Fig. 1. DAS Walkaway-VSP observation system

Fig. 2. DAS-VSP shot gathers at different offsets

Fig. 3. DAS-VSP near-offset shot gather. (a) Full wave field record; (b) P-P wave travel time record at different depths; (c) P-S wave travel time record at different depths

Fig. 4. DAS-VSP shot gathers before and after denoising. (a) Before denoising; (b) after MCA denoising; (c) removed noise

Fig. 5. DAS-VSP shot spectrograms before and after denoising. (a) Before denoising; (b) after MCA denoising; (c) removed noise

Fig. 6. Noise records removed by different denoising methods. (a) Bandpass filtering method; (b) MCA method

Fig. 7. Flow chart of DAS-VSP multi-wave data separation based on artificial intelligence methods

Fig. 8. Upgoing P-P wave record after wave field separation

Fig. 9. Upgoing P-S wave record after wave field separation

Fig. 10. FK spectrum after wave field separation of shot ③. (a) Upgoing P-P wave; (b) upgoing P-S wave

Fig. 11. Partial convolutional U-net network architecture map

Fig. 12. Flow chart of training process

Fig. 13. DAS-VSP common receiver gathers before and after AI assisted regularization. (a) Before regularization; (b) after regularization

Fig. 14. DAS-VSP final images for downgoing multiples before and after AI assisted regularization. (a) Before regularization; (b) after regularization

Fig. 15. Flow chart of VSP multi-wave imaging processing based on minimum travel time

Fig. 16. VSP imaging process based on minimum travel time. (a) 1 VSP reflected wave data; (b) travel time table corresponding to 200 grids with minimum travel time; (c) imaging profile

Fig. 17. DAS Walkaway-VSP imaging profiles obtained based on minimum travel time imaging method. (a) upgoing P-P wave imaging profile; (b) upgoing P-S wave imaging profile