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Manual seismic horizon picking is the least efficient interpretation technique in terms of time and effort. Loop-tie is a key “element” and the most time-consuming task in manual horizon picking, which ensures the accuracy of horizon picking. Auto-picking techniques have been around since the early 1980s. However, there are few studies simulating the procedure of manual seismic horizon picking and quantitatively evaluating the auto-picked horizons. In our proposed method, we perform auto-picking on inline and crossline seismic vertical slices independently, similar to the manual horizon picking procedure. We then evaluate the picked horizons using a loop-tie step similar to the loop-tie checking in manual horizon picking. To simulate the loop-tie step in manual picking, we define two dip attributes for each time sample of seismic traces, which are “left” and “right” reflector dips. We only preserve the portions of the tracked horizons that meet the defined loop-tie checking. Next, we merge the tracked horizons centered at the seeded seismic traces. The two-way traveltime of the merged horizons function as “hard” controls for the final step of auto-picking. We finally use the seismic dip attribute to track the horizons over the seismic survey under the hard controls. The real data demonstrate that our algorithm can extract accurate horizons near discontinuities such as faults and unconformities.

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Intriguing Nernst effects offer the prospect of engineering micro-and nano-sized thermoelectric devices, facilitating heat harvesting, energy conversion, and directional temperature control. In this Letter, we establish a theoretical analysis of the layer Nernst effect (LNE) in twisted moiré layers subjected to a temperature gradient. Our analysis reveals the emergence of a layer-contrasted Nernst voltage perpendicular to the direction of the temperature gradient. This phenomenon arises from the trigonal warping of the Fermi surface and the layer pseudomagnetic field. The Fermi surface's trigonal warping, breaking intra-valley inversion symmetry, leads to an imbalance between left and right movers, resulting in a non-vanishing LNE. We validate our theoretical framework by applying it to twisted bilayer graphene (TBG). Importantly, our findings indicate that the Nernst coefficient in TBG can reach values as high as µA/K, surpassing those of previously known materials by three orders of magnitude. These results provide a foundation for utilizing TBG and other twisted moiré layers as promising platforms to explore layer caloritronics and develop thermoelectric devices.

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