ORCID Profile
0000-0001-6334-8344
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Publisher: American Geophysical Union (AGU)
Date: 11-2019
DOI: 10.1029/2019WR026072
Publisher: American Geophysical Union (AGU)
Date: 04-2021
DOI: 10.1029/2020WR028700
Abstract: Bedload transport is a key process in fluvial morphodynamics, but difficult to measure. The advent of seismic monitoring techniques has provided an alternative to in‐stream monitoring, which is often costly and cannot be utilized during large floods. Seismic monitoring is a method requiring several steps to convert seismic data into bedload flux data. State‐of‐the‐art conversion approaches exploit physical models predicting the seismic signal generated by bedload transport. However, due to a lack of well‐constrained validation data, the accuracy of the resulting inversions is unknown. We use field experiments to constrain a seismic bedload model and compare the results to high‐quality independent bedload measurements. Constraining the Green's function (i.e., seismic ground properties) with an active seismic survey resulted in an average absolute difference between modeled and empirically measured seismic bedload power of 11 dB in the relevant frequency band. Using generically estimated Green's function parameters resulted in a difference of 20 dB, thus highlighting the importance of using actual field parameters. Water turbulence and grain hiding are unlikely to be the cause of differences between field observations and our analysis. Rather, they may be either due to the inverted model being particularly sensitive to the coarse tail of the grain‐size distribution, which is least well constrained from field observations, or due to the seismic model underestimating effects of the largest grains.
Publisher: Authorea, Inc.
Date: 20-07-2023
DOI: 10.22541/ESSOAR.168987133.36073109/V1
Abstract: Seismic instruments placed outside of spatially extensive hazard zones can be used to rapidly sense a range of mass movements. However, it remains challenging to automatically detect specific events of interest. Benford’s law, which states that first non-zero-digit of given datasets follow a specific probability distribution, can provide a computationally cheap approach to identifying anomalies in large datasets and potentially be used for event detection. Here, we select raw seismic signals to derive the first-digit distribution. The seismic signals generated by debris flows, landslides, lahars, and glacier-lake-outburst floods follow Benford’s law, while those generated by ambient noise, rockfalls, and bedload transports do not. Focusing on debris flows, our Benford’s-law-based detector is comparable to an existing random forest method for the Illgraben, Switzerland, but requires only single station data and three non-dimensional parameters. We suggest this computationally cheap, novel technique offers an alternative for event recognition and potentially for real-time warnings.
Location: Germany
No related grants have been discovered for Sophie Lagarde.