Given a data series (mathbf{d}(t)=left{d_{0}, d_{1}, d_{2}, ldots, d_{N} ight}) and its estimated wavelet (mathbf{w}(t)=left{w_{0} ight.), (left.w_{1},

Given a data series \(\mathbf{d}(t)=\left\{d_{0}, d_{1}, d_{2}, \ldots, d_{N}\right\}\) and its estimated wavelet \(\mathbf{w}(t)=\left\{w_{0}\right.\), \(\left.w_{1}, w_{2}, \ldots, w_{M}\right\}\), how would you deconvolve \(\mathbf{w}(t)\) from \(\mathbf{d}(t)\) ? Please provide as many approaches as possible.