Algorithm 1 FaultMap.

Information Transfer Network Inference 1:Input:N-dimensional time series $\{X^{\left(1\right)},X^{\left(2\right)},...,X^{\left(N\right)}\}$, of M samples from system X represented by: $x^{\left(i\right)}=\{x_1^{\left(i\right)},x_2^{\left(i\right)}...,x_M^{\left(i\right)}\}ϵR^M$, statistical significance threshold $\delta$ in, e.g., a rank-order test using iAAFT surrogates, embedding delay range ${\tau }_{max}$ and embedding history lengths k and l for time series i and time series $j(\ne i)$ 2:Output: adjacency matrix $WϵR^{N\times N}$, where entry ($i,j$) represents information transfer from node i to node j 3:for$i\leftarrow 1$ to N do 4:    for $j\leftarrow 1$ to $N,j\ne i$ do 5:        for $\tau \leftarrow 0$ to ${\tau }_{max}$ do 6:           compute $T{E}_{i\to j}(k,l,\tau )$ by Equation (1) 7:           if ${\displaystyle T{E}_{i\to j}=0<\delta <\underset{0⩽\tau ⩽{\tau }_{max}}{max}\left\{Equation\left(1\right)\right\}}$ then 8:               $W_{ij}\leftarrow T{E}_{i\to j}$ 9:           else 10:               $W_{ij}\leftarrow 0$ 11:           end if 12:        end for 13:    end for 14:end for Spectral Centrality Ranking 1:Input: matrix $P_{\gamma }^{{}^{\prime }}=\gamma P+\frac{1}{N}(1-\gamma )J$ where $\gamma ϵ\langle 0,1]$, ranking distance $ϵ$ 2:Output: node centrality score vector ${\pi }_1$ 3:initialize ${\pi }^{\left(0\right)}$ with probabilities $\left[\right(1/N,1/N,...,1/N\left)\right]$ 4:while$|{\pi }^{(k+1)}-{\pi }^{\left(k\right)}|>ϵ$do 5:    compute eigenvector ${\pi }^{(k+1)}$ of matrix $P_{\gamma }^{{}^{\prime }}$ associated     with eigenvalue ${\lambda }_1\left(P_{\gamma }^{{}^{\prime }}\right)=1$, such that ${\pi }_1=P_{\gamma }^{{}^{\prime }}{\pi }_1$ 6:end while