The Challenges and Solutions of Green Seismic Survey: Imaging Deep Seafloor Structures with Cruise TN-373 Reflection Data

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Seismic surveys in the past can be viewed as a trade-off between data quality and survey cost. As technology advances, the gap between these two demands has lessened. Nowadays, a new factor is emerging as more regulations are aimed at curbing the environmental impact of seismic surveys. I define “green” seismic surveys as those that employ natural or low-energy seismic sources to minimize the environmental impact on the surrounding area. However, lower energy can drastically reduce the quality of the collected seismic data. This dissertation aims to develop strategies that optimize the efficiency of future acquisition by properly balancing three factors: quality, cost, and environmental impact. Chapter 2 introduces the seismic dataset from Cruise TN-373, an NSF project that utilized small airgun sources to image deep seafloor structures. The research vessel sailed across the South Atlantic Ocean from November through December of 2019 to collect more than 3000 km of 2D reflection seismic lines. The dataset modernizes existing seismic data over the area to expand our understanding of the Walvis Ridge formation, as well as to assist in the selection of the ocean floor drilling sites for the IODP Expedition 391. Chapter 3 reviews the challenges and opportunities of future acquisition using small sources by using TN-373 as a case study. I recommend ways to optimize survey parameters for future opportunities of integrating green seismic surveys into conventional surveys. I also summarize key challenges to data fidelity including image resolution, image artifacts, and uncertainty in image position. Chapter 4 documents the processing flows of TN-373, focusing on pre-processing, deconvolution, velocity model building, and migration algorithms for imaging deep marine sediments. Common marine noises are attenuated by classifying them based on origin, coherency, and frequency to increase the signal-to-noise ratio (SNR). To improve resolution by deconvolution, I compared the far-field source wavelet derived using three common methods and discovered that accurately estimating a far-field source wavelet relies on the directivity and SNR of the data. I further assess the uncertainties in our velocity models and identify commonly observed artifacts. In Chapter 5, I study the state-of-the-art methods in the separation of simultaneous source data. The lack of amplitude in low-energy sources can be increased by raising source density and therefore increasing data quality. I demonstrate a neural network approach to effectively reduce unwanted crosstalk noise and show potential for improved efficiency and better separation than existing algorithms. This dissertation offers insights into optimizing the efficiency and quality of future acquisitions while minimizing their environmental impact. The findings and recommendations can guide prospective seismic surveys to becoming more sustainable and responsible through the ongoing energy transition.

Green seismic survey, seismic processing, small seismic sources
Portions of this document appear in: Zhou, H.-W., H. Zhou, B. Wu, and Z. Li, 2019, Assessing the source radiation pattern (SRP) of onshore seismic data: Preliminary results, SEG Technical Program Expanded Abstracts 2019, 2969-2973; and in: Li, Zhehao, Hua‐Wei Zhou, Guangkai Ma, Dongdi Cai, and Mingqiang Cao. "A study on bubble suppression for deep marine reflection data acquired by a small airgun array." Geophysical Prospecting (2023); and in: Li, Z., H.-W. Zhou, and K. Fu, 2022, Separation of simultaneous source wavefields using convolutional neural network, SEG Technical Program Expanded Abstracts 2022, 2917-2921.