<jupyter_start><jupyter_text>Lab Notebook - Rift Inversion Project This Jupyter notebooks serves as a record of modeling runs done on Stampede2 as part of the rift inversion project. Each cell includes a note of the current modeling goal and the code to generate the resulting models, as well as a report on the model results.__NOTE__: Never run all cells, model inputs are designed to be temporary<jupyter_code>import ripropagate

# Key function: generate
# Parameters in generate: lthick,evel,etime,output,ver,soft,shell,file<jupyter_output><empty_output><jupyter_text>11/1/2021 * Finished rifting of model with new inflow/outflow scheme (1025b) - making inversion for that now<jupyter_code>ripropagate.generate(etime=32.5,evel=-1,output='./110121_ri_a',file='ri_base_iso_inflow.prm',soft=0.375,shell='run_base_nal.sh',
                    ver='a')<jupyter_output><empty_output><jupyter_text>* Fastscape model with marine turned on crashed very quickly. Need to take a closer look at parameters.* Made some changes after suggestions from Derek, going to try that out again.<jupyter_code>ripropagate.generate(etime=12.5,evel=1,output='./110121_ri_b',file='ri_base_fs.prm',soft=0.375,shell='run_base_fsr.sh',
                    ver='b')<jupyter_output><empty_output><jupyter_text>11/4/2021 * Fastscape model (1101b) actually rifted successfully - trying the inversion next.<jupyter_code>ripropagate.generate(etime=32.5,evel=-1,output='./110421_ri',file='ri_base_fs.prm',soft=0.375,shell='run_base_fsr.sh')<jupyter_output><empty_output><jupyter_text>11/10/2021 * Inversions of inflow and fastscape models proceeding. Finished inverting 2 isosurfaces model.* Adding larger model domain to inflow model to see if it helps with edge effects<jupyter_code>ripropagate.generate(etime=12.5,evel=1,output='./111021_ri',file='ri_base_iso_inflow_large.prm',soft=0.375,shell='run_base_fsr.sh',
                    depth=600)
ripropagate.comp_ascii(y=600,output='./111021_ri')<jupyter_output><empty_output><jupyter_text>11/11/2021 * Realized problem is persisting even with the new outflow scheme - going to try reverting back to cell averaging instead.<jupyter_code>ripropagate.generate(etime=12.5,evel=1,output='./111121_ri_a',file='ri_base.prm',soft=0.375,shell='run_base_nal.sh',ver='a')<jupyter_output><empty_output><jupyter_text>* Also doing a version of this with 1 km resolution throughout.<jupyter_code>ripropagate.generate(etime=12.5,evel=1,output='./111121_ri_b',file='ri_base_hires.prm',soft=0.375,shell='run_base_nal.sh',ver='b')<jupyter_output><empty_output><jupyter_text>11/17/2021 * 1111a finished, so going to invert that.* But also need to try this with the new inflow scheme, so adding that.* 1111b is super slow, so not going to continue with that at the moment.<jupyter_code>ripropagate.generate(etime=32.5,evel=-1,output='./111721_ri_a',file='ri_base.prm',soft=0.375,shell='run_base_nal.sh',ver='a')
ripropagate.generate(etime=12.5,evel=1,output='./111721_ri_b',file='ri_base_inflow.prm',soft=0.375,shell='run_base_nal.sh',ver='b')<jupyter_output><empty_output><jupyter_text>11/22/2021 * Discovered that 1111a went seriously wrong (rifting initialized on edge of model), so canceled 1117a. * 1117b almost done rifting, but also need to check that the same problem didn't occur.* Pulling 1111b to see how hires version did as well.  11/24/2021 * 1117b finished rifting, inverting that now.<jupyter_code>ripropagate.generate(etime=32.5,evel=-1,output='./112421_ri',file='ri_base_inflow.prm',soft=0.375,shell='run_base_nal.sh')<jupyter_output><empty_output><jupyter_text>12/8/2021 * Going to attempt new rift model with larger model domain and significant mesh refinement.<jupyter_code>ripropagate.generate(etime=12.5,evel=1,output='./120821_ri',file='ri_base_inflow_large.prm',soft=0.375,depth=600,shell='run_base_nal.sh')
ripropagate.comp_ascii(x=1800,y=600,output='./120821_ri')<jupyter_output><empty_output><jupyter_text>12/13/2021 * A few issues with previous models - trying a model now with 400 km domain but with composition/temperature fixed at sides but vertical velocities allowed.<jupyter_code>ripropagate.generate(etime=12.5,evel=1,output='./121321_ri',file='ri_base_inflow_tcfix.prm',soft=0.375,depth=400,shell='run_base_nal.sh')<jupyter_output><empty_output><jupyter_text>12/15/2021 * That crashed after 3.5 Myr, so reverting back to no fixed parameters at size but still new inflow/outflow scheme.<jupyter_code>ripropagate.generate(etime=12.5,evel=1,output='./121521_ri',file='ri_base_inflow_nofix.prm',soft=0.375,depth=400,shell='run_base_nal.sh')<jupyter_output><empty_output><jupyter_text>12/16/2021 * That still crashed, so trying the same thing but with slightly larger model domain and coarsened resolution on the sides.* Actually, scratch that, discovered major typo in velocity function (p1 was incorrectly smaller than p2, so going back to model with fixed velocity,temp,comp on sides with correct velocities)<jupyter_code>ripropagate.generate(etime=12.5,evel=1,output='./121621_ri',file='ri_base_inflow.prm',soft=0.375,depth=400,shell='run_base_nal.sh')<jupyter_output><empty_output><jupyter_text>12/17/2021 * New model appears to have rifted successfully. Inverting now.<jupyter_code>ripropagate.generate(etime=32.5,evel=-1,output='./121721_ri',file='ri_base_inflow.prm',soft=0.375,depth=400,shell='run_base_nal.sh')<jupyter_output><empty_output><jupyter_text>12/22/2021 * Inversion is generally looking good, though needs CFL at 0.5 and timestep at 20000 to go fast enough. Trying a comparable version with mantle down to 600 km and wider transition velocity zone.<jupyter_code>ripropagate.generate(etime=12.5,evel=1,output='./122221_ri',file='ri_base_600.prm',soft=0.375,depth=600,
                     p1=400,p2=200,shell='run_base_nal.sh')
ripropagate.comp_ascii(x=1000,y=600,output='./122221_ri')<jupyter_output><empty_output><jupyter_text>12/23/2021 * That rifted successfully, so now inverting.<jupyter_code>ripropagate.generate(etime=32.5,evel=-1,output='./122321_ri',file='ri_base_600.prm',soft=0.375,depth=600,
                     p1=400,p2=200,shell='run_base_nal.sh')<jupyter_output><empty_output><jupyter_text>Disovered issue with AMR scheme - everything in asthenosphere is 4 km - refining that to 2 instead.<jupyter_code>ripropagate.generate(etime=12.5,evel=1,output='./122321_ri_b',file='ri_base_600_amr.prm',soft=0.375,depth=600,
                     p1=400,p2=200,shell='run_base_nal.sh',ver='b')<jupyter_output><empty_output>
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