Supplemental Material: How similar was the 1983 Mw 6.9 Borah Peak earthquake rupture to its surface-faulting predecessors along the northern Lost River fault zone (Idaho, USA)?

Text S1: Bayesian (OxCal) models for northern Lost River fault zone trench sites. Text S2: Bulk sediment analysis and charcoal identification; Text S3: Luminescence geochronology. Table S1: Description of stratigraphic units at the Sheep Creek trench. Table S2: Description of stratigraphic units at the Arentson Gulch trench. Figure S1: Photomosaics and large-format trench logs for the Sheep Creek trench. Figure S2: Photomosaics and large-format trench logs for the Arentson Gulch trench. Figure S3: Sheep Creek and Arentson Gulch vertical displacement measurements. Figure S4: Fault bend angles along the northern Lost River fault zone. Figure S5: Photographs of the Sheep Creek and Arentson Gulch trench sites. Figure S6: Probability density functions for Lost River fault zone ruptures.

Supplemental Material: How similar was the 1983 Mw 6.9 Borah Peak earthquake rupture to its surface-faulting predecessors along the northern Lost River fault zone (Idaho, USA)?

Text S1: Bayesian (OxCal) models for northern Lost River fault zone trench sites. Text S2: Bulk sediment analysis and charcoal identification; Text S3: Luminescence geochronology. Table S1: Description of stratigraphic units at the Sheep Creek trench. Table S2: Description of stratigraphic units at the Arentson Gulch trench. Figure S1: Photomosaics and large-format trench logs for the Sheep Creek trench. Figure S2: Photomosaics and large-format trench logs for the Arentson Gulch trench. Figure S3: Sheep Creek and Arentson Gulch vertical displacement measurements. Figure S4: Fault bend angles along the northern Lost River fault zone. Figure S5: Photographs of the Sheep Creek and Arentson Gulch trench sites. Figure S6: Probability density functions for Lost River fault zone ruptures.

A MODWT-Based Algorithm for the Identification and Removal of Jumps/Short-Term Distortions in Displacement Measurements Used for Structural Health Monitoring

Researchers have made substantial efforts to improve the measurement of structural reciprocal motion using radars in the last years. However, the signal-to-noise ratio of the radar’s received signal still plays an important role for long-term monitoring of structures that are susceptible to excessive vibration. Although the prolonged monitoring of structural deflections may provide paramount information for the assessment of structural condition, most of the existing structural health monitoring (SHM) works did not consider the challenges to handle long-term displacement measurements when the signal-to-noise ratio of the measurement is low. This may cause discontinuities in the detected reciprocal motion and can result in wrong assessments during the data analyses. This paper introduces a novel approach that uses a wavelet-based multi-resolution analysis to correct short-term distortions in the calculated displacements even when previously proposed denoising techniques are not effective. Experimental results are presented to validate and demonstrate the feasibility of the proposed algorithm. The advantages and limitations of the proposed approach are also discussed.