Final data of the adjoint-state full waveform tsunami source inversion, applied to Chile-Iquique tsunami event

We develop an adjoint-state full waveform inversion procedure to recover the initial water elevation of a tsunami event. Traditional finite-fault tsunami source inversion methods suffer from the uncertainty of fault parameters or crustal rigidity. Moreover, the heavy computational burden of calculating Green’s functions results in limited spatial resolution and hinders the real-time applicability of the traditional methods to tsunami early warning. In this work, we apply the adjoint-state full waveform inversion method to the tsunami source inversion. The benefits of the adjoint inversion are two folds: 1) independence of fault parameters, and 2) high computational efficiency, especially for dense tsunami arrays and high resolution grids. We valid this approach with synthetic tsunami sources, and apply it to the 2014 Chile-Iquique tsunami event. Both synthetic and real-data preliminary results show that the adjoint-state method is of high efficiency and high resolution, outperforming the traditional tsunami source inversions.  The data is in three comma-separated ascii files. We shared the three inversion results with different starting models. The source region is 70.3~71.5W, 18.5~21S on uniform grids. The src_TRIstart.txt is the inversion result with TRI image starting model, src_USGS_unistart.txt is the inversion result with USGS uniform slip model (https://earthquake.usgs.gov/earthquakes/eventpage/usc000nzvd/finite-fault). The src_zerostart.txt is the inversion result with zero starting model. The text file has longitude (in degrees), latitude (in degrees) and water elevation (in meters) of each column.</p

Performance of our method, PointNet, and PointNet++ when the training dataset contains 30 3D models.

The horizontal axis represents recall rate, and the vertical axis represents precision rate. Higher curves represent better performance.</p

The query results of scaling invariance testing.

The query results of scaling invariance testing.</p

Our pose-invariant feature sets for 3 horse models with different poses.

The feature set is defined in Section 3.4. The horizontal axis represents the ith element in each feature set. The vertical axis represents the corresponding zi in each feature set.</p

The query results of robustness testing.

The query results of robustness testing.</p

Supplementary document for Arbitrary trajectory control of Bessel beams with pure phase modulation - 5897623.pdf

Supplement

The model dataset used to test our algorithm.

It contains 10 horse models with different poses, 10 flamingo models with different poses, and 15 human models with different sampling rates, scaling, and noise levels.</p

Performance of our method, PointNet, and PointNet++ when the training dataset contains 60 3D models.

The horizontal axis represents recall rate, and the vertical axis represents precision rate. Higher curves represent better performance.</p

S1 Data -

(RAR)</p

Precision vs. recall curves of our method and 4 other methods on our testing dataset.

The horizontal axis represents recall rate, and the vertical axis represents precision rate. Higher curves represent better performance.</p