A computer implemented method for reconstructing at least one trace in a seismic image of a common-receiver or common mid-point gather or a common offset gather and, time domain, the image comprising traces in time domain with seismic data acquired from a seismic survey, and at least one trace to be reconstructed, the method comprising the steps: - deploying a convolutional neural network for predicting a trace, the convolutional neural network comprising at least one layer, wherein the at least one layer of the convolutional neural network comprises a kernel function with a blind-trace receptive field covering adjacent traces; - training the convolutional neural network using a plurality of traces of the seismic image; - inputting the seismic image into the convolutional neural network predicting the value of the at least one trace to be reconstructed; - assigning the predicted value of the at least one trace to be reconstructed in the seismic image at the location of said trace to be reconstructed; characterized in that the blind-trace receptive field of the kernel function covers adjacent traces with data but not covers the trace located at the position of the trace to be reconstructed, the convolutional neural network is a U-net and, the blind-trace receptive field of the kernel function is determined by the following processing steps: - limiting the receptive field to cover one side according to the direction of the trace; - duplicating the image to be inputted into the receptive field resulting into a first copy of the image and a second copy of the image, the second copy of the image being rotated 180o with respect to the first copy of the image; - the first copy and the second copy of the image are inputted into the receptive field of the U-net and the resulting images being combined into a single image. - deploying a convolutional neural network for predicting a trace, the convolutional neural network comprising at least one layer, wherein the at least one layer of the convolutional neural network comprises a kernel function with a blind-trace receptive field covering adjacent traces; - training the convolutional neural network using a plurality of traces of the seismic image; - inputting the seismic image into the convolutional neural network predicting the value of the at least one trace to be reconstructed; - assigning the predicted value of the at least one trace to be reconstructed in the seismic image at the location of said trace to be reconstructed; the blind-trace receptive field of the kernel function covers adjacent traces with data but not covers the trace located at the position of the trace to be reconstructed, the convolutional neural network is a U-net and, the blind-trace receptive field of the kernel function is determined by the following processing steps: - limiting the receptive field to cover one side according to the direction of the trace; - duplicating the image to be inputted into the receptive field resulting into a first copy of the image and a second copy of the image, the second copy of the image being rotated 180o with respect to the first copy of the image; - the first copy and the second copy of the image are inputted into the receptive field of the U-net and the resulting images being combined into a single image. - limiting the receptive field to cover one side according to the direction of the trace; - duplicating the image to be inputted into the receptive field resulting into a first copy of the image and a second copy of the image, the second copy of the image being rotated 180o with respect to the first copy of the image; - the first copy and the second copy of the image are inputted into the receptive field of the U-net and the resulting images being combined into a single image.