography methods may not be valid in the near surface given the likelihood of relatively large seismic wavelengths compared to the length scales of heterogeneities that are possible in the near surface. FDTT makes use of the frequency content in the seismic waves in both the forward and inverse modeling steps. In this application to a near-surface benchmark model, the results show that FDTT can better recover the magnitude of velocity anomalies than infinite frequency (ray-theory) traveltime tomography (IFTT). FWI can fail by converging to a local minimum when there is an absence of sufficiently low frequency data and an accurate starting model, either of which, if present, can provide long-wavelength constraints on the inverted velocity model. Both IFTT and FDTT models can serve as adequate starting models for FWI. However, FWI produces significantly better results starting from the FDTT model as compared to the IFTT model when low frequency data are not available. The final FWI models provide wavelength-scale structures allowing for direct geologic interpretation from the velocity model itself, demonstrating the effectiveness of FDTT and FWI in near-surface studies given the modest experiment and data requirements of refraction surveys.Item Comparison of Full Wavefield Synthetics with Frequency-Dependent Traveltimes Calculated Using Wavelength-Dependent Velocity Smoothing(Society of Exploration Geophysicists, 2017) Chen, Jianxiong; Zelt, Colin A.Ray theory-based traveltime calculation that assumes infinitely high frequency wave propagation is likely to be invalid in the near-surface (upper tens of meters) due to the relatively large seismic wavelength compared with the total travel path lengths and the scale of the near-surface velocity heterogeneities. The wavelength-dependent velocity smoothing (WDVS) algorithm calculates a frequency-dependent, first-arrival traveltime by assuming that using a wavelength-smoothed velocity model and conventional ray theory is equivalent to using the original unsmoothed model and a frequency-dependent calculation. This paper presents comparisons of WDVS-calculated traveltimes with band-limited full wavefield synthetics including the results from 1) different velocity models, 2) different frequency spectra, 3) different values of a free parameter in the WDVS algorithm, and 4) different levels of added noise to the synthetics. The results show that WDVS calculates frequency-dependent traveltimes that are generally consistent with the first arrivals from band-limited full wavefield synthetics. Compared to infinite-frequency traveltimes calculated using conventional ray theory, the WDVS frequency-dependent traveltimes are more consistent with the first arrivals picked from full wavefield synthetics in terms of absolute time and trace-to-trace variation. The results support the use of WDVS as the forward modeling component of a tomographic inversion method, or any seismic method that involves modeling first-arrival traveltimes.Item Frequency-Dependent Traveltime Tomography and Full Waveform Inversion for Near-Surface Seismic Refraction Data(2016-03-18) Chen, Jianxiong; Zelt, Colin AI demonstrate the utility and benefits of a combined use of frequency-dependent traveltime tomography (FDTT) and full waveform inversion (FWI) to estimate the near-surface seismic velocity that contains wavelength- and sub-wavelength-scale features. FDTT is fundamentally different from conventional ray-theory infinite-frequency traveltime tomography (IFTT) methods in the calculation of a frequency-dependent traveltime using wavelength-dependent velocity smoothing (WDVS). I justify the use of WDVS in FDTT for calculating a frequency-dependent traveltime by using forward modeling examples to show its frequency-dependent behaviors that are consistent with finite-frequency wave propagation. Compared to the conventional infinite-frequency traveltimes calculated based on ray-theory, the frequency-dependent traveltimes calculated using WDVS can better match that from synthetic seismographs. In the combined workflow of FDTT and FWI, FDTT provides a long-wavelength background seismic velocity model as the starting model, and then FWI introduces wavelength- and sub-wavelength-scale features that allow for direct geologic interpretation of the velocity models as is usually carried out in conventional imaging using seismic reflection data. I apply this workflow to seismic data generated by a near-surface realistic synthetic velocity model representing a geologic setting consisting of unconsolidated sediment overlying faulted bedrock, successfully imaging the key model features, a thin low-velocity layer in the sediments, a steep bedrock offset and a steeply dipping low-velocity fault zone. These structures are all at the wavelength-scale that are weakly presented by conventional ray-theory methods. I then apply this workflow to 2D P- and SH-waves collected in 2011 at Rice campus with a known target consisting of a buried tunnel with concrete walls and a void space inside. FDTT inverted the P- and SH-wave picked traveltimes at 250 Hz to provide long-wavelength background velocity models as the starting models for FWI. FWI inverted 18-54 Hz P-wave data and 16-50 Hz SH-wave data to produce velocity models with sub-wavelength- and wavelength-scale features. The P- and SH-wave models image the top part of the tunnel at the correct location at a depth of 1.6 m as a high-velocity anomaly. The P-wave models also image the air in the void space of the tunnel as a low-velocity anomaly. As a comparison, in both the realistic synthetic test and real data applications, conventional IFTT is also applied in a combined workflow with FWI. The comparisons of the inverted models show that both IFTT and FDTT models can serve as adequate starting models for FWI, but FDTT is favored over IFTT because: 1) The FDTT models better recover the magnitude of the velocity anomalies, and 2) The FDTT model serves as a better starting model for FWI, which results in a more accurate FWI velocity estimation with better recovery of the magnitude and location of the key features, particularly in the absence of usable low frequency data.